From c9bf0f62f315d0cf75029be7bb1b8dfa365e5c71 Mon Sep 17 00:00:00 2001
From: GitHub Actions <actions@github.com>
Date: Sun, 22 Dec 2024 21:49:27 +0000
Subject: [PATCH] Built site for admiral@1.1.1.9046: 2f70ceb

---
 dev/CODE_OF_CONDUCT.html                      |  2 +-
 dev/CONTRIBUTING.html                         |  2 +-
 dev/LICENSE.html                              |  2 +-
 dev/articles/admiral.html                     |  2 +-
 dev/articles/adsl.html                        |  2 +-
 dev/articles/bds_exposure.html                |  2 +-
 dev/articles/bds_finding.html                 |  6 +--
 dev/articles/bds_tte.html                     |  2 +-
 dev/articles/concepts_conventions.html        |  2 +-
 dev/articles/faq.html                         |  2 +-
 dev/articles/generic.html                     |  2 +-
 dev/articles/higher_order.html                |  2 +-
 dev/articles/hys_law.html                     |  2 +-
 dev/articles/imputation.html                  |  2 +-
 dev/articles/index.html                       |  2 +-
 dev/articles/lab_grading.html                 |  2 +-
 dev/articles/occds.html                       |  2 +-
 dev/articles/pk_adnca.html                    |  2 +-
 dev/articles/queries_dataset.html             |  2 +-
 dev/articles/questionnaires.html              |  2 +-
 dev/articles/visits_periods.html              |  2 +-
 dev/articles/website-versions.html            |  2 +-
 dev/authors.html                              |  6 +--
 dev/index.html                                |  2 +-
 dev/news/index.html                           |  2 +-
 dev/pkgdown.yml                               |  2 +-
 dev/reference/admiral-package.html            |  2 +-
 dev/reference/admiral_adlb.html               |  2 +-
 dev/reference/admiral_adsl.html               |  2 +-
 dev/reference/assert_parameters_argument.html |  2 +-
 dev/reference/atoxgr_criteria_ctcv4.html      |  2 +-
 dev/reference/atoxgr_criteria_ctcv5.html      |  2 +-
 dev/reference/atoxgr_criteria_daids.html      |  2 +-
 dev/reference/basket_select.html              |  2 +-
 dev/reference/call_derivation.html            |  2 +-
 dev/reference/call_user_fun.html              |  2 +-
 dev/reference/censor_source.html              |  2 +-
 dev/reference/chr2vars.html                   |  2 +-
 dev/reference/compute_age_years.html          |  2 +-
 dev/reference/compute_bmi.html                |  2 +-
 dev/reference/compute_bsa.html                |  2 +-
 dev/reference/compute_dtf.html                |  2 +-
 dev/reference/compute_duration.html           |  2 +-
 dev/reference/compute_egfr.html               |  2 +-
 dev/reference/compute_framingham.html         |  2 +-
 dev/reference/compute_map.html                |  2 +-
 dev/reference/compute_qtc.html                |  2 +-
 dev/reference/compute_qual_imputation.html    |  2 +-
 .../compute_qual_imputation_dec.html          |  2 +-
 dev/reference/compute_rr.html                 |  2 +-
 dev/reference/compute_scale.html              |  2 +-
 dev/reference/compute_tmf.html                |  2 +-
 dev/reference/consolidate_metadata.html       |  2 +-
 dev/reference/convert_blanks_to_na.html       |  2 +-
 dev/reference/convert_date_to_dtm.html        |  4 +-
 dev/reference/convert_dtc_to_dt.html          |  2 +-
 dev/reference/convert_dtc_to_dtm.html         |  2 +-
 dev/reference/convert_na_to_blanks.html       |  2 +-
 dev/reference/count_vals.html                 |  2 +-
 dev/reference/country_code_lookup.html        |  2 +-
 dev/reference/create_period_dataset.html      |  2 +-
 dev/reference/create_query_data.html          |  2 +-
 dev/reference/create_single_dose_dataset.html |  2 +-
 dev/reference/date_source.html                |  2 +-
 dev/reference/default_qtc_paramcd.html        |  2 +-
 dev/reference/derivation_slice.html           |  2 +-
 dev/reference/derive_basetype_records.html    | 38 +++++++++----------
 dev/reference/derive_expected_records.html    |  2 +-
 dev/reference/derive_extreme_event.html       |  2 +-
 dev/reference/derive_extreme_records.html     |  2 +-
 dev/reference/derive_locf_records.html        |  2 +-
 dev/reference/derive_param_bmi.html           |  2 +-
 dev/reference/derive_param_bsa.html           |  2 +-
 dev/reference/derive_param_computed.html      |  2 +-
 dev/reference/derive_param_doseint.html       |  2 +-
 dev/reference/derive_param_exist_flag.html    |  2 +-
 dev/reference/derive_param_exposure.html      |  2 +-
 .../derive_param_extreme_record.html          |  2 +-
 dev/reference/derive_param_framingham.html    |  2 +-
 dev/reference/derive_param_map.html           |  2 +-
 dev/reference/derive_param_qtc.html           |  2 +-
 dev/reference/derive_param_rr.html            |  2 +-
 dev/reference/derive_param_tte.html           |  2 +-
 dev/reference/derive_param_wbc_abs.html       |  2 +-
 dev/reference/derive_summary_records.html     |  2 +-
 dev/reference/derive_var_age_years.html       |  2 +-
 dev/reference/derive_var_analysis_ratio.html  |  2 +-
 dev/reference/derive_var_anrind.html          |  2 +-
 dev/reference/derive_var_atoxgr.html          |  2 +-
 dev/reference/derive_var_atoxgr_dir.html      |  2 +-
 dev/reference/derive_var_base.html            |  2 +-
 dev/reference/derive_var_chg.html             |  2 +-
 dev/reference/derive_var_dthcaus.html         |  2 +-
 dev/reference/derive_var_extreme_dt.html      |  2 +-
 dev/reference/derive_var_extreme_dtm.html     |  2 +-
 dev/reference/derive_var_extreme_flag.html    |  2 +-
 .../derive_var_joined_exist_flag.html         |  2 +-
 dev/reference/derive_var_merged_ef_msrc.html  |  2 +-
 .../derive_var_merged_exist_flag.html         |  2 +-
 dev/reference/derive_var_merged_summary.html  |  2 +-
 dev/reference/derive_var_obs_number.html      |  2 +-
 dev/reference/derive_var_ontrtfl.html         |  2 +-
 dev/reference/derive_var_pchg.html            |  2 +-
 dev/reference/derive_var_relative_flag.html   |  2 +-
 dev/reference/derive_var_shift.html           |  2 +-
 dev/reference/derive_var_trtdurd.html         |  2 +-
 dev/reference/derive_var_trtemfl.html         |  2 +-
 dev/reference/derive_vars_aage.html           |  2 +-
 dev/reference/derive_vars_atc.html            |  2 +-
 dev/reference/derive_vars_cat.html            |  2 +-
 dev/reference/derive_vars_computed.html       |  2 +-
 dev/reference/derive_vars_crit_flag.html      |  2 +-
 dev/reference/derive_vars_dt.html             |  2 +-
 dev/reference/derive_vars_dtm.html            |  2 +-
 dev/reference/derive_vars_dtm_to_dt.html      |  2 +-
 dev/reference/derive_vars_dtm_to_tm.html      |  2 +-
 dev/reference/derive_vars_duration.html       |  2 +-
 dev/reference/derive_vars_dy.html             |  2 +-
 dev/reference/derive_vars_extreme_event.html  |  2 +-
 dev/reference/derive_vars_joined.html         |  2 +-
 dev/reference/derive_vars_merged.html         |  2 +-
 dev/reference/derive_vars_merged_lookup.html  |  2 +-
 dev/reference/derive_vars_period.html         |  2 +-
 dev/reference/derive_vars_query.html          |  2 +-
 dev/reference/derive_vars_transposed.html     |  2 +-
 dev/reference/dose_freq_lookup.html           |  2 +-
 dev/reference/dt_level.html                   |  2 +-
 dev/reference/dthcaus_source.html             |  2 +-
 dev/reference/dtm_level.html                  |  2 +-
 dev/reference/event.html                      |  2 +-
 dev/reference/event_joined.html               |  2 +-
 dev/reference/event_source.html               |  2 +-
 dev/reference/ex_single.html                  |  2 +-
 dev/reference/example_qs.html                 |  2 +-
 dev/reference/extend_condition.html           |  2 +-
 dev/reference/extract_duplicate_records.html  |  2 +-
 dev/reference/extract_unit.html               |  2 +-
 dev/reference/filter_exist.html               |  2 +-
 dev/reference/filter_extreme.html             |  2 +-
 dev/reference/filter_joined.html              |  2 +-
 dev/reference/filter_not_exist.html           |  2 +-
 dev/reference/filter_relative.html            |  2 +-
 dev/reference/flag_event.html                 |  2 +-
 dev/reference/format.basket_select.html       |  2 +-
 dev/reference/get_admiral_option.html         |  2 +-
 dev/reference/get_duplicates_dataset.html     |  2 +-
 dev/reference/get_flagged_records.html        |  2 +-
 dev/reference/get_hori_data.html              |  2 +-
 dev/reference/get_imputation_target_date.html |  2 +-
 dev/reference/get_imputation_target_time.html |  2 +-
 dev/reference/get_joined_data.html            |  2 +-
 dev/reference/get_joined_sub_data.html        |  2 +-
 dev/reference/get_many_to_one_dataset.html    |  2 +-
 dev/reference/get_not_mapped.html             |  2 +-
 dev/reference/get_one_to_many_dataset.html    |  2 +-
 dev/reference/get_partialdatetime.html        |  2 +-
 dev/reference/get_summary_records.html        |  2 +-
 dev/reference/get_terms_from_db.html          |  2 +-
 dev/reference/get_vars_query.html             |  2 +-
 dev/reference/impute_dtc_dt.html              |  2 +-
 dev/reference/impute_dtc_dtm.html             |  2 +-
 dev/reference/index.html                      |  2 +-
 dev/reference/list_all_templates.html         |  2 +-
 dev/reference/list_tte_source_objects.html    |  2 +-
 dev/reference/max_cond.html                   |  2 +-
 dev/reference/min_cond.html                   |  2 +-
 dev/reference/negate_vars.html                |  2 +-
 dev/reference/params.html                     |  2 +-
 dev/reference/pipe.html                       |  2 +-
 dev/reference/print.adam_templates.html       |  2 +-
 dev/reference/print.duplicates.html           |  2 +-
 dev/reference/print.source.html               |  2 +-
 dev/reference/print_named_list.html           |  2 +-
 dev/reference/queries.html                    |  2 +-
 dev/reference/queries_mh.html                 |  2 +-
 dev/reference/query.html                      |  2 +-
 dev/reference/records_source.html             |  2 +-
 dev/reference/reexport-desc.html              |  2 +-
 dev/reference/reexport-exprs.html             |  2 +-
 dev/reference/restrict_derivation.html        |  2 +-
 dev/reference/restrict_imputed_dtc_dt.html    |  2 +-
 dev/reference/restrict_imputed_dtc_dtm.html   |  2 +-
 dev/reference/set_admiral_options.html        |  2 +-
 dev/reference/signal_duplicate_records.html   |  2 +-
 dev/reference/slice_derivation.html           |  2 +-
 dev/reference/transform_range.html            |  2 +-
 dev/reference/tte_source.html                 |  2 +-
 dev/reference/tte_source_objects.html         |  2 +-
 dev/reference/use_ad_template.html            |  2 +-
 dev/reference/yn_to_numeric.html              |  2 +-
 dev/search.json                               |  2 +-
 191 files changed, 214 insertions(+), 214 deletions(-)

diff --git a/dev/CODE_OF_CONDUCT.html b/dev/CODE_OF_CONDUCT.html
index c720bacc4f..15f4e403f5 100644
--- a/dev/CODE_OF_CONDUCT.html
+++ b/dev/CODE_OF_CONDUCT.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="index.html">admiral</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9046</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/CONTRIBUTING.html b/dev/CONTRIBUTING.html
index b175f6576c..9ed275bae8 100644
--- a/dev/CONTRIBUTING.html
+++ b/dev/CONTRIBUTING.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="index.html">admiral</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9046</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/LICENSE.html b/dev/LICENSE.html
index 8b79756b31..5e88ab9738 100644
--- a/dev/LICENSE.html
+++ b/dev/LICENSE.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="index.html">admiral</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9046</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/articles/admiral.html b/dev/articles/admiral.html
index 22e8787fd1..6fd2fed1f3 100644
--- a/dev/articles/admiral.html
+++ b/dev/articles/admiral.html
@@ -27,7 +27,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">admiral</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9046</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/articles/adsl.html b/dev/articles/adsl.html
index 8d7f551e09..63b9ec6f9e 100644
--- a/dev/articles/adsl.html
+++ b/dev/articles/adsl.html
@@ -27,7 +27,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">admiral</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9046</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/articles/bds_exposure.html b/dev/articles/bds_exposure.html
index 981dfb978a..f7250cf3d5 100644
--- a/dev/articles/bds_exposure.html
+++ b/dev/articles/bds_exposure.html
@@ -27,7 +27,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">admiral</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9046</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/articles/bds_finding.html b/dev/articles/bds_finding.html
index 853e99e93b..c243564b53 100644
--- a/dev/articles/bds_finding.html
+++ b/dev/articles/bds_finding.html
@@ -27,7 +27,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">admiral</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9046</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -336,8 +336,8 @@ <h3 id="paramcd">Assign <code>PARAMCD</code>, <code>PARAM</code>,
 derivations. Additional derived parameters may be added, so only
 <code>PARAMCD</code> is joined to the datasets at this point. All other
 variables related to <code>PARAMCD</code> (e.g. <code>PARAM</code>,
-<code>PARAMCAT1</code>, …) will be added when all <code>PARAMCD</code>
-are derived.</p>
+<code>PARCAT1</code>, …) will be added when all <code>PARAMCD</code> are
+derived.</p>
 <div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">advs</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/derive_vars_merged_lookup.html">derive_vars_merged_lookup</a></span><span class="op">(</span></span>
 <span>  <span class="va">advs</span>,</span>
diff --git a/dev/articles/bds_tte.html b/dev/articles/bds_tte.html
index 8933fd7022..1a2deacf5a 100644
--- a/dev/articles/bds_tte.html
+++ b/dev/articles/bds_tte.html
@@ -27,7 +27,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">admiral</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9046</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/articles/concepts_conventions.html b/dev/articles/concepts_conventions.html
index e2dac571df..46a58a3acc 100644
--- a/dev/articles/concepts_conventions.html
+++ b/dev/articles/concepts_conventions.html
@@ -27,7 +27,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">admiral</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9046</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/articles/faq.html b/dev/articles/faq.html
index 74502bf2ef..07f0b2c013 100644
--- a/dev/articles/faq.html
+++ b/dev/articles/faq.html
@@ -27,7 +27,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">admiral</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9046</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/articles/generic.html b/dev/articles/generic.html
index 08b1b1a82d..95a73a1000 100644
--- a/dev/articles/generic.html
+++ b/dev/articles/generic.html
@@ -27,7 +27,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">admiral</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9046</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/articles/higher_order.html b/dev/articles/higher_order.html
index 00e11c3307..490dd5d3e6 100644
--- a/dev/articles/higher_order.html
+++ b/dev/articles/higher_order.html
@@ -27,7 +27,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">admiral</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9046</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/articles/hys_law.html b/dev/articles/hys_law.html
index efce5d46ed..983f81040c 100644
--- a/dev/articles/hys_law.html
+++ b/dev/articles/hys_law.html
@@ -27,7 +27,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">admiral</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9046</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/articles/imputation.html b/dev/articles/imputation.html
index 0e56143dc4..ea34ee4a91 100644
--- a/dev/articles/imputation.html
+++ b/dev/articles/imputation.html
@@ -27,7 +27,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">admiral</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9046</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/articles/index.html b/dev/articles/index.html
index bda859d533..ec3fcb9cdb 100644
--- a/dev/articles/index.html
+++ b/dev/articles/index.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">admiral</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9046</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/articles/lab_grading.html b/dev/articles/lab_grading.html
index 14020f5e64..5ee9cb77d3 100644
--- a/dev/articles/lab_grading.html
+++ b/dev/articles/lab_grading.html
@@ -27,7 +27,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">admiral</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9046</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/articles/occds.html b/dev/articles/occds.html
index 7b28b0e2ea..fc0614f214 100644
--- a/dev/articles/occds.html
+++ b/dev/articles/occds.html
@@ -27,7 +27,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">admiral</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9046</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/articles/pk_adnca.html b/dev/articles/pk_adnca.html
index e168d0df25..7e419299bd 100644
--- a/dev/articles/pk_adnca.html
+++ b/dev/articles/pk_adnca.html
@@ -27,7 +27,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">admiral</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9046</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/articles/queries_dataset.html b/dev/articles/queries_dataset.html
index fdb4b9df28..26fec62c75 100644
--- a/dev/articles/queries_dataset.html
+++ b/dev/articles/queries_dataset.html
@@ -27,7 +27,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">admiral</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9046</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/articles/questionnaires.html b/dev/articles/questionnaires.html
index 210a52f808..9ad01801d4 100644
--- a/dev/articles/questionnaires.html
+++ b/dev/articles/questionnaires.html
@@ -27,7 +27,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">admiral</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9046</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/articles/visits_periods.html b/dev/articles/visits_periods.html
index 78bdd10ed8..1e3d1c89fc 100644
--- a/dev/articles/visits_periods.html
+++ b/dev/articles/visits_periods.html
@@ -27,7 +27,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">admiral</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9046</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/articles/website-versions.html b/dev/articles/website-versions.html
index d896125f77..561c3cbc7b 100644
--- a/dev/articles/website-versions.html
+++ b/dev/articles/website-versions.html
@@ -27,7 +27,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">admiral</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9046</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/authors.html b/dev/authors.html
index 83caa55241..576bd2746c 100644
--- a/dev/authors.html
+++ b/dev/authors.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="index.html">admiral</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9046</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -164,14 +164,14 @@ <h2 id="citation">Citation</h2>
 
       <p>Straub B, Bundfuss S, Dickinson J, Farrugia R, Gautier F, Gayatri G, Chakma A, Kulkarni D, Mancini E, Mascary S, Miller G, Patil L, Rothstein J, Shapcott S, Simms E, Sjoberg D, Thoma S, Webster P, Zhang K, Zhu Z (2024).
 <em>admiral: ADaM in R Asset Library</em>.
-R package version 1.1.1.9045,
+R package version 1.1.1.9046,
 https://github.com/pharmaverse/admiral, <a href="https://pharmaverse.github.io/admiral/">https://pharmaverse.github.io/admiral/</a>.
 </p>
       <pre>@Manual{,
   title = {admiral: ADaM in R Asset Library},
   author = {Ben Straub and Stefan Bundfuss and Jeffrey Dickinson and Ross Farrugia and Fanny Gautier and G Gayatri and Asha Chakma and Dinakar Kulkarni and Edoardo Mancini and Sadchla Mascary and Gordon Miller and Lina Patil and Jim Rothstein and Sophie Shapcott and Eric Simms and Daniel Sjoberg and Stefan Thoma and Phillip Webster and Kangjie Zhang and Zelos Zhu},
   year = {2024},
-  note = {R package version 1.1.1.9045,
+  note = {R package version 1.1.1.9046,
     https://github.com/pharmaverse/admiral},
   url = {https://pharmaverse.github.io/admiral/},
 }</pre>
diff --git a/dev/index.html b/dev/index.html
index 04aa172021..58a8a7978d 100644
--- a/dev/index.html
+++ b/dev/index.html
@@ -30,7 +30,7 @@
 
     <a class="navbar-brand me-2" href="index.html">admiral</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9046</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/news/index.html b/dev/news/index.html
index 72a9f30535..666fe0a959 100644
--- a/dev/news/index.html
+++ b/dev/news/index.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">admiral</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9046</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/pkgdown.yml b/dev/pkgdown.yml
index a2f95dffc8..2c7fa59145 100644
--- a/dev/pkgdown.yml
+++ b/dev/pkgdown.yml
@@ -20,7 +20,7 @@ articles:
   questionnaires: questionnaires.html
   visits_periods: visits_periods.html
   articles/website-versions: website-versions.html
-last_built: 2024-12-20T19:57Z
+last_built: 2024-12-22T21:45Z
 urls:
   reference: https://pharmaverse.github.io/admiral/reference
   article: https://pharmaverse.github.io/admiral/articles
diff --git a/dev/reference/admiral-package.html b/dev/reference/admiral-package.html
index 062408c570..8a975bc34b 100644
--- a/dev/reference/admiral-package.html
+++ b/dev/reference/admiral-package.html
@@ -9,7 +9,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">admiral</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9046</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/reference/admiral_adlb.html b/dev/reference/admiral_adlb.html
index d9f022b96e..8da0485f14 100644
--- a/dev/reference/admiral_adlb.html
+++ b/dev/reference/admiral_adlb.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">admiral</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9046</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/reference/admiral_adsl.html b/dev/reference/admiral_adsl.html
index a69e6657b5..1e96d927b0 100644
--- a/dev/reference/admiral_adsl.html
+++ b/dev/reference/admiral_adsl.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">admiral</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9046</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/reference/assert_parameters_argument.html b/dev/reference/assert_parameters_argument.html
index 2419db6894..0ce070130f 100644
--- a/dev/reference/assert_parameters_argument.html
+++ b/dev/reference/assert_parameters_argument.html
@@ -11,7 +11,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">admiral</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9046</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/reference/atoxgr_criteria_ctcv4.html b/dev/reference/atoxgr_criteria_ctcv4.html
index e83f1c290f..a76abb241b 100644
--- a/dev/reference/atoxgr_criteria_ctcv4.html
+++ b/dev/reference/atoxgr_criteria_ctcv4.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">admiral</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9046</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/reference/atoxgr_criteria_ctcv5.html b/dev/reference/atoxgr_criteria_ctcv5.html
index 3c9182002a..0ab56443ed 100644
--- a/dev/reference/atoxgr_criteria_ctcv5.html
+++ b/dev/reference/atoxgr_criteria_ctcv5.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">admiral</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9046</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/reference/atoxgr_criteria_daids.html b/dev/reference/atoxgr_criteria_daids.html
index 64ceab7ffb..d523b5763f 100644
--- a/dev/reference/atoxgr_criteria_daids.html
+++ b/dev/reference/atoxgr_criteria_daids.html
@@ -7,7 +7,7 @@
 
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-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9046</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/reference/basket_select.html b/dev/reference/basket_select.html
index 41b2c3fa5b..5727afa857 100644
--- a/dev/reference/basket_select.html
+++ b/dev/reference/basket_select.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">admiral</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9046</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/reference/call_derivation.html b/dev/reference/call_derivation.html
index a8d70be0a2..ec3236e541 100644
--- a/dev/reference/call_derivation.html
+++ b/dev/reference/call_derivation.html
@@ -9,7 +9,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">admiral</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9046</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/reference/call_user_fun.html b/dev/reference/call_user_fun.html
index 0785868fac..9315588dea 100644
--- a/dev/reference/call_user_fun.html
+++ b/dev/reference/call_user_fun.html
@@ -9,7 +9,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">admiral</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9046</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/reference/censor_source.html b/dev/reference/censor_source.html
index 816cfe54bb..efb8b5260f 100644
--- a/dev/reference/censor_source.html
+++ b/dev/reference/censor_source.html
@@ -11,7 +11,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">admiral</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9046</small>
 
 
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diff --git a/dev/reference/chr2vars.html b/dev/reference/chr2vars.html
index 7051d53839..fdda0e59cc 100644
--- a/dev/reference/chr2vars.html
+++ b/dev/reference/chr2vars.html
@@ -7,7 +7,7 @@
 
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-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
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diff --git a/dev/reference/compute_age_years.html b/dev/reference/compute_age_years.html
index f013e20677..7750472d1b 100644
--- a/dev/reference/compute_age_years.html
+++ b/dev/reference/compute_age_years.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">admiral</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9046</small>
 
 
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diff --git a/dev/reference/compute_bmi.html b/dev/reference/compute_bmi.html
index 53163f0dd2..3e2b445106 100644
--- a/dev/reference/compute_bmi.html
+++ b/dev/reference/compute_bmi.html
@@ -7,7 +7,7 @@
 
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-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
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diff --git a/dev/reference/compute_bsa.html b/dev/reference/compute_bsa.html
index 9d262f388a..8be10e1f56 100644
--- a/dev/reference/compute_bsa.html
+++ b/dev/reference/compute_bsa.html
@@ -7,7 +7,7 @@
 
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-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
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diff --git a/dev/reference/compute_dtf.html b/dev/reference/compute_dtf.html
index 3bc501b7ac..107dbf92eb 100644
--- a/dev/reference/compute_dtf.html
+++ b/dev/reference/compute_dtf.html
@@ -9,7 +9,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">admiral</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
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diff --git a/dev/reference/compute_duration.html b/dev/reference/compute_duration.html
index 78a14354cc..c6b9708500 100644
--- a/dev/reference/compute_duration.html
+++ b/dev/reference/compute_duration.html
@@ -9,7 +9,7 @@
 
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-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
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diff --git a/dev/reference/compute_egfr.html b/dev/reference/compute_egfr.html
index 6ebf65dd38..79a00e7b67 100644
--- a/dev/reference/compute_egfr.html
+++ b/dev/reference/compute_egfr.html
@@ -15,7 +15,7 @@
 
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-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9046</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/reference/compute_framingham.html b/dev/reference/compute_framingham.html
index 9eef4039f2..d26d097618 100644
--- a/dev/reference/compute_framingham.html
+++ b/dev/reference/compute_framingham.html
@@ -13,7 +13,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">admiral</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9046</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/reference/compute_map.html b/dev/reference/compute_map.html
index 658e164cb2..56ae9e93d4 100644
--- a/dev/reference/compute_map.html
+++ b/dev/reference/compute_map.html
@@ -9,7 +9,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">admiral</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9046</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/reference/compute_qtc.html b/dev/reference/compute_qtc.html
index f50c456678..db2203b8ec 100644
--- a/dev/reference/compute_qtc.html
+++ b/dev/reference/compute_qtc.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">admiral</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9046</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/reference/compute_qual_imputation.html b/dev/reference/compute_qual_imputation.html
index 94324d086c..68728b1bd7 100644
--- a/dev/reference/compute_qual_imputation.html
+++ b/dev/reference/compute_qual_imputation.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">admiral</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9046</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/reference/compute_qual_imputation_dec.html b/dev/reference/compute_qual_imputation_dec.html
index 8ef1f44401..2a87ebdb5e 100644
--- a/dev/reference/compute_qual_imputation_dec.html
+++ b/dev/reference/compute_qual_imputation_dec.html
@@ -17,7 +17,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">admiral</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9046</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/reference/compute_rr.html b/dev/reference/compute_rr.html
index 9b578d22df..bbfff87382 100644
--- a/dev/reference/compute_rr.html
+++ b/dev/reference/compute_rr.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">admiral</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9046</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/reference/compute_scale.html b/dev/reference/compute_scale.html
index 400acd20a7..f1e838bbc3 100644
--- a/dev/reference/compute_scale.html
+++ b/dev/reference/compute_scale.html
@@ -13,7 +13,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">admiral</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9046</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/reference/compute_tmf.html b/dev/reference/compute_tmf.html
index c3d08f5e7e..a0b23aa197 100644
--- a/dev/reference/compute_tmf.html
+++ b/dev/reference/compute_tmf.html
@@ -9,7 +9,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">admiral</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9046</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/reference/consolidate_metadata.html b/dev/reference/consolidate_metadata.html
index 40eadfa113..d06a5f53f1 100644
--- a/dev/reference/consolidate_metadata.html
+++ b/dev/reference/consolidate_metadata.html
@@ -11,7 +11,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">admiral</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9046</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/reference/convert_blanks_to_na.html b/dev/reference/convert_blanks_to_na.html
index 1eae287126..eb2b4c915b 100644
--- a/dev/reference/convert_blanks_to_na.html
+++ b/dev/reference/convert_blanks_to_na.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">admiral</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9046</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/reference/convert_date_to_dtm.html b/dev/reference/convert_date_to_dtm.html
index cfc0384668..ea6fec28d5 100644
--- a/dev/reference/convert_date_to_dtm.html
+++ b/dev/reference/convert_date_to_dtm.html
@@ -11,7 +11,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">admiral</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9046</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -214,7 +214,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="fu">convert_date_to_dtm</span><span class="op">(</span><span class="st">"2019-07-18T15:25:00"</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] "2019-07-18 15:25:00 UTC"</span>
 <span class="r-in"><span><span class="fu">convert_date_to_dtm</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Sys.time.html" class="external-link">Sys.time</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "2024-12-20 19:57:32 UTC"</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "2024-12-22 21:46:00 UTC"</span>
 <span class="r-in"><span><span class="fu">convert_date_to_dtm</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/as.Date.html" class="external-link">as.Date</a></span><span class="op">(</span><span class="st">"2019-07-18"</span><span class="op">)</span>, time_imputation <span class="op">=</span> <span class="st">"23:59:59"</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] "2019-07-18 23:59:59 UTC"</span>
 <span class="r-in"><span><span class="fu">convert_date_to_dtm</span><span class="op">(</span><span class="st">"2019-07-18"</span>, time_imputation <span class="op">=</span> <span class="st">"23:59:59"</span><span class="op">)</span></span></span>
diff --git a/dev/reference/convert_dtc_to_dt.html b/dev/reference/convert_dtc_to_dt.html
index 2a89e8d9ab..3dc9f627c7 100644
--- a/dev/reference/convert_dtc_to_dt.html
+++ b/dev/reference/convert_dtc_to_dt.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">admiral</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9046</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/reference/convert_dtc_to_dtm.html b/dev/reference/convert_dtc_to_dtm.html
index 5ec1c1a887..794424216e 100644
--- a/dev/reference/convert_dtc_to_dtm.html
+++ b/dev/reference/convert_dtc_to_dtm.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">admiral</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9046</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/reference/convert_na_to_blanks.html b/dev/reference/convert_na_to_blanks.html
index 0ee9d4bbdc..6ffc80018f 100644
--- a/dev/reference/convert_na_to_blanks.html
+++ b/dev/reference/convert_na_to_blanks.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">admiral</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9046</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/reference/count_vals.html b/dev/reference/count_vals.html
index 7217123d68..3704bbc1f4 100644
--- a/dev/reference/count_vals.html
+++ b/dev/reference/count_vals.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">admiral</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9046</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/reference/country_code_lookup.html b/dev/reference/country_code_lookup.html
index cdff79978c..3d7f80750c 100644
--- a/dev/reference/country_code_lookup.html
+++ b/dev/reference/country_code_lookup.html
@@ -11,7 +11,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">admiral</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9046</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/reference/create_period_dataset.html b/dev/reference/create_period_dataset.html
index a72e9e654b..cc7fcfdfae 100644
--- a/dev/reference/create_period_dataset.html
+++ b/dev/reference/create_period_dataset.html
@@ -15,7 +15,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">admiral</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9046</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/reference/create_query_data.html b/dev/reference/create_query_data.html
index db35e5ecc2..6c46b95baa 100644
--- a/dev/reference/create_query_data.html
+++ b/dev/reference/create_query_data.html
@@ -9,7 +9,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">admiral</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9046</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/reference/create_single_dose_dataset.html b/dev/reference/create_single_dose_dataset.html
index 45ad4782b1..ccba693e1e 100644
--- a/dev/reference/create_single_dose_dataset.html
+++ b/dev/reference/create_single_dose_dataset.html
@@ -11,7 +11,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">admiral</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9046</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/reference/date_source.html b/dev/reference/date_source.html
index 1cfa9967a4..72e274b684 100644
--- a/dev/reference/date_source.html
+++ b/dev/reference/date_source.html
@@ -13,7 +13,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">admiral</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9046</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/reference/default_qtc_paramcd.html b/dev/reference/default_qtc_paramcd.html
index cccbcf8250..254de2007f 100644
--- a/dev/reference/default_qtc_paramcd.html
+++ b/dev/reference/default_qtc_paramcd.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">admiral</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9046</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/reference/derivation_slice.html b/dev/reference/derivation_slice.html
index 3fcfd98137..57024576ef 100644
--- a/dev/reference/derivation_slice.html
+++ b/dev/reference/derivation_slice.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">admiral</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9046</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/reference/derive_basetype_records.html b/dev/reference/derive_basetype_records.html
index 11b72d064e..49cbad1a59 100644
--- a/dev/reference/derive_basetype_records.html
+++ b/dev/reference/derive_basetype_records.html
@@ -1,19 +1,19 @@
 <!DOCTYPE html>
 <!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Derive Basetype Variable — derive_basetype_records • admiral</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><script src="../deps/MathJax-3.2.2/tex-chtml.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Derive Basetype Variable — derive_basetype_records"><meta name="description" content="Baseline Type BASETYPE is needed when there is more than one definition of
 baseline for a given Analysis Parameter PARAM in the same dataset.  For a
-given parameter, if Baseline Value BASE is populated, and there is more than
-one definition of baseline, then BASETYPE must be non-null on all records of
-any type for that parameter. Each value of BASETYPE refers to a definition of
-baseline that characterizes the value of BASE on that row.  Please see
-section 4.2.1.6 of the ADaM Implementation Guide, version 1.3 for further
-background."><meta property="og:description" content="Baseline Type BASETYPE is needed when there is more than one definition of
+given parameter, if Baseline Value BASE or BASEC are derived and there
+is more than one definition of baseline, then BASETYPE must be non-null on
+all records of any type for that parameter. Each value of BASETYPE refers
+to a definition of baseline that characterizes the value of BASE on that
+row.  Please see section 4.2.1.6 of the ADaM Implementation Guide, version
+1.3 for further background."><meta property="og:description" content="Baseline Type BASETYPE is needed when there is more than one definition of
 baseline for a given Analysis Parameter PARAM in the same dataset.  For a
-given parameter, if Baseline Value BASE is populated, and there is more than
-one definition of baseline, then BASETYPE must be non-null on all records of
-any type for that parameter. Each value of BASETYPE refers to a definition of
-baseline that characterizes the value of BASE on that row.  Please see
-section 4.2.1.6 of the ADaM Implementation Guide, version 1.3 for further
-background."><meta property="og:image" content="https://pharmaverse.github.io/admiral/logo.png"><meta name="robots" content="noindex"></head><body>
+given parameter, if Baseline Value BASE or BASEC are derived and there
+is more than one definition of baseline, then BASETYPE must be non-null on
+all records of any type for that parameter. Each value of BASETYPE refers
+to a definition of baseline that characterizes the value of BASE on that
+row.  Please see section 4.2.1.6 of the ADaM Implementation Guide, version
+1.3 for further background."><meta property="og:image" content="https://pharmaverse.github.io/admiral/logo.png"><meta name="robots" content="noindex"></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
 
@@ -21,7 +21,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">admiral</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9046</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -84,12 +84,12 @@
     <div class="ref-description section level2">
     <p>Baseline Type <code>BASETYPE</code> is needed when there is more than one definition of
 baseline for a given Analysis Parameter <code>PARAM</code> in the same dataset.  For a
-given parameter, if Baseline Value <code>BASE</code> is populated, and there is more than
-one definition of baseline, then <code>BASETYPE</code> must be non-null on all records of
-any type for that parameter. Each value of <code>BASETYPE</code> refers to a definition of
-baseline that characterizes the value of <code>BASE</code> on that row.  Please see
-section 4.2.1.6 of the ADaM Implementation Guide, version 1.3 for further
-background.</p>
+given parameter, if Baseline Value <code>BASE</code> or <code>BASEC</code> are derived and there
+is more than one definition of baseline, then <code>BASETYPE</code> must be non-null on
+all records of any type for that parameter. Each value of <code>BASETYPE</code> refers
+to a definition of baseline that characterizes the value of <code>BASE</code> on that
+row.  Please see section 4.2.1.6 of the ADaM Implementation Guide, version
+1.3 for further background.</p>
     </div>
 
     <div class="section level2">
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index d952f37262..3b6b33614a 100644
--- a/dev/reference/derive_expected_records.html
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-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
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index ac6a05514e..f478f24737 100644
--- a/dev/reference/derive_extreme_event.html
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@@ -17,7 +17,7 @@
 
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index 5f9446dbee..f61748d1d7 100644
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index 62742b465b..8832f11cce 100644
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index a9907bf886..fcf762e7b6 100644
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index 14e2df6713..20ef658eac 100644
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index 8ab7bab349..135847bcc7 100644
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index c972feed37..a3341f76d1 100644
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index 83f713ef53..474e6c1ffe 100644
--- a/dev/reference/derive_param_exist_flag.html
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@@ -13,7 +13,7 @@
 
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index 1c47eaa642..534d1eee92 100644
--- a/dev/reference/derive_param_exposure.html
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-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
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index a42972c249..42a2901998 100644
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index 451ade7f2f..ea06facbe4 100644
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index 49b3ffbed4..865ee10ae7 100644
--- a/dev/reference/get_joined_data.html
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index 00b22f618d..483216ef6e 100644
--- a/dev/reference/get_joined_sub_data.html
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index 067e51dec0..6dc8a01289 100644
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index 541e7c2a7f..ecfc43adb0 100644
--- a/dev/reference/get_not_mapped.html
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@@ -7,7 +7,7 @@
 
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index d67d5b0c24..01ec97cb17 100644
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@@ -7,7 +7,7 @@
 
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index 4823d39520..c559bc4033 100644
--- a/dev/reference/get_partialdatetime.html
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@@ -7,7 +7,7 @@
 
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index 3659af8bb3..591b61bc83 100644
--- a/dev/reference/get_summary_records.html
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index 04e02d001d..c039655777 100644
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index 0b75d2348d..8382b05d05 100644
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@@ -9,7 +9,7 @@
 
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index 7cecc08074..bb9232b79b 100644
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@@ -7,7 +7,7 @@
 
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index ed89ae25b2..a98260b6b9 100644
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@@ -7,7 +7,7 @@
 
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index 5279cc7c00..c652636ad3 100644
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@@ -7,7 +7,7 @@
 
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index 24a6647457..ed071158b6 100644
--- a/dev/reference/max_cond.html
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@@ -9,7 +9,7 @@
 
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index d4d1f8d8e3..02bb8cb1ce 100644
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@@ -9,7 +9,7 @@
 
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index 4bb9c84967..19cc573129 100644
--- a/dev/reference/negate_vars.html
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@@ -7,7 +7,7 @@
 
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index 97a8cd097c..374244145b 100644
--- a/dev/reference/params.html
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@@ -7,7 +7,7 @@
 
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index 09543599db..ad27f0a23c 100644
--- a/dev/reference/pipe.html
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@@ -7,7 +7,7 @@
 
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index c8cfe4a13d..62bd6804e8 100644
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index a6dca4ff09..3a1821d96d 100644
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@@ -7,7 +7,7 @@
 
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index 41827b31bb..6ceaf74fb1 100644
--- a/dev/reference/print.source.html
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@@ -7,7 +7,7 @@
 
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index 80d6df922b..43b082c68c 100644
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@@ -7,7 +7,7 @@
 
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index 3e5b071f87..ae72f50234 100644
--- a/dev/reference/queries.html
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@@ -7,7 +7,7 @@
 
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index 092c4cf305..633537235e 100644
--- a/dev/reference/queries_mh.html
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@@ -7,7 +7,7 @@
 
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index 19f8f14344..7e99b24c76 100644
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index bf2b59f55a..0d96f99961 100644
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index c72fed8029..9b8e5f046b 100644
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@@ -7,7 +7,7 @@
 
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index 5a11bbd132..7760be0cbb 100644
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@@ -7,7 +7,7 @@
 
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diff --git a/dev/reference/restrict_derivation.html b/dev/reference/restrict_derivation.html
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diff --git a/dev/reference/restrict_imputed_dtc_dt.html b/dev/reference/restrict_imputed_dtc_dt.html
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-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">1.1.1.9045</small>
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diff --git a/dev/reference/restrict_imputed_dtc_dtm.html b/dev/reference/restrict_imputed_dtc_dtm.html
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diff --git a/dev/reference/set_admiral_options.html b/dev/reference/set_admiral_options.html
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diff --git a/dev/reference/signal_duplicate_records.html b/dev/reference/signal_duplicate_records.html
index 9a2f8c0d85..500339a7e9 100644
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diff --git a/dev/reference/slice_derivation.html b/dev/reference/slice_derivation.html
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diff --git a/dev/reference/transform_range.html b/dev/reference/transform_range.html
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diff --git a/dev/reference/tte_source.html b/dev/reference/tte_source.html
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diff --git a/dev/reference/tte_source_objects.html b/dev/reference/tte_source_objects.html
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diff --git a/dev/reference/use_ad_template.html b/dev/reference/use_ad_template.html
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diff --git a/dev/reference/yn_to_numeric.html b/dev/reference/yn_to_numeric.html
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diff --git a/dev/search.json b/dev/search.json
index a2479a7936..6f0d9f9dd3 100644
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+++ b/dev/search.json
@@ -1 +1 @@
-[{"path":"https://pharmaverse.github.io/admiral/dev/CODE_OF_CONDUCT.html","id":null,"dir":"","previous_headings":"","what":"Contributor Code of Conduct","title":"Contributor Code of Conduct","text":"contributors maintainers project, pledge respect people contribute reporting issues, posting feature requests, updating documentation, submitting pull requests patches, activities. committed making participation project harassment-free experience everyone, regardless level experience, gender, gender identity expression, sexual orientation, disability, personal appearance, body size, race, ethnicity, age, religion. Examples unacceptable behavior participants include use sexual language imagery, derogatory comments personal attacks, trolling, public private harassment, insults, unprofessional conduct. Project maintainers right responsibility remove, edit, reject comments, commits, code, wiki edits, issues, contributions aligned Code Conduct. Project maintainers follow Code Conduct may removed project team. Instances abusive, harassing, otherwise unacceptable behavior may reported opening issue contacting one project maintainers. Code Conduct adapted Contributor Covenant (http://www.contributor-covenant.org), version 1.0.0, available http://www.contributor-covenant.org/version/1/0/0/","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/CONTRIBUTING.html","id":null,"dir":"","previous_headings":"","what":"Contribution to {admiral}","title":"Contribution to {admiral}","text":"outlines propose change admiral package. detailed info contributing {admiral}, pharmaverse packages, please see Contribution Guide well Developer Guides Articles section {admiraldev} website. Please note try align best practices used R packages’ development processes - veteran developers familiar processes. However, deviate slightly best practices advise new contributors review package documentation accordingly.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/CONTRIBUTING.html","id":"basics-of-contribution","dir":"","previous_headings":"","what":"Basics of Contribution","title":"Contribution to {admiral}","text":"🦋 new contribution, user creates issue issue tab GitHub put backlog. issues can range bug identification /fixes, enhancements functions, documentation, tests new features. 🦋 advise contact us issue created via Slack (don’t access, use link join). can discuss details align expectations familiar admiral philosophy programming strategy. team try review issues within next backlog meeting give initial feedback. Since 100% fully resourced software development team might issues take longer respond depending amount overall issues. 🦋 advise familiarize programming strategy, guidance GitHub usage unit testing. 🦋 newly created issues reviewed within next backlog meeting creator receive initial feedback via comment. Someone core development team triage new issues assigning appropriate labels (“user request” can easily identify new requests). 🦋 Issues meant taken users Pharma programming community exclusively admiral team GSK Roche.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/CONTRIBUTING.html","id":"type-1-contribution-without-code","dir":"","previous_headings":"","what":"Type 1: Contribution without Code","title":"Contribution to {admiral}","text":"🦋 User creates issue ideally contacts admiral team member via Slack. 🦋 admiral core development team contact issue creator soon possible discuss details.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/CONTRIBUTING.html","id":"type-2-contribution-with-code","dir":"","previous_headings":"","what":"Type 2: Contribution with Code","title":"Contribution to {admiral}","text":"🦋 First, user creates issue comments existing issue notify ’d like contribute code. 🦋 Follow development process step--step guide. 🦋 advise contact admiral core development team directly via Slack submitting code complex functionality.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/CONTRIBUTING.html","id":"detailed-development-process","dir":"","previous_headings":"","what":"Detailed Development Process","title":"Contribution to {admiral}","text":"decide contribute code ’re ready make first code contribution, detailed development process step--step guide help tie detailed vignettes together give simplest experience helping grow enhance codebase. Create new feature branch main branch following naming convention pull latest changes - detailed GitHub usage guide. Familiarize admiral programming strategy, make required code updates. code formatted according tidyverse style guide? create/add appropriate unit tests? removed/replaced function /function parameters, fully follow deprecation guidance? update [documentation]https://pharmaverse.github.io/admiraldev/articles/programming_strategy.html#function-header-documentation)? , remember run devtools::document() include updated NAMESPACE .Rd files man/. code update impact ADaM template R scripts stored inst/templates? code update impact vignettes stored vignettes? update Changelog NEWS.md? build admiral site pkgdown::build_site() check affected examples displayed correctly new functions occur “Reference” page? happy updates, make pull request merge main branch link issue closes successful merging. Check merge conflicts. , fix requesting review. See solving merge conflicts guidance. Check results automated R-CMD check lintr checks issues consult guide. Assign reviewer admiral core development team - anyone discussed issue previously via Slack GitHub. unsure, add comment pull request ready review add @pharmaverse/admiral tag . review completed, reviewer merge PR automatically delete feature branch. Finally, just note say core developers hugely appreciate taking time contribute admiral. Don’t offended review send requests back , expectations high can ensure admiral codebase remains robust consistent. best way learn always jump get involved, please don’t afraid ’ll make mistakes along way – continue , ’s reviews . Also ever get stuck don’t hesitate reach support via Slack channel. Welcome admiral community!","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"Apache License","title":"Apache License","text":"Version 2.0, January 2004 <http://www.apache.org/licenses/>","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/LICENSE.html","id":"id_1-definitions","dir":"","previous_headings":"Terms and Conditions for use, reproduction, and distribution","what":"1. Definitions","title":"Apache License","text":"“License” shall mean terms conditions use, reproduction, distribution defined Sections 1 9 document. “Licensor” shall mean copyright owner entity authorized copyright owner granting License. “Legal Entity” shall mean union acting entity entities control, controlled , common control entity. purposes definition, “control” means () power, direct indirect, cause direction management entity, whether contract otherwise, (ii) ownership fifty percent (50%) outstanding shares, (iii) beneficial ownership entity. “” (“”) shall mean individual Legal Entity exercising permissions granted License. “Source” form shall mean preferred form making modifications, including limited software source code, documentation source, configuration files. “Object” form shall mean form resulting mechanical transformation translation Source form, including limited compiled object code, generated documentation, conversions media types. “Work” shall mean work authorship, whether Source Object form, made available License, indicated copyright notice included attached work (example provided Appendix ). “Derivative Works” shall mean work, whether Source Object form, based (derived ) Work editorial revisions, annotations, elaborations, modifications represent, whole, original work authorship. purposes License, Derivative Works shall include works remain separable , merely link (bind name) interfaces , Work Derivative Works thereof. “Contribution” shall mean work authorship, including original version Work modifications additions Work Derivative Works thereof, intentionally submitted Licensor inclusion Work copyright owner individual Legal Entity authorized submit behalf copyright owner. purposes definition, “submitted” means form electronic, verbal, written communication sent Licensor representatives, including limited communication electronic mailing lists, source code control systems, issue tracking systems managed , behalf , Licensor purpose discussing improving Work, excluding communication conspicuously marked otherwise designated writing copyright owner “Contribution.” “Contributor” shall mean Licensor individual Legal Entity behalf Contribution received Licensor subsequently incorporated within Work.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/LICENSE.html","id":"id_2-grant-of-copyright-license","dir":"","previous_headings":"Terms and Conditions for use, reproduction, and distribution","what":"2. Grant of Copyright License","title":"Apache License","text":"Subject terms conditions License, Contributor hereby grants perpetual, worldwide, non-exclusive, -charge, royalty-free, irrevocable copyright license reproduce, prepare Derivative Works , publicly display, publicly perform, sublicense, distribute Work Derivative Works Source Object form.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/LICENSE.html","id":"id_3-grant-of-patent-license","dir":"","previous_headings":"Terms and Conditions for use, reproduction, and distribution","what":"3. Grant of Patent License","title":"Apache License","text":"Subject terms conditions License, Contributor hereby grants perpetual, worldwide, non-exclusive, -charge, royalty-free, irrevocable (except stated section) patent license make, made, use, offer sell, sell, import, otherwise transfer Work, license applies patent claims licensable Contributor necessarily infringed Contribution(s) alone combination Contribution(s) Work Contribution(s) submitted. institute patent litigation entity (including cross-claim counterclaim lawsuit) alleging Work Contribution incorporated within Work constitutes direct contributory patent infringement, patent licenses granted License Work shall terminate date litigation filed.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/LICENSE.html","id":"id_4-redistribution","dir":"","previous_headings":"Terms and Conditions for use, reproduction, and distribution","what":"4. Redistribution","title":"Apache License","text":"may reproduce distribute copies Work Derivative Works thereof medium, without modifications, Source Object form, provided meet following conditions: () must give recipients Work Derivative Works copy License; (b) must cause modified files carry prominent notices stating changed files; (c) must retain, Source form Derivative Works distribute, copyright, patent, trademark, attribution notices Source form Work, excluding notices pertain part Derivative Works; (d) Work includes “NOTICE” text file part distribution, Derivative Works distribute must include readable copy attribution notices contained within NOTICE file, excluding notices pertain part Derivative Works, least one following places: within NOTICE text file distributed part Derivative Works; within Source form documentation, provided along Derivative Works; , within display generated Derivative Works, wherever third-party notices normally appear. contents NOTICE file informational purposes modify License. may add attribution notices within Derivative Works distribute, alongside addendum NOTICE text Work, provided additional attribution notices construed modifying License. may add copyright statement modifications may provide additional different license terms conditions use, reproduction, distribution modifications, Derivative Works whole, provided use, reproduction, distribution Work otherwise complies conditions stated License.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/LICENSE.html","id":"id_5-submission-of-contributions","dir":"","previous_headings":"Terms and Conditions for use, reproduction, and distribution","what":"5. Submission of Contributions","title":"Apache License","text":"Unless explicitly state otherwise, Contribution intentionally submitted inclusion Work Licensor shall terms conditions License, without additional terms conditions. Notwithstanding , nothing herein shall supersede modify terms separate license agreement may executed Licensor regarding Contributions.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/LICENSE.html","id":"id_6-trademarks","dir":"","previous_headings":"Terms and Conditions for use, reproduction, and distribution","what":"6. Trademarks","title":"Apache License","text":"License grant permission use trade names, trademarks, service marks, product names Licensor, except required reasonable customary use describing origin Work reproducing content NOTICE file.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/LICENSE.html","id":"id_7-disclaimer-of-warranty","dir":"","previous_headings":"Terms and Conditions for use, reproduction, and distribution","what":"7. Disclaimer of Warranty","title":"Apache License","text":"Unless required applicable law agreed writing, Licensor provides Work (Contributor provides Contributions) “” BASIS, WITHOUT WARRANTIES CONDITIONS KIND, either express implied, including, without limitation, warranties conditions TITLE, NON-INFRINGEMENT, MERCHANTABILITY, FITNESS PARTICULAR PURPOSE. solely responsible determining appropriateness using redistributing Work assume risks associated exercise permissions License.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/LICENSE.html","id":"id_8-limitation-of-liability","dir":"","previous_headings":"Terms and Conditions for use, reproduction, and distribution","what":"8. Limitation of Liability","title":"Apache License","text":"event legal theory, whether tort (including negligence), contract, otherwise, unless required applicable law (deliberate grossly negligent acts) agreed writing, shall Contributor liable damages, including direct, indirect, special, incidental, consequential damages character arising result License use inability use Work (including limited damages loss goodwill, work stoppage, computer failure malfunction, commercial damages losses), even Contributor advised possibility damages.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/LICENSE.html","id":"id_9-accepting-warranty-or-additional-liability","dir":"","previous_headings":"Terms and Conditions for use, reproduction, and distribution","what":"9. Accepting Warranty or Additional Liability","title":"Apache License","text":"redistributing Work Derivative Works thereof, may choose offer, charge fee , acceptance support, warranty, indemnity, liability obligations /rights consistent License. However, accepting obligations, may act behalf sole responsibility, behalf Contributor, agree indemnify, defend, hold Contributor harmless liability incurred , claims asserted , Contributor reason accepting warranty additional liability. END TERMS CONDITIONS","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/LICENSE.html","id":"appendix-how-to-apply-the-apache-license-to-your-work","dir":"","previous_headings":"","what":"APPENDIX: How to apply the Apache License to your work","title":"Apache License","text":"apply Apache License work, attach following boilerplate notice, fields enclosed brackets [] replaced identifying information. (Don’t include brackets!) text enclosed appropriate comment syntax file format. also recommend file class name description purpose included “printed page” copyright notice easier identification within third-party archives.","code":"Copyright 2021 F. Hoffmann-La Roche AG and GlaxoSmithKline LLC  Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at    http://www.apache.org/licenses/LICENSE-2.0  Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License."},{"path":"https://pharmaverse.github.io/admiral/dev/articles/admiral.html","id":"main-idea","dir":"Articles","previous_headings":"","what":"Main Idea","title":"Get Started","text":"main idea admiral ADaM dataset built sequence derivations. derivation adds one variables records processed dataset. modular approach makes easy adjust code adding, removing, modifying derivations. derivation function call. vignette explore different type derivation functions offered admiral, well argument conventions follow best start admiral script.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/admiral.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Get Started","text":"help examples showcased, load packages set example data manipulate later. dplyr, lubridate stringr packages tidyverse packages used heavily throughout vignette. admiral package also leverages pharmaversesdtm package example SDTM datasets CDISC Pilot Study. adsl advs objects now prepared showcase addition variables addition records later. Note: example , reading dummy R data pharmaversesdtm. However, using SAS datasets starting point, sure consult section Handling Missing Values Programming Concepts Conventions User Guide learn use function convert_blanks_to_na() process.","code":"# Uncomment line below if you need to install these packages # install.packages(c(\"dplyr\", \"lubridate\", \"stringr\", \"tibble\", \"pharmaversesdtm\", \"admiral\"))  library(dplyr, warn.conflicts = FALSE) library(lubridate) library(stringr) library(tibble) library(pharmaversesdtm) library(admiral)  # Read in SDTM datasets dm <- pharmaversesdtm::dm ds <- pharmaversesdtm::ds ex <- pharmaversesdtm::ex vs <- pharmaversesdtm::vs admiral_adsl <- admiral::admiral_adsl ex_ext <- ex %>%   derive_vars_dtm(     dtc = EXSTDTC,     new_vars_prefix = \"EXST\"   )  vs <- vs %>%   filter(     USUBJID %in% c(       \"01-701-1015\", \"01-701-1023\", \"01-703-1086\",       \"01-703-1096\", \"01-707-1037\", \"01-716-1024\"     ) &       VSTESTCD %in% c(\"SYSBP\", \"DIABP\") &       VSPOS == \"SUPINE\"   )  adsl <- admiral_adsl %>%   select(-TRTSDTM, -TRTSTMF)  advs <- vs %>%   mutate(     PARAM = VSTEST, PARAMCD = VSTESTCD, AVAL = VSSTRESN, AVALU = VSORRESU,     AVISIT = VISIT, AVISITN = VISITNUM   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/admiral.html","id":"derivation-functions","dir":"Articles","previous_headings":"","what":"Derivation Functions","title":"Get Started","text":"important functions admiral derivations. Derivations add variables observations/records input dataset. Existing variables observations input dataset changed. Derivation functions start derive_. first argument functions expects input dataset. allows us string together derivations using %>% operator. Functions derive dedicated variable start derive_var_ followed variable name, e.g., derive_var_trtdurd() derives TRTDURD variable. Functions can derive multiple variables start derive_vars_ followed variable name, e.g., derive_vars_dtm() can derive TRTSDTM TRTSTMF variables. Functions derive dedicated parameter start derive_param_ followed parameter name, e.g., derive_param_bmi() derives BMI parameter.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/admiral.html","id":"addvars","dir":"Articles","previous_headings":"Derivation Functions","what":"Example: Adding Variables","title":"Get Started","text":"can see example call one common derivation functions, derive_vars_merged(). function adds variable(s) input dataset based contents another dataset. example, add treatment start datetime corresponding imputation flag (EXSTTMF) adsl identifying first record ex non-missing Exposure Start Datetime (EXSTDTM) sorting EXSTDM EXSEQ.","code":"adsl <- adsl %>%   derive_vars_merged(     dataset_add = ex_ext,     filter_add = !is.na(EXSTDTM),     new_vars = exprs(TRTSDTM = EXSTDTM, TRTSTMF = EXSTTMF),     order = exprs(EXSTDTM, EXSEQ),     mode = \"first\",     by_vars = exprs(STUDYID, USUBJID)   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/admiral.html","id":"addrecs","dir":"Articles","previous_headings":"Derivation Functions","what":"Example: Adding Records","title":"Get Started","text":"Another common derivation function derive_param_computed(). function adds derived parameter input dataset. example , use derive Mean Arterial Pressure (MAP) Systolic Diastolic values blood pressure. parameters needed derivation specified parameters argument, within set_values_to set variable values new derived record. Note: users’ convenience, admiral actually provides derive_param_map() (wrapper derive_param_computed()) derive MAP. example serves illustrative purposes .","code":"advs <- advs %>%   derive_param_computed(     by_vars = exprs(USUBJID, AVISIT, AVISITN),     parameters = c(\"SYSBP\", \"DIABP\"),     set_values_to = exprs(       AVAL = (AVAL.SYSBP + 2 * AVAL.DIABP) / 3,       PARAMCD = \"MAP\",       PARAM = \"Mean Arterial Pressure (mmHg)\",       AVALU = \"mmHg\"     )   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/admiral.html","id":"other-types-of-functions","dir":"Articles","previous_headings":"","what":"Other Types of Functions","title":"Get Started","text":"Along derivation functions, admiral provides large arsenal functions help ADaM derivations. categories.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/admiral.html","id":"higher-order-functions","dir":"Articles","previous_headings":"Other Types of Functions","what":"Higher Order Functions","title":"Get Started","text":"Higher order functions admiral functions take functions input. enhance existing portfolio derivation functions allowing greater customization latter’s behavior. done allowing derivation function : call_derivation() - Called multiple times, varying input arguments. restrict_derivation() - Executed subset input dataset. slice_derivation() - Executed differently subsets input dataset. Higher order functions relatively advanced topic within admiral; can can read dedicated vignette Higher Order Functions.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/admiral.html","id":"computation-functions","dir":"Articles","previous_headings":"Other Types of Functions","what":"Computation Functions","title":"Get Started","text":"Computations expect vectors input return vector. Usually computation functions can used %>%. functions can used expressions like convert_dtc_to_dt() derivation Final Lab Visit Date (FINLABDT) example : Computations can also used inside mutate() statement (similar), can leveraged variable derivations require calls specific admiral functions:","code":"# Add the date of the final lab visit to ADSL adsl <- dm %>%   derive_vars_merged(     dataset_add = ds,     by_vars = exprs(USUBJID),     new_vars = exprs(FINLABDT = convert_dtc_to_dt(DSSTDTC)),     filter_add = DSDECOD == \"FINAL LAB VISIT\"   ) adsl <- adsl %>%   mutate(RFSTDT = convert_dtc_to_dt(RFSTDTC))"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/admiral.html","id":"filter-functions","dir":"Articles","previous_headings":"Other Types of Functions","what":"Filter Functions","title":"Get Started","text":"Filter functions admiral utilities filter input dataset different manners, instance returning records fit/don’t fit certain condition first/last observation group. functions form important internal backbone admiral functions, can also used explore manipulate dataset. instance, example use filter_extreme() extract recent MAP records advs:","code":"advs_lastmap <- advs %>%   filter(PARAMCD == \"MAP\") %>%   filter_extreme(     by_vars = exprs(USUBJID),     order = exprs(AVISITN, PARAMCD),     mode = \"last\"   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/admiral.html","id":"argument-conventions","dir":"Articles","previous_headings":"","what":"Argument Conventions","title":"Get Started","text":"Within admiral package, arguments expect variable names expressions variable names, symbols expressions must specified rather strings. arguments expect single variable name, name can specified without quotes quotation, e.g. new_var = TEMPBL arguments expect one variable names, list symbols expected, e.g. by_vars = exprs(PARAMCD, AVISIT) arguments expect single expression, expression needs passed “”, e.g. filter = PARAMCD == \"TEMP\" arguments expect one expressions, list expressions expected, e.g. order = exprs(AVISIT, desc(AESEV)) new expressions, consider reading Expressions Scripts section Concepts Conventions User Guide learn .","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/admiral.html","id":"starting-a-script","dir":"Articles","previous_headings":"","what":"Starting a Script","title":"Get Started","text":"ADaM data structures, overview flow example function calls common steps provided following vignettes: Creating ADSL Creating OCCDS ADaM Creating BDS Findings ADaM admiral also provides template R scripts starting point. can created calling use_ad_template(), e.g., list available templates can obtained list_all_templates():","code":"use_ad_template(   adam_name = \"adsl\",   save_path = \"./ad_adsl.R\" ) library(admiral) list_all_templates() #> Existing ADaM templates in package 'admiral': #> • ADAE #> • ADCM #> • ADEG #> • ADEX #> • ADLB #> • ADLBHY #> • ADMH #> • ADPC #> • ADPP #> • ADPPK #> • ADSL #> • ADVS"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/admiral.html","id":"support","dir":"Articles","previous_headings":"","what":"Support","title":"Get Started","text":"Support provided via admiral Slack channel. Additionally, please feel free raise issues GitHub repository.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/admiral.html","id":"see-also","dir":"Articles","previous_headings":"","what":"See also","title":"Get Started","text":"Template scripts Programming Concepts Conventions Programming Strategy","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/adsl.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Creating ADSL","text":"article describes creating ADSL ADaM. Examples currently presented tested using DM, EX , AE, LB DS SDTM domains. However, domains used. Note: examples assume CDISC SDTM /ADaM format input unless otherwise specified.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/adsl.html","id":"programming-flow","dir":"Articles","previous_headings":"","what":"Programming Flow","title":"Creating ADSL","text":"Read Data Derive Period, Subperiod, Phase Variables (e.g. APxxSDT, APxxEDT, …) Derive Treatment Variables (TRT0xP, TRT0xA) Derive/Impute Numeric Treatment Date/Time Duration (TRTSDT, TRTEDT, TRTDURD) Disposition Dates (e.g. EOSDT) Disposition Status (e.g. EOSTT) Disposition Reason(s) (e.g. DCSREAS, DCSREASP) Randomization Date (RANDDT) Death Date (DTHDT) Cause Death (DTHCAUS) Duration Relative Death Derive Last Known Date Alive (LSTALVDT) Grouping (e.g. AGEGR1 REGION1) Population Flags (e.g. SAFFL) Derive Variables Add Labels Attributes","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/adsl.html","id":"readdata","dir":"Articles","previous_headings":"Programming Flow","what":"Read in Data","title":"Creating ADSL","text":"start, data frames needed creation ADSL read environment. company specific process. data frames needed may DM, EX, DS, AE, LB. example purpose, CDISC Pilot SDTM datasets—included pharmaversesdtm—used. DM domain used basis ADSL:","code":"library(admiral) library(dplyr, warn.conflicts = FALSE) library(pharmaversesdtm) library(lubridate) library(stringr)  dm <- pharmaversesdtm::dm ds <- pharmaversesdtm::ds ex <- pharmaversesdtm::ex ae <- pharmaversesdtm::ae lb <- pharmaversesdtm::lb  dm <- convert_blanks_to_na(dm) ds <- convert_blanks_to_na(ds) ex <- convert_blanks_to_na(ex) ae <- convert_blanks_to_na(ae) lb <- convert_blanks_to_na(lb) adsl <- dm %>%   select(-DOMAIN)"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/adsl.html","id":"periodvars","dir":"Articles","previous_headings":"Programming Flow","what":"Derive Period, Subperiod, and Phase Variables (e.g. APxxSDT, APxxEDT, …)","title":"Creating ADSL","text":"See “Visit Period Variables” vignette information. variables derived based period reference dataset, may derived later point flow. example, phases like “Treatment Phase” “Follow ” derived based treatment start end date.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/adsl.html","id":"treatmentvar","dir":"Articles","previous_headings":"Programming Flow","what":"Derive Treatment Variables (TRT0xP, TRT0xA)","title":"Creating ADSL","text":"mapping treatment variables left ADaM programmer. example mapping study without periods may : studies periods see “Visit Period Variables” vignette.","code":"adsl <- dm %>%   mutate(TRT01P = ARM, TRT01A = ACTARM)"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/adsl.html","id":"trtdatetime","dir":"Articles","previous_headings":"Programming Flow","what":"Derive/Impute Numeric Treatment Date/Time and Duration (TRTSDTM, TRTEDTM, TRTDURD)","title":"Creating ADSL","text":"function derive_vars_merged() can used derive treatment start end date/times using ex domain. pre-processing step ex required convert variable EXSTDTC EXSTDTC datetime variables impute missing date time components. Conversion imputation done derive_vars_dtm(). Example calls: call returns original data frame column TRTSDTM, TRTSTMF, TRTEDTM, TRTETMF added. Exposure observations incomplete date zero doses non placebo treatments ignored. Missing time parts imputed first last start end date respectively. datetime variables returned can converted dates using derive_vars_dtm_to_dt() function. Now, TRTSDT TRTEDT derived, function derive_var_trtdurd() can used calculate Treatment duration (TRTDURD).","code":"# impute start and end time of exposure to first and last respectively, # do not impute date ex_ext <- ex %>%   derive_vars_dtm(     dtc = EXSTDTC,     new_vars_prefix = \"EXST\"   ) %>%   derive_vars_dtm(     dtc = EXENDTC,     new_vars_prefix = \"EXEN\",     time_imputation = \"last\"   )  adsl <- adsl %>%   derive_vars_merged(     dataset_add = ex_ext,     filter_add = (EXDOSE > 0 |       (EXDOSE == 0 &         str_detect(EXTRT, \"PLACEBO\"))) & !is.na(EXSTDTM),     new_vars = exprs(TRTSDTM = EXSTDTM, TRTSTMF = EXSTTMF),     order = exprs(EXSTDTM, EXSEQ),     mode = \"first\",     by_vars = exprs(STUDYID, USUBJID)   ) %>%   derive_vars_merged(     dataset_add = ex_ext,     filter_add = (EXDOSE > 0 |       (EXDOSE == 0 &         str_detect(EXTRT, \"PLACEBO\"))) & !is.na(EXENDTM),     new_vars = exprs(TRTEDTM = EXENDTM, TRTETMF = EXENTMF),     order = exprs(EXENDTM, EXSEQ),     mode = \"last\",     by_vars = exprs(STUDYID, USUBJID)   ) adsl <- adsl %>%   derive_vars_dtm_to_dt(source_vars = exprs(TRTSDTM, TRTEDTM)) adsl <- adsl %>%   derive_var_trtdurd()"},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/articles/adsl.html","id":"disposition_date","dir":"Articles","previous_headings":"Programming Flow > Derive Disposition Variables","what":"Disposition Dates (e.g. EOSDT)","title":"Creating ADSL","text":"functions derive_vars_dt() derive_vars_merged() can used derive disposition date. First character disposition date (DS.DSSTDTC) converted numeric date (DSSTDT) calling derive_vars_dt(). DS dataset extended DSSTDT variable date required derivations, e.g., RANDDT well. relevant disposition date selected adjusting filter_add argument. add End Study date (EOSDT) input dataset, call : ds_ext dataset: adsl dataset: derive_vars_dt() function allows impute partial dates well. imputation needed missing days imputed first month missing months first month year, set highest_imputation = \"M\".","code":"# convert character date to numeric date without imputation ds_ext <- derive_vars_dt(   ds,   dtc = DSSTDTC,   new_vars_prefix = \"DSST\" )  adsl <- adsl %>%   derive_vars_merged(     dataset_add = ds_ext,     by_vars = exprs(STUDYID, USUBJID),     new_vars = exprs(EOSDT = DSSTDT),     filter_add = DSCAT == \"DISPOSITION EVENT\" & DSDECOD != \"SCREEN FAILURE\"   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/adsl.html","id":"disposition_status","dir":"Articles","previous_headings":"Programming Flow > Derive Disposition Variables","what":"Disposition Status (e.g. EOSSTT)","title":"Creating ADSL","text":"function derive_vars_merged() can used derive End Study status (EOSSTT) based DSCAT DSDECOD DS. relevant observations selected adjusting filter_add argument. function mapping DSDECOD values EOSSTT values can defined used new_vars argument. mapping call \"COMPLETED\" DSDECOD == \"COMPLETED\" NA_character_ DSDECOD \"SCREEN FAILURE\" \"DISCONTINUED\" otherwise Example function format_eosstt(): customized mapping function format_eosstt() can now passed main function. subjects without disposition event end study status set \"ONGOING\" specifying missing_values argument. call return input dataset column EOSSTT added. derivation must changed, user can create /function map DSDECOD suitable EOSSTT value.","code":"format_eosstt <- function(x) {   case_when(     x %in% c(\"COMPLETED\") ~ \"COMPLETED\",     x %in% c(\"SCREEN FAILURE\") ~ NA_character_,     TRUE ~ \"DISCONTINUED\"   ) } adsl <- adsl %>%   derive_vars_merged(     dataset_add = ds,     by_vars = exprs(STUDYID, USUBJID),     filter_add = DSCAT == \"DISPOSITION EVENT\",     new_vars = exprs(EOSSTT = format_eosstt(DSDECOD)),     missing_values = exprs(EOSSTT = \"ONGOING\")   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/adsl.html","id":"disposition_reason","dir":"Articles","previous_headings":"Programming Flow > Derive Disposition Variables","what":"Disposition Reason(s) (e.g. DCSREAS, DCSREASP)","title":"Creating ADSL","text":"main reason discontinuation usually stored DSDECOD DSTERM provides additional details regarding subject’s discontinuation (e.g., description \"\"). function derive_vars_merged() can used derive disposition reason (along details, required) specific timepoint. relevant observations selected adjusting filter_add argument. derive End Study reason(s) (DCSREAS DCSREASP), function map DCSREAS DSDECOD, DCSREASP DSTERM DSDECOD \"COMPLETED\", \"SCREEN FAILURE\", NA, NA otherwise. call return input dataset column DCSREAS DCSREASP added. derivation must changed, user can define derivation filter_add argument function map DSDECOD DSTERM suitable DCSREAS/DCSREASP value. call maps DCSREAS DCREASP follows: DCSREAS DSDECOD DSDECOD \"COMPLETED\" NA, NA otherwise DCSREASP DSTERM DSDECOD equal , NA otherwise","code":"adsl <- adsl %>%   derive_vars_merged(     dataset_add = ds,     by_vars = exprs(USUBJID),     new_vars = exprs(DCSREAS = DSDECOD, DCSREASP = DSTERM),     filter_add = DSCAT == \"DISPOSITION EVENT\" &       !(DSDECOD %in% c(\"SCREEN FAILURE\", \"COMPLETED\", NA))   ) adsl <- adsl %>%   derive_vars_merged(     dataset_add = ds,     by_vars = exprs(USUBJID),     new_vars = exprs(DCSREAS = DSDECOD),     filter_add = DSCAT == \"DISPOSITION EVENT\" &       DSDECOD %notin% c(\"SCREEN FAILURE\", \"COMPLETED\", NA)   ) %>%   derive_vars_merged(     dataset_add = ds,     by_vars = exprs(USUBJID),     new_vars = exprs(DCSREASP = DSTERM),     filter_add = DSCAT == \"DISPOSITION EVENT\" & DSDECOD %in% \"OTHER\"   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/adsl.html","id":"randomization_date","dir":"Articles","previous_headings":"Programming Flow > Derive Disposition Variables","what":"Randomization Date (RANDDT)","title":"Creating ADSL","text":"function derive_vars_merged() can used derive randomization date variable. map Randomization Date (RANDDT), call : call return input dataset column RANDDT added.","code":"adsl <- adsl %>%   derive_vars_merged(     dataset_add = ds_ext,     filter_add = DSDECOD == \"RANDOMIZED\",     by_vars = exprs(STUDYID, USUBJID),     new_vars = exprs(RANDDT = DSSTDT)   )"},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/articles/adsl.html","id":"death_date","dir":"Articles","previous_headings":"Programming Flow > Derive Death Variables","what":"Death Date (DTHDT)","title":"Creating ADSL","text":"function derive_vars_dt() can used derive DTHDT. function allows user impute date well. Example calls: call return input dataset columns DTHDT added , default, associated date imputation flag (DTHDTF) populated controlled terminology outlined ADaM IG date imputations. imputation flag required, user must set argument flag_imputation \"none\". imputation needed date imputed first day month/year call : See also Date Time Imputation.","code":"adsl <- adsl %>%   derive_vars_dt(     new_vars_prefix = \"DTH\",     dtc = DTHDTC   ) adsl <- adsl %>%   derive_vars_dt(     new_vars_prefix = \"DTH\",     dtc = DTHDTC,     date_imputation = \"first\"   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/adsl.html","id":"death_cause","dir":"Articles","previous_headings":"Programming Flow > Derive Death Variables","what":"Cause of Death (DTHCAUS)","title":"Creating ADSL","text":"cause death DTHCAUS can derived using function derive_vars_extreme_event(). Since cause death collected/mapped different domains (e.g. DS, AE, DD), important user specifies right source(s) derive cause death . example, date death collected AE form AE Fatal, cause death set preferred term (AEDECOD) Fatal AE, date death collected DS form, cause death set disposition term (DSTERM). achieve , event() objects within derive_vars_extreme_event() must specified defined fit study requirement. example call derive_vars_extreme_event() : function also offers option add traceability variables (e.g. DTHDOM store domain date death collected, DTHSEQ store xxSEQ value domain). traceability variables added event() calls included new_vars parameter derive_vars_extreme_event(). Following derivation DTHCAUS related traceability variables, possible derive grouping variables death categories (DTHCGRx) using standard tidyverse code.","code":"adsl <- adsl %>%   derive_vars_extreme_event(     by_vars = exprs(STUDYID, USUBJID),     events = list(       event(         dataset_name = \"ae\",         condition = AEOUT == \"FATAL\",         set_values_to = exprs(DTHCAUS = AEDECOD),       ),       event(         dataset_name = \"ds\",         condition = DSDECOD == \"DEATH\" & grepl(\"DEATH DUE TO\", DSTERM),         set_values_to = exprs(DTHCAUS = DSTERM),       )     ),     source_datasets = list(ae = ae, ds = ds),     tmp_event_nr_var = event_nr,     order = exprs(event_nr),     mode = \"first\",     new_vars = exprs(DTHCAUS)   ) adsl <- adsl %>%   select(-DTHCAUS) %>% # remove it before deriving it again   derive_vars_extreme_event(     by_vars = exprs(STUDYID, USUBJID),     events = list(       event(         dataset_name = \"ae\",         condition = AEOUT == \"FATAL\",         set_values_to = exprs(DTHCAUS = AEDECOD, DTHDOM = \"AE\", DTHSEQ = AESEQ),       ),       event(         dataset_name = \"ds\",         condition = DSDECOD == \"DEATH\" & grepl(\"DEATH DUE TO\", DSTERM),         set_values_to = exprs(DTHCAUS = DSTERM, DTHDOM = \"DS\", DTHSEQ = DSSEQ),       )     ),     source_datasets = list(ae = ae, ds = ds),     tmp_event_nr_var = event_nr,     order = exprs(event_nr),     mode = \"first\",     new_vars = exprs(DTHCAUS, DTHDOM, DTHSEQ)   ) adsl <- adsl %>%   mutate(DTHCGR1 = case_when(     is.na(DTHDOM) ~ NA_character_,     DTHDOM == \"AE\" ~ \"ADVERSE EVENT\",     str_detect(DTHCAUS, \"(PROGRESSIVE DISEASE|DISEASE RELAPSE)\") ~ \"PROGRESSIVE DISEASE\",     TRUE ~ \"OTHER\"   ))"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/adsl.html","id":"death_other","dir":"Articles","previous_headings":"Programming Flow > Derive Death Variables","what":"Duration Relative to Death","title":"Creating ADSL","text":"function derive_vars_duration() can used derive duration relative death like Relative Day Death (DTHADY) numbers days last dose death (LDDTHELD). Example calls: Relative Day Death Elapsed Days Last Dose Death","code":"adsl <- adsl %>%   derive_vars_duration(     new_var = DTHADY,     start_date = TRTSDT,     end_date = DTHDT   ) adsl <- adsl %>%   derive_vars_duration(     new_var = LDDTHELD,     start_date = TRTEDT,     end_date = DTHDT,     add_one = FALSE   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/adsl.html","id":"lstalvdt","dir":"Articles","previous_headings":"Programming Flow","what":"Derive the Last Date Known Alive (LSTALVDT)","title":"Creating ADSL","text":"Similarly cause death (DTHCAUS), last known alive date (LSTALVDT) can derived multiples sources using derive_vars_extreme_event(). example (DTC dates converted numeric dates imputing missing day month first): Traceability variables can added specifying variables set_values_to parameter event() function.","code":"adsl <- adsl %>%   derive_vars_extreme_event(     by_vars = exprs(STUDYID, USUBJID),     events = list(       event(         dataset_name = \"ae\",         order = exprs(AESTDTC, AESEQ),         condition = !is.na(AESTDTC),         set_values_to = exprs(           LSTALVDT = convert_dtc_to_dt(AESTDTC, highest_imputation = \"M\"),           seq = AESEQ         ),       ),       event(         dataset_name = \"ae\",         order = exprs(AEENDTC, AESEQ),         condition = !is.na(AEENDTC),         set_values_to = exprs(           LSTALVDT = convert_dtc_to_dt(AEENDTC, highest_imputation = \"M\"),           seq = AESEQ         ),       ),       event(         dataset_name = \"lb\",         order = exprs(LBDTC, LBSEQ),         condition = !is.na(LBDTC),         set_values_to = exprs(           LSTALVDT = convert_dtc_to_dt(LBDTC, highest_imputation = \"M\"),           seq = LBSEQ         ),       ),       event(         dataset_name = \"adsl\",         condition = !is.na(TRTEDT),         set_values_to = exprs(LSTALVDT = TRTEDT, seq = 0),       )     ),     source_datasets = list(ae = ae, lb = lb, adsl = adsl),     tmp_event_nr_var = event_nr,     order = exprs(LSTALVDT, seq, event_nr),     mode = \"last\",     new_vars = exprs(LSTALVDT)   ) adsl <- adsl %>%   select(-LSTALVDT) %>% # created in the previous call   derive_vars_extreme_event(     by_vars = exprs(STUDYID, USUBJID),     events = list(       event(         dataset_name = \"ae\",         order = exprs(AESTDTC, AESEQ),         condition = !is.na(AESTDTC),         set_values_to = exprs(           LSTALVDT = convert_dtc_to_dt(AESTDTC, highest_imputation = \"M\"),           LALVSEQ = AESEQ,           LALVDOM = \"AE\",           LALVVAR = \"AESTDTC\"         ),       ),       event(         dataset_name = \"ae\",         order = exprs(AEENDTC, AESEQ),         condition = !is.na(AEENDTC),         set_values_to = exprs(           LSTALVDT = convert_dtc_to_dt(AEENDTC, highest_imputation = \"M\"),           LALVSEQ = AESEQ,           LALVDOM = \"AE\",           LALVVAR = \"AEENDTC\"         ),       ),       event(         dataset_name = \"lb\",         order = exprs(LBDTC, LBSEQ),         condition = !is.na(LBDTC),         set_values_to = exprs(           LSTALVDT = convert_dtc_to_dt(LBDTC, highest_imputation = \"M\"),           LALVSEQ = LBSEQ,           LALVDOM = \"LB\",           LALVVAR = \"LBDTC\"         ),       ),       event(         dataset_name = \"adsl\",         condition = !is.na(TRTEDT),         set_values_to = exprs(LSTALVDT = TRTEDT, LALVSEQ = NA_integer_, LALVDOM = \"ADSL\", LALVVAR = \"TRTEDTM\"),       )     ),     source_datasets = list(ae = ae, lb = lb, adsl = adsl),     tmp_event_nr_var = event_nr,     order = exprs(LSTALVDT, LALVSEQ, event_nr),     mode = \"last\",     new_vars = exprs(LSTALVDT, LALVSEQ, LALVDOM, LALVVAR)   )"},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/articles/adsl.html","id":"groupings_ex","dir":"Articles","previous_headings":"Programming Flow > Derive Groupings and Populations","what":"Grouping (e.g. AGEGR1 or REGION1)","title":"Creating ADSL","text":"Numeric categorical variables (AGE, RACE, COUNTRY, etc.) may need grouped perform required analysis. admiral provides derive_vars_cat() function create groups. function especially useful one variable needs created condition, e.g., AGEGR1 AGEGR1N. Additionally, one needs careful considering order conditions lookup table. category assigned based first match. means catch-conditions must come specific conditions, e.g. !.na(AGE) must come AGE < 18. Alternatively, can also solve task custom functions:","code":"# create lookup tables agegr1_lookup <- exprs(   ~condition,           ~AGEGR1,   AGE < 18,               \"<18\",   between(AGE, 18, 64), \"18-64\",   AGE > 64,               \">64\",   is.na(AGE),         \"Missing\" )  region1_lookup <- exprs(   ~condition,                          ~REGION1,   COUNTRY %in% c(\"CAN\", \"USA\"), \"North America\",   !is.na(COUNTRY),          \"Rest of the World\",   is.na(COUNTRY),                     \"Missing\" ) adsl <- adsl %>%   derive_vars_cat(     definition = agegr1_lookup   ) %>%   derive_vars_cat(     definition = region1_lookup   ) format_agegr1 <- function(var_input) {   case_when(     var_input < 18 ~ \"<18\",     between(var_input, 18, 64) ~ \"18-64\",     var_input > 64 ~ \">64\",     TRUE ~ \"Missing\"   ) } format_region1 <- function(var_input) {   case_when(     var_input %in% c(\"CAN\", \"USA\") ~ \"North America\",     !is.na(var_input) ~ \"Rest of the World\",     TRUE ~ \"Missing\"   ) }  adsl %>%   mutate(     AGEGR1 = format_agegr1(AGE),     REGION1 = format_region1(COUNTRY)   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/adsl.html","id":"popflag","dir":"Articles","previous_headings":"Programming Flow > Derive Groupings and Populations","what":"Population Flags (e.g. SAFFL)","title":"Creating ADSL","text":"Since populations flags mainly company/study specific dedicated functions provided, cases can easily derived using derive_var_merged_exist_flag. example implementation :","code":"adsl <- adsl %>%   derive_var_merged_exist_flag(     dataset_add = ex,     by_vars = exprs(STUDYID, USUBJID),     new_var = SAFFL,     condition = (EXDOSE > 0 | (EXDOSE == 0 & str_detect(EXTRT, \"PLACEBO\")))   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/adsl.html","id":"other","dir":"Articles","previous_headings":"Programming Flow","what":"Derive Other Variables","title":"Creating ADSL","text":"users can add specific code cover need analysis. following functions helpful many ADSL derivations: derive_vars_merged() - Merge Variables Dataset Input Dataset derive_var_merged_exist_flag() - Merge Existence Flag derive_var_merged_summary() - Merge Summary Variables See also Generic Functions.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/adsl.html","id":"attributes","dir":"Articles","previous_headings":"Programming Flow","what":"Add Labels and Attributes","title":"Creating ADSL","text":"Adding labels attributes SAS transport files supported following packages: metacore: establish common foundation use metadata within R session. metatools: enable use metacore objects. Metatools can used build datasets enhance columns existing datasets well checking datasets metadata. xportr: functionality associate metadata information local R data frame, perform data set level validation checks convert transport v5 file(xpt). NOTE: packages experimental phase, vision associated End End pipeline umbrella pharmaverse. example applying metadata perform associated checks can found pharmaverse E2E example.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_exposure.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Creating a BDS Exposure ADaM","text":"article describes creating Exposure ADaM using BDS structure. Examples currently presented using underlying EX domain EX domain represents data collected CRF ADEX ADaM output. However, examples can applied situations EC domain used input /ADEC another exposure ADaM created. many different approaches modeling exposure data. vignette gives examples creating PARAMCD AVAL combinations using exposure data. vignette meant guide standard structure exposure analysis datasets. Note: examples assume CDISC SDTM /ADaM format input unless otherwise specified.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_exposure.html","id":"programming-workflow","dir":"Articles","previous_headings":"","what":"Programming Workflow","title":"Creating a BDS Exposure ADaM","text":"Read Data Derive/Impute Numeric Date/Time Analysis Day (ADT, ADTM, ADY, ADTF, ATMF) Compute Duration Record Create 1:1 Mapping Records Create Summary Records Assign PARAMCD, PARAMN, etc. Reference Tables Derive Categorization Variables (AVALCATy) Assign ASEQ Add ADSL variables ASEQ Add Labels Attributes","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_exposure.html","id":"readdata","dir":"Articles","previous_headings":"Programming Workflow","what":"Read in Data","title":"Creating a BDS Exposure ADaM","text":"start, data frames needed creation ADEX read environment. company specific process. data frames needed may EX ADSL. example purpose, CDISC Pilot SDTM ADaM datasets—included pharmaversesdtm—used. step, may useful join ADSL EX domain well. ADSL variables used derivations selected step. rest relevant ADSL variables added later. CDISC pilot EX domain data contain dose adjustment flag planned dose information. demonstration purposes, added data.","code":"library(admiral) library(dplyr, warn.conflicts = FALSE) library(pharmaversesdtm) library(lubridate) library(stringr) library(tibble)  ex <- pharmaversesdtm::ex adsl <- admiral::admiral_adsl  ex <- convert_blanks_to_na(ex) adsl_vars <- exprs(TRTSDT, TRTSDTM, TRTEDT, TRTEDTM)  adex <- derive_vars_merged(   ex,   dataset_add = adsl,   new_vars = adsl_vars,   by_vars = get_admiral_option(\"subject_keys\") ) adex <- adex %>%   mutate(     EXADJ = case_when(       USUBJID == \"01-701-1028\" & VISIT %in% c(\"WEEK 2\") ~ \"ADVERSE EVENT\",       USUBJID == \"01-701-1148\" & VISIT %in% c(\"WEEK 2\", \"WEEK 24\") ~ \"MEDICATION ERROR\",       TRUE ~ NA_character_     ),     EXDOSE = case_when(       USUBJID == \"01-701-1028\" & VISIT %in% c(\"WEEK 2\") ~ 0,       USUBJID == \"01-701-1148\" & VISIT %in% c(\"WEEK 2\", \"WEEK 24\") ~ 0,       TRUE ~ EXDOSE     )   ) %>%   mutate(EXPLDOS = if_else(EXTRT == \"PLACEBO\", 0, 54))  distinct(adex, EXTRT, EXPLDOS) #> # A tibble: 2 × 2 #>   EXTRT      EXPLDOS #>   <chr>        <dbl> #> 1 PLACEBO          0 #> 2 XANOMELINE      54 count(adex, EXADJ) #> # A tibble: 1 × 2 #>   EXADJ     n #>   <chr> <int> #> 1 NA       13"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_exposure.html","id":"datetime","dir":"Articles","previous_headings":"Programming Workflow","what":"Derive/Impute Numeric Date/Time and Analysis Day (ADT, ADTM, ADY, ADTF, ATMF)","title":"Creating a BDS Exposure ADaM","text":"function derive_vars_dt() can used derive ADT. function allows user impute date well. Example calls: next examples demonstrates datetime imputation features available derive_vars_dtm() function, time imputed “00:00:00”: example imputes start date first first day month imputes end date last day month. Please see Date Time Imputation additional examples calculating imputing analysis dates. Next, analysis study days can derived:","code":"adex <- derive_vars_dt(adex, new_vars_prefix = \"AST\", dtc = EXSTDTC) adex <- derive_vars_dt(adex, new_vars_prefix = \"AEN\", dtc = EXENDTC) adex <- derive_vars_dtm(   adex,   dtc = EXSTDTC,   highest_imputation = \"M\",   new_vars_prefix = \"AST\" )  adex <- derive_vars_dtm(   adex,   dtc = EXENDTC,   highest_imputation = \"M\",   date_imputation = \"last\",   new_vars_prefix = \"AEN\" ) adex <-   derive_vars_dy(adex,     reference_date = TRTSDT,     source_vars = exprs(ASTDT, AENDT)   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_exposure.html","id":"duration","dir":"Articles","previous_headings":"Programming Workflow","what":"Compute duration for a record","title":"Creating a BDS Exposure ADaM","text":"compute duration treatment exposure record, derive_vars_duration() function can used. units calculated duration can also changed. example, duration output years: Please refer derive_vars_duration() documentation detailed information input parameters. may necessary calculate additional intermediate values. example, cumulative doses received cumulative planned doses may calculated : may additional interest turn single record containing dosing summary information set multiple single records, representing single dose interval specified summary record. another approach deriving total dose parameter EXDOSFRQ != . function create_single_dose_dataset() can used expand record containing start date, end date, dosing frequency corresponding set records representing one dose (.e. EXDOSFRQ == \"\").","code":"adex <- adex %>%   derive_vars_duration(     new_var = EXDURD,     start_date = ASTDT,     end_date = AENDT   ) adex <- adex %>%   derive_vars_duration(     new_var = EXDURDY,     out_unit = \"years\",     start_date = ASTDT,     end_date = AENDT   ) adex <- adex %>%   mutate(     DOSEO = EXDOSE * EXDURD,     PDOSEO = EXPLDOS * EXDURD   ) single_dose <- adex %>%   filter(USUBJID == \"01-701-1015\" & EXSTDY == 1) %>%   create_single_dose_dataset(keep_source_vars = exprs(USUBJID, EXDOSE, EXPLDOS, EXDOSFRQ, ASTDT, AENDT))"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_exposure.html","id":"onetoone","dir":"Articles","previous_headings":"Programming Workflow","what":"Create 1:1 mapping records","title":"Creating a BDS Exposure ADaM","text":"first set exposure records create records mapped 1:1 existing collected exposure record SDTM. records, AVAL AVALC calculated using columns exist data summarizing records necessary. records may used input summary records used individually summarization outputs. examples may exposure duration, dose administered, dose adjusted, etc. based one exposure record SDTM. records can derived using simple dplyr::mutate assignments combined:","code":"adex_durd <- adex %>%   mutate(     PARAMCD = \"DURD\",     AVAL = EXDURD   )  adex_dose <- adex %>%   mutate(     PARAMCD = \"DOSE\",     AVAL = DOSEO   )  adex_pldos <- adex %>%   mutate(     PARAMCD = \"PLDOSE\",     AVAL = PDOSEO   )  adex_adj <- adex %>%   mutate(     PARAMCD = \"ADJ\",     AVALC = if_else(!is.na(EXADJ), \"Y\", NA_character_)   )  adex_adjae <- adex %>%   mutate(     PARAMCD = \"ADJAE\",     AVALC = if_else(EXADJ == \"ADVERSE EVENT\", \"Y\", NA_character_)   )  adex <- bind_rows(   adex_durd,   adex_dose,   adex_pldos,   adex_adj,   adex_adjae ) %>%   mutate(PARCAT1 = \"INDIVIDUAL\")  count(adex, PARAMCD) #> # A tibble: 5 × 2 #>   PARAMCD     n #>   <chr>   <int> #> 1 ADJ        13 #> 2 ADJAE      13 #> 3 DOSE       13 #> 4 DURD       13 #> 5 PLDOSE     13"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_exposure.html","id":"summaryrec","dir":"Articles","previous_headings":"Programming Workflow","what":"Create Summary Records","title":"Creating a BDS Exposure ADaM","text":"Exposure commonly analyzed timing interval (e.g. APHASE, APERIOD, AVISIT, etc.). types calculations, derive_param_exposure() function may used. addition creating summarized AVAL, function also compute minimum maximum dates record. example, calculate total dose subject treatment, record PARAMCD == \"TDOSE\" created PARCAT1 set \"OVERALL\" using records ADEX PARAMCD == \"DOSE\" summing AVAL. addition, ASTDT, AENDT created minimum maximum date/times associated by_vars grouping. Note , addition PARAMCD, PARCAT1, AVAL, ASTDT AENDT, variables specified by_vars argument populated new records. Multiple parameters (records) may created one time using call_derivation() function: Dose intensity can calculated using function derive_param_doseint(). planned dose administered dose passed function new record created dose intensity calculation. , variables specified by_vars argument populated new record. default calculation dose intensity : Administered Doses / Planned Doses * 100. Please see derive_param_doseint() documentation see planned doses 0 NA handled.","code":"adex <- derive_param_exposure(   adex,   dataset_add = adex,   by_vars = c(get_admiral_option(\"subject_keys\"), adsl_vars),   input_code = \"DOSE\",   set_values_to = exprs(     PARAMCD = \"TDOSE\",     PARCAT1 = \"OVERALL\",     AVAL = sum(AVAL, na.rm = TRUE)   ) ) adex <- adex %>%   call_derivation(     derivation = derive_param_exposure,     variable_params = list(       params(         set_values_to = exprs(           PARAMCD = \"TDOSE\",           PARCAT1 = \"OVERALL\",           AVAL = sum(AVAL, na.rm = TRUE)         ),         input_code = \"DOSE\"       ),       params(         set_values_to = exprs(           PARAMCD = \"TPDOSE\",           PARCAT1 = \"OVERALL\",           AVAL = sum(AVAL, na.rm = TRUE)         ),         input_code = \"PLDOSE\"       ),       params(         set_values_to = exprs(           PARAMCD = \"TDURD\",           PARCAT1 = \"OVERALL\",           AVAL = sum(AVAL, na.rm = TRUE)         ),         input_code = \"DURD\"       ),       params(         set_values_to = exprs(           PARAMCD = \"TADJ\",           PARCAT1 = \"OVERALL\",           AVALC = if_else(sum(!is.na(AVALC)) > 0, \"Y\", NA_character_)         ),         input_code = \"ADJ\"       ),       params(         set_values_to = exprs(           PARAMCD = \"TADJAE\",           PARCAT1 = \"OVERALL\",           AVALC = if_else(sum(!is.na(AVALC)) > 0, \"Y\", NA_character_)         ),         input_code = \"ADJAE\"       )     ),     dataset_add = adex,     by_vars = c(get_admiral_option(\"subject_keys\"), adsl_vars)   )  count(adex, PARAMCD, PARCAT1) #> # A tibble: 10 × 3 #>    PARAMCD PARCAT1        n #>    <chr>   <chr>      <int> #>  1 ADJ     INDIVIDUAL    13 #>  2 ADJAE   INDIVIDUAL    13 #>  3 DOSE    INDIVIDUAL    13 #>  4 DURD    INDIVIDUAL    13 #>  5 PLDOSE  INDIVIDUAL    13 #>  6 TADJ    OVERALL        6 #>  7 TADJAE  OVERALL        6 #>  8 TDOSE   OVERALL        6 #>  9 TDURD   OVERALL        6 #> 10 TPDOSE  OVERALL        6 adex <- adex %>%   derive_param_doseint(     by_vars = c(get_admiral_option(\"subject_keys\"), adsl_vars),     set_values_to = exprs(PARAMCD = \"TNDOSINT\"),     tadm_code = \"TDOSE\",     tpadm_code = \"TPDOSE\"   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_exposure.html","id":"paramcd","dir":"Articles","previous_headings":"Programming Workflow","what":"Assign PARAMCD, PARAMN, etc. from Reference tables","title":"Creating a BDS Exposure ADaM","text":"assign parameter level values PARAM, PARAMN, PARCAT1, etc., lookup can created join source data. example, creating ADEX, lookup based ADaM PARAMCD value may created: Please note, example additional columns may needed join depending lookup/metadata table.","code":"adex <- derive_vars_merged(   adex,   dataset_add = param_lookup,   by_vars = exprs(PARAMCD) )  count(adex, PARAMCD, PARAM, PARAMN) #> # A tibble: 11 × 4 #>    PARAMCD  PARAM                                                   PARAMN     n #>    <chr>    <chr>                                                    <dbl> <int> #>  1 ADJ      Dose adjusted during constant dosing interval                4    13 #>  2 ADJAE    Dose adjusted  due to AE during constant dosing interv…      5    13 #>  3 DOSE     Dose administered during constant dosing interval (mg)       2    13 #>  4 DURD     Study drug duration during constant dosing interval (d…      1    13 #>  5 PLDOSE   Planned dose during constant dosing interval (mg)            3    13 #>  6 TADJ     Dose adjusted during study                                  10     6 #>  7 TADJAE   Dose adjusted during study due to AE                        11     6 #>  8 TDOSE    Total dose administered (mg)                                 7     6 #>  9 TDURD    Overall duration (days)                                      6     6 #> 10 TNDOSINT Overall dose intensity (%)                                  12     6 #> 11 TPDOSE   Total planned dose (mg)                                      9     6"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_exposure.html","id":"cat","dir":"Articles","previous_headings":"Programming Workflow","what":"Derive Categorization Variables (AVALCATy)","title":"Creating a BDS Exposure ADaM","text":"can use derive_vars_cat() function derive categorization variables.","code":"avalcax_lookup <- exprs(   ~PARAMCD,            ~condition,             ~AVALCAT1,   \"TDURD\",             AVAL >= 90,          \">= 90 days\",   \"TDURD\", AVAL >= 30 & AVAL < 90, \">= 30 and < 90 days\",   \"TDURD\",              AVAL < 30,           \"< 30 days\",   \"TDOSE\",            AVAL < 1000,           \"< 1000 mg\",   \"TDOSE\",           AVAL >= 1000,          \">= 1000 mg\",   \"TPDOSE\",           AVAL < 1000,           \"< 1000 mg\",   \"TPDOSE\",          AVAL >= 1000,          \">= 1000 mg\" )  adex <- adex %>%   derive_vars_cat(     definition = avalcax_lookup,     by_vars = exprs(PARAMCD)   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_exposure.html","id":"aseq","dir":"Articles","previous_headings":"Programming Workflow","what":"Assign ASEQ","title":"Creating a BDS Exposure ADaM","text":"admiral function derive_var_obs_number() can used derive ASEQ. example call :","code":"adex <- derive_var_obs_number(   adex,   new_var = ASEQ,   by_vars = get_admiral_option(\"subject_keys\"),   order = exprs(PARCAT1, ASTDT, VISIT, VISITNUM, EXSEQ, PARAMN),   check_type = \"error\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_exposure.html","id":"adsl_vars","dir":"Articles","previous_headings":"Programming Workflow","what":"Add the ADSL variables","title":"Creating a BDS Exposure ADaM","text":"needed, ADSL variables can now added:","code":"adex <- adex %>%   derive_vars_merged(     dataset_add = select(adsl, !!!negate_vars(adsl_vars)),     by_vars = get_admiral_option(\"subject_keys\")   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_exposure.html","id":"attributes","dir":"Articles","previous_headings":"Programming Workflow","what":"Add Labels and Attributes","title":"Creating a BDS Exposure ADaM","text":"Adding labels attributes SAS transport files supported following packages: metacore: establish common foundation use metadata within R session. metatools: enable use metacore objects. Metatools can used build datasets enhance columns existing datasets well checking datasets metadata. xportr: functionality associate metadata information local R data frame, perform data set level validation checks convert transport v5 file(xpt). NOTE: packages experimental phase, vision associated End End pipeline umbrella pharmaverse. example applying metadata perform associated checks can found pharmaverse E2E example.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_finding.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Creating a BDS Finding ADaM","text":"article describes creating BDS finding ADaM. Examples currently presented tested context ADVS. However, examples applied BDS Finding ADaMs ADEG, ADLB, etc. single result captured SDTM Finding domain single date /time. Note: examples assume CDISC SDTM /ADaM format input unless otherwise specified.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_finding.html","id":"programming-workflow","dir":"Articles","previous_headings":"","what":"Programming Workflow","title":"Creating a BDS Finding ADaM","text":"Read Data Derive/Impute Numeric Date/Time Analysis Day (ADT, ADTM, ADY, ADTF, ATMF) Assign PARAMCD, PARAM, PARAMN, PARCAT1 Derive Results (AVAL, AVALC) Derive Additional Parameters (e.g. BSA, BMI, MAP ADVS) Derive Timing Variables (e.g. APHASE, AVISIT, APERIOD) Timing Flag Variables (e.g. ONTRTFL) Assign Reference Range Indicator (ANRIND) Derive Baseline (BASETYPE, ABLFL, BASE, BASEC, BNRIND) Derive Change Baseline (CHG, PCHG) Derive Shift (e.g.SHIFT1) Derive Analysis Ratio (e.g. R2BASE) Derive Analysis Flags (e.g. ANL01FL) Assign Treatment (TRTA, TRTP) Assign ASEQ Derive Categorization Variables (AVALCATy) Derive Criterion Variables (CRITy, CRITyFL, CRITyFN) Add ADSL variables Derive New Rows Add Labels Attributes","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_finding.html","id":"readdata","dir":"Articles","previous_headings":"Programming Workflow","what":"Read in Data","title":"Creating a BDS Finding ADaM","text":"start, data frames needed creation ADVS read environment. company specific process. data frames needed may VS ADSL. example purpose, CDISC Pilot SDTM ADaM datasets—included pharmaversesdtm—used. step, may useful join ADSL VS domain. ADSL variables used derivations selected step. rest relevant ADSL variables added later.","code":"library(admiral) library(dplyr, warn.conflicts = FALSE) library(pharmaversesdtm) library(lubridate) library(stringr) library(tibble)  vs <- pharmaversesdtm::vs adsl <- admiral::admiral_adsl  vs <- convert_blanks_to_na(vs) adsl_vars <- exprs(TRTSDT, TRTEDT, TRT01A, TRT01P)  advs <- derive_vars_merged(   vs,   dataset_add = adsl,   new_vars = adsl_vars,   by_vars = exprs(STUDYID, USUBJID) )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_finding.html","id":"datetime","dir":"Articles","previous_headings":"Programming Workflow","what":"Derive/Impute Numeric Date/Time and Analysis Day (ADT, ADTM, ADY, ADTF, ATMF)","title":"Creating a BDS Finding ADaM","text":"function derive_vars_dt() can used derive ADT. function allows user impute date well. Example calls: imputation needed date imputed first month, call : Similarly, ADTM may created using function derive_vars_dtm(). Imputation may done date time components ADTM. default, variable ADTF derive_vars_dt() ADTF ATMF derive_vars_dtm() created populated controlled terminology outlined ADaM IG date imputations. See also Date Time Imputation. ADT derived, function derive_vars_dy() can used derive ADY. example assumes ADT TRTSDT exist data frame.","code":"advs <- derive_vars_dt(advs, new_vars_prefix = \"A\", dtc = VSDTC) advs <- derive_vars_dt(   advs,   new_vars_prefix = \"A\",   dtc = VSDTC,   highest_imputation = \"M\" ) # CDISC Pilot data does not contain times and the output of the derivation # ADTM is not presented. advs <- derive_vars_dtm(   advs,   new_vars_prefix = \"A\",   dtc = VSDTC,   highest_imputation = \"M\" ) advs <-   derive_vars_dy(advs, reference_date = TRTSDT, source_vars = exprs(ADT))"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_finding.html","id":"paramcd","dir":"Articles","previous_headings":"Programming Workflow","what":"Assign PARAMCD, PARAM, PARAMN, PARCAT1","title":"Creating a BDS Finding ADaM","text":"assign parameter level values PARAMCD, PARAM, PARAMN, PARCAT1, etc., lookup can created join source data. example, creating ADVS, lookup based SDTM --TESTCD value may created: lookup may now joined source data: stage, PARAMCD required perform derivations. Additional derived parameters may added, PARAMCD joined datasets point. variables related PARAMCD (e.g. PARAM, PARAMCAT1, …) added PARAMCD derived. Please note, may necessary include variables join. example, perhaps PARAMCD based VSTESTCD VSPOS, may necessary expand lookup create separate look PARAMCD. one lookup table, e.g., company parameter mappings project parameter mappings, available, consolidate_metadata() can used consolidate single lookup table.","code":"advs <- derive_vars_merged_lookup(   advs,   dataset_add = param_lookup,   new_vars = exprs(PARAMCD),   by_vars = exprs(VSTESTCD) ) #> All `VSTESTCD` are mapped."},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_finding.html","id":"aval","dir":"Articles","previous_headings":"Programming Workflow","what":"Derive Results (AVAL, AVALC)","title":"Creating a BDS Finding ADaM","text":"mapping AVAL AVALC left ADaM programmer. example mapping may : example, often case ADVS, AVAL values numeric without corresponding non-redundant text value AVALC. Per recommendation ADaMIG v1.3 map AVALC.","code":"advs <- mutate(   advs,   AVAL = VSSTRESN )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_finding.html","id":"derive_param","dir":"Articles","previous_headings":"Programming Workflow","what":"Derive Additional Parameters (e.g. BSA, BMI or MAP for ADVS)","title":"Creating a BDS Finding ADaM","text":"Optionally derive new parameters creating PARAMCD AVAL. Note variables specified by_vars argument populated newly created records. relevant functions derive_param_map, derive_param_bsa, derive_param_bmi, derive_param_qtc. example creating Mean Arterial Pressure ADVS, see also Example 3 section Derive New Rows alternative way creating new parameters. Likewise, function call , create parameter Body Surface Area (BSA) Body Mass Index (BMI) ADVS domain. Note height collected use constant_by_vars specify subject-level variable merge . Otherwise BSA BMI calculated visits collected. Similarly, ADEG, parameters QTCBF QTCBS QTCL can created function call. See example PARAMCD = QTCF. Similarly, ADLB, function derive_param_wbc_abs() can used create new parameter lab differentials converted absolute values. See example : PARAMCD derived added dataset, information look-table (PARAM, PARAMCAT1,…) added.","code":"advs <- derive_param_map(   advs,   by_vars = exprs(STUDYID, USUBJID, !!!adsl_vars, VISIT, VISITNUM, ADT, ADY, VSTPT, VSTPTNUM),   set_values_to = exprs(PARAMCD = \"MAP\"),   get_unit_expr = VSSTRESU,   filter = VSSTAT != \"NOT DONE\" | is.na(VSSTAT) ) advs <- derive_param_bsa(   advs,   by_vars = exprs(STUDYID, USUBJID, !!!adsl_vars, VISIT, VISITNUM, ADT, ADY, VSTPT, VSTPTNUM),   method = \"Mosteller\",   set_values_to = exprs(PARAMCD = \"BSA\"),   get_unit_expr = VSSTRESU,   filter = VSSTAT != \"NOT DONE\" | is.na(VSSTAT),   constant_by_vars = exprs(USUBJID) )  advs <- derive_param_bmi(   advs,   by_vars = exprs(STUDYID, USUBJID, !!!adsl_vars, VISIT, VISITNUM, ADT, ADY, VSTPT, VSTPTNUM),   set_values_to = exprs(PARAMCD = \"BMI\"),   get_unit_expr = VSSTRESU,   filter = VSSTAT != \"NOT DONE\" | is.na(VSSTAT),   constant_by_vars = exprs(USUBJID) ) adeg <- tibble::tribble(   ~USUBJID, ~EGSTRESU, ~PARAMCD, ~AVAL,          ~VISIT,   \"P01\",       \"msec\",     \"QT\",   350, \"CYCLE 1 DAY 1\",   \"P01\",       \"msec\",     \"QT\",   370, \"CYCLE 2 DAY 1\",   \"P01\",       \"msec\",     \"RR\",   842, \"CYCLE 1 DAY 1\",   \"P01\",       \"msec\",     \"RR\",   710, \"CYCLE 2 DAY 1\" )  adeg <- derive_param_qtc(   adeg,   by_vars = exprs(USUBJID, VISIT),   method = \"Fridericia\",   set_values_to = exprs(PARAMCD = \"QTCFR\"),   get_unit_expr = EGSTRESU ) adlb <- tibble::tribble(   ~USUBJID, ~PARAMCD, ~AVAL,                        ~PARAM,          ~VISIT,   \"P01\",       \"WBC\",    33,    \"Leukocyte Count (10^9/L)\", \"CYCLE 1 DAY 1\",   \"P01\",       \"WBC\",    38,    \"Leukocyte Count (10^9/L)\", \"CYCLE 2 DAY 1\",   \"P01\",     \"LYMLE\",  0.90, \"Lymphocytes (fraction of 1)\", \"CYCLE 1 DAY 1\",   \"P01\",     \"LYMLE\",  0.70, \"Lymphocytes (fraction of 1)\", \"CYCLE 2 DAY 1\" )  derive_param_wbc_abs(   dataset = adlb,   by_vars = exprs(USUBJID, VISIT),   set_values_to = exprs(     PARAMCD = \"LYMPH\",     PARAM = \"Lymphocytes Abs (10^9/L)\",     DTYPE = \"CALCULATION\"   ),   get_unit_expr = extract_unit(PARAM),   wbc_code = \"WBC\",   diff_code = \"LYMLE\",   diff_type = \"fraction\" ) # Derive PARAM and PARAMN advs <- derive_vars_merged(   advs,   dataset_add = select(param_lookup, -VSTESTCD),   by_vars = exprs(PARAMCD) )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_finding.html","id":"timing","dir":"Articles","previous_headings":"Programming Workflow","what":"Derive Timing Variables (e.g. APHASE, AVISIT, APERIOD)","title":"Creating a BDS Finding ADaM","text":"Categorical timing variables protocol analysis dependent. simple example. assigning visits based time windows deriving periods, subperiods, phase variables see “Visit Period Variables” vignette.","code":"advs <- advs %>%   mutate(     AVISIT = case_when(       str_detect(VISIT, \"SCREEN\") ~ NA_character_,       str_detect(VISIT, \"UNSCHED\") ~ NA_character_,       str_detect(VISIT, \"RETRIEVAL\") ~ NA_character_,       str_detect(VISIT, \"AMBUL\") ~ NA_character_,       !is.na(VISIT) ~ str_to_title(VISIT)     ),     AVISITN = as.numeric(case_when(       VISIT == \"BASELINE\" ~ \"0\",       str_detect(VISIT, \"WEEK\") ~ str_trim(str_replace(VISIT, \"WEEK\", \"\"))     )),     ATPT = VSTPT,     ATPTN = VSTPTNUM   )  count(advs, VISITNUM, VISIT, AVISITN, AVISIT) #> # A tibble: 15 × 5 #>    VISITNUM VISIT               AVISITN AVISIT       n #>       <dbl> <chr>                 <dbl> <chr>    <int> #>  1      1   SCREENING 1              NA NA         102 #>  2      2   SCREENING 2              NA NA          78 #>  3      3   BASELINE                  0 Baseline    96 #>  4      3.5 AMBUL ECG PLACEMENT      NA NA          65 #>  5      4   WEEK 2                    2 Week 2      96 #>  6      5   WEEK 4                    4 Week 4      80 #>  7      6   AMBUL ECG REMOVAL        NA NA          52 #>  8      7   WEEK 6                    6 Week 6      48 #>  9      8   WEEK 8                    8 Week 8      48 #> 10      9   WEEK 12                  12 Week 12     48 #> 11     10   WEEK 16                  16 Week 16     48 #> 12     11   WEEK 20                  20 Week 20     32 #> 13     12   WEEK 24                  24 Week 24     32 #> 14     13   WEEK 26                  26 Week 26     32 #> 15    201   RETRIEVAL                NA NA          26  count(advs, VSTPTNUM, VSTPT, ATPTN, ATPT) #> # A tibble: 4 × 5 #>   VSTPTNUM VSTPT                          ATPTN ATPT                           n #>      <dbl> <chr>                          <dbl> <chr>                      <int> #> 1      815 AFTER LYING DOWN FOR 5 MINUTES   815 AFTER LYING DOWN FOR 5 MI…   232 #> 2      816 AFTER STANDING FOR 1 MINUTE      816 AFTER STANDING FOR 1 MINU…   232 #> 3      817 AFTER STANDING FOR 3 MINUTES     817 AFTER STANDING FOR 3 MINU…   232 #> 4       NA NA                                NA NA                           187"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_finding.html","id":"timingflag","dir":"Articles","previous_headings":"Programming Workflow","what":"Timing Flag Variables (e.g. ONTRTFL)","title":"Creating a BDS Finding ADaM","text":"analyses, may necessary flag observation -treatment. admiral function derive_var_ontrtfl() can used. example, -treatment defined observation treatment start treatment end, flag may derived : function returns original data frame column ONTRTFL added. Additionally, function functionality handle window ref_end_date. example, -treatment defined treatment start treatment end plus 60 days, call : addition, function allow filter pre-treatment observations occurred start date. example, observations VSTPT == PRE considered -treatment observation date falls treatment start end date, user may specify using filter_pre_timepoint parameter: Lastly, function allow create -treatment flag based analysis needs. example, variable ONTR01FL needed, showing -treatment flag Period 01, need set new var = ONTR01FL. addition, Period 01 Start Date Period 01 End Date, need ref_start_date = AP01SDT ref_end_date = AP01EDT.","code":"advs <- derive_var_ontrtfl(   advs,   start_date = ADT,   ref_start_date = TRTSDT,   ref_end_date = TRTEDT ) advs <- derive_var_ontrtfl(   advs,   start_date = ADT,   ref_start_date = TRTSDT,   ref_end_date = TRTEDT,   ref_end_window = 60 ) advs <- derive_var_ontrtfl(   advs,   start_date = ADT,   ref_start_date = TRTSDT,   ref_end_date = TRTEDT,   filter_pre_timepoint = ATPT == \"AFTER LYING DOWN FOR 5 MINUTES\" ) advs <- derive_var_ontrtfl(   advs,   new_var = ONTR01FL,   start_date = ASTDT,   end_date = AENDT,   ref_start_date = AP01SDT,   ref_end_date = AP01EDT,   span_period = TRUE )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_finding.html","id":"referencerange","dir":"Articles","previous_headings":"Programming Workflow","what":"Assign Reference Range Indicator (ANRIND)","title":"Creating a BDS Finding ADaM","text":"admiral function derive_var_anrind() may used derive reference range indicator ANRIND. function requires reference range boundaries exist data frame (ANRLO, ANRHI) also accommodates additional boundaries A1LO A1HI. function called :","code":"advs <- derive_var_anrind(advs)"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_finding.html","id":"baseline","dir":"Articles","previous_headings":"Programming Workflow","what":"Derive Baseline (BASETYPE, ABLFL, BASE, BNRIND)","title":"Creating a BDS Finding ADaM","text":"BASETYPE derived using function derive_basetype_records(). parameter basetypes function requires named list expression detailing BASETYPE assigned. Note, record falls multiple expressions within basetypes expression, row produced BASETYPE. important derive BASETYPE first can utilized subsequent derivations. important data frame contains multiple values BASETYPE. Next, analysis baseline flag ABLFL can derived using admiral function derive_var_extreme_flag(). example, baseline defined last non-missing AVAL prior TRTSDT, function call ABLFL : Note: Additional examples derive_var_extreme_flag() function can found . Lastly, BASE, BNRIND columns can derived using admiral function derive_var_base(). Example calls :","code":"advs <- derive_basetype_records(   dataset = advs,   basetypes = exprs(     \"LAST: AFTER LYING DOWN FOR 5 MINUTES\" = ATPTN == 815,     \"LAST: AFTER STANDING FOR 1 MINUTE\" = ATPTN == 816,     \"LAST: AFTER STANDING FOR 3 MINUTES\" = ATPTN == 817,     \"LAST\" = is.na(ATPTN)   ) )  count(advs, ATPT, ATPTN, BASETYPE) #> # A tibble: 4 × 4 #>   ATPT                           ATPTN BASETYPE                                n #>   <chr>                          <dbl> <chr>                               <int> #> 1 AFTER LYING DOWN FOR 5 MINUTES   815 LAST: AFTER LYING DOWN FOR 5 MINUT…   232 #> 2 AFTER STANDING FOR 1 MINUTE      816 LAST: AFTER STANDING FOR 1 MINUTE     232 #> 3 AFTER STANDING FOR 3 MINUTES     817 LAST: AFTER STANDING FOR 3 MINUTES    232 #> 4 NA                                NA LAST                                  187 advs <- restrict_derivation(   advs,   derivation = derive_var_extreme_flag,   args = params(     by_vars = exprs(STUDYID, USUBJID, BASETYPE, PARAMCD),     order = exprs(ADT, ATPTN, VISITNUM),     new_var = ABLFL,     mode = \"last\"   ),   filter = (!is.na(AVAL) & ADT <= TRTSDT & !is.na(BASETYPE)) ) advs <- derive_var_base(   advs,   by_vars = exprs(STUDYID, USUBJID, PARAMCD, BASETYPE),   source_var = AVAL,   new_var = BASE )  advs <- derive_var_base(   advs,   by_vars = exprs(STUDYID, USUBJID, PARAMCD, BASETYPE),   source_var = ANRIND,   new_var = BNRIND )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_finding.html","id":"bchange","dir":"Articles","previous_headings":"Programming Workflow","what":"Derive Change from Baseline (CHG, PCHG)","title":"Creating a BDS Finding ADaM","text":"Change percent change baseline can derived using admiral functions derive_var_chg() derive_var_pchg(). functions expect AVAL BASE exist data frame. CHG simply AVAL - BASE PCHG (AVAL - BASE) / absolute value (BASE) * 100. Examples calls : variables derived records, e.g., post-baseline records , restrict_derivation() can used.","code":"advs <- derive_var_chg(advs)  advs <- derive_var_pchg(advs)"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_finding.html","id":"shift","dir":"Articles","previous_headings":"Programming Workflow","what":"Derive Shift (e.g. SHIFT1)","title":"Creating a BDS Finding ADaM","text":"Shift variables can derived using admiral function derive_var_shift(). function derives character shift variable concatenating shift values based user-defined pairing, e.g., shift baseline reference range BNRIND analysis reference range ANRIND. Examples calls : variables derived records, e.g., post-baseline records , restrict_derivation() can used.","code":"advs <- derive_var_shift(advs,   new_var = SHIFT1,   from_var = BNRIND,   to_var = ANRIND )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_finding.html","id":"analysisratio","dir":"Articles","previous_headings":"Programming Workflow","what":"Derive Analysis Ratio (R2BASE)","title":"Creating a BDS Finding ADaM","text":"Analysis ratio variables can derived using admiral function derive_var_analysis_ratio(). function derives ratio variable based user-specified pair. example, Ratio Baseline calculated AVAL / BASE function appends new variable R2BASE dataset. Examples calls : variables derived records, e.g., post-baseline records , restrict_derivation() can used.","code":"advs <- derive_var_analysis_ratio(advs,   numer_var = AVAL,   denom_var = BASE )  advs <- derive_var_analysis_ratio(advs,   numer_var = AVAL,   denom_var = ANRLO,   new_var = R01ANRLO )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_finding.html","id":"analysisrec","dir":"Articles","previous_headings":"Programming Workflow","what":"Derive Analysis Flags (e.g. ANL01FL)","title":"Creating a BDS Finding ADaM","text":"finding ADaMs, analysis flag derived identify appropriate observation(s) use particular analysis subject multiple observations within particular timing period. situation, analysis flag (e.g. ANLxxFL) may used choose appropriate record analysis. flag may derived using admiral function derive_var_extreme_flag(). example, assume like choose latest highest value USUBJID, PARAMCD, AVISIT, ATPT. Another common example flagging worst value subject, parameter, visit. example, assume 3 PARAMCD values (SYSBP, DIABP, RESP). also assume high worst SYSBP DIABP low worst RESP.","code":"advs <- restrict_derivation(   advs,   derivation = derive_var_extreme_flag,   args = params(     by_vars = exprs(STUDYID, USUBJID, BASETYPE, PARAMCD, AVISIT),     order = exprs(ADT, ATPTN, AVAL),     new_var = ANL01FL,     mode = \"last\"   ),   filter = !is.na(AVISITN) ) advs <- slice_derivation(   advs,   derivation = derive_var_extreme_flag,   args = params(     by_vars = exprs(STUDYID, USUBJID, BASETYPE, PARAMCD, AVISIT),     order = exprs(ADT, ATPTN),     new_var = WORSTFL,     mode = \"first\"   ),   derivation_slice(     filter = PARAMCD %in% c(\"SYSBP\", \"DIABP\") & (!is.na(AVISIT) & !is.na(AVAL))   ),   derivation_slice(     filter = PARAMCD %in% \"PULSE\" & (!is.na(AVISIT) & !is.na(AVAL)),     args = params(mode = \"last\")   ) ) %>%   arrange(STUDYID, USUBJID, BASETYPE, PARAMCD, AVISIT)"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_finding.html","id":"treatment","dir":"Articles","previous_headings":"Programming Workflow","what":"Assign Treatment (TRTA, TRTP)","title":"Creating a BDS Finding ADaM","text":"TRTA TRTP must match least one value character treatment variables ADSL (e.g., TRTxxA/TRTxxP, TRTSEQA/TRTSEQP, TRxxAGy/TRxxPGy). example simple implementation study without periods : studies periods see “Visit Period Variables” vignette.","code":"advs <- mutate(advs, TRTP = TRT01P, TRTA = TRT01A)  count(advs, TRTP, TRTA, TRT01P, TRT01A) #> # A tibble: 2 × 5 #>   TRTP                TRTA                TRT01P              TRT01A           n #>   <chr>               <chr>               <chr>               <chr>        <int> #> 1 Placebo             Placebo             Placebo             Placebo        640 #> 2 Xanomeline Low Dose Xanomeline Low Dose Xanomeline Low Dose Xanomeline …   243"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_finding.html","id":"aseq","dir":"Articles","previous_headings":"Programming Workflow","what":"Assign ASEQ","title":"Creating a BDS Finding ADaM","text":"admiral function derive_var_obs_number() can used derive ASEQ. example call :","code":"advs <- derive_var_obs_number(   advs,   new_var = ASEQ,   by_vars = exprs(STUDYID, USUBJID),   order = exprs(PARAMCD, ADT, AVISITN, VISITNUM, ATPTN),   check_type = \"error\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_finding.html","id":"cat","dir":"Articles","previous_headings":"Programming Workflow","what":"Derive Categorization Variables (AVALCATy)","title":"Creating a BDS Finding ADaM","text":"can use derive_vars_cat() function derive categorization variables.","code":"avalcat_lookup <- exprs(   ~PARAMCD,  ~condition,   ~AVALCAT1, ~AVALCA1N,   \"HEIGHT\",  AVAL > 140,   \">140 cm\",         1,   \"HEIGHT\", AVAL <= 140, \"<= 140 cm\",         2 ) advs <- advs %>%   derive_vars_cat(     definition = avalcat_lookup,     by_vars = exprs(PARAMCD)   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_finding.html","id":"crit_vars","dir":"Articles","previous_headings":"Programming Workflow","what":"Derive Criterion Variables (CRITy, CRITyFL, CRITyFN)","title":"Creating a BDS Finding ADaM","text":"deriving criterion variables (CRITy, CRITyFL, CRITyFN) admiral provides derive_vars_crit_flag(). ensures derived ADaM-compliant way (see documentation function details). cases criterion depends parameter. higher order functions restrict_derivation() slice_derivation() useful case. following example criterion flags systolic diastolic blood pressure ADaM IG derived. first criterion based AVAL derived systolic diastolic blood pressure. slice_derivation() us used specify condition description criterion depending parameter. second criterion based AVAL CHG derived systolic blood pressure . Thus restrict_derivation() used.","code":"advs <- advs %>%   slice_derivation(     derivation = derive_vars_crit_flag,     args = params(       values_yn = TRUE,       create_numeric_flag = TRUE     ),     derivation_slice(       filter = PARAMCD == \"SYSBP\",       args = params(         condition = AVAL > 160,         description = \"Systolic Pressure > 160\"       )     ),     derivation_slice(       filter = PARAMCD == \"DIABP\",       args = params(         condition = AVAL > 95,         description = \"Diastolic Pressure > 95\"       )     )   ) advs <- advs %>%   restrict_derivation(     derivation = derive_vars_crit_flag,     args = params(       condition = AVAL > 160 & CHG > 10,       description = \"Systolic Pressure > 160 and Change from Baseline in Systolic Pressure > 10\",       crit_nr = 2,       values_yn = TRUE,       create_numeric_flag = TRUE     ),     filter = PARAMCD == \"SYSBP\"   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_finding.html","id":"adsl_vars","dir":"Articles","previous_headings":"Programming Workflow","what":"Add ADSL variables","title":"Creating a BDS Finding ADaM","text":"needed, ADSL variables can now added. List ADSL variables already merged held vector adsl_vars","code":"advs <- advs %>%   derive_vars_merged(     dataset_add = select(adsl, !!!negate_vars(adsl_vars)),     by_vars = exprs(STUDYID, USUBJID)   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_finding.html","id":"additional","dir":"Articles","previous_headings":"Programming Workflow","what":"Derive New Rows","title":"Creating a BDS Finding ADaM","text":"deriving new rows data frame, essential programmer takes time insert derivation correct location code. location vary depending previous computations retained new record computations must done new records.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_finding.html","id":"example-1-creating-a-new-record","dir":"Articles","previous_headings":"Programming Workflow > Derive New Rows","what":"Example 1 (Creating a New Record):","title":"Creating a BDS Finding ADaM","text":"add new record based selection certain criterion (e.g. minimum, maximum) derive_extreme_records() can used. new records include variables selected records.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_finding.html","id":"adding-a-new-record-for-the-last-value","dir":"Articles","previous_headings":"Programming Workflow > Derive New Rows > Example 1 (Creating a New Record):","what":"Adding a New Record for the Last Value","title":"Creating a BDS Finding ADaM","text":"subject Vital Signs parameter, add record holding last valid observation end treatment. Set AVISIT \"End Treatment\" assign unique AVISITN value.","code":"advs_ex1 <- advs %>%   derive_extreme_records(     dataset_add = advs,     by_vars = exprs(STUDYID, USUBJID, PARAMCD),     order = exprs(ADT, AVISITN, ATPTN, AVAL),     mode = \"last\",     filter_add = (4 < AVISITN & AVISITN <= 12 & ANL01FL == \"Y\"),     set_values_to = exprs(       AVISIT = \"End of Treatment\",       AVISITN = 99,       DTYPE = \"LOV\"     )   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_finding.html","id":"adding-a-new-record-for-the-minimum-value","dir":"Articles","previous_headings":"Programming Workflow > Derive New Rows > Example 1 (Creating a New Record):","what":"Adding a New Record for the Minimum Value","title":"Creating a BDS Finding ADaM","text":"subject Vital Signs parameter, add record holding minimum value end treatment. minimum attained multiple observations first one selected. Set AVISIT \"Minimum Treatment\" assign unique AVISITN value.","code":"advs_ex1 <- advs %>%   derive_extreme_records(     dataset_add = advs,     by_vars = exprs(STUDYID, USUBJID, PARAMCD),     order = exprs(AVAL, ADT, AVISITN, ATPTN),     mode = \"first\",     filter_add = (4 < AVISITN & AVISITN <= 12 & ANL01FL == \"Y\" & !is.na(AVAL)),     set_values_to = exprs(       AVISIT = \"Minimum on Treatment\",       AVISITN = 98,       DTYPE = \"MINIMUM\"     )   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_finding.html","id":"example-2-deriving-a-summary-record","dir":"Articles","previous_headings":"Programming Workflow > Derive New Rows","what":"Example 2 (Deriving a Summary Record)","title":"Creating a BDS Finding ADaM","text":"adding new records based aggregating records derive_summary_records() can used. new records variables specified by_vars set_values_to populated. subject, Vital Signs parameter, visit, date add record holding average value observations date. Set DTYPE AVERAGE.","code":"advs_ex2 <- derive_summary_records(   advs,   dataset_add = advs,   by_vars = exprs(STUDYID, USUBJID, PARAMCD, VISITNUM, ADT),   set_values_to = exprs(     AVAL = mean(AVAL, na.rm = TRUE),     DTYPE = \"AVERAGE\"   ) )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_finding.html","id":"example-3-deriving-a-new-paramcd","dir":"Articles","previous_headings":"Programming Workflow > Derive New Rows","what":"Example 3 (Deriving a New PARAMCD)","title":"Creating a BDS Finding ADaM","text":"Use function derive_param_computed() create new PARAMCD. Note variables specified by_vars argument populated newly created records. example creating Mean Arterial Pressure (PARAMCD = MAP2) alternative formula.","code":"advs_ex3 <- derive_param_computed(   advs,   by_vars = exprs(USUBJID, VISIT, ATPT),   parameters = c(\"SYSBP\", \"DIABP\"),   set_values_to = exprs(     AVAL = (AVAL.SYSBP - AVAL.DIABP) / 3 + AVAL.DIABP,     PARAMCD = \"MAP2\",     PARAM = \"Mean Arterial Pressure 2 (mmHg)\"   ) )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_finding.html","id":"attributes","dir":"Articles","previous_headings":"Programming Workflow","what":"Add Labels and Attributes","title":"Creating a BDS Finding ADaM","text":"Adding labels attributes SAS transport files supported following packages: metacore: establish common foundation use metadata within R session. metatools: enable use metacore objects. Metatools can used build datasets enhance columns existing datasets well checking datasets metadata. xportr: functionality associate metadata information local R data frame, perform data set level validation checks convert transport v5 file(xpt). NOTE: packages experimental phase, vision associated End End pipeline umbrella pharmaverse. example applying metadata perform associated checks can found pharmaverse E2E example.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_tte.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Creating a BDS Time-to-Event ADaM","text":"article describes creating BDS time--event ADaM. main part programming time--event dataset definition events censoring times. admiral supports single events like death composite events like disease progression death. one source dataset can used definition event censoring times. Note: examples assume CDISC SDTM /ADaM format input unless otherwise specified.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_tte.html","id":"required-packages","dir":"Articles","previous_headings":"Introduction","what":"Required Packages","title":"Creating a BDS Time-to-Event ADaM","text":"examples vignette require following packages.","code":"library(admiral) library(dplyr, warn.conflicts = FALSE) library(pharmaversesdtm)"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_tte.html","id":"programming-workflow","dir":"Articles","previous_headings":"","what":"Programming Workflow","title":"Creating a BDS Time-to-Event ADaM","text":"Read Data Derive Parameters (CNSR, ADT, STARTDT) Derive Analysis Value (AVAL) Derive Analysis Sequence Number (ASEQ) Add ADSL Variables Add Labels Attributes","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_tte.html","id":"readdata","dir":"Articles","previous_headings":"Programming Workflow","what":"Read in Data","title":"Creating a BDS Time-to-Event ADaM","text":"start, datasets needed creation time--event dataset read environment. company specific process. example purpose, ADSL dataset—included admiral—SDTM datasets pharmaversesdtm used. following code creates minimally viable ADAE dataset used throughout following examples.","code":"ae <- pharmaversesdtm::ae adsl <- admiral::admiral_adsl  ae <- convert_blanks_to_na(ae) adae <- ae %>%   left_join(adsl, by = c(\"STUDYID\", \"USUBJID\")) %>%   derive_vars_dt(     new_vars_prefix = \"AST\",     dtc = AESTDTC,     highest_imputation = \"M\"   ) %>%   derive_vars_dt(     new_vars_prefix = \"AEN\",     dtc = AEENDTC,     highest_imputation = \"M\",     date_imputation = \"last\"   ) %>%   mutate(TRTEMFL = if_else(ASTDT >= TRTSDT &     AENDT <= TRTEDT + days(30), \"Y\", NA_character_))"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_tte.html","id":"parameters","dir":"Articles","previous_headings":"Programming Workflow","what":"Derive Parameters (CNSR, ADT, STARTDT)","title":"Creating a BDS Time-to-Event ADaM","text":"derive parameter dependent variables like CNSR, ADT, STARTDT, EVNTDESC, SRCDOM, PARAMCD, … derive_param_tte() function can used. adds one parameter input dataset one observation per subject. Usually called several times. subject determined event occurred. affirmative analysis date ADT set earliest event date. event occurred, analysis date set latest censoring date. events censorings defined event_source() censor_source() class respectively. defines observations (filter parameter) source dataset (dataset_name parameter) potential events censorings, value CNSR variable (censor parameter), variable provides date (date parameter). date can provided date (--DT variable) datetime (--DTM variable). CDISC strongly recommends CNSR = 0 events positive integers censorings. admiral enforces recommendation. Therefore censor parameter available censor_source() . defaulted 1. dataset_name parameter expects character value used identifier. actual data used derivation parameter provided via source_datasets parameter derive_param_tte(). expects named list datasets. names correspond identifiers specified dataset_name parameter. allows define events censoring independent data.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_tte.html","id":"pre-defined-time-to-event-source-objects","dir":"Articles","previous_headings":"Programming Workflow > Derive Parameters (CNSR, ADT, STARTDT)","what":"Pre-Defined Time-to-Event Source Objects","title":"Creating a BDS Time-to-Event ADaM","text":"table shows pre-defined tte_source objects cover common use cases. pre-defined objects can passed directly derive_param_tte() create new time--event parameter.","code":"adtte <- derive_param_tte(   dataset_adsl = adsl,   start_date = TRTSDT,   event_conditions = list(ae_ser_event),   censor_conditions = list(lastalive_censor),   source_datasets = list(adsl = adsl, adae = adae),   set_values_to = exprs(PARAMCD = \"TTAESER\", PARAM = \"Time to First Serious AE\") )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_tte.html","id":"single-event","dir":"Articles","previous_headings":"Programming Workflow > Derive Parameters (CNSR, ADT, STARTDT)","what":"Single Event","title":"Creating a BDS Time-to-Event ADaM","text":"example, overall survival time defined treatment start death. Patients alive lost follow-censored last alive date. following call defines death event based ADSL variables. corresponding censoring based last known alive date can defined following call. definitions can passed derive_param_tte() create new time--event parameter. Note practice efficacy parameters might use randomization date time event origin date.","code":"death <- event_source(   dataset_name = \"adsl\",   filter = DTHFL == \"Y\",   date = DTHDT ) lstalv <- censor_source(   dataset_name = \"adsl\",   date = LSTALVDT ) adtte <- derive_param_tte(   dataset_adsl = adsl,   source_datasets = list(adsl = adsl),   start_date = TRTSDT,   event_conditions = list(death),   censor_conditions = list(lstalv),   set_values_to = exprs(PARAMCD = \"OS\", PARAM = \"Overall Survival\") )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_tte.html","id":"add-additional-information-for-events-and-censoring-evntdesc-srcvar","dir":"Articles","previous_headings":"Programming Workflow > Derive Parameters (CNSR, ADT, STARTDT)","what":"Add Additional Information for Events and Censoring (EVNTDESC, SRCVAR, …)","title":"Creating a BDS Time-to-Event ADaM","text":"add additional information like event censoring description (EVNTDESC) source variable (SRCVAR) set_values_to parameter can specified event/censoring definition.","code":"# define death event # death <- event_source(   dataset_name = \"adsl\",   filter = DTHFL == \"Y\",   date = DTHDT,   set_values_to = exprs(     EVNTDESC = \"DEATH\",     SRCDOM = \"ADSL\",     SRCVAR = \"DTHDT\"   ) )  # define censoring at last known alive date # lstalv <- censor_source(   dataset_name = \"adsl\",   date = LSTALVDT,   set_values_to = exprs(     EVNTDESC = \"LAST KNOWN ALIVE DATE\",     SRCDOM = \"ADSL\",     SRCVAR = \"LSTALVDT\"   ) )  # derive time-to-event parameter # adtte <- derive_param_tte(   dataset_adsl = adsl,   source_datasets = list(adsl = adsl),   event_conditions = list(death),   censor_conditions = list(lstalv),   set_values_to = exprs(PARAMCD = \"OS\", PARAM = \"Overall Survival\") )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_tte.html","id":"handling-subjects-without-assessment","dir":"Articles","previous_headings":"Programming Workflow > Derive Parameters (CNSR, ADT, STARTDT)","what":"Handling Subjects Without Assessment","title":"Creating a BDS Time-to-Event ADaM","text":"subject event record meeting censoring rule, included output dataset. order record subject output dataset, another censoring_source() object created specify patients censored. Therefore start censoring defined achieve subjects without data adrs censored start date. ADaM IG requires computed date must accompanied imputation flags. Thus, function detects --DTF /--TMF variable corresponding start_date STARTDTF STARTTMF set automatically values variables. date variable one event censoring source datasets imputed, imputation flag can specified set_values_to parameter event_source() censor_source() (see definition start censoring ). CDISC pilot contain RS dataset, following example progression free survival uses manually created datasets. event progression free survival occurs progression disease observed subject dies. Therefore two event_source() objects defined: pd progression disease death death. subjects may experience events. case first one selected derive_param_tte(). Subjects without event must censored last tumor assessment. censoring lastvisit object defined tumor assessments. Please note necessary select last one exclude assessments resulted progression disease. handled within derive_param_tte(). Patients without tumor assessment censored start date. Therefore start object defined treatment start date censoring date. necessary exclude patient tumor assessment definition start derive_param_tte() selects last date across censor_source() objects censoring date.","code":"View(adsl) View(adrs) # progressive disease event # pd <- event_source(   dataset_name = \"adrs\",   filter = AVALC == \"PD\",   date = ADT,   set_values_to = exprs(     EVNTDESC = \"PD\",     SRCDOM = \"ADRS\",     SRCVAR = \"ADT\",     SRCSEQ = ASEQ   ) )  # death event # death <- event_source(   dataset_name = \"adsl\",   filter = DTHFL == \"Y\",   date = DTHDT,   set_values_to = exprs(     EVNTDESC = \"DEATH\",     SRCDOM = \"ADSL\",     SRCVAR = \"DTHDT\"   ) ) # last tumor assessment censoring (CNSR = 1 by default) # lastvisit <- censor_source(   dataset_name = \"adrs\",   date = ADT,   set_values_to = exprs(     EVNTDESC = \"LAST TUMOR ASSESSMENT\",     SRCDOM = \"ADRS\",     SRCVAR = \"ADT\"   ) ) # start date censoring (for patients without tumor assessment) (CNSR = 2) # start <- censor_source(   dataset_name = \"adsl\",   date = TRTSDT,   censor = 2,   set_values_to = exprs(     EVNTDESC = \"TREATMENT START\",     SRCDOM = \"ADSL\",     SRCVAR = \"TRTSDT\",     ADTF = TRTSDTF   ) )  # derive time-to-event parameter # adtte <- derive_param_tte(   dataset_adsl = adsl,   source_datasets = list(adsl = adsl, adrs = adrs),   start_date = TRTSDT,   event_conditions = list(pd, death),   censor_conditions = list(lastvisit, start),   set_values_to = exprs(PARAMCD = \"PFS\", PARAM = \"Progression Free Survival\") )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_tte.html","id":"deriving-a-series-of-time-to-event-parameters","dir":"Articles","previous_headings":"Programming Workflow > Derive Parameters (CNSR, ADT, STARTDT)","what":"Deriving a Series of Time-to-Event Parameters","title":"Creating a BDS Time-to-Event ADaM","text":"several similar time--event parameters need derived call_derivation() function useful. following example parameters time first AE, time first serious AE, time first related AE derived. censoring three. definition event differs.","code":"# define censoring # observation_end <- censor_source(   dataset_name = \"adsl\",   date = pmin(TRTEDT + days(30), EOSDT),   censor = 1,   set_values_to = exprs(     EVNTDESC = \"END OF TREATMENT\",     SRCDOM = \"ADSL\",     SRCVAR = \"TRTEDT\"   ) )  # define time to first AE # tt_ae <- event_source(   dataset_name = \"ae\",   date = ASTDT,   set_values_to = exprs(     EVNTDESC = \"ADVERSE EVENT\",     SRCDOM = \"AE\",     SRCVAR = \"AESTDTC\"   ) )  # define time to first serious AE # tt_ser_ae <- event_source(   dataset_name = \"ae\",   filter = AESER == \"Y\",   date = ASTDT,   set_values_to = exprs(     EVNTDESC = \"SERIOUS ADVERSE EVENT\",     SRCDOM = \"AE\",     SRCVAR = \"AESTDTC\"   ) )  # define time to first related AE # tt_rel_ae <- event_source(   dataset_name = \"ae\",   filter = AEREL %in% c(\"PROBABLE\", \"POSSIBLE\", \"REMOTE\"),   date = ASTDT,   set_values_to = exprs(     EVNTDESC = \"RELATED ADVERSE EVENT\",     SRCDOM = \"AE\",     SRCVAR = \"AESTDTC\"   ) )  # derive all three time to event parameters # adaette <- call_derivation(   derivation = derive_param_tte,   variable_params = list(     params(       event_conditions = list(tt_ae),       set_values_to = exprs(PARAMCD = \"TTAE\")     ),     params(       event_conditions = list(tt_ser_ae),       set_values_to = exprs(PARAMCD = \"TTSERAE\")     ),     params(       event_conditions = list(tt_rel_ae),       set_values_to = exprs(PARAMCD = \"TTRELAE\")     )   ),   dataset_adsl = adsl,   source_datasets = list(     adsl = adsl,     ae = filter(adae, TRTEMFL == \"Y\")   ),   censor_conditions = list(observation_end) )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_tte.html","id":"deriving-time-to-event-parameters-using-by-groups","dir":"Articles","previous_headings":"Programming Workflow > Derive Parameters (CNSR, ADT, STARTDT)","what":"Deriving Time-to-Event Parameters Using By Groups","title":"Creating a BDS Time-to-Event ADaM","text":"time--event parameters need derived group source dataset, by_vars parameter can specified. time--event parameter derived group. Please note CDISC requires separate parameters (PARAMCD, PARAM) groups. Therefore variables specified by_vars parameter included output dataset. PARAMCD variable specified set_value_to parameter using expression right hand side results unique value group. values variables included output dataset, can stored PARCATn variables. following example time--event parameter preferred term AE dataset derived.","code":"View(adsl) View(ae) # define time to first adverse event event # ttae <- event_source(   dataset_name = \"ae\",   date = AESTDT,   set_values_to = exprs(     EVNTDESC = \"AE\",     SRCDOM = \"AE\",     SRCVAR = \"AESTDTC\",     SRCSEQ = AESEQ   ) )  # define censoring at end of study # eos <- censor_source(   dataset_name = \"adsl\",   date = EOSDT,   set_values_to = exprs(     EVNTDESC = \"END OF STUDY\",     SRCDOM = \"ADSL\",     SRCVAR = \"EOSDT\"   ) )  # derive time-to-event parameter # adtte <- derive_param_tte(   dataset_adsl = adsl,   by_vars = exprs(AEDECOD),   start_date = TRTSDT,   event_conditions = list(ttae),   censor_conditions = list(eos),   source_datasets = list(adsl = adsl, ae = ae),   set_values_to = exprs(     PARAMCD = paste0(\"TTAE\", as.numeric(as.factor(AEDECOD))),     PARAM = paste(\"Time to First\", AEDECOD, \"Adverse Event\"),     PARCAT1 = \"TTAE\",     PARCAT2 = AEDECOD   ) )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_tte.html","id":"aval","dir":"Articles","previous_headings":"Programming Workflow","what":"Derive Analysis Value (AVAL)","title":"Creating a BDS Time-to-Event ADaM","text":"analysis value (AVAL) can derived calling derive_vars_duration(). example derives time event days. units can requested specifying out_unit parameter.","code":"adtte <- derive_vars_duration(   adtte,   new_var = AVAL,   start_date = STARTDT,   end_date = ADT )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_tte.html","id":"aseq","dir":"Articles","previous_headings":"Programming Workflow","what":"Derive Analysis Sequence Number (ASEQ)","title":"Creating a BDS Time-to-Event ADaM","text":"admiral function derive_var_obs_number() can used derive ASEQ:","code":"adtte <- derive_var_obs_number(   adtte,   by_vars = exprs(STUDYID, USUBJID),   order = exprs(PARAMCD),   check_type = \"error\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_tte.html","id":"adslvars","dir":"Articles","previous_headings":"Programming Workflow","what":"Add ADSL Variables","title":"Creating a BDS Time-to-Event ADaM","text":"Variables ADSL required time--event analyses, e.g., treatment variables covariates can added using derive_vars_merged().","code":"adtte <- derive_vars_merged(   adtte,   dataset_add = adsl,   new_vars = exprs(ARMCD, ARM, ACTARMCD, ACTARM, AGE, SEX),   by_vars = exprs(STUDYID, USUBJID) )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_tte.html","id":"attributes","dir":"Articles","previous_headings":"Programming Workflow","what":"Add Labels and Attributes","title":"Creating a BDS Time-to-Event ADaM","text":"Adding labels attributes SAS transport files supported following packages: metacore: establish common foundation use metadata within R session. metatools: enable use metacore objects. Metatools can used build datasets enhance columns existing datasets well checking datasets metadata. xportr: functionality associate metadata information local R data frame, perform data set level validation checks convert transport v5 file(xpt). NOTE: packages experimental phase, vision associated End End pipeline umbrella pharmaverse. example applying metadata perform associated checks can found pharmaverse E2E example.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/concepts_conventions.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Programming Concepts and Conventions","text":"vignette aims discuss common programming concepts conventions adopted within admiral family packages. intended user-facing version Programming Strategy vignette, users can also read latter becoming familiar package expand topics interest. common admiral FAQ, visit corresponding FAQ page provided drop menu vignette.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/concepts_conventions.html","id":"input-and-output","dir":"Articles","previous_headings":"","what":"Input and Output","title":"Programming Concepts and Conventions","text":"expected input dataset grouped. Otherwise error issued. output dataset ungrouped. observations ordered dedicated way. particular, order observations input dataset may preserved.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/concepts_conventions.html","id":"admiral-functions-and-options","dir":"Articles","previous_headings":"","what":"{admiral} Functions and Options","title":"Programming Concepts and Conventions","text":"general principle, behavior admiral functions determined input, global object, .e. inputs like datasets, variable names, options, etc. must provided function arguments. Correspondingly, general functions side-effects like creating modifying global objects, printing, writing files, etc. exception principle found approach package options (see get_admiral_option() set_admiral_options()), allow user-defined defaults commonly used function arguments. instance, option subject_keys currently pre-defined exprs(STUDYID, USUBJID), can modified using set_admiral_options(subject_keys = exprs(...)) top script. full discussion admiral Inputs, Outputs Options, see section developer-facing Programming Strategy.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/concepts_conventions.html","id":"missing","dir":"Articles","previous_headings":"","what":"Handling of Missing Values","title":"Programming Concepts and Conventions","text":"using haven package read SAS datasets R, SAS-style character missing values, .e. \"\", converted proper R NA values. Rather kept . problematic downstream data processing R handles \"\" just string. Thus, data manipulation performed SAS blanks converted R NAs using admiral’s convert_blanks_to_na() function, e.g. Note logical operator applied NA value always returns NA rather TRUE FALSE. exception .na() returns TRUE input NA. Thus, filter visits \"Baseline\" following condition need used. Also note aggregation functions, like mean() max(), also return NA element input vector missing. avoid behavior one explicitly set na.rm = TRUE. important keep mind using admiral’s aggregation functions derive_summary_records(). handling NAs sorting variables see Sort Order.","code":"dm <- haven::read_sas(\"dm.sas7bdat\") %>%    convert_blanks_to_na() visits <- c(\"Baseline\", NA, \"Screening\", \"Week 1 Day 7\") visits != \"Baseline\" #> [1] FALSE    NA  TRUE  TRUE is.na(visits) #> [1] FALSE  TRUE FALSE FALSE visits != \"Baseline\" | is.na(visits) #> [1] FALSE  TRUE  TRUE  TRUE mean(c(1, NA, 2)) #> [1] NA mean(c(1, NA, 2), na.rm = TRUE) #> [1] 1.5"},{"path":[]},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/articles/concepts_conventions.html","id":"expr-and-exprs","dir":"Articles","previous_headings":"Expressions in Scripts > Quoting and Unquoting: Introducing expr(), exprs(), !! and !!!","what":"expr() and exprs()","title":"Programming Concepts and Conventions","text":"expr() function rlang package, used create expression. expression evaluated - rather, passed derivation function evaluates environment. exprs() plural version expr(), accepts multiple comma-separated items returns list expressions. used within contest admiral derivation function, expr() exprs() allow function evaluate expressions context input dataset. example, expr() exprs() allow users pass variable names datasets function without wrapping quotation marks. expressions framework powerful users able intuitively “inject code” admiral functions (function parameters) using similar syntax writing open code, exception possibly outer exprs() wrapper. instance, derive_vars_merged() call , user merging adsl ex able filter ex prior merge using expression passed filter_add parameter. filter_add accepts expressions, user full power filter dataset please. vein, user able create new variables wish merge using new_vars argument, pass list expressions containing “standard” R code.","code":"library(rlang)  adae <- data.frame(USUBJID = \"XXX-1\", AEDECOD = \"HEADACHE\")  # Return the adae object adae #>   USUBJID  AEDECOD #> 1   XXX-1 HEADACHE  # Return an expression expr(adae) #> adae derive_vars_merged(   adsl,   dataset_add = ex,   filter_add = !is.na(EXENDTM),   by_vars = exprs(STUDYID, USUBJID),   new_vars = exprs(     TRTEDTM = EXENDTM,     TRTETMF = EXENTMF,     COMPTRT = if_else(!is.na(EXENDTM), \"Y\", \"N\")   ),   order = exprs(EXENDTM),   mode = \"last\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/concepts_conventions.html","id":"unquoting","dir":"Articles","previous_headings":"Expressions in Scripts > Quoting and Unquoting: Introducing expr(), exprs(), !! and !!!","what":"Bang-Bang (!!) and Bang-Bang-Bang (!!!)","title":"Programming Concepts and Conventions","text":"Sometimes may want construct expression using , pre-existing expressions. However, ’s immediately clear achieve expressions inherently pause evaluation code ’s executed: !! (bang-bang) comes : provided rlang package, allows inject contents expression another expression, meaning using !! can modify code inside expression R evaluates . using !! unquoting expression, .e. evaluating pass onwards. can see example !! comes handy within admiral code Common Pitfall 1, contents expression unquoted can passed derive_vars_merged(). !!! (bang-bang-bang) plural version !! can used unquote list expressions: Within admiral, operator can useful need unquote list variables (stored expressions) use inside admiral even dplyr call. One example admiral subject keys: want use subject keys stored within admiral option subset dataset, need use !!! unquote list. Let’s construct dummy example illustrate point: can see another example !!! action line admiral ADEX template script, used dynamically control variables passed admiral function.","code":"a <- expr(2) b <- expr(3)  expr(a + b) #> a + b # NOT 2 + 3 expr(!!a + !!b) #> 2 + 3 exprs(!!!list(a, b)) #> [[1]] #> [1] 2 #>  #> [[2]] #> [1] 3 get_admiral_option(\"subject_keys\") #> [[1]] #> STUDYID #>  #> [[2]] #> USUBJID adcm <- data.frame(STUDYID = \"XXX\", USUBJID = \"XXX-1\", CMTRT = \"ASPIRIN\") adcm #>   STUDYID USUBJID   CMTRT #> 1     XXX   XXX-1 ASPIRIN  # This doesn't work as we are not unquoting the subject keys adcm %>% select(get_admiral_option(\"subject_keys\")) #> Error in `select()`: #> ! Can't select columns with `get_admiral_option(\"subject_keys\")`. #> ✖ `get_admiral_option(\"subject_keys\")` must be numeric or character, not a list.  # This works because we are unquoting the subject keys adcm %>% select(!!!get_admiral_option(\"subject_keys\")) #>   STUDYID USUBJID #> 1     XXX   XXX-1"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/concepts_conventions.html","id":"summary","dir":"Articles","previous_headings":"Expressions in Scripts > Quoting and Unquoting: Introducing expr(), exprs(), !! and !!!","what":"Summary","title":"Programming Concepts and Conventions","text":"summary, although expressions framework may seem slightly clunky mysterious begin , allows power flexibility forms key part admiral package. comprehensive treatment expressions, see Chapter 18 Chapter 19 Advanced R textbook. Chapter 19 specifically covers much detail concept unquoting.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/concepts_conventions.html","id":"common-pitfalls","dir":"Articles","previous_headings":"Expressions in Scripts","what":"Common pitfalls","title":"Programming Concepts and Conventions","text":"Expressions powerful, can also lead misunderstandings functionality. Let’s set dummy data explore common issues new (experienced!) programmers may encounter dealing expressions.","code":"library(dplyr, warn.conflicts = FALSE) library(admiral)  vs <- tribble(   ~USUBJID, ~VSTESTCD, ~VISIT, ~VSSTRESN, ~VSSTRESU, ~VSDTC,   \"01-1301\", \"WEIGHT\", \"SCREENING\", 82.1, \"kg\", \"2013-08-29\",   \"01-1301\", \"WEIGHT\", \"WEEK 2\", 81.19, \"kg\", \"2013-09-15\",   \"01-1301\", \"WEIGHT\", \"WEEK 4\", 82.56, \"kg\", \"2013-09-24\",   \"01-1302\", \"BMI\", \"SCREENING\", 20.1, \"kg/m2\", \"2013-08-29\",   \"01-1302\", \"BMI\", \"WEEK 2\", 20.2, \"kg/m2\", \"2013-09-15\",   \"01-1302\", \"BMI\", \"WEEK 4\", 19.9, \"kg/m2\", \"2013-09-24\" )  dm <- tribble(   ~USUBJID, ~AGE,   \"01-1301\", 18 )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/concepts_conventions.html","id":"pitfall1","dir":"Articles","previous_headings":"Expressions in Scripts > Common pitfalls","what":"1. Mistakenly passing something that isn’t an expression to an argument","title":"Programming Concepts and Conventions","text":"writing complex admiral code can easy mistakenly pass wrong input argument expects expression. example, code fails my_expression expression - name object global environment containing expression. fix code, need unquote my_expression expression holding passed correctly derive_vars_merged():","code":"my_expression <- expr(VSTESTCD == \"WEIGHT\" & VISIT == \"SCREENING\")  derive_vars_merged(   dm,   dataset_add = select(vs, USUBJID, VSTESTCD, VISIT),   by_vars = exprs(USUBJID),   filter_add = my_expression ) #> Error in `derive_vars_merged()`: #> ! Argument `filter_add` must be a filter condition, but is a symbol derive_vars_merged(   dm,   dataset_add = select(vs, USUBJID, VSTESTCD, VISIT),   by_vars = exprs(USUBJID),   filter_add = !!my_expression ) #> # A tibble: 1 × 4 #>   USUBJID   AGE VSTESTCD VISIT     #>   <chr>   <dbl> <chr>    <chr>     #> 1 01-1301    18 WEIGHT   SCREENING"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/concepts_conventions.html","id":"forgetting-that-expressions-must-be-evaluable-in-the-dataset","dir":"Articles","previous_headings":"Expressions in Scripts > Common pitfalls","what":"2. Forgetting that expressions must be evaluable in the dataset","title":"Programming Concepts and Conventions","text":"similar vein , even actual expression passed argument, must make sure can evaluated within dataset interest. may seem trivial, common pitfall expressions delay evaluation code can delay identification issues. instance, consider example: second call fails hidden within expression mention VSTPT, dropped vs filter_vs_and_merge().","code":"filter_vs_and_merge <- function(my_expression) {   derive_vars_merged(     dm,     dataset_add = select(vs, USUBJID, VSTESTCD, VISIT),     by_vars = exprs(USUBJID),     filter_add = !!my_expression   ) }  # This works filter_vs_and_merge(expr(VSTESTCD == \"WEIGHT\" & VISIT == \"SCREENING\")) #> # A tibble: 1 × 4 #>   USUBJID   AGE VSTESTCD VISIT     #>   <chr>   <dbl> <chr>    <chr>     #> 1 01-1301    18 WEIGHT   SCREENING  # This fails filter_vs_and_merge(expr(VSTESTCD == \"WEIGHT\" & VISIT == \"SCREENING\" & VSTPT == \"PREDOSE\")) #> Error in `filter()`: #> ℹ In argument: `VSTESTCD == \"WEIGHT\" & VISIT == \"SCREENING\" & VSTPT == #>   \"PREDOSE\"`. #> Caused by error: #> ! object 'VSTPT' not found"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/concepts_conventions.html","id":"see-also","dir":"Articles","previous_headings":"","what":"See also","title":"Programming Concepts and Conventions","text":"Programming Strategy","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/faq.html","id":"what-is-admiral","dir":"Articles","previous_headings":"","what":"What is admiral?","title":"FAQ","text":"block stand alone purpose (function provides specific functionality) Data Scientists can create blocks (create R functions) Constructing ADaM dataset become like building blocks based admiral modular functions user created modular functions.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/faq.html","id":"why-did-we-decide-to-start-admiral","dir":"Articles","previous_headings":"","what":"Why did we decide to start admiral?","title":"FAQ","text":"Data analysis challenges clinical trials vary depending scientific goals, therapeutic areas, indications, data sources data quality. face challenge limit company-level adoption crowd-sourcing create ADaM datasets? Build ADaMs via collaboration co-creation Early engagement like-minded companies moving towards R lead solution shared open source framework contribution across-industry Building ADaMs like modular building blocks, everyone can contribute module clear input output enable re-usable solutions Users can “slot ” modules address specific company/TA/Molecule/Study requirements TA specific requirements can open sourced transformed common ADaM approach analysis long-term gain consistent way producing ADaM wider community across-industry developers contributing grow codebase cover infinite array possibilities Contributors: option make name Pharma open-source community & avenue collaborate like-minded people across industry Imagine ADaMs built consistent manner code openly maintained functions impact Health Authorities, readable code, QC, talent flow","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/faq.html","id":"why-did-we-use-r-as-a-programming-language","dir":"Articles","previous_headings":"","what":"Why did we use R as a programming language?","title":"FAQ","text":"R isolated software product, everyone can contribute (open source principal) People University/Statistical talent pipeline likely come R skills rather proprietary language seems strong data science/analytics R community FDA open accepting R submissions heavy users Top line visualization/graphics - R-Shiny interactive data displays also R Markdown offers great report writing functionality R popular among statisticians new statistical methods likely implemented R language might equally suited programming languages - however stage make decision :)","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/faq.html","id":"admiral-offers-a-toolbox-of-functions-to-facilitate-adam--what-does-that-mean","dir":"Articles","previous_headings":"","what":"Admiral offers a toolbox of functions to facilitate ADaM. What does that mean?","title":"FAQ","text":"e.g. derive_vars_aage parameterized start end-date unit. Depending parameters results may vary specification. Functions serve toolbox user can create ADaM according requirements. principles, programming strategy documentation admiral considered framework users contribute.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/faq.html","id":"how-does-a-user-know-what-a-function-does-exactly","dir":"Articles","previous_headings":"","what":"How does a user know what a function does exactly?","title":"FAQ","text":"Function details purpose, requirements, parameters, dependencies examples documented header function. Complex functions potentially vignette admiral homepage provide details. admiral provide link explicit specification define.xml.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/faq.html","id":"where-a-user-can-quickly-find-some-references-or-advice-to-use-a-function","dir":"Articles","previous_headings":"","what":"Where a user can quickly find some references or advice to use a function?","title":"FAQ","text":"Cheat Sheet available, providing examples many admiral functions. detailed description, please refer Reference section.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/faq.html","id":"would-admiral-create-a-whole-adam-dataset","dir":"Articles","previous_headings":"","what":"Would {admiral} create a whole ADaM dataset?","title":"FAQ","text":"admiral meant toolbox enable Data Scientists build ADaMs according varying analysis needs admiral meant “click button, comes ADaM” tool admiral webpage, example scripts provided can used starting point create ADaM (see end vignette)","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/faq.html","id":"in-which-order-does-a-user-need-to-execute-the-functions","dir":"Articles","previous_headings":"","what":"In which order does a user need to execute the functions?","title":"FAQ","text":"Guidance provided ADSL, BDS OCCDS ADaM structure including template scripts.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/faq.html","id":"is-the-admiral-package-validated","dir":"Articles","previous_headings":"","what":"Is the {admiral} package validated?","title":"FAQ","text":"functions reviewed tested (see provided around function testing?) ensure work described documentation. Test cases function part R package. Users can add tests provide additional feedback. testing admiral team function replace QC validation process company. GitHub action (using open source packages) exists generate validation report R package, option company use. example report using earlier version admiral exists illustration.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/faq.html","id":"what-will-be-provided-around-function-testing","dir":"Articles","previous_headings":"","what":"What will be provided around function testing?","title":"FAQ","text":"Unit tests reliability function - available part open source release integration testing done ensure functions can called together create ADaM (e.g. even via internal testing teams) validation able use package company-specific SCE GxP purposes associated audit evidence strategy use admiral fits company-specific quality assurance process (double programming comparison versus company-specific legacy ADaM solution appropriate confidence builds) see guidance unit testing","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/faq.html","id":"will-admiral-provide-harmonized-define-xml-or-submittable-specifications-for-functions","dir":"Articles","previous_headings":"","what":"Will admiral provide harmonized define.xml or submittable specifications for functions?","title":"FAQ","text":". functions documented via programming headers, define.xml responsibility end user. Functions usually generalized specific. (see Admiral offers toolbox functions facilitate ADaM. mean?) users responsible make sure use functions parameters right way ensure alignment define.xml","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/faq.html","id":"will-admiral-provide-adam-ig-cdisc-compliant-datasets","dir":"Articles","previous_headings":"","what":"Will {admiral} provide ADaM IG CDISC compliant datasets?","title":"FAQ","text":"Although admiral follows CDISC standards claim dataset resulting calling admiral functions ADaM compliant. ensured user.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/faq.html","id":"how-much-of-the-adam-ig-is-covered-by-admiral","dir":"Articles","previous_headings":"","what":"How much of the ADaM IG is covered by admiral?","title":"FAQ","text":"ADaM IG standard framework without specific number datasets variables, used specific baseline answer question. provide guidance ADaM dataset structure (ADSL, OCCDS BDS) highlight functionality admiral covers. (see order user need execute functions?) guidance also highlight gaps filled user (e.g. timing, ranges). standard ADaM datasets (ADAE, ADCM, …) can provide estimated coverage based early adopters Roche/GSK ADaM implementation","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/faq.html","id":"will-there-be-a-usercontribution-guide","dir":"Articles","previous_headings":"","what":"Will there be a user/contribution guide?","title":"FAQ","text":"programming strategy serves framework users create functions. Please see contribution model website detailed description contribute","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/faq.html","id":"how-has-admiral-been-tested-externally-to-rochegsk","dir":"Articles","previous_headings":"","what":"How has {admiral} been tested externally to Roche/GSK?","title":"FAQ","text":"Sept/Oct 2021, limited release testing conducted 18 companies (>50 individuals) order assess compatibility admiral toolkit different company standards implementations test usability functions, e.g. clarity, reliability, robustness, flexibility. foundational version admiral achieved 7.9 / 10 average score survey respondents >75% said ’d advocate using admiral ADaM transformations R. “Extremely easy learn get , well thought planned. Plenty minor functions instead aiming create large”jack trades” framework. toolkit attempt become large one-button ADaM generator (fantastic).” “huge advantage Pharma companies common functions common stuff develop. easier authorities foundation ADaM programs. development goes faster every one develop across companies, bug-fixing faster many using package likely find potential bugs.” “huge proponent shared solutions within Pharma. Overall impressed admiral project – design, development, documentation, validation details available teams readily adopt.”","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/faq.html","id":"are-there-any-presentations-available-about-admiral","dir":"Articles","previous_headings":"","what":"Are there any presentations available about {admiral}?","title":"FAQ","text":"full collection admiral conference presentations years, please travel Presentation Archive.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/generic.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Generic Derivations","text":"vignette provides overview important admiral functions, generic derivations. derive specific variable parameter specific dataset can used many different derivations many different datasets. important concepts examples presented . full details examples see documentation functions.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/generic.html","id":"required-packages","dir":"Articles","previous_headings":"Introduction","what":"Required Packages","title":"Generic Derivations","text":"examples vignette require following packages.","code":"library(admiral) library(tibble)"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/generic.html","id":"characterization-of-derivations","dir":"Articles","previous_headings":"","what":"Characterization of Derivations","title":"Generic Derivations","text":"generic functions can characterized following three properties: Variables Records/Parameters single source dataset Multiple source datasets Selection: new values derived selecting records, e.g., baseline records, last exposure record, … Summary: new values derived summarizing values, e.g., sum, average, concatenation, … Computation: new values derived computation one value input, e.g., deriving BMI BSA height weight","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/generic.html","id":"overview-of-derivations","dir":"Articles","previous_headings":"Characterization of Derivations","what":"Overview of Derivations","title":"Generic Derivations","text":"Using three properties makes easy find appropriate function particular derivation. following interactive table lists generic functions properties.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/generic.html","id":"source-data","dir":"Articles","previous_headings":"","what":"Source Data","title":"Generic Derivations","text":"derivation functions expect single source dataset. multiple source datasets can specified. cases way specify source datasets across generic functions.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/generic.html","id":"single-source-dataset","dir":"Articles","previous_headings":"Source Data","what":"Single Source Dataset","title":"Generic Derivations","text":"functions expecting single source dataset data provided dataset_add argument. mandatory argument. data provided dataset argument used1. dataset_add argument provided, data dataset used (derive_var_extreme_flag()).","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/generic.html","id":"multiple-source-datasets","dir":"Articles","previous_headings":"Source Data","what":"Multiple Source Datasets","title":"Generic Derivations","text":"functions expecting multiple source datasets data provided source_datasets argument. datasets referred dataset_name element source objects. example, consider derivation response parameter. three possible responses defined event() objects. objects define events include data. Instead dataset_name field set (character) id. id used source_datasets argument derivation function link data events. .e., first two events (complete_response partial_response) dataset adrs_ovr used last event dataset myadsl used. allows define source objects independent data. .e., source object can used different source datasets. example, parameter derived data second reporter just changing source_dataset: source objects dataset_name element optional, e.g., event(). specified, input dataset (dataset) used.","code":"complete_response <- event(   description = \"Define complete response\",   dataset_name = \"ovr\",   condition = AVALC == \"CR\",   set_values_to = exprs(AVALC = \"COMPLETE RESPONSE\") )  partial_response <- event(   description = \"Define partial response\",   dataset_name = \"ovr\",   condition = AVALC == \"PR\",   set_values_to = exprs(AVALC = \"PARTIAL RESPONSE\") )  no_response <- event(   description = \"Define no response for all patients in adsl\",   dataset_name = \"adsl\",   condition = TRUE,   set_values_to = exprs(AVALC = \"NO RESPONSE\") )  derive_extreme_event(   ...   events = list(complete_response, partial_response, no_response),   source_datasets = list(ovr = adrs_ovr, adsl = myadsl),   ... ) derive_extreme_event(   ...   events = list(complete_response, partial_response, no_response),   source_datasets = list(ovr = adrs_ovr_reporter2, adsl = myadsl),   ... )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/generic.html","id":"methods","dir":"Articles","previous_headings":"","what":"Methods","title":"Generic Derivations","text":"generic derivations use three different methods deriving values new variables records. Usually derivation applies method several times, group input source data. groups defined by_vars argument, e.g., by_vars = exprs(USUBJID) “subject” by_vars = exprs(USUBJID, PARAMCD) “subject parameter”.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/generic.html","id":"selection","dir":"Articles","previous_headings":"Methods","what":"Selection","title":"Generic Derivations","text":"common method selection method. selects record source dataset(s) adds information selected record input dataset. information just flag indicating record exists, one variables selected records, new variables created selected record.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/generic.html","id":"options-for-selection","dir":"Articles","previous_headings":"Methods > Selection","what":"Options for Selection","title":"Generic Derivations","text":"simplest case record selected condition. condition specified filter_add filter argument. following example baseline weight added ADSL. Sometimes possible select record interest condition, e.g., first, last, best, worst, lowest, highest, … value derived. case mode order argument can specified select first last record respect variables specified order. day last valid dose added ADSL. also possible select record based records input source dataset. type selection admiral provides functions derive_vars_joined(), derive_var_joined_exist_flag(), derive_extreme_event(). provide filter_join argument accepts conditions variables input dataset (dataset) additional dataset (dataset_add). example consider deriving day dose last study treatment adverse event: filter_join condition applied temporary dataset created left joining input dataset additional dataset (restricted filter_add): “joined” function can also used condition selecting depends previous subsequent records dataset. case dataset specified dataset dataest_add. Consider following example \"HIGH\" results flagged confirmed second \"HIGH\" result least ten days later. join_type argument set \"\" restrict joined records subsequent results. variables included dataset dataset_add, dataset_add renamed adding suffix “.join”. variables dataset_add used filter_join must specified join_vars. temporary dataset applying filter_join : possible use summary functions like () () filter_join. Assume previous example records flagged results flagged record confirmation record \"HIGH\". can achieved specifying first_cond_upper argument set condition confirmation. first_cond_upper argument specified records joined dataset restricted first records condition fulfilled: Thereafter filter_join applied restricted joined dataset. .e., () function considers results confirmation records ignores subsequent results. Note: principle, actually achieve every result derive_vars_merged() derive_var_merged_exist_flag() using derive_vars_joined() derive_var_joined_exist_flag() respectively. However, “joined” functions require much resources (time memory), hence recommended use really required, .e., condition selecting records depends variables datasets.","code":"adsl <- tribble(   ~USUBJID,   \"1\",   \"2\",   \"3\" )  advs <- tribble(   ~USUBJID, ~PARAMCD, ~AVISIT,    ~ABLFL, ~AVAL, ~AVALU,   \"1\",      \"WEIGHT\", \"BASELINE\", \"Y\",     58.7, \"kg\",   \"1\",      \"HEIGHT\", \"BASELINE\", \"Y\",    169.2, \"cm\",   \"1\",      \"WEIGHT\", \"WEEK 3\",   NA,      59.3, \"kg\",   \"2\",      \"WEIGHT\", \"BASELINE\", \"Y\",     72.5, \"kg\",   \"2\",      \"WEIGHT\", \"WEKK 3\",   NA,      71.9, \"kg\", )  derive_vars_merged(   adsl,   dataset_add = advs,   by_vars = exprs(USUBJID),   filter_add = PARAMCD == \"WEIGHT\" & ABLFL == \"Y\",   new_vars = exprs(WGTBL = AVAL) ) #> # A tibble: 3 × 2 #>   USUBJID WGTBL #>   <chr>   <dbl> #> 1 1        58.7 #> 2 2        72.5 #> 3 3        NA adsl <- tribble(   ~USUBJID,   \"1\",   \"2\",   \"3\" )  ex <- tribble(   ~USUBJID, ~EXSTDY, ~EXDOSE,   \"1\",            1,      50,   \"1\",            7,      70,   \"1\",           14,       0,   \"2\",            1,      75,   \"2\",            9,      70 )  derive_vars_merged(   adsl,   dataset_add = ex,   by_vars = exprs(USUBJID),   filter_add = EXDOSE > 0,   order = exprs(EXSTDY),   mode = \"last\",   new_vars = exprs(TRTEDY = EXSTDY) ) #> # A tibble: 3 × 2 #>   USUBJID TRTEDY #>   <chr>    <dbl> #> 1 1            7 #> 2 2            9 #> 3 3           NA adae <- tribble(   ~USUBJID, ~ASTDY, ~AESEQ,   \"1\",           3,      1,   \"1\",           3,      2,   \"1\",          15,      3 )  ex <- tribble(   ~USUBJID, ~EXSTDY, ~EXDOSE,   \"1\",            1,      50,   \"1\",            7,      70,   \"1\",           14,       0,   \"2\",            1,      75,   \"2\",            9,      70 )  derive_vars_joined(   adae,   dataset_add = ex,   by_vars = exprs(USUBJID),   filter_add = EXDOSE > 0,   filter_join = EXSTDY <= ASTDY,   join_type = \"all\",   order = exprs(EXSTDY),   mode = \"last\",   new_vars = exprs(LSTDOSDY = EXSTDY, LASTDOS = EXDOSE) ) #> # A tibble: 3 × 5 #>   USUBJID ASTDY AESEQ LSTDOSDY LASTDOS #>   <chr>   <dbl> <dbl>    <dbl>   <dbl> #> 1 1           3     1        1      50 #> 2 1           3     2        1      50 #> 3 1          15     3        7      70 #> # A tibble: 6 × 5 #>   USUBJID ASTDY AESEQ EXSTDY EXDOSE #>   <chr>   <dbl> <dbl>  <dbl>  <dbl> #> 1 1           3     1      1     50 #> 2 1           3     1      7     70 #> 3 1           3     2      1     50 #> 4 1           3     2      7     70 #> 5 1          15     3      1     50 #> 6 1          15     3      7     70 adlb <- tribble(   ~USUBJID, ~PARAMCD, ~ADY, ~ANRIND,   \"1\",      \"AST\",       1, \"HIGH\",   \"1\",      \"AST\",       7, \"HIGH\",   \"1\",      \"AST\",      14, \"NORMAL\",   \"1\",      \"ALT\",       1, \"HIGH\",   \"1\",      \"ALT\",       7, \"NORMAL\",   \"1\",      \"ALT\",      14, \"HIGH\",   \"2\",      \"AST\",       1, \"HIGH\",   \"2\",      \"AST\",      15, \"HIGH\",   \"2\",      \"AST\",      22, \"NORMAL\",   \"2\",      \"ALT\",       1, \"HIGH\" )  derive_var_joined_exist_flag(   adlb,   dataset_add = adlb,   by_vars = exprs(USUBJID, PARAMCD),   order = exprs(ADY),   join_vars = exprs(ADY, ANRIND),   join_type = \"after\",   filter_join = ANRIND == \"HIGH\" & ANRIND.join == \"HIGH\" & ADY.join > ADY + 10,   new_var = HICONFFL ) #> # A tibble: 10 × 5 #>    USUBJID PARAMCD   ADY ANRIND HICONFFL #>    <chr>   <chr>   <dbl> <chr>  <chr>    #>  1 1       AST         1 HIGH   NA       #>  2 1       AST         7 HIGH   NA       #>  3 1       AST        14 NORMAL NA       #>  4 1       ALT         1 HIGH   Y        #>  5 1       ALT         7 NORMAL NA       #>  6 1       ALT        14 HIGH   NA       #>  7 2       AST         1 HIGH   Y        #>  8 2       AST        15 HIGH   NA       #>  9 2       AST        22 NORMAL NA       #> 10 2       ALT         1 HIGH   NA #> # A tibble: 9 × 6 #>   USUBJID PARAMCD   ADY ANRIND ADY.join ANRIND.join #>   <chr>   <chr>   <dbl> <chr>     <dbl> <chr>       #> 1 1       ALT         1 HIGH          7 NORMAL      #> 2 1       ALT         1 HIGH         14 HIGH        #> 3 1       ALT         7 NORMAL       14 HIGH        #> 4 1       AST         1 HIGH          7 HIGH        #> 5 1       AST         1 HIGH         14 NORMAL      #> 6 1       AST         7 HIGH         14 NORMAL      #> 7 2       AST         1 HIGH         15 HIGH        #> 8 2       AST         1 HIGH         22 NORMAL      #> 9 2       AST        15 HIGH         22 NORMAL derive_var_joined_exist_flag(   adlb,   dataset_add = adlb,   by_vars = exprs(USUBJID, PARAMCD),   order = exprs(ADY),   join_vars = exprs(ADY, ANRIND),   join_type = \"after\",   first_cond_upper = ANRIND.join == \"HIGH\" & ADY.join > ADY + 10,   filter_join = ANRIND == \"HIGH\" & all(ANRIND.join == \"HIGH\"),   new_var = HICONFFL ) #> # A tibble: 10 × 5 #>    USUBJID PARAMCD   ADY ANRIND HICONFFL #>    <chr>   <chr>   <dbl> <chr>  <chr>    #>  1 1       AST         1 HIGH   NA       #>  2 1       AST         7 HIGH   NA       #>  3 1       AST        14 NORMAL NA       #>  4 1       ALT         1 HIGH   NA       #>  5 1       ALT         7 NORMAL NA       #>  6 1       ALT        14 HIGH   NA       #>  7 2       AST         1 HIGH   Y        #>  8 2       AST        15 HIGH   NA       #>  9 2       AST        22 NORMAL NA       #> 10 2       ALT         1 HIGH   NA #> # A tibble: 3 × 6 #>   USUBJID PARAMCD   ADY ANRIND ADY.join ANRIND.join #>   <chr>   <chr>   <dbl> <chr>     <dbl> <chr>       #> 1 1       ALT         1 HIGH          7 NORMAL      #> 2 1       ALT         1 HIGH         14 HIGH        #> 3 2       AST         1 HIGH         15 HIGH"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/generic.html","id":"sort_order","dir":"Articles","previous_headings":"Methods > Selection","what":"Sort Order","title":"Generic Derivations","text":"admiral functions use dplyr::arrange() sorting, .e., NAs always sorted end (regardless whether desc() used ). Consider example following derivation last visit flag. record AVISITN == NA flagged NA sorted end. order argument accepts expressions. allows specify NAs handled. example, following sorts NA start. Thus AVISITN == 3 record flagged. can achieved following, also works character variables.","code":"advs <- tribble(   ~USUBJID, ~PARAMCD, ~AVISITN, ~AVAL,   \"1\",      \"WEIGHT\",       NA,  62.1,   \"1\",      \"WEIGHT\",        1,  62.3,   \"1\",      \"WEIGHT\",        2,  62.5,   \"1\",      \"WEIGHT\",        3,  62.4 )  derive_var_extreme_flag(   advs,   by_vars = exprs(USUBJID, PARAMCD),   order = exprs(AVISITN),   mode = \"last\",   new_var = LSTVISFL ) #> # A tibble: 4 × 5 #>   USUBJID PARAMCD AVISITN  AVAL LSTVISFL #>   <chr>   <chr>     <dbl> <dbl> <chr>    #> 1 1       WEIGHT        1  62.3 NA       #> 2 1       WEIGHT        2  62.5 NA       #> 3 1       WEIGHT        3  62.4 NA       #> 4 1       WEIGHT       NA  62.1 Y derive_var_extreme_flag(   advs,   by_vars = exprs(USUBJID, PARAMCD),   order = exprs(if_else(is.na(AVISITN), -Inf, AVISITN)),   mode = \"last\",   new_var = LSTVISFL ) #> # A tibble: 4 × 5 #>   USUBJID PARAMCD AVISITN  AVAL LSTVISFL #>   <chr>   <chr>     <dbl> <dbl> <chr>    #> 1 1       WEIGHT       NA  62.1 NA       #> 2 1       WEIGHT        1  62.3 NA       #> 3 1       WEIGHT        2  62.5 NA       #> 4 1       WEIGHT        3  62.4 Y derive_var_extreme_flag(   advs,   by_vars = exprs(USUBJID, PARAMCD),   order = exprs(!is.na(AVISITN), AVISITN),   mode = \"last\",   new_var = LSTVISFL ) #> # A tibble: 4 × 5 #>   USUBJID PARAMCD AVISITN  AVAL LSTVISFL #>   <chr>   <chr>     <dbl> <dbl> <chr>    #> 1 1       WEIGHT       NA  62.1 NA       #> 2 1       WEIGHT        1  62.3 NA       #> 3 1       WEIGHT        2  62.5 NA       #> 4 1       WEIGHT        3  62.4 Y"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/generic.html","id":"new-values","dir":"Articles","previous_headings":"Methods > Selection","what":"New Values","title":"Generic Derivations","text":"(new) variables set depends whether variables, flag, records added derivation. flag needs added, flag functions (derive_var_merged_exist_flag(), derive_var_joined_exist_flag()) can used. name new variable specified new_var argument values flag true_value false_value. new variables selected record needs added, name new variables values specified new_vars argument. addition flag added, exist_flag argument true_value false_value argument can used. new records added, variables values defined set_values_to argument.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/generic.html","id":"summary","dir":"Articles","previous_headings":"Methods","what":"Summary","title":"Generic Derivations","text":"new values derived summarizing values, e.g., sum, average, concatenation, …, functions derive_summary_records() derive_var_merged_summary() can used. example, adding average dose parameter ADEX can done following: summary function, source variable, new variable can specified set_values_to argument. Variables specified set_values_to by_vars set NA new records. average dose added variable ADSL, consider following: summary function, source variable, new variable specified new_vars argument. subjects without exposure observations value new variable can defined missing_values argument.","code":"adex <- tribble(   ~USUBJID, ~ASTDY, ~AVAL, ~PARAMCD,   \"1\",           1,    50, \"DOSE\",   \"1\",           7,    70, \"DOSE\",   \"1\",          14,     0, \"DOSE\",   \"2\",           1,    75, \"DOSE\",   \"2\",           9,    70, \"DOSE\" )  derive_summary_records(   adex,   dataset_add = adex,   filter_add = AVAL > 0,   by_vars = exprs(USUBJID),   set_values_to = exprs(     AVAL = mean(AVAL),     PARAMCD = \"AVERAGE DOSE\"   ) ) #> # A tibble: 7 × 4 #>   USUBJID ASTDY  AVAL PARAMCD      #>   <chr>   <dbl> <dbl> <chr>        #> 1 1           1  50   DOSE         #> 2 1           7  70   DOSE         #> 3 1          14   0   DOSE         #> 4 2           1  75   DOSE         #> 5 2           9  70   DOSE         #> 6 1          NA  60   AVERAGE DOSE #> 7 2          NA  72.5 AVERAGE DOSE adsl <- tribble(   ~USUBJID,   \"1\",   \"2\",   \"3\" )  derive_var_merged_summary(   adsl,   dataset_add = adex,   filter_add = AVAL > 0,   by_vars = exprs(USUBJID),   new_vars = exprs(     AVERDOSE = mean(AVAL)   ),   missing_values = exprs(AVERDOSE = 0) ) #> # A tibble: 3 × 2 #>   USUBJID AVERDOSE #>   <chr>      <dbl> #> 1 1           60   #> 2 2           72.5 #> 3 3            0"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/generic.html","id":"computed","dir":"Articles","previous_headings":"Methods","what":"Computed","title":"Generic Derivations","text":"new values computed different parameters source dataset, derive_param_computed() can used. computed value can specified set_values_to argument. values source variable parameter can referred temporary variables form <variable name>.<parameter name>. common computations like BMI admiral offers computation functions. previous example compute_bmi() used instead formula BMI:","code":"advs <- tribble(   ~USUBJID, ~AVISIT,    ~PARAMCD, ~AVAL, ~AVALU,   \"1\",      \"BASELINE\", \"WEIGHT\",  32.6, \"kg\",   \"1\",      \"BASELINE\", \"HEIGHT\", 155.4, \"cm\",   \"1\",      \"MONTH 6\",  \"WEIGHT\",  33.2, \"kg\",   \"1\",      \"MONTH 6\",  \"HEIGHT\", 155.8, \"cm\",   \"2\",      \"BASELINE\", \"WEIGHT\",  44.2, \"kg\",   \"2\",      \"BASELINE\", \"HEIGHT\", 145.3, \"cm\",   \"2\",      \"MONTH 6\",  \"WEIGHT\",  42.0, \"kg\",   \"2\",      \"MONTH 6\",  \"HEIGHT\", 146.4, \"cm\" )  derive_param_computed(   advs,   by_vars = exprs(USUBJID, AVISIT),   parameters = c(\"WEIGHT\", \"HEIGHT\"),   set_values_to = exprs(     AVAL = AVAL.WEIGHT / (AVAL.HEIGHT / 100)^2,     PARAMCD = \"BMI\",     AVALU = \"kg/m^2\"   ) ) #> # A tibble: 12 × 5 #>    USUBJID AVISIT   PARAMCD  AVAL AVALU  #>    <chr>   <chr>    <chr>   <dbl> <chr>  #>  1 1       BASELINE WEIGHT   32.6 kg     #>  2 1       BASELINE HEIGHT  155.  cm     #>  3 1       MONTH 6  WEIGHT   33.2 kg     #>  4 1       MONTH 6  HEIGHT  156.  cm     #>  5 2       BASELINE WEIGHT   44.2 kg     #>  6 2       BASELINE HEIGHT  145.  cm     #>  7 2       MONTH 6  WEIGHT   42   kg     #>  8 2       MONTH 6  HEIGHT  146.  cm     #>  9 1       BASELINE BMI      13.5 kg/m^2 #> 10 1       MONTH 6  BMI      13.7 kg/m^2 #> 11 2       BASELINE BMI      20.9 kg/m^2 #> 12 2       MONTH 6  BMI      19.6 kg/m^2 derive_param_computed(   advs,   by_vars = exprs(USUBJID, AVISIT),   parameters = c(\"WEIGHT\", \"HEIGHT\"),   set_values_to = exprs(     AVAL = compute_bmi(weight = AVAL.WEIGHT, height = AVAL.HEIGHT),     PARAMCD = \"BMI\",     AVALU = \"kg/m^2\"   ) ) #> # A tibble: 12 × 5 #>    USUBJID AVISIT   PARAMCD  AVAL AVALU  #>    <chr>   <chr>    <chr>   <dbl> <chr>  #>  1 1       BASELINE WEIGHT   32.6 kg     #>  2 1       BASELINE HEIGHT  155.  cm     #>  3 1       MONTH 6  WEIGHT   33.2 kg     #>  4 1       MONTH 6  HEIGHT  156.  cm     #>  5 2       BASELINE WEIGHT   44.2 kg     #>  6 2       BASELINE HEIGHT  145.  cm     #>  7 2       MONTH 6  WEIGHT   42   kg     #>  8 2       MONTH 6  HEIGHT  146.  cm     #>  9 1       BASELINE BMI      13.5 kg/m^2 #> 10 1       MONTH 6  BMI      13.7 kg/m^2 #> 11 2       BASELINE BMI      20.9 kg/m^2 #> 12 2       MONTH 6  BMI      19.6 kg/m^2"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/higher_order.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Higher Order Functions","text":"vignette explains advanced options admiral related higher order functions. higher order function function takes another function input. introducing higher order functions, intend give user greater power derivation functions, whilst trying negate need adding additional admiral functions arguments, user needing many separate steps. functions covered : call_derivation(): Call single derivation multiple times arguments fixed across iterations others varying restrict_derivation(): Execute single derivation subset input dataset slice_derivation(): input dataset split slices (subsets) slice single derivation called separately. arguments derivation may vary depending slice.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/higher_order.html","id":"required-packages","dir":"Articles","previous_headings":"Introduction","what":"Required Packages","title":"Higher Order Functions","text":"examples vignette require following packages. example purpose, ADSL dataset—included admiral—SDTM datasets pharmaversesdtm used. following code creates minimally viable ADAE dataset used needed following examples.","code":"library(admiral) library(pharmaversesdtm) library(dplyr, warn.conflicts = FALSE)  ae <- pharmaversesdtm::ae vs <- pharmaversesdtm::vs adsl <- admiral::admiral_adsl  ae <- convert_blanks_to_na(ae) vs <- convert_blanks_to_na(vs) adae <- ae %>%   left_join(adsl, by = c(\"STUDYID\", \"USUBJID\")) %>%   derive_vars_dt(     new_vars_prefix = \"AST\",     dtc = AESTDTC,     highest_imputation = \"M\"   ) %>%   mutate(TRTEMFL = if_else(ASTDT >= TRTSDT, \"Y\", NA_character_))"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/higher_order.html","id":"call-derivation","dir":"Articles","previous_headings":"","what":"Call Derivation","title":"Higher Order Functions","text":"function exists purely convenience save user repeating numerous similar derivation function calls. best used multiple derived variables similar specifications slight variations. example, imagine case parameters BDS ADaM required highest value flag lowest value flag. example achieve without using call_derivation(): example achieve using call_derivation(), different arguments passed using params(): example, can see higher order functions, derivation user supplies name derivation function apply, trailing parentheses required. variable_params used pass list different arguments needed derived variable. advantage higher order function highlighted examples two variable derivations similar needs, case multiple time AE parameters derived one call. Note example relies pre-defined tte_source objects, explained Creating BDS Time--Event ADaM. Developing ADaM scripts way using call_derivation() give following benefits: code becomes efficient readable maintenance eased case specification changes downstream quality checking require less effort","code":"vs_without <- vs %>%   derive_var_extreme_flag(     by_vars = exprs(USUBJID, VSTESTCD),     order = exprs(VSORRES, VSSEQ),     new_var = AHIFL,     mode = \"last\"   ) %>%   derive_var_extreme_flag(     by_vars = exprs(USUBJID, VSTESTCD),     order = exprs(VSORRES, VSSEQ),     new_var = ALOFL,     mode = \"first\"   ) vs_with <- vs %>%   call_derivation(     derivation = derive_var_extreme_flag,     variable_params = list(       params(new_var = AHIFL, mode = \"last\"),       params(new_var = ALOFL, mode = \"first\")     ),     by_vars = exprs(USUBJID, VSTESTCD),     order = exprs(VSORRES, VSSEQ)   ) adaette <- call_derivation(   derivation = derive_param_tte,   variable_params = list(     params(       event_conditions = list(ae_event),       set_values_to = exprs(PARAMCD = \"TTAE\")     ),     params(       event_conditions = list(ae_ser_event),       set_values_to = exprs(PARAMCD = \"TTSERAE\")     ),     params(       event_conditions = list(ae_sev_event),       set_values_to = exprs(PARAMCD = \"TTSEVAE\")     ),     params(       event_conditions = list(ae_wd_event),       set_values_to = exprs(PARAMCD = \"TTWDAE\")     )   ),   dataset_adsl = adsl,   source_datasets = list(adsl = adsl, adae = adae),   censor_conditions = list(lastalive_censor) )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/higher_order.html","id":"restrict-derivation","dir":"Articles","previous_headings":"","what":"Restrict Derivation","title":"Higher Order Functions","text":"idea behind function sometimes want apply derivation certain records input dataset. Introducing restrict_derivation() therefore gives users ability achieve across function, without function needing argument allow . example wanted flag first occurring AE highest severity patient, wanted records occurring study day 1. achieve using restrict_derivation(), function arguments passed using params() restriction criteria given using filter:","code":"ae <- ae %>%   mutate(TEMP_AESEVN = as.integer(factor(AESEV, levels = c(\"SEVERE\", \"MODERATE\", \"MILD\")))) %>%   restrict_derivation(     derivation = derive_var_extreme_flag,     args = params(       new_var = AHSEVFL,       by_vars = exprs(USUBJID),       order = exprs(TEMP_AESEVN, AESTDY, AESEQ),       mode = \"first\"     ),     filter = AESTDY >= 1   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/higher_order.html","id":"slice-derivation","dir":"Articles","previous_headings":"","what":"Slice Derivation","title":"Higher Order Functions","text":"function way combines features two. allows single derivation applied different arguments different slices (subsets) records input dataset. separate restrict_derivation() calls different set records, slice_derivation() allows achieve one call. example wanted achieve derivation records occurring study day 1, pre-treatment AEs wanted flag last occurring AE. achieve using slice_derivation(), function arguments passed using params() via different slices controlled filter: can see example, derivation_slice ordering important. addressed AEs study day 1 first, used filter = TRUE option catch remaining records (case pre-treatment AEs). ordering perhaps shown even look example three slices taken. Remember observations match one slice considered first matching slice. case ’re creating flag patient record first severe AE, first moderate AE, finally flagging last occurring AE severe moderate. order important slices mutually exclusive, case moderate AE slice severe AE slice, example, difference result. However third slice come last check remaining (.e. severe moderate) records .","code":"ae <- ae %>%   slice_derivation(     derivation = derive_var_extreme_flag,     args = params(       new_var = AHSEV2FL,       by_vars = exprs(USUBJID)     ),     derivation_slice(       filter = AESTDY >= 1,       args = params(order = exprs(TEMP_AESEVN, AESTDY, AESEQ), mode = \"first\")     ),     derivation_slice(       filter = TRUE,       args = params(order = exprs(AESTDY, AESEQ), mode = \"last\")     )   ) ae <- ae %>%   slice_derivation(     derivation = derive_var_extreme_flag,     args = params(       new_var = AHSEV3FL,       by_vars = exprs(USUBJID)     ),     derivation_slice(       filter = AESEV == \"SEVERE\",       args = params(order = exprs(AESTDY, AESEQ), mode = \"first\")     ),     derivation_slice(       filter = AESEV == \"MODERATE\",       args = params(order = exprs(AESTDY, AESEQ), mode = \"first\")     ),     derivation_slice(       filter = TRUE,       args = params(order = exprs(AESTDY, AESEQ), mode = \"last\")     )   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/hys_law.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Hy's Law Implementation","text":"drug development process, clinical trials often required assess potential experimental drug can cause severe liver injury, known drug induced liver injury (DILI) Drug-Induced Liver Injury: Premarketing Clinical Evaluation. multiple criteria need evaluated determine classify DILI “Event”. Hy’s Law, common rule thumb DILI Event , usually comprised three parts: Elevated alanine aminotransferase (ALT) aspartate aminotransferase (AST) 3-times greater upper limit normal. Elevated serum total bilirubin (BILI) 2-times greater within window time, ~14 days elevated ALT/AST event. reason explain increased lab values like preexisting liver disease.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/hys_law.html","id":"required-packages","dir":"Articles","previous_headings":"Introduction","what":"Required Packages","title":"Hy's Law Implementation","text":"examples vignette require following packages.","code":"library(admiral) library(dplyr, warn.conflicts = FALSE)"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/hys_law.html","id":"programming-workflow","dir":"Articles","previous_headings":"","what":"Programming Workflow","title":"Hy's Law Implementation","text":"Read Data Flagging Elevated Values (CRITy, CRITyFL) Subsetting LBTESTCD Joining Potential Events Create New Parameters Rows Conclusion","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/hys_law.html","id":"readdata","dir":"Articles","previous_headings":"Programming Workflow","what":"Read in Data","title":"Hy's Law Implementation","text":"assume ADLB dataset available 1. First read ADLB parameters required Hy’s Law parameters:","code":"adlb <- admiral::admiral_adlb %>%   filter(PARAMCD %in% c(\"AST\", \"ALT\", \"BILI\") & is.na(DTYPE))"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/hys_law.html","id":"columns","dir":"Articles","previous_headings":"Programming Workflow","what":"Flagging Elevated Values (CRITy, CRITyFL)","title":"Hy's Law Implementation","text":"standard convention ADLBHY datasets, various CRITy CRITyFL columns describe conditions necessary reach particular criterion Hy’s Law actual flag indicate whether condition reached. Using mutate(), call_derivation() derive_var_merged_exist_flag(), can create columns indicate 3-fold greater upper limit normal ALT/AST 2-fold greater upper limit normal BILI. increase visibility simplicity, retain columns relevant Hy’s Law analysis now.","code":"adlb_annotated <- adlb %>%   slice_derivation(     derive_vars_crit_flag,     args = params(       values_yn = TRUE     ),     derivation_slice(       filter = PARAMCD %in% c(\"AST\", \"ALT\"),       args = params(         condition = AVAL / ANRHI >= 3,         description = paste(PARAMCD, \">=3xULN\")       )     ),     derivation_slice(       filter = PARAMCD == \"BILI\",       args = params(         condition = AVAL / ANRHI >= 2,         description = \"BILI >= 2xULN\"       )     )   ) %>%   select(STUDYID, USUBJID, TRT01A, PARAMCD, LBSEQ, ADT, AVISIT, ADY, AVAL, ANRHI, CRIT1, CRIT1FL)"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/hys_law.html","id":"joins","dir":"Articles","previous_headings":"Programming Workflow","what":"Subsetting by LBTESTCD and Joining by Potential Events","title":"Hy's Law Implementation","text":"elevated ALT/AST event reaches threshold Hy’s Law, need search elevated BILI events within certain time-window, usually 14 days elevated ALT/AST event (window may vary organization). , Splitting dataset ALT/AST BILI subsets, respectively, Joining two datasets using derive_vars_joined() using filter_join argument join together relevant flagged BILI records corresponding flagged ALT/AST record (prior 14 days may vary trial/organization) indicate potential Hy’s Law event, resulting dataset helpful deriving additional parameters. dataset may also prove useful listing display two lab-records one row showcase potential event.","code":"altast_records <- adlb_annotated %>%   filter(PARAMCD %in% c(\"AST\", \"ALT\"))  bili_records <- adlb_annotated %>%   filter(PARAMCD %in% c(\"BILI\"))  hylaw_records <- derive_vars_joined(   dataset = altast_records,   dataset_add = bili_records,   by_vars = exprs(STUDYID, USUBJID),   order = exprs(ADY),   join_type = \"all\",   filter_join = 0 <= ADT.join - ADT & ADT.join - ADT <= 14 & CRIT1FL == \"Y\" & CRIT1FL.join == \"Y\",   new_vars = exprs(BILI_DT = ADT, BILI_CRITFL = CRIT1FL),   mode = \"first\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/hys_law.html","id":"newparams","dir":"Articles","previous_headings":"Programming Workflow","what":"How to Create New Parameters and Rows","title":"Hy's Law Implementation","text":"Using derive_param_exist_flag() can create variety parameters final dataset AVAL = 1/0 specific Hy’s Law analysis. example indicate potential Hy’s Law event, PARAMCD set \"HYSLAW\" PARAM set \"ALT/AST >= 3xULN BILI >= 2xULN\" patient using flags prior dataset. method allows flexibility well, parameters visit desired, add AVISIT ADT select() by_vars lines denoted following code. Additional modifications can made : Parameter indicate worsening condition sort baseline/post-baseline based analysis Flags lab values like ALP modified last step binding rows back whatever previous dataset appropriate based data specifications, case, best suited bind back adlb_annotated object.","code":"hylaw_records_pts_visits <- hylaw_records %>%   select(STUDYID, USUBJID, TRT01A) %>% # add AVISIT, ADT for by visit   distinct()  hylaw_records_fls <- hylaw_records %>%   select(STUDYID, USUBJID, TRT01A, CRIT1FL, BILI_CRITFL) %>% # add AVISIT, ADT for by visit   distinct()  hylaw_params <- derive_param_exist_flag(   dataset_ref = hylaw_records_pts_visits,   dataset_add = hylaw_records_fls,   condition = CRIT1FL == \"Y\" & BILI_CRITFL == \"Y\",   false_value = \"N\",   missing_value = \"N\",   by_vars = exprs(STUDYID, USUBJID, TRT01A), # add AVISIT, ADT for by visit   set_values_to = exprs(     PARAMCD = \"HYSLAW\",     PARAM = \"ALT/AST >= 3xULN and BILI >= 2xULN\",     AVAL = yn_to_numeric(AVALC)   ) )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/hys_law.html","id":"conclusion","dir":"Articles","previous_headings":"Programming Workflow","what":"Conclusion","title":"Hy's Law Implementation","text":"demonstrated base-case may asked trial programmer. reality Hy’s Law assessing potential DILI events can get rather complex quite quickly. Differences assessment across organizations specific trials might require modifications, may include: additional CRITy CRITyFL columns different cutoffs like 5xULN, 10xULN, 20xULN checking elevated values additional labs like alkaline phosphatase (ALP) appearance certain adverse events associated elevated lab-values different criteria cutoffs depend baseline values characteristics parameters worsening condition hope demonstrating flexibility admiral functions using general workflow create necessary parameters ADLBHY, creating final dataset becomes simplified easily scalable. Ideally, ready organization’s standard macros previous code TLFs outputs well. first attempt breaking summarizing topic. welcome feedback ideas improve guide!","code":"adlbhy <- adlb_annotated %>%   bind_rows(hylaw_params)"},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/articles/imputation.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Date and Time Imputation","text":"vignette broken three major sections. first section briefly explores imputation rules used admiral. second section focuses imputation functions work vectors lots small examples explore imputation rules. vector-based functions form backbone admiral’s powerful functions derive_vars_dt() derive_vars_dtm() building ADaM dataset. final section moves detailed examples user might face working ADaMs need ---DT ---DTM variables.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/imputation.html","id":"required-packages","dir":"Articles","previous_headings":"Introduction","what":"Required Packages","title":"Date and Time Imputation","text":"examples vignette require following packages.","code":"library(admiral) library(lubridate) library(tibble) library(dplyr, warn.conflicts = FALSE)"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/imputation.html","id":"imputation-rules","dir":"Articles","previous_headings":"","what":"Imputation Rules","title":"Date and Time Imputation","text":"Date time collected SDTM character values using extended ISO 8601 format. example, \"2019-10-9T13:42:00\". allows parts date time missing, e.g., \"2019-10\" day time unknown. ADaM timing variables like ADTM (Analysis Datetime) ADY (Analysis Relative Day) numeric variables. can derived date datetime complete. Therefore admiral provides imputation functions fill missing date time parts according certain imputation rules. admiral users primarily use two functions derive_vars_dt() derive_vars_dtm() date datetime imputations respectively. functions dates can passed argument, expect full dates datetimes (unless otherwise specified), possibility partials functions used first step make required imputation. functions need date/time imputation follow rule called Highest Imputation, corresponding argument functions called highest_imputation. rule best explained working examples , put briefly, rule allows user control components DTC value imputed missing. default imputation _dtm() functions, e.g. impute_dtc_dtm(), derive_vars_dtm(), “h” (hours). user can specify imputation done setting highest_imputation = n. However, _dt() functions, e.g. impute_dtc_dt(), derive_vars_dt() default imputation already set highest_imputation = \"n\". Care must taken deciding level imputation. component higher level highest imputation level missing, NA_character_ returned. example, highest_imputation = \"D\" \"2020\" results NA_character_ month missing. encourage readers explore detail highest_imputation options _dtm() _dt() function documentations examples .","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/imputation.html","id":"imputation-on-a-vector","dir":"Articles","previous_headings":"Imputation Rules","what":"Imputation on a Vector","title":"Date and Time Imputation","text":"first example, make use impute_dtc_dtm() 2019-10 setting highest_imputation = \"M\". argument date_imputation time_imputation given expressed inputs imputation like see done. Next impute using 2019-02, done naively can result invalid dates, e.g., Therefore keywords \"first\" \"last\" can specified date_imputation request missing parts replaced first last possible value - giving us valid date! dates, additional option use keyword \"mid\" impute missing day 15 missing day month 06-30, note different behavior depending preserve argument case month missing: wanted achieve similar result replacing missing part date fixed value 06-15, also possible, note difference days cases month missing:","code":"impute_dtc_dtm(   \"2019-10\",   highest_imputation = \"M\",   date_imputation = \"01-01\",   time_imputation = \"00:00:00\" ) #> [1] \"2019-10-01T00:00:00\" impute_dtc_dtm(   \"2019-02\",   highest_imputation = \"M\",   date_imputation = \"02-31\",   time_imputation = \"00:00:00\" ) #> [1] \"2019-02-31T00:00:00\" impute_dtc_dtm(   \"2019-02\",   highest_imputation = \"M\",   date_imputation = \"last\",   time_imputation = \"00:00:00\" ) #> [1] \"2019-02-28T00:00:00\" dates <- c(   \"2019-02\",   \"2019\",   \"2019---01\" ) impute_dtc_dtm(   dates,   highest_imputation = \"M\",   date_imputation = \"mid\",   time_imputation = \"00:00:00\",   preserve = FALSE ) #> [1] \"2019-02-15T00:00:00\" \"2019-06-30T00:00:00\" \"2019-06-30T00:00:00\" impute_dtc_dtm(   dates,   highest_imputation = \"M\",   date_imputation = \"mid\",   time_imputation = \"00:00:00\",   preserve = TRUE ) #> [1] \"2019-02-15T00:00:00\" \"2019-06-30T00:00:00\" \"2019-06-01T00:00:00\" dates <- c(   \"2019-02\",   \"2019\",   \"2019---01\" ) impute_dtc_dtm(   dates,   highest_imputation = \"M\",   date_imputation = \"06-15\",   time_imputation = \"00:00:00\" ) #> [1] \"2019-02-15T00:00:00\" \"2019-06-15T00:00:00\" \"2019-06-15T00:00:00\""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/imputation.html","id":"imputation-level","dir":"Articles","previous_headings":"Imputation Rules","what":"Imputation Level","title":"Date and Time Imputation","text":"imputation level, .e., components imputed missing, controlled highest_imputation argument. components specified level imputed. imputation years (highest_imputation = \"Y\") see next section.","code":"dates <- c(   \"2019-02-03T12:30:15\",   \"2019-02-03T12:30\",   \"2019-02-03\",   \"2019-02\",   \"2019\" )  # Do not impute impute_dtc_dtm(   dates,   highest_imputation = \"n\" ) #> [1] \"2019-02-03T12:30:15\" NA                    NA                    #> [4] NA                    NA  # Impute seconds only impute_dtc_dtm(   dates,   highest_imputation = \"s\" ) #> [1] \"2019-02-03T12:30:15\" \"2019-02-03T12:30:00\" NA                    #> [4] NA                    NA  # Impute time (hours, minutes, seconds) only impute_dtc_dtm(   dates,   highest_imputation = \"h\" ) #> [1] \"2019-02-03T12:30:15\" \"2019-02-03T12:30:00\" \"2019-02-03T00:00:00\" #> [4] NA                    NA  # Impute days and time impute_dtc_dtm(   dates,   highest_imputation = \"D\" ) #> [1] \"2019-02-03T12:30:15\" \"2019-02-03T12:30:00\" \"2019-02-03T00:00:00\" #> [4] \"2019-02-01T00:00:00\" NA  # Impute date (months and days) and time impute_dtc_dtm(   dates,   highest_imputation = \"M\" ) #> [1] \"2019-02-03T12:30:15\" \"2019-02-03T12:30:00\" \"2019-02-03T00:00:00\" #> [4] \"2019-02-01T00:00:00\" \"2019-01-01T00:00:00\""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/imputation.html","id":"minimummaximum-dates","dir":"Articles","previous_headings":"Imputation Rules","what":"Minimum/Maximum Dates","title":"Date and Time Imputation","text":"scenarios imputed date certain dates. example imputed date data cut date death date desirable. admiral imputation functions provide min_dates max_dates argument specify dates. example: ensured imputed date specified dates. dates range possible dates DTC value considered. possible dates defined missing parts DTC date, .e., “2019-02” possible dates range “2019-02-01” “2019-02-28”. Thus “2019-01-14” ignored. ensures non-missing parts DTC date changed. min_dates max_dates argument specified, also possible impute completely missing dates. date_imputation = \"first\" min_dates argument must specified date_imputation = \"last\" max_dates argument. imputation rules imputing year possible.","code":"impute_dtc_dtm(   \"2019-02\",   highest_imputation = \"M\",   date_imputation = \"last\",   time_imputation = \"last\",   max_dates = list(ymd(\"2019-01-14\"), ymd(\"2019-02-25\")) ) #> [1] \"2019-02-25T23:59:59\" # Impute year to first impute_dtc_dtm(   c(\"2019-02\", NA),   highest_imputation = \"Y\",   min_dates = list(     ymd(\"2019-01-14\", NA),     ymd(\"2019-02-25\", \"2020-01-01\")   ) ) #> [1] \"2019-02-25T00:00:00\" \"2020-01-01T00:00:00\"  # Impute year to last impute_dtc_dtm(   c(\"2019-02\", NA),   highest_imputation = \"Y\",   date_imputation = \"last\",   time_imputation = \"last\",   max_dates = list(     ymd(\"2019-01-14\", NA),     ymd(\"2019-02-25\", \"2020-01-01\")   ) ) #> [1] \"2019-02-25T23:59:59\" \"2020-01-01T23:59:59\""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/imputation.html","id":"imputation-flags","dir":"Articles","previous_headings":"","what":"Imputation Flags","title":"Date and Time Imputation","text":"ADaM requires date datetime variables imputation used accompanied date /time imputation flag variables (*DTF *TMF, e.g., ADTF ATMF ADTM). variables indicate highest level imputed, e.g., minutes seconds imputed, imputation flag set \"M\". admiral functions derive imputed variables also adding corresponding imputation flag variables. Note: admiral datetime imputation function provides ignore_seconds_flag argument can set TRUE cases seconds never collected. due following ADaM IG: given SDTM DTC variable, hours minutes ever collected, seconds imputed *DTM 00, necessary set *TMF \"S\".","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/imputation.html","id":"imputation-functions","dir":"Articles","previous_headings":"","what":"Imputation Functions","title":"Date and Time Imputation","text":"admiral provides following functions imputation: derive_vars_dt(): Adds date variable date imputation flag variable (optional) based –DTC variable imputation rules. derive_vars_dtm(): Adds datetime variable, date imputation flag variable, time imputation flag variable (optional) based –DTC variable imputation rules. impute_dtc_dtm(): Returns complete ISO 8601 datetime NA based partial ISO 8601 datetime imputation rules. impute_dtc_dt(): Returns complete ISO 8601 date (without time) NA based partial ISO 8601 date(time) imputation rules. convert_dtc_to_dt(): Returns date input ISO 8601 date complete. Otherwise, NA returned. convert_dtc_to_dtm(): Returns datetime input ISO 8601 date complete (missing time replaced \"00:00:00\" default). Otherwise, NA returned. compute_dtf(): Returns date imputation flag. compute_tmf(): Returns time imputation flag.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/articles/imputation.html","id":"create-an-imputed-datetime-and-date-variable-and-imputation-flag-variables","dir":"Articles","previous_headings":"Examples","what":"Create an Imputed Datetime and Date Variable and Imputation Flag Variables","title":"Date and Time Imputation","text":"derive_vars_dtm() function derives imputed datetime variable corresponding date time imputation flags. imputed date variable can derived using derive_vars_dtm_to_dt() function. necessary advisable perform imputation date variable already done datetime variable. CDISC considers datetime date variable two representations date. Thus imputation must imputation flags valid datetime date variable.","code":"ae <- tribble(   ~AESTDTC,   \"2019-08-09T12:34:56\",   \"2019-04-12\",   \"2010-09\",   NA_character_ ) %>%   derive_vars_dtm(     dtc = AESTDTC,     new_vars_prefix = \"AST\",     highest_imputation = \"M\",     date_imputation = \"first\",     time_imputation = \"first\"   ) %>%   derive_vars_dtm_to_dt(exprs(ASTDTM))"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/imputation.html","id":"create-an-imputed-date-variable-and-imputation-flag-variable","dir":"Articles","previous_headings":"Examples","what":"Create an Imputed Date Variable and Imputation Flag Variable","title":"Date and Time Imputation","text":"imputed date variable without corresponding datetime variable required, can derived derive_vars_dt() function.","code":"ae <- tribble(   ~AESTDTC,   \"2019-08-09T12:34:56\",   \"2019-04-12\",   \"2010-09\",   NA_character_ ) %>%   derive_vars_dt(     dtc = AESTDTC,     new_vars_prefix = \"AST\",     highest_imputation = \"M\",     date_imputation = \"first\"   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/imputation.html","id":"impute-time-without-imputing-date","dir":"Articles","previous_headings":"Examples","what":"Impute Time without Imputing Date","title":"Date and Time Imputation","text":"time imputed date, highest_imputation argument set \"h\". results NA date partial. date imputed date imputation flag created.","code":"ae <- tribble(   ~AESTDTC,   \"2019-08-09T12:34:56\",   \"2019-04-12\",   \"2010-09\",   NA_character_ ) %>%   derive_vars_dtm(     dtc = AESTDTC,     new_vars_prefix = \"AST\",     highest_imputation = \"h\",     time_imputation = \"first\"   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/imputation.html","id":"avoid-imputed-dates-before-a-particular-date","dir":"Articles","previous_headings":"Examples","what":"Avoid Imputed Dates Before a Particular Date","title":"Date and Time Imputation","text":"Usually adverse event start date imputed earliest date possible dates filling missing parts. result may date treatment start date. desirable adverse event considered treatment emergent excluded adverse event summaries. can avoided specifying treatment start date variable (TRTSDTM) min_dates argument. Please note TRTSDTM used imputed date non missing date time parts AESTDTC coincide TRTSDTM. Therefore 2019-10 imputed 2019-11-11 12:34:56. ensures collected information changed imputation.","code":"ae <- tribble(   ~AESTDTC,              ~TRTSDTM,   \"2019-08-09T12:34:56\", ymd_hms(\"2019-11-11T12:34:56\"),   \"2019-10\",             ymd_hms(\"2019-11-11T12:34:56\"),   \"2019-11\",             ymd_hms(\"2019-11-11T12:34:56\"),   \"2019-12-04\",          ymd_hms(\"2019-11-11T12:34:56\") ) %>%   derive_vars_dtm(     dtc = AESTDTC,     new_vars_prefix = \"AST\",     highest_imputation = \"M\",     date_imputation = \"first\",     time_imputation = \"first\",     min_dates = exprs(TRTSDTM)   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/imputation.html","id":"avoid-imputed-dates-after-a-particular-date","dir":"Articles","previous_headings":"Examples","what":"Avoid Imputed Dates After a Particular Date","title":"Date and Time Imputation","text":"date imputed latest date possible dates filling missing parts, result dates data cut death. can achieved specifying dates max_dates argument. Please note non missing date parts changed. Thus 2019-12-04 imputed 2019-12-04 23:59:59 although data cut date. may make sense replace data cut date part imputation. done separate data cleaning data cut step.","code":"ae <- tribble(   ~AEENDTC,              ~DTHDT,            ~DCUTDT,   \"2019-08-09T12:34:56\", ymd(\"2019-11-11\"), ymd(\"2019-12-02\"),   \"2019-11\",             ymd(\"2019-11-11\"), ymd(\"2019-12-02\"),   \"2019-12\",             NA,                ymd(\"2019-12-02\"),   \"2019-12-04\",          NA,                ymd(\"2019-12-02\") ) %>%   derive_vars_dtm(     dtc = AEENDTC,     new_vars_prefix = \"AEN\",     highest_imputation = \"M\",     date_imputation = \"last\",     time_imputation = \"last\",     max_dates = exprs(DTHDT, DCUTDT)   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/imputation.html","id":"imputation-without-creating-a-new-variable","dir":"Articles","previous_headings":"Examples","what":"Imputation Without Creating a New Variable","title":"Date and Time Imputation","text":"imputation required without creating new variable convert_dtc_to_dt() function can called obtain vector imputed dates. can used example conditions:","code":"mh <- tribble(   ~MHSTDTC,     ~TRTSDT,   \"2019-04\",    ymd(\"2019-04-15\"),   \"2019-04-01\", ymd(\"2019-04-15\"),   \"2019-05\",    ymd(\"2019-04-15\"),   \"2019-06-21\", ymd(\"2019-04-15\") ) %>%   filter(     convert_dtc_to_dt(       MHSTDTC,       highest_imputation = \"M\",       date_imputation = \"first\"     ) < TRTSDT   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/imputation.html","id":"using-more-than-one-imputation-rule-for-a-variable","dir":"Articles","previous_headings":"Examples","what":"Using More Than One Imputation Rule for a Variable","title":"Date and Time Imputation","text":"Using different imputation rules depending observation can done using slice_derivation().","code":"vs <- tribble(   ~VSDTC,                ~VSTPT,   \"2019-08-09T12:34:56\", NA,   \"2019-10-12\",          \"PRE-DOSE\",   \"2019-11-10\",          NA,   \"2019-12-04\",          NA ) %>%   slice_derivation(     derivation = derive_vars_dtm,     args = params(       dtc = VSDTC,       new_vars_prefix = \"A\"     ),     derivation_slice(       filter = VSTPT == \"PRE-DOSE\",       args = params(time_imputation = \"first\")     ),     derivation_slice(       filter = TRUE,       args = params(time_imputation = \"last\")     )   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/lab_grading.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Lab Grading","text":"Within ADLB ADaM data set concept lab grading, set criteria particular lab tests grade severity abnormality lab value. grades 0 4, grade 0 can viewed generally “NORMAL” value. higher grade severe abnormal lab value . several sets lab grading criteria, currently admiral implemented NCI-CTCAEv4, NCI-CTCAEv5 DAIDS grading criteria. future releases admiral may look implement grading criteria. NCI-CTCAE version 4 5 grading criteria can found : https://ctep.cancer.gov/protocoldevelopment/electronic_applications/ctc.htm . NCI-CTCAEv4 criteria can found heading Common Terminology Criteria Adverse Events (CTCAE)v4.0 NCI-CTCAEv5 criteria can found heading Common Terminology Criteria Adverse Events (CTCAE)v5.0 DAIDS grading criteria can found : https://rsc.niaid.nih.gov/clinical-research-sites/daids-adverse-event-grading-tables . DAIDS criteria can found heading DAIDS Table Grading Severity Adult Pediatric Adverse Events Corrected Version 2.1","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/lab_grading.html","id":"grading-metadata","dir":"Articles","previous_headings":"","what":"Grading metadata","title":"Lab Grading","text":"admiral store metadata data set set grading criteria data folder admiral. Currently, atoxgr_criteria_ctcv4() NCI-CTCAEv4, atoxgr_criteria_ctcv5() NCI-CTCAEv5 atoxgr_criteria_daids() DAIDS. metadata data set required variables optional variables, optional variables purely transparency, contain detailed information grading criteria. required variables used admiral create grade.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/lab_grading.html","id":"structure-of-metadata-set","dir":"Articles","previous_headings":"Grading metadata","what":"Structure of metadata set","title":"Lab Grading","text":"metadata data set following structure required variables: metadata data set following structure optional variables:","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/lab_grading.html","id":"handling-floating-points-when-comparing-numeric-values","dir":"Articles","previous_headings":"","what":"Handling floating points when comparing numeric values","title":"Lab Grading","text":"comparing numeric values, example AVAL > 1.1*ANRHI, unexpected results can occur due floating point issues. solve issue {admiral} used signif() function side equation, number significant digits used compare passed function derive_var_atoxgr_dir() via argument signif_dig. Please see documentation function details blog post admiral handles floating points context.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/articles/lab_grading.html","id":"mapping-adlb-to-the-term-variable-in-the-admiral-metadata-data-set","dir":"Articles","previous_headings":"Creating the Lab Grade","what":"Mapping ADLB to the TERM variable in the {admiral} metadata data set","title":"Lab Grading","text":"company needs map lab test term describes criteria applied. list terms defined admiral metadata implement NCI-CTCAEv4 :  Likewise, list terms defined admiral metadata implement NCI-CTCAEv5 : (Terms identical NCI-CTCAEv4, except Hyperglycemia, Hyperglycemia (Fasting) Hypophosphatemia) present NCI-CTCAEv5.  Finally, list terms defined admiral metadata implement DAIDS : Using CDISC data lab tests can mapped correct terms, firstly create PARAMCD, PARAM, AVAL, ANRLO ANRHI, also lab grading criteria require BASE PCHG, also need created running derive_var_atoxgr_dir() function. Another look-table used add ATOXDSCL ATOXDSCH using PARAMCD. ATOXDSCL holds terms grading low lab values, ATOXDSCH holds terms grading high lab values. names variables can user-defined. ATOXDSCL ATOXDSCH link ADLB data admiral metadata holds grading criteria. now straightforward create grade, low lab values grade held ATOXGRL high lab values grade held ATOXGRH. Note: NCICTCAEv5 grading, update meta_criteria parameter atoxgr_criteria_ctcv5 DAIDS grading update meta_criteria parameter atoxgr_criteria_daids Note: admiral grade ‘Anemia’ ‘Hemoglobin Increased’ metadata based SI unit ‘g/L’, however CDISC data SI unit ‘mmol/L’. Please see SI_UNIT_CHECK variable admiral metadata atoxgr_criteria_ctcv4() atoxgr_criteria_ctcv5() atoxgr_criteria_daids(), metadata data folder admiral.  admiral also gives option combine ATOXGRL ATOXGRH one variable, namely ATOXGR. Grades held ATOXGRL given negative value ATOXGR distinguish low high values.","code":"library(admiral) library(pharmaversesdtm) library(dplyr, warn.conflicts = FALSE) library(stringr) library(tibble)  lb <- pharmaversesdtm::lb adsl <- admiral::admiral_adsl  lb <- convert_blanks_to_na(lb) # Look-up tables ----  # Assign PARAMCD, PARAM, and PARAMN param_lookup <- tibble::tribble(   ~LBTESTCD, ~PARAMCD,  ~PARAM,                                             ~PARAMN,   \"ALB\",     \"ALB\",     \"Albumin (g/L)\",                                    1,   \"ALP\",     \"ALKPH\",   \"Alkaline Phosphatase (U/L)\",                       2,   \"ALT\",     \"ALT\",     \"Alanine Aminotransferase (U/L)\",                   3,   \"ANISO\",   \"ANISO\",   \"Anisocytes\",                                       4,   \"AST\",     \"AST\",     \"Aspartate Aminotransferase (U/L)\",                 5,   \"BASO\",    \"BASO\",    \"Basophils (10^9/L)\",                               6,   \"BASOLE\",  \"BASOLE\",  \"Basophils/Leukocytes (FRACTION)\",                  7,   \"BILI\",    \"BILI\",    \"Bilirubin (umol/L)\",                               8,   \"BUN\",     \"BUN\",     \"Blood Urea Nitrogen (mmol/L)\",                     9,   \"CA\",      \"CA\",      \"Calcium (mmol/L)\",                                 10,   \"CHOL\",    \"CHOLES\",  \"Cholesterol (mmol/L)\",                             11,   \"CK\",      \"CK\",      \"Creatinine Kinase (U/L)\",                          12,   \"CL\",      \"CL\",      \"Chloride (mmol/L)\",                                13,   \"COLOR\",   \"COLOR\",   \"Color\",                                            14,   \"CREAT\",   \"CREAT\",   \"Creatinine (umol/L)\",                              15,   \"EOS\",     \"EOS\",     \"Eosinophils (10^9/L)\",                             16,   \"EOSLE\",   \"EOSLE\",   \"Eosinophils/Leukocytes (FRACTION)\",                17,   \"GGT\",     \"GGT\",     \"Gamma Glutamyl Transferase (U/L)\",                 18,   \"GLUC\",    \"GLUC\",    \"Glucose (mmol/L)\",                                 19,   \"HBA1C\",   \"HBA1C\",   \"Hemoglobin A1C (1)\",                               20,   \"HCT\",     \"HCT\",     \"Hematocrit (1)\",                                   21,   \"HGB\",     \"HGB\",     \"Hemoglobin (mmol/L)\",                              22,   \"K\",       \"POTAS\",   \"Potassium (mmol/L)\",                               23,   \"KETONES\", \"KETON\",   \"Ketones\",                                          24,   \"LYM\",     \"LYMPH\",   \"Lymphocytes (10^9/L)\",                             25,   \"LYMLE\",   \"LYMPHLE\", \"Lymphocytes/Leukocytes (FRACTION)\",                26,   \"MACROCY\", \"MACROC\",  \"Macrocytes\",                                       27,   \"MCH\",     \"MCH\",     \"Ery. Mean Corpuscular Hemoglobin (fmol(Fe))\",      28,   \"MCHC\",    \"MCHC\",    \"Ery. Mean Corpuscular HGB Concentration (mmol/L)\", 29,   \"MCV\",     \"MCV\",     \"Ery. Mean Corpuscular Volume (f/L)\",               30,   \"MICROCY\", \"MICROC\",  \"Microcytes\",                                       31,   \"MONO\",    \"MONO\",    \"Monocytes (10^9/L)\",                               32,   \"MONOLE\",  \"MONOLE\",  \"Monocytes/Leukocytes (FRACTION)\",                  33,   \"PH\",      \"PH\",      \"pH\",                                               34,   \"PHOS\",    \"PHOS\",    \"Phosphate (mmol/L)\",                               35,   \"PLAT\",    \"PLAT\",    \"Platelet (10^9/L)\",                                36,   \"POIKILO\", \"POIKIL\",  \"Poikilocytes\",                                     37,   \"POLYCHR\", \"POLYCH\",  \"Polychromasia\",                                    38,   \"PROT\",    \"PROT\",    \"Protein (g/L)\",                                    39,   \"RBC\",     \"RBC\",     \"Erythrocytes (TI/L)\",                              40,   \"SODIUM\",  \"SODIUM\",  \"Sodium (mmol/L)\",                                  41,   \"SPGRAV\",  \"SPGRAV\",  \"Specific Gravity\",                                 42,   \"TSH\",     \"TSH\",     \"Thyrotropin (mU/L)\",                               43,   \"URATE\",   \"URATE\",   \"Urate (umol/L)\",                                   44,   \"UROBIL\",  \"UROBIL\",  \"Urobilinogen\",                                     45,   \"VITB12\",  \"VITB12\",  \"Vitamin B12 (pmol/L)\",                             46,   \"WBC\",     \"WBC\",     \"Leukocytes (10^9/L)\",                              47 )  adlb <- lb %>%   ## Add PARAMCD PARAM and PARAMN - from LOOK-UP table   derive_vars_merged_lookup(     dataset_add = param_lookup,     new_vars = exprs(PARAMCD, PARAM, PARAMN),     by_vars = exprs(LBTESTCD)   ) %>%   ## Calculate PARCAT1 AVAL AVALC ANRLO ANRHI   ## Dummy the values for BASE   mutate(     PARCAT1 = LBCAT,     AVAL = LBSTRESN,     AVALC = ifelse(       is.na(LBSTRESN) | as.character(LBSTRESN) != LBSTRESC,       LBSTRESC,       NA     ),     ANRLO = LBSTNRLO,     ANRHI = LBSTNRHI,     BASE = AVAL - 10   ) #> All `LBTESTCD` are mapped. # Assign ATOXDSCL and ATOXDSCH to hold lab grading terms # ATOXDSCL and ATOXDSCH hold terms defined by NCI-CTCAEv4. grade_lookup <- tibble::tribble(   ~PARAMCD, ~ATOXDSCL,                    ~ATOXDSCH,   \"ALB\",    \"Hypoalbuminemia\",            NA_character_,   \"ALKPH\",  NA_character_,                \"Alkaline phosphatase increased\",   \"ALT\",    NA_character_,                \"Alanine aminotransferase increased\",   \"AST\",    NA_character_,                \"Aspartate aminotransferase increased\",   \"BILI\",   NA_character_,                \"Blood bilirubin increased\",   \"CA\",     \"Hypocalcemia\",               \"Hypercalcemia\",   \"CHOLES\", NA_character_,                \"Cholesterol high\",   \"CK\",     NA_character_,                \"CPK increased\",   \"CREAT\",  NA_character_,                \"Creatinine increased\",   \"GGT\",    NA_character_,                \"GGT increased\",   \"GLUC\",   \"Hypoglycemia\",               \"Hyperglycemia\",   \"HGB\",    \"Anemia\",                     \"Hemoglobin increased\",   \"POTAS\",  \"Hypokalemia\",                \"Hyperkalemia\",   \"LYMPH\",  \"CD4 lymphocytes decreased\",  NA_character_,   \"PHOS\",   \"Hypophosphatemia\",           NA_character_,   \"PLAT\",   \"Platelet count decreased\",   NA_character_,   \"SODIUM\", \"Hyponatremia\",               \"Hypernatremia\",   \"WBC\",    \"White blood cell decreased\", \"Leukocytosis\", )  adlb <- adlb %>%   derive_vars_merged(     dataset_add = grade_lookup,     by_vars = exprs(PARAMCD),   ) adlb <- adlb %>%   derive_var_atoxgr_dir(     new_var = ATOXGRL,     tox_description_var = ATOXDSCL,     meta_criteria = atoxgr_criteria_ctcv4,     criteria_direction = \"L\",     get_unit_expr = extract_unit(PARAM)   ) %>%   derive_var_atoxgr_dir(     new_var = ATOXGRH,     tox_description_var = ATOXDSCH,     meta_criteria = atoxgr_criteria_ctcv4,     criteria_direction = \"H\",     get_unit_expr = extract_unit(PARAM)   ) adlb <- adlb %>%   derive_var_atoxgr()"},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/articles/lab_grading.html","id":"terms-graded","dir":"Articles","previous_headings":"NCI-CTCAEV4 implementation","what":"Terms graded","title":"Lab Grading","text":"Grading implemented lab tests lab value included grading definition, admiral try read data determine grade, ADLB dataset used. following CTCAE v4.0 SOC values identified grading, “Investigations”, “Metabolism nutrition disorders” “Blood lymphatic system disorders”. SOC values following terms criteria implemented admiral SOC = “Investigations” 21 CTCAE v4.0 Terms: Activated partial thromboplastin time prolonged Alanine aminotransferase increased Alkaline phosphatase increased Aspartate aminotransferase increased Blood bilirubin increased CD4 lymphocytes decreased Cholesterol high CPK increased Creatinine increased Fibrinogen decreased GGT increased Haptoglobin decreased Hemoglobin increased INR increased Lipase increased Lymphocyte count decreased Lymphocyte count increased Neutrophil count decreased Platelet count decreased Serum amylase increased White blood cell decreased SOC = “Metabolism nutrition disorders” 14 CTCAE v4.0 Terms: Hypercalcemia Hyperglycemia Hyperkalemia Hypermagnesemia Hypernatremia Hypertriglyceridemia Hyperuricemia Hypoalbuminemia Hypocalcemia Hypoglycemia Hypokalemia Hypomagnesemia Hyponatremia Hypophosphatemia SOC = “Blood lymphatic system disorders” 2 CTCAE v4.0 Terms: Anemia Leukocytosis","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/lab_grading.html","id":"updates-made-to-term","dir":"Articles","previous_headings":"NCI-CTCAEV4 implementation","what":"Updates made to TERM","title":"Lab Grading","text":"terms “Hypocalcemia” “Hypercalcemia” criteria provided Calcium Ionized Calcium, therefore admiral created row metadata, noted COMMENT variable metadata:  Similarly, criteria applicable Fasting Glucose well non-Fasting Glucose “Hyperglycemia” split 2 rows, noted COMMENT variable. Note “Hypoglycemia” require split 2 rows:","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/lab_grading.html","id":"assumptions-made-when-grading","dir":"Articles","previous_headings":"NCI-CTCAEV4 implementation","what":"Assumptions made when grading","title":"Lab Grading","text":"term “INR Increased” following criteria:   admiral assumed worst case used parts criteria grading, comparing lab value ULN also BASE. decision made put COMMENT field.   TERM “Hyperuricemia”, criteria Grade 1 Grade 3 respect lab value, worse case assumed grade 3. decision made put COMMENT field.   similar approach taken TERM “Hypokalemia” Grade 1 Grade 2 criteria respect lab value, worse case assumed grade 2. decision made put COMMENT field.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/articles/lab_grading.html","id":"terms-graded-1","dir":"Articles","previous_headings":"NCI-CTCAEV5 implementation","what":"Terms graded","title":"Lab Grading","text":"Grading implemented lab tests lab value included grading definition, admiral try read data determine grade, ADLB dataset used. following CTCAE v5.0 SOC values identified grading, “Investigations”, “Metabolism nutrition disorders” “Blood lymphatic system disorders”. SOC values following terms criteria implemented admiral SOC = “Investigations” 21 CTCAE v5.0 Terms: Activated partial thromboplastin time prolonged Alanine aminotransferase increased Alkaline phosphatase increased Aspartate aminotransferase increased Blood bilirubin increased CD4 lymphocytes decreased Cholesterol high CPK increased Creatinine increased Fibrinogen decreased GGT increased Haptoglobin decreased Hemoglobin increased INR increased Lipase increased Lymphocyte count decreased Lymphocyte count increased Neutrophil count decreased Platelet count decreased Serum amylase increased White blood cell decreased Note: terms identified NCI-CTCAEv4. SOC = “Metabolism nutrition disorders” 12 CTCAE v4.0 Terms: Hypercalcemia Hyperkalemia Hypermagnesemia Hypernatremia Hypertriglyceridemia Hyperuricemia Hypoalbuminemia Hypocalcemia Hypoglycemia Hypokalemia Hypomagnesemia Hyponatremia Note: terms identified NCI-CTCAEv4, except “Hypophosphatemia” “Hyperglycemia” NCICTCAEv5 grading criteria. SOC = “Blood lymphatic system disorders” 2 CTCAE v4.0 Terms: Anemia Leukocytosis Note: terms identified NCI-CTCAEv4.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/lab_grading.html","id":"updates-made-to-term-1","dir":"Articles","previous_headings":"NCI-CTCAEV5 implementation","what":"Updates made to TERM","title":"Lab Grading","text":"terms “Hypocalcemia” “Hypercalcemia” criteria provided Calcium Ionized Calcium, therefore admiral created row metadata, noted COMMENT variable metadata:","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/lab_grading.html","id":"assumptions-made-when-grading-1","dir":"Articles","previous_headings":"NCI-CTCAEV5 implementation","what":"Assumptions made when grading","title":"Lab Grading","text":"terms “Alanine aminotransferase increased”, “Alkaline phosphatase increased”, “Aspartate aminotransferase increased”, “Blood bilirubin increased” “GGT increased” criteria dependent Baseline Value normal abnormal. use variable BNRIND determine , users can pass value(s) BNRIND indicate baseline abnormal via argument abnormal_indicator function derive_var_atoxgr_dir(). example, setting abnormal_indicator = \"HIGH\" mean observations BNRIND = \"HIGH\" deemed abnormal Baseline Value. Note: can pass multiple values, abnormal_indicator = c(\"H\", \"HIGH\") 5 terms, means BNRIND abnormal grade always zero baseline observation. term “INR Increased” following criteria:   admiral assumed worst case used parts criteria grading, comparing lab value ULN also BASE. decision made put COMMENT field.  Similarly, terms “Lipase Increased” “Serum amylase increased” following criteria:   admiral assumed worst case implemented highest grade possible. decision made put COMMENT field.   TERM “Hyperuricemia”, criteria Grade 1 Grade 3 respect lab value, worse case assumed grade 3. decision made put COMMENT field.   similar approach taken TERM “Hypokalemia” “Hyponatremia”. “Hypokalemia”, Grade 1 Grade 2 criteria respect lab value, worse case assumed grade 2. “Hyponatremia”, Grade 2 Grade 2 criteria respect lab value, worse case assumed grade 3. decision made put COMMENT field.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/articles/lab_grading.html","id":"terms-graded-2","dir":"Articles","previous_headings":"DAIDS implementation","what":"Terms graded","title":"Lab Grading","text":"Grading implemented lab tests lab value included grading definition, admiral try read data determine grade, ADLB dataset used. following DAIDS SOC values identified grading, “Chemistries” “Hematology”. SOC values following terms criteria implemented admiral SOC = “Chemistries” 31 DAIDS Terms: Acidosis Albumin, Low Alkaline Phosphatase, High Alkalosis ALT, High Amylase, High AST, High Bicarbonate, Low Direct Bilirubin, High Total Bilirubin, High Calcium, High Calcium (Ionized), High Calcium, Low Calcium (Ionized), Low Creatine Kinase, High Creatinine, High Glucose Fasting, High Glucose Nonfasting, High Glucose, Low Lactate, High Lipase, High Cholesterol, Fasting, High LDL, Fasting, High Triglycerides, Fasting, High Magnesium, Low Phosphate, Low Potassium, High Potassium, Low Sodium, High Sodium, Low Uric Acid, High Note: {admiral} grade TERM = “Total Bilirubin, High” AGE <= 28 days, criteria Appendix DAIDS Table Grading Severity Adult Pediatric Adverse Events Corrected Version 2.1. SOC = “Hematology” 11 DAIDS Terms: Absolute CD4+ Count, Low Absolute Lymphocyte Count, Low Absolute Neutrophil Count (ANC), Low Fibrinogen Decreased Hemoglobin, Low INR, High Methemoglobin PTT, High Platelets, Decreased PT, High WBC, Decreased","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/lab_grading.html","id":"terms-with-age-or-sex-dependent-grading-criteria","dir":"Articles","previous_headings":"DAIDS implementation","what":"Terms with age or sex dependent grading criteria","title":"Lab Grading","text":"terms defined DAIDS age sex dependent grading criteria, {admiral} handles variable FILTER metadata. use {admiral} function compute_duration calculate age, see TERM = “Cholesterol, Fasting, High”:  Note: possible values must covered TERM defined, TERM = “Absolute Lymphocyte Count, Low” “Absolute CD4+ Count, Low” grading criteria defined age > 5 years. Therefore, add another row age <= 5 years set grade missing. Similarly, TERM = “LDL, Fasting, High” grading criteria defined age > 2 years. Therefore, add another row age <= 2 years set grade missing.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/lab_grading.html","id":"assumptions-made-when-grading-2","dir":"Articles","previous_headings":"DAIDS implementation","what":"Assumptions made when grading","title":"Lab Grading","text":"terms “INR, High”, “PT, High” “PTT, High”, criteria based subjects “anticoagulation therapy”, captured COMMENT field.   Similarly, terms “Absolute CD4+ Count, Low” “Absolute Lymphocyte Count, Low”, criteria based subjects “HIV infected”, captured COMMENT field.   term “Acidosis”, “Alkalosis” “Direct Bilirubin, High (> 28 days age)”, {admiral} grades high possible, assumes worst case subject “life-threatening consequences”. captured COMMENT field.  Similarly, term “Lactate, High”, {admiral} grade 1 2, following criteria:   admiral assumed worst case assume “without acidosis”. decision made put COMMENT field.   TERM “Direct Bilirubin, High (<= 28 days age)” “Uric Acid, High” criteria given SI unit. conversion SI unit comment field.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/lab_grading.html","id":"conclusion","dir":"Articles","previous_headings":"","what":"Conclusion","title":"Lab Grading","text":"NCI-CTCAEv4, NCI-CTCAEv5 DAIDS grading now implemented, {admiral} may look implement industry standard grading criteria. Providing tools users easily interact metadata update criteria, based companies needs also looked . Ideally, users able create metadata company specific grading schemes.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/occds.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Creating an OCCDS ADaM","text":"article describes creating OCCDS ADaM. Examples currently presented tested context ADAE. However, examples applied OCCDS ADaMs ADCM, ADMH, ADDV, etc. Note: examples assume CDISC SDTM /ADaM format input unless otherwise specified.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/occds.html","id":"programming-workflow","dir":"Articles","previous_headings":"","what":"Programming Workflow","title":"Creating an OCCDS ADaM","text":"Read Data Derive/Impute End Start Analysis Date/time Relative Day Derive Durations Derive ATC variables Derive Planned Actual Treatment Derive Date/Date-time Last Dose Derive Severity, Causality, Toxicity Grade Derive Treatment Emergent Flag Derive Occurrence Flags Derive Query Variables Add ADSL variables Derive Analysis Sequence Number Add Labels Attributes","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/occds.html","id":"readdata","dir":"Articles","previous_headings":"Programming Workflow","what":"Read in Data","title":"Creating an OCCDS ADaM","text":"start, data frames needed creation ADAE read environment. company specific process. data frames needed may AE ADSL example purpose, CDISC Pilot SDTM ADaM datasets —included pharmaversesdtm— used. step, may useful join ADSL AE domain well. ADSL variables used derivations selected step. rest relevant ADSL variables added later.","code":"library(admiral) library(dplyr, warn.conflicts = FALSE) library(pharmaversesdtm) library(lubridate)  ae <- pharmaversesdtm::ae adsl <- admiral::admiral_adsl ex_single <- admiral::ex_single  ae <- convert_blanks_to_na(ae) adsl_vars <- exprs(TRTSDT, TRTEDT, TRT01A, TRT01P, DTHDT, EOSDT)  adae <- derive_vars_merged(   ae,   dataset_add = adsl,   new_vars = adsl_vars,   by = exprs(STUDYID, USUBJID) )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/occds.html","id":"datetime","dir":"Articles","previous_headings":"Programming Workflow","what":"Derive/Impute End and Start Analysis Date/time and Relative Day","title":"Creating an OCCDS ADaM","text":"part derives ASTDTM, ASTDT, ASTDY, AENDTM, AENDT, AENDY. function derive_vars_dtm() can used derive ASTDTM AENDTM ASTDTM company-specific. ASTDT AENDT can derived ASTDTM AENDTM, respectively, using function derive_vars_dtm_to_dt(). derive_vars_dy() can used create ASTDY AENDY. See also Date Time Imputation.","code":"adae <- adae %>%   derive_vars_dtm(     dtc = AESTDTC,     new_vars_prefix = \"AST\",     highest_imputation = \"M\",     min_dates = exprs(TRTSDT)   ) %>%   derive_vars_dtm(     dtc = AEENDTC,     new_vars_prefix = \"AEN\",     highest_imputation = \"M\",     date_imputation = \"last\",     time_imputation = \"last\",     max_dates = exprs(DTHDT, EOSDT)   ) %>%   derive_vars_dtm_to_dt(exprs(ASTDTM, AENDTM)) %>%   derive_vars_dy(     reference_date = TRTSDT,     source_vars = exprs(ASTDT, AENDT)   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/occds.html","id":"duration","dir":"Articles","previous_headings":"Programming Workflow","what":"Derive Durations","title":"Creating an OCCDS ADaM","text":"function derive_vars_duration() can used create variables ADURN ADURU.","code":"adae <- adae %>%   derive_vars_duration(     new_var = ADURN,     new_var_unit = ADURU,     start_date = ASTDT,     end_date = AENDT   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/occds.html","id":"atc","dir":"Articles","previous_headings":"Programming Workflow","what":"Derive ATC variables","title":"Creating an OCCDS ADaM","text":"function derive_vars_atc() can used derive ATC Class Variables. helps add Anatomical Therapeutic Chemical class variables FACM ADCM. expected result input dataset ATC variables added.","code":"cm <- tibble::tribble(   ~STUDYID,  ~USUBJID,       ~CMGRPID, ~CMREFID,  ~CMDECOD,   \"STUDY01\", \"BP40257-1001\", \"14\",     \"1192056\", \"PARACETAMOL\",   \"STUDY01\", \"BP40257-1001\", \"18\",     \"2007001\", \"SOLUMEDROL\",   \"STUDY01\", \"BP40257-1002\", \"19\",     \"2791596\", \"SPIRONOLACTONE\" ) facm <- tibble::tribble(   ~STUDYID,  ~USUBJID,       ~FAGRPID, ~FAREFID,  ~FATESTCD,  ~FASTRESC,   \"STUDY01\", \"BP40257-1001\", \"1\",      \"1192056\", \"CMATC1CD\", \"N\",   \"STUDY01\", \"BP40257-1001\", \"1\",      \"1192056\", \"CMATC2CD\", \"N02\",   \"STUDY01\", \"BP40257-1001\", \"1\",      \"1192056\", \"CMATC3CD\", \"N02B\",   \"STUDY01\", \"BP40257-1001\", \"1\",      \"1192056\", \"CMATC4CD\", \"N02BE\",   \"STUDY01\", \"BP40257-1001\", \"1\",      \"2007001\", \"CMATC1CD\", \"D\",   \"STUDY01\", \"BP40257-1001\", \"1\",      \"2007001\", \"CMATC2CD\", \"D10\",   \"STUDY01\", \"BP40257-1001\", \"1\",      \"2007001\", \"CMATC3CD\", \"D10A\",   \"STUDY01\", \"BP40257-1001\", \"1\",      \"2007001\", \"CMATC4CD\", \"D10AA\",   \"STUDY01\", \"BP40257-1001\", \"2\",      \"2007001\", \"CMATC1CD\", \"D\",   \"STUDY01\", \"BP40257-1001\", \"2\",      \"2007001\", \"CMATC2CD\", \"D07\",   \"STUDY01\", \"BP40257-1001\", \"2\",      \"2007001\", \"CMATC3CD\", \"D07A\",   \"STUDY01\", \"BP40257-1001\", \"2\",      \"2007001\", \"CMATC4CD\", \"D07AA\",   \"STUDY01\", \"BP40257-1001\", \"3\",      \"2007001\", \"CMATC1CD\", \"H\",   \"STUDY01\", \"BP40257-1001\", \"3\",      \"2007001\", \"CMATC2CD\", \"H02\",   \"STUDY01\", \"BP40257-1001\", \"3\",      \"2007001\", \"CMATC3CD\", \"H02A\",   \"STUDY01\", \"BP40257-1001\", \"3\",      \"2007001\", \"CMATC4CD\", \"H02AB\",   \"STUDY01\", \"BP40257-1002\", \"1\",      \"2791596\", \"CMATC1CD\", \"C\",   \"STUDY01\", \"BP40257-1002\", \"1\",      \"2791596\", \"CMATC2CD\", \"C03\",   \"STUDY01\", \"BP40257-1002\", \"1\",      \"2791596\", \"CMATC3CD\", \"C03D\",   \"STUDY01\", \"BP40257-1002\", \"1\",      \"2791596\", \"CMATC4CD\", \"C03DA\" )  derive_vars_atc(cm, dataset_facm = facm, id_vars = exprs(FAGRPID)) #> # A tibble: 5 × 9 #>   STUDYID USUBJID      CMGRPID CMREFID CMDECOD       ATC1CD ATC2CD ATC3CD ATC4CD #>   <chr>   <chr>        <chr>   <chr>   <chr>         <chr>  <chr>  <chr>  <chr>  #> 1 STUDY01 BP40257-1001 14      1192056 PARACETAMOL   N      N02    N02B   N02BE  #> 2 STUDY01 BP40257-1001 18      2007001 SOLUMEDROL    D      D10    D10A   D10AA  #> 3 STUDY01 BP40257-1001 18      2007001 SOLUMEDROL    D      D07    D07A   D07AA  #> 4 STUDY01 BP40257-1001 18      2007001 SOLUMEDROL    H      H02    H02A   H02AB  #> 5 STUDY01 BP40257-1002 19      2791596 SPIRONOLACTO… C      C03    C03D   C03DA"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/occds.html","id":"trtpa","dir":"Articles","previous_headings":"Programming Workflow","what":"Derive Planned and Actual Treatment","title":"Creating an OCCDS ADaM","text":"TRTA TRTP must match least one value character treatment variables ADSL (e.g., TRTxxA/TRTxxP, TRTSEQA/TRTSEQP, TRxxAGy/TRxxPGy). example simple implementation study without periods : studies periods see “Visit Period Variables” vignette.","code":"adae <- mutate(adae, TRTP = TRT01P, TRTA = TRT01A)  count(adae, TRTP, TRTA, TRT01P, TRT01A) #> # A tibble: 2 × 5 #>   TRTP                TRTA                TRT01P              TRT01A           n #>   <chr>               <chr>               <chr>               <chr>        <int> #> 1 Placebo             Placebo             Placebo             Placebo         10 #> 2 Xanomeline Low Dose Xanomeline Low Dose Xanomeline Low Dose Xanomeline …     6"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/occds.html","id":"last_dose","dir":"Articles","previous_headings":"Programming Workflow","what":"Derive Date/Date-time of Last Dose","title":"Creating an OCCDS ADaM","text":"function derive_vars_joined() can used derive last dose date start event.","code":"ex_single <- derive_vars_dtm(   ex_single,   dtc = EXSTDTC,   new_vars_prefix = \"EXST\",   flag_imputation = \"none\" )  adae <- derive_vars_joined(   adae,   ex_single,   by_vars = exprs(STUDYID, USUBJID),   new_vars = exprs(LDOSEDTM = EXSTDTM),   join_vars = exprs(EXSTDTM),   join_type = \"all\",   order = exprs(EXSTDTM),   filter_add = (EXDOSE > 0 | (EXDOSE == 0 & grepl(\"PLACEBO\", EXTRT))) & !is.na(EXSTDTM),   filter_join = EXSTDTM <= ASTDTM,   mode = \"last\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/occds.html","id":"severity","dir":"Articles","previous_headings":"Programming Workflow","what":"Derive Severity, Causality, and Toxicity Grade","title":"Creating an OCCDS ADaM","text":"variables ASEV, AREL, ATOXGR can added using simple dplyr::mutate() assignments, imputation required.","code":"adae <- adae %>%   mutate(     ASEV = AESEV,     AREL = AEREL   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/occds.html","id":"trtflag","dir":"Articles","previous_headings":"Programming Workflow","what":"Derive Treatment Emergent Flag","title":"Creating an OCCDS ADaM","text":"derive treatment emergent flag TRTEMFL, one can call derive_var_trtemfl(). example , use 30 days flag derivation. derive -treatment flag (ONTRTFL) ADaM dataset single occurrence date, use derive_var_ontrtfl(). expected result input dataset additional column named ONTRTFL value \"Y\" NA. want also check end date, add end_date argument. Note scenario set span_period = TRUE want occurrences started prior drug intake, ongoing ended time considered -treatment.","code":"adae <- adae %>%   derive_var_trtemfl(     trt_start_date = TRTSDT,     trt_end_date = TRTEDT,     end_window = 30   ) bds1 <- tibble::tribble(   ~USUBJID, ~ADT,              ~TRTSDT,           ~TRTEDT,   \"P01\",    ymd(\"2020-02-24\"), ymd(\"2020-01-01\"), ymd(\"2020-03-01\"),   \"P02\",    ymd(\"2020-01-01\"), ymd(\"2020-01-01\"), ymd(\"2020-03-01\"),   \"P03\",    ymd(\"2019-12-31\"), ymd(\"2020-01-01\"), ymd(\"2020-03-01\") ) derive_var_ontrtfl(   bds1,   start_date = ADT,   ref_start_date = TRTSDT,   ref_end_date = TRTEDT ) #> # A tibble: 3 × 5 #>   USUBJID ADT        TRTSDT     TRTEDT     ONTRTFL #>   <chr>   <date>     <date>     <date>     <chr>   #> 1 P01     2020-02-24 2020-01-01 2020-03-01 Y       #> 2 P02     2020-01-01 2020-01-01 2020-03-01 Y       #> 3 P03     2019-12-31 2020-01-01 2020-03-01 NA  bds2 <- tibble::tribble(   ~USUBJID, ~ADT,              ~TRTSDT,           ~TRTEDT,   \"P01\",    ymd(\"2020-07-01\"), ymd(\"2020-01-01\"), ymd(\"2020-03-01\"),   \"P02\",    ymd(\"2020-04-30\"), ymd(\"2020-01-01\"), ymd(\"2020-03-01\"),   \"P03\",    ymd(\"2020-03-15\"), ymd(\"2020-01-01\"), ymd(\"2020-03-01\") ) derive_var_ontrtfl(   bds2,   start_date = ADT,   ref_start_date = TRTSDT,   ref_end_date = TRTEDT,   ref_end_window = 60 ) #> # A tibble: 3 × 5 #>   USUBJID ADT        TRTSDT     TRTEDT     ONTRTFL #>   <chr>   <date>     <date>     <date>     <chr>   #> 1 P01     2020-07-01 2020-01-01 2020-03-01 NA      #> 2 P02     2020-04-30 2020-01-01 2020-03-01 Y       #> 3 P03     2020-03-15 2020-01-01 2020-03-01 Y  bds3 <- tibble::tribble(   ~ADTM,              ~TRTSDTM,           ~TRTEDTM,           ~TPT,   \"2020-01-02T12:00\", \"2020-01-01T12:00\", \"2020-03-01T12:00\", NA,   \"2020-01-01T12:00\", \"2020-01-01T12:00\", \"2020-03-01T12:00\", \"PRE\",   \"2019-12-31T12:00\", \"2020-01-01T12:00\", \"2020-03-01T12:00\", NA ) %>%   mutate(     ADTM = ymd_hm(ADTM),     TRTSDTM = ymd_hm(TRTSDTM),     TRTEDTM = ymd_hm(TRTEDTM)   ) derive_var_ontrtfl(   bds3,   start_date = ADTM,   ref_start_date = TRTSDTM,   ref_end_date = TRTEDTM,   filter_pre_timepoint = TPT == \"PRE\" ) #> # A tibble: 3 × 5 #>   ADTM                TRTSDTM             TRTEDTM             TPT   ONTRTFL #>   <dttm>              <dttm>              <dttm>              <chr> <chr>   #> 1 2020-01-02 12:00:00 2020-01-01 12:00:00 2020-03-01 12:00:00 NA    Y       #> 2 2020-01-01 12:00:00 2020-01-01 12:00:00 2020-03-01 12:00:00 PRE   NA      #> 3 2019-12-31 12:00:00 2020-01-01 12:00:00 2020-03-01 12:00:00 NA    NA"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/occds.html","id":"occflag","dir":"Articles","previous_headings":"Programming Workflow","what":"Derive Occurrence Flags","title":"Creating an OCCDS ADaM","text":"function derive_var_extreme_flag() can help derive variables AOCCIFL, AOCCPIFL, AOCCSIFL, AOCCzzFL. grades collected, following can used flag first occurrence maximum toxicity grade. Similarly, ASEV can also used derive occurrence flags, severity collected. case, variable need recoded numeric variable. Flag first occurrence severe adverse event:","code":"adae <- adae %>%   restrict_derivation(     derivation = derive_var_extreme_flag,     args = params(       by_vars = exprs(USUBJID),       order = exprs(desc(ATOXGR), ASTDTM, AESEQ),       new_var = AOCCIFL,       mode = \"first\"     ),     filter = TRTEMFL == \"Y\"   ) adae <- adae %>%   restrict_derivation(     derivation = derive_var_extreme_flag,     args = params(       by_vars = exprs(USUBJID),       order = exprs(         as.integer(factor(           ASEV,           levels = c(\"DEATH THREATENING\", \"SEVERE\", \"MODERATE\", \"MILD\")         )),         ASTDTM, AESEQ       ),       new_var = AOCCIFL,       mode = \"first\"     ),     filter = TRTEMFL == \"Y\"   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/occds.html","id":"query","dir":"Articles","previous_headings":"Programming Workflow","what":"Derive Query Variables","title":"Creating an OCCDS ADaM","text":"deriving query variables SMQzzNAM, SMQzzCD, SMQzzSC, SMQzzSCN, CQzzNAM derive_vars_query() function can used. input expects queries dataset, provides definition queries. See Queries dataset documentation detailed description queries dataset. create_query_data() function can used create queries datasets. following example shows derive query variables Standardized MedDRA Queries (SMQs) ADAE. Similarly SMQ, derive_vars_query() function can used derive Standardized Drug Groupings (SDG).","code":"queries <- admiral::queries adae1 <- tibble::tribble(   ~USUBJID, ~ASTDTM, ~AETERM, ~AESEQ, ~AEDECOD, ~AELLT, ~AELLTCD,   \"01\", \"2020-06-02 23:59:59\", \"ALANINE AMINOTRANSFERASE ABNORMAL\",   3, \"Alanine aminotransferase abnormal\", NA_character_, NA_integer_,   \"02\", \"2020-06-05 23:59:59\", \"BASEDOW'S DISEASE\",   5, \"Basedow's disease\", NA_character_, 1L,   \"03\", \"2020-06-07 23:59:59\", \"SOME TERM\",   2, \"Some query\", \"Some term\", NA_integer_,   \"05\", \"2020-06-09 23:59:59\", \"ALVEOLAR PROTEINOSIS\",   7, \"Alveolar proteinosis\", NA_character_, NA_integer_ )  adae_query <- derive_vars_query(dataset = adae1, dataset_queries = queries) sdg <- tibble::tribble(   ~PREFIX, ~GRPNAME,          ~GRPID, ~SCOPE,  ~SCOPEN, ~SRCVAR,   ~TERMCHAR,          ~TERMNUM,   \"SDG01\", \"Diuretics\",           11, \"BROAD\", 1,       \"CMDECOD\", \"Diuretic 1\",       NA,   \"SDG01\", \"Diuretics\",           11, \"BROAD\", 1,       \"CMDECOD\", \"Diuretic 2\",       NA,   \"SDG02\", \"Costicosteroids\",     12, \"BROAD\", 1,       \"CMDECOD\", \"Costicosteroid 1\", NA,   \"SDG02\", \"Costicosteroids\",     12, \"BROAD\", 1,       \"CMDECOD\", \"Costicosteroid 2\", NA,   \"SDG02\", \"Costicosteroids\",     12, \"BROAD\", 1,       \"CMDECOD\", \"Costicosteroid 3\", NA, ) adcm <- tibble::tribble(   ~USUBJID, ~ASTDTM,               ~CMDECOD,   \"01\",     \"2020-06-02 23:59:59\", \"Diuretic 1\",   \"02\",     \"2020-06-05 23:59:59\", \"Diuretic 1\",   \"03\",     \"2020-06-07 23:59:59\", \"Costicosteroid 2\",   \"05\",     \"2020-06-09 23:59:59\", \"Diuretic 2\" ) adcm_query <- derive_vars_query(adcm, sdg)"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/occds.html","id":"adsl_vars","dir":"Articles","previous_headings":"Programming Workflow","what":"Add the ADSL variables","title":"Creating an OCCDS ADaM","text":"needed, ADSL variables can now added:","code":"adae <- adae %>%   derive_vars_merged(     dataset_add = select(adsl, !!!negate_vars(adsl_vars)),     by_vars = exprs(STUDYID, USUBJID)   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/occds.html","id":"aseq","dir":"Articles","previous_headings":"Programming Workflow","what":"Derive Analysis Sequence Number","title":"Creating an OCCDS ADaM","text":"function derive_var_obs_number() can used deriving ASEQ variable ensure uniqueness subject records within dataset. example, can multiple records present ADCM single subject ASTDTM CMSEQ variables. records still differ ATC level:","code":"adcm <- tibble::tribble(   ~USUBJID,       ~ASTDTM,          ~CMSEQ, ~CMDECOD,         ~ATC1CD, ~ATC2CD, ~ATC3CD, ~ATC4CD,   \"BP40257-1001\", \"2013-07-05 UTC\", \"14\",   \"PARACETAMOL\",    \"N\",     \"N02\",   \"N02B\",  \"N02BE\",   \"BP40257-1001\", \"2013-08-15 UTC\", \"18\",   \"SOLUMEDROL\",     \"D\",     \"D10\",   \"D10A\",  \"D10AA\",   \"BP40257-1001\", \"2013-08-15 UTC\", \"18\",   \"SOLUMEDROL\",     \"D\",     \"D07\",   \"D07A\",  \"D07AA\",   \"BP40257-1001\", \"2013-08-15 UTC\", \"18\",   \"SOLUMEDROL\",     \"H\",     \"H02\",   \"H02A\",  \"H02AB\",   \"BP40257-1002\", \"2012-12-15 UTC\", \"19\",   \"SPIRONOLACTONE\", \"C\",     \"C03\",   \"C03D\",  \"C03DA\" )  adcm_aseq <- adcm %>%   derive_var_obs_number(     by_vars    = exprs(USUBJID),     order      = exprs(ASTDTM, CMSEQ, ATC1CD, ATC2CD, ATC3CD, ATC4CD),     new_var    = ASEQ,     check_type = \"error\"   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/occds.html","id":"attributes","dir":"Articles","previous_headings":"Programming Workflow","what":"Add Labels and Attributes","title":"Creating an OCCDS ADaM","text":"Adding labels attributes SAS transport files supported following packages: metacore: establish common foundation use metadata within R session. metatools: enable use metacore objects. Metatools can used build datasets enhance columns existing datasets well checking datasets metadata. xportr: functionality associate metadata information local R data frame, perform data set level validation checks convert transport v5 file(xpt). NOTE: packages experimental phase, vision associated End End pipeline umbrella pharmaverse. example applying metadata perform associated checks can found pharmaverse E2E example.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/articles/pk_adnca.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Creating a PK NCA or Population PK ADaM","text":"article describes creating Pharmacokinetics (PK) Non-compartmental analysis (NCA) ADaM (ADNCA/ADPC) Population PK ADaM (ADPPK). first part article describes NCA file creation second part describes Population PK. initial steps files similar combined one script desired.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/pk_adnca.html","id":"programming-pk-nca-adpcadnca-analysis-data","dir":"Articles","previous_headings":"","what":"Programming PK NCA (ADPC/ADNCA) Analysis Data","title":"Creating a PK NCA or Population PK ADaM","text":"Non-compartmental analysis (NCA) ADaM uses CDISC Implementation Guide (https://www.cdisc.org/standards/foundational/adam/adamig-non-compartmental-analysis-input-data-v1-0). example presented uses underlying EX PC domains EX PC domains represent data collected ADPC ADaM output. However, example can applied situations EC domain used input instead EX /ADNCA another ADaM created. One important aspects dataset derivation relative timing variables. variables consist nominal actual times, refer time first dose time recent reference dose. reference dose pre-dose records may upcoming dose. CDISC Implementation Guide makes use duplicated records analysis, allows record used respect previous dose next upcoming dose. illustrated later vignette. relative time variables use. correspond names CDISC Implementation Guide. Note: examples assume CDISC SDTM /ADaM format input unless otherwise specified.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/pk_adnca.html","id":"adpc-programming-workflow","dir":"Articles","previous_headings":"","what":"ADPC Programming Workflow","title":"Creating a PK NCA or Population PK ADaM","text":"Read Data Expand Dosing Records Find First Dose Find Reference Dose Dates Corresponding PK Records Combine PC EX Records Derive Relative Time Variables Derive Analysis Variables Create Duplicated Records Analysis Combine ADPC data Duplicated Records Calculate Change Baseline Assign ASEQ Add Additional Baseline Variables Add ADSL variables Add Labels Attributes","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/pk_adnca.html","id":"readdata","dir":"Articles","previous_headings":"ADPC Programming Workflow","what":"Read in Data","title":"Creating a PK NCA or Population PK ADaM","text":"start, data frames needed creation ADPC read environment. company specific process. data frames needed may PC, EX, ADSL. Additional domains VS LB may used additional baseline variables needed. may come either SDTM ADaM source. purpose example, CDISC Pilot SDTM ADaM datasets—included pharmaversesdtm—used. step, may useful join ADSL PC EX domains well. ADSL variables used derivations selected step. rest relevant ADSL variables added later. case keep TRTSDT/TRTSDTM day derivation TRT01P/TRT01A planned actual treatments. segment use derive_vars_merged() join ADSL variables following admiral functions derive analysis dates, times days: derive_vars_dtm(), derive_vars_dtm_to_dt(), derive_vars_dtm_to_tm(), derive_vars_dy(). also create NFRLT PC data based PCTPTNUM. create event ID (EVID) 0 concentration records 1 dosing records. traditional variable provide handy tool identify records dropped final dataset example. Next also join ADSL data EX derive dates/times. section uses admiral functions derive_vars_merged(), derive_vars_dtm(), derive_vars_dtm_to_dt(). Time imputed 00:00:00 reasons specific sample data. imputation times may used based study details. create NFRLT EX data based VISITDY using dplyr::mutate().","code":"library(dplyr, warn.conflicts = FALSE) library(admiral) library(pharmaversesdtm) library(lubridate) library(stringr) library(tibble)  ex <- pharmaversesdtm::ex pc <- pharmaversesdtm::pc vs <- pharmaversesdtm::vs lb <- pharmaversesdtm::lb adsl <- admiral::admiral_adsl  ex <- convert_blanks_to_na(ex) pc <- convert_blanks_to_na(pc) vs <- convert_blanks_to_na(vs) lb <- convert_blanks_to_na(lb) %>%   filter(LBBLFL == \"Y\")  # ---- Lookup tables ---- param_lookup <- tibble::tribble(   ~PCTESTCD, ~PARAMCD, ~PARAM, ~PARAMN,   \"XAN\", \"XAN\", \"Pharmacokinetic concentration of Xanomeline\", 1,   \"DOSE\", \"DOSE\", \"Xanomeline Patch Dose\", 2, ) adsl_vars <- exprs(TRTSDT, TRTSDTM, TRT01P, TRT01A)  pc_dates <- pc %>%   # Join ADSL with PC (need TRTSDT for ADY derivation)   derive_vars_merged(     dataset_add = adsl,     new_vars = adsl_vars,     by_vars = exprs(STUDYID, USUBJID)   ) %>%   # Derive analysis date/time   # Impute missing time to 00:00:00   derive_vars_dtm(     new_vars_prefix = \"A\",     dtc = PCDTC,     time_imputation = \"00:00:00\"   ) %>%   # Derive dates and times from date/times   derive_vars_dtm_to_dt(exprs(ADTM)) %>%   derive_vars_dtm_to_tm(exprs(ADTM)) %>%   derive_vars_dy(reference_date = TRTSDT, source_vars = exprs(ADT)) %>%   # Derive event ID and nominal relative time from first dose (NFRLT)   mutate(     EVID = 0,     DRUG = PCTEST,     NFRLT = if_else(PCTPTNUM < 0, 0, PCTPTNUM), .after = USUBJID   ) # ---- Get dosing information ----  ex_dates <- ex %>%   derive_vars_merged(     dataset_add = adsl,     new_vars = adsl_vars,     by_vars = exprs(STUDYID, USUBJID)   ) %>%   # Keep records with nonzero dose   filter(EXDOSE > 0) %>%   # Add time and set missing end date to start date   # Impute missing time to 00:00:00   # Note all times are missing for dosing records in this example data   # Derive Analysis Start and End Dates   derive_vars_dtm(     new_vars_prefix = \"AST\",     dtc = EXSTDTC,     time_imputation = \"00:00:00\"   ) %>%   derive_vars_dtm(     new_vars_prefix = \"AEN\",     dtc = EXENDTC,     time_imputation = \"00:00:00\"   ) %>%   # Derive event ID and nominal relative time from first dose (NFRLT)   mutate(     EVID = 1,     NFRLT = case_when(       VISITDY == 1 ~ 0,       TRUE ~ 24 * VISITDY     )   ) %>%   # Set missing end dates to start date   mutate(AENDTM = case_when(     is.na(AENDTM) ~ ASTDTM,     TRUE ~ AENDTM   )) %>%   # Derive dates from date/times   derive_vars_dtm_to_dt(exprs(ASTDTM)) %>%   derive_vars_dtm_to_dt(exprs(AENDTM))"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/pk_adnca.html","id":"expand","dir":"Articles","previous_headings":"ADPC Programming Workflow","what":"Expand Dosing Records","title":"Creating a PK NCA or Population PK ADaM","text":"function create_single_dose_dataset() can used expand dosing records start date end date. nominal time also expanded based values EXDOSFRQ, example “QD” result nominal time incremented 24 hours “BID” result nominal time incremented 12 hours. new feature create_single_dose_dataset(). Dates times derived expansion using derive_vars_dtm_to_dt() derive_vars_dtm_to_tm(). example study define analysis visit (AVISIT) based nominal day value NFRLT give format, “Day 1”, “Day 2”, “Day 3”, etc. important creating BASETYPE variable later. DRUG created EXTRT . useful linking treatment data concentration data multiple drugs /analytes, variable also dropped final dataset example.","code":"# ---- Expand dosing records between start and end dates ----  ex_exp <- ex_dates %>%   create_single_dose_dataset(     dose_freq = EXDOSFRQ,     start_date = ASTDT,     start_datetime = ASTDTM,     end_date = AENDT,     end_datetime = AENDTM,     nominal_time = NFRLT,     lookup_table = dose_freq_lookup,     lookup_column = CDISC_VALUE,     keep_source_vars = exprs(       STUDYID, USUBJID, EVID, EXDOSFRQ, EXDOSFRM,       NFRLT, EXDOSE, EXDOSU, EXTRT, ASTDT, ASTDTM, AENDT, AENDTM,       VISIT, VISITNUM, VISITDY, TRT01A, TRT01P, DOMAIN, EXSEQ, !!!adsl_vars     )   ) %>%   # Derive AVISIT based on nominal relative time   # Derive AVISITN to nominal time in whole days using integer division   # Define AVISIT based on nominal day   mutate(     AVISITN = NFRLT %/% 24 + 1,     AVISIT = paste(\"Day\", AVISITN),     ADTM = ASTDTM,     DRUG = EXTRT,   ) %>%   # Derive dates and times from datetimes   derive_vars_dtm_to_dt(exprs(ADTM)) %>%   derive_vars_dtm_to_tm(exprs(ADTM)) %>%   derive_vars_dtm_to_tm(exprs(ASTDTM)) %>%   derive_vars_dtm_to_tm(exprs(AENDTM)) %>%   derive_vars_dy(reference_date = TRTSDT, source_vars = exprs(ADT))"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/pk_adnca.html","id":"firstdose","dir":"Articles","previous_headings":"ADPC Programming Workflow","what":"Find First Dose","title":"Creating a PK NCA or Population PK ADaM","text":"section find first dose subject drug, using derive_vars_merged(). also create analysis visit (AVISIT) based NFRLT. first dose datetime analyte FANLDTM calculated minimum ADTM dosing records subject drug.","code":"# ---- Find first dose per treatment per subject ---- # ---- Join with ADPC data and keep only subjects with dosing ----  adpc_first_dose <- pc_dates %>%   derive_vars_merged(     dataset_add = ex_exp,     filter_add = (EXDOSE > 0 & !is.na(ADTM)),     new_vars = exprs(FANLDTM = ADTM),     order = exprs(ADTM, EXSEQ),     mode = \"first\",     by_vars = exprs(STUDYID, USUBJID, DRUG)   ) %>%   filter(!is.na(FANLDTM)) %>%   # Derive AVISIT based on nominal relative time   # Derive AVISITN to nominal time in whole days using integer division   # Define AVISIT based on nominal day   mutate(     AVISITN = NFRLT %/% 24 + 1,     AVISIT = paste(\"Day\", AVISITN)   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/pk_adnca.html","id":"dosedates","dir":"Articles","previous_headings":"ADPC Programming Workflow","what":"Find Reference Dose Dates Corresponding to PK Records","title":"Creating a PK NCA or Population PK ADaM","text":"Use derive_vars_joined() find previous dose data. join expanded EX data ADPC based analysis date ADTM. Note filter_join parameter. addition date previous dose (ADTM_prev), also keep actual dose amount EXDOSE_prev analysis visit dose AVISIT_prev. Similarly, find next dose information using derive_vars_joined() filter_join parameter ADTM <= ADTM.join. keep next dose analysis date ADTM_next, next actual dose EXDOSE_next, next analysis visit AVISIT_next. Use method find previous next nominal times. Note data sorted nominal time rather actual time. tell us previous dose next dose supposed occur. Sometimes differ actual times study. keep previous nominal dose time NFRLT_prev next nominal dose time NFRLT_next. Note filter_join parameter uses nominal relative times, e.g. NFRLT > NFRLT.join.","code":"# ---- Find previous dose  ----  adpc_prev <- adpc_first_dose %>%   derive_vars_joined(     dataset_add = ex_exp,     by_vars = exprs(USUBJID),     order = exprs(ADTM),     new_vars = exprs(       ADTM_prev = ADTM, EXDOSE_prev = EXDOSE, AVISIT_prev = AVISIT,       AENDTM_prev = AENDTM     ),     join_vars = exprs(ADTM),     join_type = \"all\",     filter_add = NULL,     filter_join = ADTM > ADTM.join,     mode = \"last\",     check_type = \"none\"   ) # ---- Find next dose  ----  adpc_next <- adpc_prev %>%   derive_vars_joined(     dataset_add = ex_exp,     by_vars = exprs(USUBJID),     order = exprs(ADTM),     new_vars = exprs(       ADTM_next = ADTM, EXDOSE_next = EXDOSE, AVISIT_next = AVISIT,       AENDTM_next = AENDTM     ),     join_vars = exprs(ADTM),     join_type = \"all\",     filter_add = NULL,     filter_join = ADTM <= ADTM.join,     mode = \"first\",     check_type = \"none\"   ) # ---- Find previous nominal time ----  adpc_nom_prev <- adpc_next %>%   derive_vars_joined(     dataset_add = ex_exp,     by_vars = exprs(USUBJID),     order = exprs(NFRLT),     new_vars = exprs(NFRLT_prev = NFRLT),     join_vars = exprs(NFRLT),     join_type = \"all\",     filter_add = NULL,     filter_join = NFRLT > NFRLT.join,     mode = \"last\",     check_type = \"none\"   )  # ---- Find next nominal time ----  adpc_nom_next <- adpc_nom_prev %>%   derive_vars_joined(     dataset_add = ex_exp,     by_vars = exprs(USUBJID),     order = exprs(NFRLT),     new_vars = exprs(NFRLT_next = NFRLT),     join_vars = exprs(NFRLT),     join_type = \"all\",     filter_add = NULL,     filter_join = NFRLT <= NFRLT.join,     mode = \"first\",     check_type = \"none\"   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/pk_adnca.html","id":"relative","dir":"Articles","previous_headings":"ADPC Programming Workflow","what":"Combine PC and EX Records and Derive Relative Time Variables","title":"Creating a PK NCA or Population PK ADaM","text":"Combine PC EX records derive additional relative time variables. Often NCA data keep dosing concentration records. keep . Sometimes see ADPC concentration records. desired, dosing records can dropped saving final dataset. use admiral function derive_vars_duration() calculate actual relative time first dose (AFRLT) actual relative time recent dose (ARRLT). Note use parameter add_one = FALSE . also create variable representing actual time next dose (AXRLT) kept, used create duplicated records analysis pre-dose records. now, update missing values ARRLT corresponding pre-dose records AXRLT, dosing records set zero. also calculate reference dates FANLDTM (First Datetime Dose Analyte) PCRFTDTM (Reference Datetime Dose Analyte) corresponding date time variables. calculate maximum date concentration records keep dosing records date. nominal relative times calculate NRRLT generally NFRLT - NFRLT_prev NXRLT NFRLT - NFRLT_next.","code":"# ---- Combine ADPC and EX data ---- # Derive Relative Time Variables  adpc_arrlt <- bind_rows(adpc_nom_next, ex_exp) %>%   group_by(USUBJID, DRUG) %>%   mutate(     FANLDTM = min(FANLDTM, na.rm = TRUE),     min_NFRLT = min(NFRLT_prev, na.rm = TRUE),     maxdate = max(ADT[EVID == 0], na.rm = TRUE), .after = USUBJID   ) %>%   arrange(USUBJID, ADTM) %>%   ungroup() %>%   filter(ADT <= maxdate) %>%   # Derive Actual Relative Time from First Dose (AFRLT)   derive_vars_duration(     new_var = AFRLT,     start_date = FANLDTM,     end_date = ADTM,     out_unit = \"hours\",     floor_in = FALSE,     add_one = FALSE   ) %>%   # Derive Actual Relative Time from Reference Dose (ARRLT)   derive_vars_duration(     new_var = ARRLT,     start_date = ADTM_prev,     end_date = ADTM,     out_unit = \"hours\",     floor_in = FALSE,     add_one = FALSE   ) %>%   # Derive Actual Relative Time from Next Dose (AXRLT not kept)   derive_vars_duration(     new_var = AXRLT,     start_date = ADTM_next,     end_date = ADTM,     out_unit = \"hours\",     floor_in = FALSE,     add_one = FALSE   ) %>%   mutate(     ARRLT = case_when(       EVID == 1 ~ 0,       is.na(ARRLT) ~ AXRLT,       TRUE ~ ARRLT     ),      # Derive Reference Dose Date     PCRFTDTM = case_when(       EVID == 1 ~ ADTM,       is.na(ADTM_prev) ~ ADTM_next,       TRUE ~ ADTM_prev     )   ) %>%   # Derive dates and times from datetimes   derive_vars_dtm_to_dt(exprs(FANLDTM)) %>%   derive_vars_dtm_to_tm(exprs(FANLDTM)) %>%   derive_vars_dtm_to_dt(exprs(PCRFTDTM)) %>%   derive_vars_dtm_to_tm(exprs(PCRFTDTM)) adpc_nrrlt <- adpc_arrlt %>%   # Derive Nominal Relative Time from Reference Dose (NRRLT)   mutate(     NRRLT = case_when(       EVID == 1 ~ 0,       is.na(NFRLT_prev) ~ NFRLT - min_NFRLT,       TRUE ~ NFRLT - NFRLT_prev     ),     NXRLT = case_when(       EVID == 1 ~ 0,       TRUE ~ NFRLT - NFRLT_next     )   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/pk_adnca.html","id":"analysis","dir":"Articles","previous_headings":"ADPC Programming Workflow","what":"Derive Analysis Variables","title":"Creating a PK NCA or Population PK ADaM","text":"Using dplyr::mutate derive number analysis variables including analysis value (AVAL), analysis time point (ATPT) analysis timepoint reference (ATPTREF) baseline type (BASETYPE). set ATPT PCTPT concentration records “Dose” dosing records. analysis timepoint reference ATPTREF correspond dosing visit. use AVISIT_prev AVISIT_next derive. baseline type concatenation ATPTREF “Baseline” values “Day 1 Baseline”, “Day 2 Baseline”, etc. baseline flag ABLFL set “Y” pre-dose records. Analysis value AVAL example comes PCSTRESN concentration records. addition including dose value EXDOSE dosing records setting BLQ (Limit Quantitation) records 0 first dose 1/2 LLOQ (Lower Limit Quantitation) records first dose. (Additional tests whether 1/3 records BLQ may required done example.) also create listing-ready variable AVALCAT1 includes “BLQ” record indicator formats numeric values three significant digits. derive actual dose DOSEA based EXDOSE_prev EXDOSE_next planned dose DOSEP based planned treatment TRT01P. addition add units dose variables relative time variables.","code":"# ---- Derive Analysis Variables ---- # Derive ATPTN, ATPT, ATPTREF, ABLFL and BASETYPE # Derive planned dose DOSEP, actual dose DOSEA and units # Derive PARAMCD and relative time units # Derive AVAL, AVALU and AVALCAT1  adpc_aval <- adpc_nrrlt %>%   mutate(     PARCAT1 = PCSPEC,     ATPTN = case_when(       EVID == 1 ~ 0,       TRUE ~ PCTPTNUM     ),     ATPT = case_when(       EVID == 1 ~ \"Dose\",       TRUE ~ PCTPT     ),     ATPTREF = case_when(       EVID == 1 ~ AVISIT,       is.na(AVISIT_prev) ~ AVISIT_next,       TRUE ~ AVISIT_prev     ),     # Derive baseline flag for pre-dose records     ABLFL = case_when(       ATPT == \"Pre-dose\" ~ \"Y\",       TRUE ~ NA_character_     ),     # Derive BASETYPE     BASETYPE = paste(ATPTREF, \"Baseline\"),      # Derive Actual Dose     DOSEA = case_when(       EVID == 1 ~ EXDOSE,       is.na(EXDOSE_prev) ~ EXDOSE_next,       TRUE ~ EXDOSE_prev     ),     # Derive Planned Dose     DOSEP = case_when(       TRT01P == \"Xanomeline High Dose\" ~ 81,       TRT01P == \"Xanomeline Low Dose\" ~ 54     ),     DOSEU = \"mg\",   ) %>%   # Derive relative time units   mutate(     FRLTU = \"h\",     RRLTU = \"h\",     # Derive PARAMCD     PARAMCD = coalesce(PCTESTCD, \"DOSE\"),     ALLOQ = PCLLOQ,     # Derive AVAL     AVAL = case_when(       EVID == 1 ~ EXDOSE,       PCSTRESC == \"<BLQ\" & NFRLT == 0 ~ 0,       PCSTRESC == \"<BLQ\" & NFRLT > 0 ~ 0.5 * ALLOQ,       TRUE ~ PCSTRESN     ),     AVALU = case_when(       EVID == 1 ~ EXDOSU,       TRUE ~ PCSTRESU     ),     AVALCAT1 = if_else(PCSTRESC == \"<BLQ\", PCSTRESC, prettyNum(signif(AVAL, digits = 3))),   ) %>%   # Add SRCSEQ   mutate(     SRCDOM = DOMAIN,     SRCVAR = \"SEQ\",     SRCSEQ = coalesce(PCSEQ, EXSEQ)   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/pk_adnca.html","id":"copy","dir":"Articles","previous_headings":"ADPC Programming Workflow","what":"Create Duplicated Records for Analysis","title":"Creating a PK NCA or Population PK ADaM","text":"mentioned , CDISC ADaM Implementation Guide Non-compartmental Analysis uses duplicated records analysis record needs used one way. example 24 hour post-dose record also used pre-dose record “Day 2” dose. addition 24 hour post-dose records, situations may include pre-dose records “Cycle 2 Day 1”, etc. general, select records interest update relative time variables duplicated records. case select nominal relative time next dose zero. (Note need duplicate first dose record since prior dose.) DTYPE set “COPY” duplicated records original PCSEQ value retained. case change “24h Post-dose” “Pre-dose”. ABLFL set “Y” since records serve baseline “Day 2” dose. DOSEA set EXDOSE_next PCRFTDTM set ADTM_next.","code":"# ---- Create DTYPE copy records ----  dtype <- adpc_aval %>%   filter(NFRLT > 0 & NXRLT == 0 & EVID == 0 & !is.na(AVISIT_next)) %>%   select(-PCRFTDT, -PCRFTTM) %>%   # Re-derive variables in for DTYPE copy records   mutate(     ABLFL = NA_character_,     ATPTREF = AVISIT_next,     ARRLT = AXRLT,     NRRLT = NXRLT,     PCRFTDTM = ADTM_next,     DOSEA = EXDOSE_next,     BASETYPE = paste(AVISIT_next, \"Baseline\"),     ATPT = \"Pre-dose\",     ATPTN = -0.5,     ABLFL = \"Y\",     DTYPE = \"COPY\"   ) %>%   derive_vars_dtm_to_dt(exprs(PCRFTDTM)) %>%   derive_vars_dtm_to_tm(exprs(PCRFTDTM))"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/pk_adnca.html","id":"combine","dir":"Articles","previous_headings":"ADPC Programming Workflow","what":"Combine ADPC data with Duplicated Records","title":"Creating a PK NCA or Population PK ADaM","text":"Now duplicated records combined original records. also derive modified relative time reference dose MRRLT. case, negative values ARRLT set zero. also opportunity derive analysis flags e.g. ANL01FL , ANL02FL etc. example ANL01FL set “Y” records ANL02FL set “Y” records except duplicated records DTYPE = “COPY”. Additional flags may used select full profile records /select records included tables figures, etc.","code":"# ---- Combine original records and DTYPE copy records ----  adpc_dtype <- bind_rows(adpc_aval, dtype) %>%   arrange(STUDYID, USUBJID, BASETYPE, ADTM, NFRLT) %>%   mutate(     # Derive MRRLT, ANL01FL and ANL02FL     MRRLT = if_else(ARRLT < 0, 0, ARRLT),     ANL01FL = \"Y\",     ANL02FL = if_else(is.na(DTYPE), \"Y\", NA_character_),   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/pk_adnca.html","id":"aseq","dir":"Articles","previous_headings":"ADPC Programming Workflow","what":"Calculate Change from Baseline and Assign ASEQ","title":"Creating a PK NCA or Population PK ADaM","text":"admiral function derive_var_base() used derive BASE function derive_var_chg() used derive change baseline CHG. also now derive ASEQ using derive_var_obs_number() drop intermediate variables ending “_prev” “_next”. Finally derive PARAM PARAMN lookup table.","code":"# ---- Derive BASE and Calculate Change from Baseline ----  adpc_base <- adpc_dtype %>%   # Derive BASE   derive_var_base(     by_vars = exprs(STUDYID, USUBJID, PARAMCD, PARCAT1, BASETYPE),     source_var = AVAL,     new_var = BASE,     filter = ABLFL == \"Y\"   )  # Calculate CHG for post-baseline records # The decision on how to populate pre-baseline and baseline values of CHG is left to producer choice adpc_chg <- restrict_derivation(   adpc_base,   derivation = derive_var_chg,   filter = AVISITN > 0 )  # ---- Add ASEQ ----  adpc_aseq <- adpc_chg %>%   # Calculate ASEQ   derive_var_obs_number(     new_var = ASEQ,     by_vars = exprs(STUDYID, USUBJID),     order = exprs(ADTM, BASETYPE, EVID, AVISITN, ATPTN, PARCAT1, DTYPE),     check_type = \"error\"   ) %>%   # Remove temporary variables   select(     -DOMAIN, -PCSEQ, -starts_with(\"orig\"), -starts_with(\"min\"),     -starts_with(\"max\"), -starts_with(\"EX\"), -ends_with(\"next\"),     -ends_with(\"prev\"), -DRUG, -EVID, -AXRLT, -NXRLT, -VISITDY   ) %>%   # Derive PARAM and PARAMN   derive_vars_merged(     dataset_add = select(param_lookup, -PCTESTCD), by_vars = exprs(PARAMCD)   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/pk_adnca.html","id":"baselines","dir":"Articles","previous_headings":"ADPC Programming Workflow","what":"Add Additional Baseline Variables","title":"Creating a PK NCA or Population PK ADaM","text":"derive additional baseline values VS baseline height HTBL weight WTBL compute body mass index (BMI) compute_bmi(). values also obtained ADVS available. Baseline lab values also derived LB ADLB similar manner.","code":"# Derive additional baselines from VS adpc_baselines <- adpc_aseq %>%   derive_vars_merged(     dataset_add = vs,     filter_add = VSTESTCD == \"HEIGHT\",     by_vars = exprs(STUDYID, USUBJID),     new_vars = exprs(HTBL = VSSTRESN, HTBLU = VSSTRESU)   ) %>%   derive_vars_merged(     dataset_add = vs,     filter_add = VSTESTCD == \"WEIGHT\" & VSBLFL == \"Y\",     by_vars = exprs(STUDYID, USUBJID),     new_vars = exprs(WTBL = VSSTRESN, WTBLU = VSSTRESU)   ) %>%   mutate(     BMIBL = compute_bmi(height = HTBL, weight = WTBL),     BMIBLU = \"kg/m^2\"   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/pk_adnca.html","id":"adsl_vars","dir":"Articles","previous_headings":"ADPC Programming Workflow","what":"Add the ADSL variables","title":"Creating a PK NCA or Population PK ADaM","text":"needed, ADSL variables can now added: Adding attributes ADPC file discussed . now turn Population PK example.","code":"# Add all ADSL variables adpc <- adpc_baselines %>%   derive_vars_merged(     dataset_add = select(adsl, !!!negate_vars(adsl_vars)),     by_vars = exprs(STUDYID, USUBJID)   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/pk_adnca.html","id":"programming-population-pk-adppk-analysis-data","dir":"Articles","previous_headings":"","what":"Programming Population PK (ADPPK) Analysis Data","title":"Creating a PK NCA or Population PK ADaM","text":"Population PK Analysis Data (ADPPK) follows CDISC Implementation Guide (https://www.cdisc.org/standards/foundational/adam/basic-data-structure-adam-poppk-implementation-guide-v1-0). programming workflow Population PK (ADPPK) Analysis Data similar NCA Programming flow key differences. Population PK models generally make use nonlinear mixed effects models require numeric variables. data used models include dosing concentration records, relative time variables, numeric covariate variables. DV dependent variable often expected. equivalent ADaM AVAL variable included addition AVAL ADPPK. ADPPK file duplicated records analysis found NCA. relative time variables use ADPPK data. correspond names forthcoming CDISC Implementation Guide. ADPPK require numeric Event ID (EVID) defined ADPC keep.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/pk_adnca.html","id":"adppk-programming-workflow","dir":"Articles","previous_headings":"","what":"ADPPK Programming Workflow","title":"Creating a PK NCA or Population PK ADaM","text":"Read Data (ADPC) Expand Dosing Records (ADPC) Find First Dose Find Previous Dose Combine PC EX Records ADPPK Derive Analysis Variables Dependent Variable DV Add ASEQ Remove Temporary Variables Derive Numeric Covariates Derive Additional Covariates VS LB Combine Covariates ADPPK Data","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/pk_adnca.html","id":"ppkfirst","dir":"Articles","previous_headings":"ADPPK Programming Workflow","what":"Find First Dose ADPPK","title":"Creating a PK NCA or Population PK ADaM","text":"initial programming steps ADPPK follow sequence ADPC. includes reading pharmaversesdtm data, deriving analysis dates, defining nominal relative time first dose NFRLT, expanding dosing records. detail see steps (Read Data). pick stage find first dose concentration records. use derive_vars_merged() ADPC.","code":"# ---- Find first dose per treatment per subject ---- # ---- Join with ADPC data and keep only subjects with dosing ----  adppk_first_dose <- pc_dates %>%   derive_vars_merged(     dataset_add = ex_exp,     filter_add = (!is.na(ADTM)),     new_vars = exprs(FANLDTM = ADTM, EXDOSE_first = EXDOSE),     order = exprs(ADTM, EXSEQ),     mode = \"first\",     by_vars = exprs(STUDYID, USUBJID, DRUG)   ) %>%   filter(!is.na(FANLDTM)) %>%   # Derive AVISIT based on nominal relative time   # Derive AVISITN to nominal time in whole days using integer division   # Define AVISIT based on nominal day   mutate(     AVISITN = NFRLT %/% 24 + 1,     AVISIT = paste(\"Day\", AVISITN),   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/pk_adnca.html","id":"prevdose","dir":"Articles","previous_headings":"ADPPK Programming Workflow","what":"Find Previous Dose","title":"Creating a PK NCA or Population PK ADaM","text":"ADPPK find previous dose respect actual time nominal time. use derive_vars_joined() ADPC, note need find next dose ADPC.","code":"# ---- Find previous dose  ----  adppk_prev <- adppk_first_dose %>%   derive_vars_joined(     dataset_add = ex_exp,     by_vars = exprs(USUBJID),     order = exprs(ADTM),     new_vars = exprs(       ADTM_prev = ADTM, EXDOSE_prev = EXDOSE, AVISIT_prev = AVISIT,       AENDTM_prev = AENDTM     ),     join_vars = exprs(ADTM),     join_type = \"all\",     filter_add = NULL,     filter_join = ADTM > ADTM.join,     mode = \"last\",     check_type = \"none\"   )  # ---- Find previous nominal dose ----  adppk_nom_prev <- adppk_prev %>%   derive_vars_joined(     dataset_add = ex_exp,     by_vars = exprs(USUBJID),     order = exprs(NFRLT),     new_vars = exprs(NFRLT_prev = NFRLT),     join_vars = exprs(NFRLT),     join_type = \"all\",     filter_add = NULL,     filter_join = NFRLT > NFRLT.join,     mode = \"last\",     check_type = \"none\"   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/pk_adnca.html","id":"aprlt","dir":"Articles","previous_headings":"ADPPK Programming Workflow","what":"Combine PC and EX Records for ADPPK","title":"Creating a PK NCA or Population PK ADaM","text":"ADPC now combine PC EX records. derive relative time variables AFRLT (Actual Relative Time First Dose), APRLT (Actual Relative Time Previous Dose), NPRLT (Nominal Relative Time Previous Dose). Use derive_vars_duration() derive AFRLT APRLT. Note defined EVID values 0 observation records 1 dosing records.","code":"# ---- Combine ADPPK and EX data ---- # Derive Relative Time Variables  adppk_aprlt <- bind_rows(adppk_nom_prev, ex_exp) %>%   group_by(USUBJID, DRUG) %>%   mutate(     FANLDTM = min(FANLDTM, na.rm = TRUE),     min_NFRLT = min(NFRLT, na.rm = TRUE),     maxdate = max(ADT[EVID == 0], na.rm = TRUE), .after = USUBJID   ) %>%   arrange(USUBJID, ADTM) %>%   ungroup() %>%   filter(ADT <= maxdate) %>%   # Derive Actual Relative Time from First Dose (AFRLT)   derive_vars_duration(     new_var = AFRLT,     start_date = FANLDTM,     end_date = ADTM,     out_unit = \"hours\",     floor_in = FALSE,     add_one = FALSE   ) %>%   # Derive Actual Relative Time from Reference Dose (APRLT)   derive_vars_duration(     new_var = APRLT,     start_date = ADTM_prev,     end_date = ADTM,     out_unit = \"hours\",     floor_in = FALSE,     add_one = FALSE   ) %>%   # Derive APRLT   mutate(     APRLT = case_when(       EVID == 1 ~ 0,       is.na(APRLT) ~ AFRLT,       TRUE ~ APRLT     ),     NPRLT = case_when(       EVID == 1 ~ 0,       is.na(NFRLT_prev) ~ NFRLT - min_NFRLT,       TRUE ~ NFRLT - NFRLT_prev     )   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/pk_adnca.html","id":"dv","dir":"Articles","previous_headings":"ADPPK Programming Workflow","what":"Derive Analysis Variables and Dependent Variable DV","title":"Creating a PK NCA or Population PK ADaM","text":"expected analysis variable ADPPK DV dependent variable. example DV set numeric concentration value PCSTRESN. also include AVAL equivalent DV consistency CDISC ADaM standards. MDV missing dependent variable also included.","code":"# ---- Derive Analysis Variables ---- # Derive actual dose DOSEA and planned dose DOSEP, # Derive AVAL and DV  adppk_aval <- adppk_aprlt %>%   mutate(     # Derive Actual Dose     DOSEA = case_when(       EVID == 1 ~ EXDOSE,       is.na(EXDOSE_prev) ~ EXDOSE_first,       TRUE ~ EXDOSE_prev     ),     # Derive Planned Dose     DOSEP = case_when(       TRT01P == \"Xanomeline High Dose\" ~ 81,       TRT01P == \"Xanomeline Low Dose\" ~ 54,       TRT01P == \"Placebo\" ~ 0     ),     # Derive PARAMCD     PARAMCD = case_when(       EVID == 1 ~ \"DOSE\",       TRUE ~ PCTESTCD     ),     ALLOQ = PCLLOQ,     # Derive CMT     CMT = case_when(       EVID == 1 ~ 1,       PCSPEC == \"PLASMA\" ~ 2,       TRUE ~ 3     ),     # Derive BLQFL/BLQFN     BLQFL = case_when(       PCSTRESC == \"<BLQ\" ~ \"Y\",       TRUE ~ \"N\"     ),     BLQFN = case_when(       PCSTRESC == \"<BLQ\" ~ 1,       TRUE ~ 0     ),     AMT = case_when(       EVID == 1 ~ EXDOSE,       TRUE ~ NA_real_     ),     # Derive DV and AVAL     DV = PCSTRESN,     AVAL = DV,     DVL = case_when(       DV != 0 ~ log(DV),       TRUE ~ NA_real_     ),     # Derive MDV     MDV = case_when(       EVID == 1 ~ 1,       is.na(DV) ~ 1,       TRUE ~ 0     ),     AVALU = case_when(       EVID == 1 ~ NA_character_,       TRUE ~ PCSTRESU     ),     UDTC = format_ISO8601(ADTM),     II = if_else(EVID == 1, 1, 0),     SS = if_else(EVID == 1, 1, 0)   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/pk_adnca.html","id":"ppkaseq","dir":"Articles","previous_headings":"ADPPK Programming Workflow","what":"Add ASEQ and Remove Temporary Variables","title":"Creating a PK NCA or Population PK ADaM","text":"derive ASEQ using derive_var_obs_number(). add PROJID based drug include numeric version PROJIDN, drop drop intermediate variables ending “_prev”.","code":"# ---- Add ASEQ ----  adppk_aseq <- adppk_aval %>%   # Calculate ASEQ   derive_var_obs_number(     new_var = ASEQ,     by_vars = exprs(STUDYID, USUBJID),     order = exprs(AFRLT, EVID, CMT),     check_type = \"error\"   ) %>%   # Derive PARAM and PARAMN   derive_vars_merged(dataset_add = select(param_lookup, -PCTESTCD), by_vars = exprs(PARAMCD)) %>%   mutate(     PROJID = DRUG,     PROJIDN = 1   ) %>%   # Remove temporary variables   select(     -DOMAIN, -starts_with(\"min\"), -starts_with(\"max\"), -starts_with(\"EX\"),     -starts_with(\"PC\"), -ends_with(\"first\"), -ends_with(\"prev\"),     -ends_with(\"DTM\"), -ends_with(\"DT\"), -ends_with(\"TM\"), -starts_with(\"VISIT\"),     -starts_with(\"AVISIT\"), -ends_with(\"TMF\"), -starts_with(\"TRT\"),     -starts_with(\"ATPT\"), -DRUG   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/pk_adnca.html","id":"covar","dir":"Articles","previous_headings":"ADPPK Programming Workflow","what":"Derive Numeric Covariates","title":"Creating a PK NCA or Population PK ADaM","text":"key feature Population PK modeling presence numeric covariates. create numeric versions many standard CDISC demographic variables including STUDYIDN, USUBJIDN, SEXN, RACEN, ETHNICN.","code":"#---- Derive Covariates ---- # Include numeric values for STUDYIDN, USUBJIDN, SEXN, RACEN etc.  covar <- adsl %>%   derive_vars_merged(     dataset_add = country_code_lookup,     new_vars = exprs(COUNTRYN = country_number, COUNTRYL = country_name),     by_vars = exprs(COUNTRY = country_code),   ) %>%   mutate(     STUDYIDN = as.numeric(word(USUBJID, 1, sep = fixed(\"-\"))),     SITEIDN = as.numeric(word(USUBJID, 2, sep = fixed(\"-\"))),     USUBJIDN = as.numeric(word(USUBJID, 3, sep = fixed(\"-\"))),     SUBJIDN = as.numeric(SUBJID),     SEXN = case_when(       SEX == \"M\" ~ 1,       SEX == \"F\" ~ 2,       TRUE ~ 3     ),     RACEN = case_when(       RACE == \"AMERICAN INDIAN OR ALASKA NATIVE\" ~ 1,       RACE == \"ASIAN\" ~ 2,       RACE == \"BLACK OR AFRICAN AMERICAN\" ~ 3,       RACE == \"NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER\" ~ 4,       RACE == \"WHITE\" ~ 5,       TRUE ~ 6     ),     ETHNICN = case_when(       ETHNIC == \"HISPANIC OR LATINO\" ~ 1,       ETHNIC == \"NOT HISPANIC OR LATINO\" ~ 2,       TRUE ~ 3     ),     ARMN = case_when(       ARM == \"Placebo\" ~ 0,       ARM == \"Xanomeline Low Dose\" ~ 1,       ARM == \"Xanomeline High Dose\" ~ 2,       TRUE ~ 3     ),     ACTARMN = case_when(       ACTARM == \"Placebo\" ~ 0,       ACTARM == \"Xanomeline Low Dose\" ~ 1,       ACTARM == \"Xanomeline High Dose\" ~ 2,       TRUE ~ 3     ),     COHORT = ARMN,     COHORTC = ARM,     ROUTE = unique(ex$EXROUTE),     ROUTEN = case_when(       ROUTE == \"TRANSDERMAL\" ~ 3,       TRUE ~ NA_real_     ),     FORM = unique(ex$EXDOSFRM),     FORMN = case_when(       FORM == \"PATCH\" ~ 3,       TRUE ~ 4     )   ) %>%   select(     STUDYID, STUDYIDN, SITEID, SITEIDN, USUBJID, USUBJIDN,     SUBJID, SUBJIDN, AGE, SEX, SEXN, COHORT, COHORTC, ROUTE, ROUTEN,     RACE, RACEN, ETHNIC, ETHNICN, FORM, FORMN, COUNTRY, COUNTRYN, COUNTRYL   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/pk_adnca.html","id":"addcovar","dir":"Articles","previous_headings":"ADPPK Programming Workflow","what":"Derive Additional Covariates from VS and LB","title":"Creating a PK NCA or Population PK ADaM","text":"add additional covariates baseline height HTBL weight WTBL VS select baseline lab values CREATBL, ALTBL, ASTBL TBILBL LB. calculate BMI BSA height weight using compute_bmi() compute_bsa(). calculate creatinine clearance CRCLBL estimated glomerular filtration rate (eGFR) EGFRBL using compute_egfr() function.","code":"#---- Derive additional baselines from VS and LB ----  labsbl <- lb %>%   filter(LBBLFL == \"Y\" & LBTESTCD %in% c(\"CREAT\", \"ALT\", \"AST\", \"BILI\")) %>%   mutate(LBTESTCDB = paste0(LBTESTCD, \"BL\")) %>%   select(STUDYID, USUBJID, LBTESTCDB, LBSTRESN)  covar_vslb <- covar %>%   derive_vars_merged(     dataset_add = vs,     filter_add = VSTESTCD == \"HEIGHT\",     by_vars = exprs(STUDYID, USUBJID),     new_vars = exprs(HTBL = VSSTRESN)   ) %>%   derive_vars_merged(     dataset_add = vs,     filter_add = VSTESTCD == \"WEIGHT\" & VSBLFL == \"Y\",     by_vars = exprs(STUDYID, USUBJID),     new_vars = exprs(WTBL = VSSTRESN)   ) %>%   derive_vars_transposed(     dataset_merge = labsbl,     by_vars = exprs(STUDYID, USUBJID),     key_var = LBTESTCDB,     value_var = LBSTRESN   ) %>%   mutate(     BMIBL = compute_bmi(height = HTBL, weight = WTBL),     BSABL = compute_bsa(       height = HTBL,       weight = HTBL,       method = \"Mosteller\"     ),     # Derive CRCLBL and EGFRBL using new function     CRCLBL = compute_egfr(       creat = CREATBL, creatu = \"SI\", age = AGE, weight = WTBL, sex = SEX,       method = \"CRCL\"     ),     EGFRBL = compute_egfr(       creat = CREATBL, creatu = \"SI\", age = AGE, weight = WTBL, sex = SEX,       method = \"CKD-EPI\"     )   ) %>%   rename(TBILBL = BILIBL)"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/pk_adnca.html","id":"final","dir":"Articles","previous_headings":"ADPPK Programming Workflow","what":"Combine Covariates with ADPPK Data","title":"Creating a PK NCA or Population PK ADaM","text":"Finally, combine covariates ADPPK data.","code":"# Combine covariates with APPPK data  adppk <- adppk_aseq %>%   derive_vars_merged(     dataset_add = covar_vslb,     by_vars = exprs(STUDYID, USUBJID)   ) %>%   arrange(STUDYIDN, USUBJIDN, AFRLT, EVID) %>%   mutate(RECSEQ = row_number())"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/pk_adnca.html","id":"attributes","dir":"Articles","previous_headings":"","what":"Add Labels and Attributes","title":"Creating a PK NCA or Population PK ADaM","text":"Adding labels attributes SAS transport files supported following packages: metacore: establish common foundation use metadata within R session. metatools: enable use metacore objects. Metatools can used build datasets enhance columns existing datasets well checking datasets metadata. xportr: functionality associate metadata information local R data frame, perform data set level validation checks convert transport v5 file(xpt). NOTE: packages experimental phase, vision associated End End pipeline umbrella pharmaverse. example applying metadata perform associated checks can found pharmaverse E2E example.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/articles/queries_dataset.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Queries Dataset Documentation","text":"support safety analysis, quite common define specific grouping events. One common ways group events medications specific medical concept Standard MedDRA Queries (SMQs) -Drug Standardized Drug Groupings (SDGs). help derivation variables, {admiral} function derive_vars_query() can used. function takes input dataset (dataset) grouping must occur (e.g ADAE) dataset containing required information perform derivation grouping variables (dataset_queries). dataset passed dataset_queries argument derive_vars_query() function can created create_query_data() function. SMQs SDGs company-specific functions accessing SMQ SDG database need passed create_query_data() function (see description get_terms_fun argument details). vignette describes expected structure content dataset passed dataset_queries argument derive_vars_query() function.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/articles/queries_dataset.html","id":"variables","dir":"Articles","previous_headings":"Structure of the Query Dataset","what":"Variables","title":"Queries Dataset Documentation","text":"Bold variables required dataset_queries: error issued variables missing. TERMCHAR REQUIRED character variable named SRCVAR. TERMNUM REQUIRED numeric variable named SRCVAR. SRCVAR contains character numeric variables, TERMCHAR TERMNUM required. variables optional. VERSION variable used derive_vars_query() can used check dictionary version queries dataset analysis dataset line.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/queries_dataset.html","id":"required-content","dir":"Articles","previous_headings":"Structure of the Query Dataset","what":"Required Content","title":"Queries Dataset Documentation","text":"row must unique within dataset. described , variables PREFIX, GRPNAME, SRCVAR, TERMCHAR TERMNUM required. combination variables allow creation grouping variable.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/queries_dataset.html","id":"input","dir":"Articles","previous_headings":"Structure of the Query Dataset > Required Content","what":"Input","title":"Queries Dataset Documentation","text":"PREFIX must character string starting 2 3 letters, followed 2-digits number (e.g. “CQ01”). GRPNAME must non missing character string must unique within PREFIX. SRCVAR must non missing character string. value SRCVAR represents variable dataset used define grouping variables (e.g. AEDECOD,AEBODSYS, AELLTCD). function derive_vars_query() check value given SRCVAR corresponding variable input dataset issue error otherwise. Different SRCVAR variables may specified within PREFIX. TERMCHAR must character string. must populated TERMNUM missing. TERMNUM must integer. must populated TERMCHAR missing.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/queries_dataset.html","id":"output","dir":"Articles","previous_headings":"Structure of the Query Dataset > Required Content","what":"Output","title":"Queries Dataset Documentation","text":"PREFIX used create grouping variable appending suffix “NAM”. variable now referred ABCzzNAM: name grouping variable. E.g. PREFIX == \"SMQ01\" create SMQ01NAM variable. PREFIX, new ABCzzNAM variable created dataset. GRPNAME used populate corresponding ABCzzNAM variable. SRCVAR used identify variables dataset used perform grouping (e.g. AEDECOD,AEBODSYS, AELLTCD). TERMCHAR (character variables), TERMNUM (numeric variables) used identify records meeting criteria dataset based variable defined SRCVAR. Result: record dataset, variable defined SRCVAR match term TERMCHAR (character variables) TERMNUM (numeric variables) datasets_queries, ABCzzNAM populated GRPNAME. Note: type (numeric character) variable defined SRCVAR checked dataset. variable character variable (e.g. AEDECOD), expected TERMCHAR populated, numeric variable (e.g. AEBDSYCD), expected TERMNUM populated, otherwise error issued.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/queries_dataset.html","id":"example","dir":"Articles","previous_headings":"Structure of the Query Dataset > Required Content","what":"Example","title":"Queries Dataset Documentation","text":"example, one standard MedDRA query (PREFIX = \"SMQ01\") one customized query (PREFIX = \"CQ02\") defined analyze adverse events. standard MedDRA query variable SMQ01NAM [PREFIX] populated “Standard Query 1” [GRPNAME] preferred term (AEDECOD) [SRCVAR] dataset equal “AE1” “AE2” [TERMCHAR] customized query (CQ02NAM) [PREFIX] populated “Query 2” [GRPNAME] Low Level Term Code (AELLTCD) [SRCVAR] dataset equal 10 [TERMNUM] preferred term (AEDECOD) [SRCVAR] dataset equal “AE4” [TERMCHAR].","code":""},{"path":[]},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/articles/queries_dataset.html","id":"output-dataset","dir":"Articles","previous_headings":"Structure of the Query Dataset > Required Content > Example","what":"Output Dataset","title":"Queries Dataset Documentation","text":"Generated calling derive_vars_query(dataset = ae, dataset_queries = ds_query). Subject 0001 one event meeting Standard Query 1 criteria (AEDECOD = \"AE1\") two events meeting customized query (AELLTCD = 10 AEDECOD = \"AE4\").","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/queries_dataset.html","id":"optional-content","dir":"Articles","previous_headings":"Structure of the Query Dataset","what":"Optional Content","title":"Queries Dataset Documentation","text":"standardized MedDRA Queries added dataset, expected name query (ABCzzNAM) populated along number code (ABCzzCD), Broad Narrow scope (ABCzzSC). following variables can added queries_datset derive information.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/queries_dataset.html","id":"input-1","dir":"Articles","previous_headings":"Structure of the Query Dataset > Optional Content","what":"Input","title":"Queries Dataset Documentation","text":"GRPID must integer. SCOPE must character string. Possible values : “BROAD”, “NARROW” NA. SCOPEN must integer. Possible values : 1, 2 NA.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/queries_dataset.html","id":"output-1","dir":"Articles","previous_headings":"Structure of the Query Dataset > Optional Content","what":"Output","title":"Queries Dataset Documentation","text":"GRPID, SCOPE SCOPEN used way GRPNAME (see ) help creation ABCzzCD, ABCzzSC ABCzzSCN variables.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/queries_dataset.html","id":"output-variables","dir":"Articles","previous_headings":"Structure of the Query Dataset > Optional Content","what":"Output Variables","title":"Queries Dataset Documentation","text":"variables optional populated dataset_queries, corresponding output variable created:","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/questionnaires.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Creating Questionnaire ADaMs","text":"article describes creating questionnaire ADaMs. Although questionnaire data collected single SDTM dataset (QS), usually make sense create single ADQS dataset questionnaire analyses. example, univariate analysis scores visit requires different variables time--event analysis. Therefore vignette provide programming workflow complete dataset, provides examples deriving common types questionnaire parameters. moment, admiral provide functions metadata specific questionnaires functionality handling vast amount questionnaires related parameters, e.g. metadata structure storing parameter definitions functions reading metadata. plan provide future releases. Note: examples assume CDISC SDTM /ADaM format input unless otherwise specified.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/questionnaires.html","id":"required-packages","dir":"Articles","previous_headings":"Introduction","what":"Required Packages","title":"Creating Questionnaire ADaMs","text":"examples vignette require following packages.","code":"library(dplyr) library(tidyr) library(tibble) library(admiral)"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/questionnaires.html","id":"example-data","dir":"Articles","previous_headings":"Introduction","what":"Example Data","title":"Creating Questionnaire ADaMs","text":"vignette use example data CDISC ADaM Supplements (Generalized Anxiety Disorder 7-Item Version 2 (GAD-7), Geriatric Depression Scale Short Form (GDS-SF))1:","code":"qs <- admiral::example_qs adsl <- tribble(   ~STUDYID, ~USUBJID, ~SITEID, ~ITTFL, ~TRTSDT,                      ~DTHCAUS,   \"STUDYX\",  \"P0001\",     13L,    \"Y\", lubridate::ymd(\"2012-11-16\"), NA_character_,   \"STUDYX\",  \"P0002\",     11L,    \"Y\", lubridate::ymd(\"2012-11-16\"), \"PROGRESSIVE DISEASE\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/questionnaires.html","id":"original-items","dir":"Articles","previous_headings":"","what":"Original Items","title":"Creating Questionnaire ADaMs","text":"original items, .e. answers questionnaire questions, can handled way BDS finding ADaM. example: handle unscheduled visits normal visits. deriving visits based time-windows, see Visit Period Variables. flagging values used analysis, see derive_var_extreme_flag().","code":"adqs <- qs %>%   # Add ADSL variables   derive_vars_merged(     dataset_add = adsl,     new_vars = exprs(TRTSDT, DTHCAUS),     by_vars = exprs(STUDYID, USUBJID)   ) %>%   # Add analysis parameter variables   mutate(     PARAMCD = QSTESTCD,     PARAM = QSTEST,     PARCAT1 = QSCAT,     AVALC = QSORRES,     AVAL = QSSTRESN   ) %>%   # Add timing variables   derive_vars_dt(new_vars_prefix = \"A\", dtc = QSDTC) %>%   derive_vars_dy(reference_date = TRTSDT, source_vars = exprs(ADT)) %>%   mutate(     AVISIT = if_else(ADT <= TRTSDT, \"BASELINE\", VISIT),     AVISITN = if_else(ADT <= TRTSDT, 0, VISITNUM)   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/questionnaires.html","id":"transformed-items","dir":"Articles","previous_headings":"","what":"Transformed Items","title":"Creating Questionnaire ADaMs","text":"Please note example data, numeric values answers mapped SDTM (QSSTRESN) can used deriving scores. Depending question, QSORRES == \"YES\" mapped QSSTRESN = 0 QSSTRESN = 1. QSSTRESN values ready used deriving scores require transformation, recommended QSSTRESN kept ADaM dataset traceability, transformed value stored AVAL, since ’s used score calculation. may also necessary transform range numeric values original items. example scale derived average range contributing items varies. case values linearly transformed unified range like [0, 100]. computation function transform_range() can used transformation.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/questionnaires.html","id":"scales-and-scores","dir":"Articles","previous_headings":"","what":"Scales and Scores","title":"Creating Questionnaire ADaMs","text":"Scales Scores often derived sum average across subset items. GAD-7 questionnaire, total score derived sum. derive_summary_records() function sum() can used derive new parameter. selecting parameters summarized, regular expressions like example may helpful. example derive separate ADaM dataset questionnaire. Depending analysis needs, also possible ADaM contains one questionnaire questionnaires. GDS-SF questionnaire, total score defined average item values transformed range [0, 15] rounded next integer. five items missing, total score considered missing. parameter can derived compute_scale() derive_summary_records(): deriving scores visit, baseline change baseline variables can derived:","code":"adgad7 <- adqs %>%   # Select records to keep in the GAD-7 ADaM   filter(PARCAT1 == \"GAD-7 V2\") %>%   derive_summary_records(     dataset = .,     dataset_add = .,     by_vars = exprs(STUDYID, USUBJID, AVISIT, ADT, ADY, TRTSDT, DTHCAUS),     # Select records contributing to total score     filter_add = str_detect(PARAMCD, \"GAD020[1-7]\"),     set_values_to = exprs(       AVAL = sum(AVAL, na.rm = TRUE),       PARAMCD = \"GAD02TS\",       PARAM = \"GAD02-Total Score - Analysis\"     )   ) adgdssf <- adqs %>%   # Select records to keep in the GDS-SF ADaM   filter(PARCAT1 == \"GDS SHORT FORM\") %>%   derive_summary_records(     dataset = .,     dataset_add = .,     by_vars = exprs(STUDYID, USUBJID, AVISIT, ADT, ADY, TRTSDT, DTHCAUS),     # Select records contributing to total score     filter_add = str_detect(PARAMCD, \"GDS02[01][0-9]\"),     set_values_to = exprs(       AVAL = compute_scale(         AVAL,         source_range = c(0, 1),         target_range = c(0, 15),         min_n = 10       ) %>%         ceiling(),       PARAMCD = \"GDS02TS\",       PARAM = \"GDS02- Total Score - Analysis\"     )   ) adgdssf <- adgdssf %>%   # Flag baseline records (last before treatement start)   restrict_derivation(     derivation = derive_var_extreme_flag,     args = params(       by_vars = exprs(STUDYID, USUBJID, PARAMCD),       order = exprs(ADT),       new_var = ABLFL,       mode = \"last\"     ),     filter = !is.na(AVAL) & ADT <= TRTSDT   ) %>%   # Derive baseline and change from baseline variables   derive_var_base(     by_vars = exprs(STUDYID, USUBJID, PARAMCD),     source_var = AVAL,     new_var = BASE   ) %>%   # Calculate CHG for post-baseline records   # The decision on how to populate pre-baseline and baseline values of CHG is left to producer choice   restrict_derivation(     derivation = derive_var_chg,     filter = AVISITN > 0   ) %>%   # Calculate PCHG for post-baseline records   # The decision on how to populate pre-baseline and baseline values of PCHG is left to producer choice   restrict_derivation(     derivation = derive_var_pchg,     filter = AVISITN > 0   ) %>%   # Derive sequence number   derive_var_obs_number(     by_vars = exprs(STUDYID, USUBJID),     order = exprs(PARAMCD, ADT),     check_type = \"error\"   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/questionnaires.html","id":"timetodeterioration","dir":"Articles","previous_headings":"","what":"Time to Deterioration/Improvement","title":"Creating Questionnaire ADaMs","text":"time event parameters require specific variables like CNSR, STARTDT, EVNTDESC, makes sense create separate time event dataset . However, might useful create flags categorization variables ADQS. example: time deterioration parameter can derived :","code":"# Create AVALCATx lookup table avalcat_lookup <- exprs(   ~PARAMCD, ~condition, ~AVALCAT1, ~AVALCAT1N,   \"GDS02TS\", AVAL <= 5, \"Normal\", 0L,   \"GDS02TS\", AVAL <= 10 & AVAL > 5, \"Possible Depression\", 1L,   \"GDS02TS\", AVAL > 10, \"Likely Depression\", 2L ) # Create CHGCAT1 lookup table chgcat_lookup <- exprs(   ~condition, ~CHGCAT1,   AVALCAT1N > BASECA1N, \"WORSENED\",   AVALCAT1N == BASECA1N, \"NO CHANGE\",   AVALCAT1N < BASECA1N, \"IMPROVED\" )  adgdssf <- adgdssf %>%   derive_vars_cat(     definition = avalcat_lookup,     by_vars = exprs(PARAMCD)   ) %>%   derive_var_base(     by_vars = exprs(STUDYID, USUBJID, PARAMCD),     source_var = AVALCAT1,     new_var = BASECAT1   ) %>%   derive_var_base(     by_vars = exprs(STUDYID, USUBJID, PARAMCD),     source_var = AVALCAT1N,     new_var = BASECA1N   ) %>%   derive_vars_cat(     definition = chgcat_lookup   ) # Define event deterioration_event <- event_source(   dataset_name = \"adqs\",   filter = PARAMCD == \"GDS02TS\" & CHGCAT1 == \"WORSENED\",   date = ADT,   set_values_to = exprs(     EVNTDESC = \"DEPRESSION WORSENED\",     SRCDOM = \"ADQS\",     SRCVAR = \"ADT\",     SRCSEQ = ASEQ   ) )  # Define censoring at last assessment last_valid_assessment <- censor_source(   dataset_name = \"adqs\",   filter = PARAMCD == \"GDS02TS\" & !is.na(CHGCAT1),   date = ADT,   set_values_to = exprs(     EVNTDESC = \"LAST ASSESSMENT\",     SRCDOM = \"ADQS\",     SRCVAR = \"ADT\",     SRCSEQ = ASEQ   ) )  # Define censoring at treatment start (for subjects without assessment) start <- censor_source(   dataset_name = \"adsl\",   date = TRTSDT,   set_values_to = exprs(     EVNTDESC = \"TREATMENT START\",     SRCDOM = \"ADSL\",     SRCVAR = \"TRTSDT\"   ) )  adgdstte <- derive_param_tte(   dataset_adsl = adsl,   source_datasets = list(adsl = adsl, adqs = adgdssf),   start_date = TRTSDT,   event_conditions = list(deterioration_event),   censor_conditions = list(last_valid_assessment, start),   set_values_to = exprs(     PARAMCD = \"TTDEPR\",     PARAM = \"Time to depression\"   ) ) %>%   derive_vars_duration(     new_var = AVAL,     start_date = STARTDT,     end_date = ADT   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/questionnaires.html","id":"time-to-confirmeddefinitive-deteriorationimprovement","dir":"Articles","previous_headings":"","what":"Time to Confirmed/Definitive Deterioration/Improvement","title":"Creating Questionnaire ADaMs","text":"derivation confirmed/definitive deterioration/improvement parameters similar unconfirmed deterioration parameters except event based CHGCATy, confirmation flag variable. confirmation flag can derived derive_var_joined_exist_flag(). example, flagging deteriorations, confirmed second assessment least seven days later: flagging deteriorations two consecutive assessments considering death due progression last assessment confirmation, tmp_obs_nr_var argument helpful: definitive deterioration (deterioration following assessments), parameter summary functions like () can used filter condition: time--event parameter can derived way unconfirmed parameters (see Time Deterioration/Improvement).","code":"adgdssf <- adgdssf %>%   derive_var_joined_exist_flag(     dataset_add = adgdssf,     by_vars = exprs(USUBJID, PARAMCD),     order = exprs(ADT),     new_var = CDETFL,     join_vars = exprs(CHGCAT1, ADY),     join_type = \"after\",     filter_join = CHGCAT1 == \"WORSENED\" &       CHGCAT1.join == \"WORSENED\" &       ADY.join >= ADY + 7   ) # Flagging deterioration at two consecutive assessments adgdssf <- adgdssf %>%   derive_var_joined_exist_flag(     dataset_add = adgdssf,     by_vars = exprs(USUBJID, PARAMCD),     order = exprs(ADT),     new_var = CONDETFL,     join_vars = exprs(CHGCAT1),     join_type = \"after\",     tmp_obs_nr_var = tmp_obs_nr,     filter_join = CHGCAT1 == \"WORSENED\" &       CHGCAT1.join == \"WORSENED\" &       tmp_obs_nr.join == tmp_obs_nr + 1   ) %>%   # Flagging deterioration confirmed by   # - a second deterioration at least 7 days later or   # - deterioration at the last assessment and death due to progression   derive_var_joined_exist_flag(     .,     dataset_add = .,     by_vars = exprs(USUBJID, PARAMCD),     order = exprs(ADT),     new_var = CDTDTHFL,     join_vars = exprs(CHGCAT1, ADY),     join_type = \"all\",     tmp_obs_nr_var = tmp_obs_nr,     filter_join = CHGCAT1 == \"WORSENED\" & (       CHGCAT1.join == \"WORSENED\" & ADY.join >= ADY + 7 |         tmp_obs_nr == max(tmp_obs_nr.join) & DTHCAUS == \"PROGRESSIVE DISEASE\")   ) adgdssf <- adgdssf %>%   derive_var_joined_exist_flag(     dataset_add = adgdssf,     by_vars = exprs(USUBJID, PARAMCD),     order = exprs(ADT),     new_var = DEFDETFL,     join_vars = exprs(CHGCAT1),     join_type = \"after\",     filter_join = CHGCAT1 == \"WORSENED\" & all(CHGCAT1.join == \"WORSENED\")   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/questionnaires.html","id":"worstbest-answer","dir":"Articles","previous_headings":"","what":"Worst/Best Answer","title":"Creating Questionnaire ADaMs","text":"class parameters can used worst answer set yes/answers selected. example, yes/answers “sleep”, “Waking three times”, “30 minutes fall asleep” collected, parameter worst sleeping problems derived. example, “sleeping problems” assumed questions answered “”.","code":"adsp <- adqs %>%   filter(PARCAT1 == \"SLEEPING PROBLEMS\") %>%   derive_extreme_event(     by_vars = exprs(USUBJID, AVISIT),     tmp_event_nr_var = event_nr,     order = exprs(event_nr, ADY, QSSEQ),     mode = \"first\",     events = list(       event(         condition = PARAMCD == \"SP0101\" & AVALC == \"YES\",         set_values_to = exprs(           AVALC = \"No sleep\",           AVAL = 1         )       ),       event(         condition = PARAMCD == \"SP0102\" & AVALC == \"YES\",         set_values_to = exprs(           AVALC = \"Waking up more than three times\",           AVAL = 2         )       ),       event(         condition = PARAMCD == \"SP0103\" & AVALC == \"YES\",         set_values_to = exprs(           AVALC = \"More than 30 mins to fall asleep\",           AVAL = 3         )       ),       event(         condition = all(AVALC == \"NO\"),         set_values_to = exprs(           AVALC = \"No sleeping problems\",           AVAL = 4         )       ),       event(         condition = TRUE,         set_values_to = exprs(           AVALC = \"Missing\",           AVAL = 99         )       )     ),     set_values_to = exprs(       PARAMCD = \"SP01WSP\",       PARAM = \"Worst Sleeping Problems\"     )   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/questionnaires.html","id":"completion","dir":"Articles","previous_headings":"","what":"Completion","title":"Creating Questionnaire ADaMs","text":"Parameters completion, like “least 90% questions answered”, can derived derive_summary_records(). Please note denominator may depend answers questions. example, given questionnaire might direct someone go question #4 directly question #8 based response question #4, questions #5, #6 #7 apply case. missed visits need taken account, expected records can added input dataset calling derive_expected_records():","code":"adgdssf <- adgdssf %>%   derive_summary_records(     dataset_add = adgdssf,     filter_add = str_detect(PARAMCD, \"GDS02[01][0-9]\"),     by_vars = exprs(USUBJID, AVISIT),     set_values_to = exprs(       AVAL = sum(!is.na(AVAL)) / 15 >= 0.9,       PARAMCD = \"COMPL90P\",       PARAM = \"Completed at least 90% of questions?\",       AVALC = if_else(AVAL == 1, \"YES\", \"NO\")     )   ) # Create dataset with expected visits and parameters (GDS0201 - GDS0215) parm_visit_ref <- crossing(   tribble(     ~AVISIT,    ~AVISITN,     \"BASELINE\",        0,     \"VISIT 2\",         2,     \"VISIT 3\",         3,     \"VISIT 4\",         4,     \"VISIT 5\",         5   ),   tibble(PARAMCD = sprintf(\"GDS02%02d\", seq(1, 15))) )  adgdssf <- adgdssf %>%   derive_expected_records(     dataset_ref = parm_visit_ref,     by_vars = exprs(USUBJID),     set_values_to = exprs(       filled_in = 1     )   ) %>%   derive_summary_records(     dataset = .,     dataset_add = .,     filter_add = str_detect(PARAMCD, \"GDS02[01][0-9]\"),     by_vars = exprs(USUBJID, AVISIT),     set_values_to = exprs(       AVAL = all(!is.na(AVAL)),       PARAMCD = \"COMPLALL\",       PARAM = \"Completed all questions?\",       AVALC = if_else(AVAL == 1, \"YES\", \"NO\")     )   ) %>%   filter(is.na(filled_in)) %>%   select(-filled_in)"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/visits_periods.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Visit and Period Variables","text":"derivation visit variables like AVISIT, AVISITN, AWLO, AWHI, … period, subperiod, phase variables like APERIOD, TRT01A, TRT02A, ASPER, PHSDTM, PHEDTM, … highly study-specific. Therefore admiral provide functions derive variables. However, common scenarios like visit assignments based time windows deriving BDS period variables ADSL period variables, functions provided support derivations.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/visits_periods.html","id":"required-packages","dir":"Articles","previous_headings":"Introduction","what":"Required Packages","title":"Visit and Period Variables","text":"examples vignette require following packages.","code":"library(admiral) #> Error in get(paste0(generic, \".\", class), envir = get_method_env()) :  #>   object 'type_sum.accel' not found library(tibble) library(dplyr, warn.conflicts = FALSE) library(lubridate)"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/visits_periods.html","id":"visits","dir":"Articles","previous_headings":"","what":"Visit variables (AVISIT, AVISITN, AWLO, AWHI, …)","title":"Visit and Period Variables","text":"common ways deriving AVISIT AVISITN : variables set collected visits (VISIT VISITNUM). variables set based time windows. former can achieved simply calling mutate(), like vignettes template scripts. latter (study-specific) reference dataset needs created provides visit start end day (AWLO AWHI) values visit related variables (AVISIT, AVISITN, AWTARGET, …). visits can assigned based analysis day (ADY) calling derive_vars_joined():","code":"windows <- tribble(   ~AVISIT,    ~AWLO, ~AWHI, ~AVISITN, ~AWTARGET,   \"BASELINE\",   -30,     1,        0,         1,   \"WEEK 1\",       2,     7,        1,         5,   \"WEEK 2\",       8,    15,        2,        11,   \"WEEK 3\",      16,    22,        3,        19,   \"WEEK 4\",      23,    30,        4,        26 ) adbds <- tribble(   ~USUBJID, ~ADY,   \"1\",       -33,   \"1\",        -2,   \"1\",         3,   \"1\",        24,   \"2\",        NA, )  adbds1 <- adbds %>%   derive_vars_joined(     dataset_add = windows,     filter_join = AWLO <= ADY & ADY <= AWHI,     join_type = \"all\",   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/visits_periods.html","id":"period-subperiod-and-phase-variables","dir":"Articles","previous_headings":"","what":"Period, Subperiod, and Phase Variables","title":"Visit and Period Variables","text":"periods, subperiods, phases used, simpler ADaM applications corresponding variables consistent across datasets. can achieved defining periods, subperiods, phases using definition datasets. definition can stored ADSL separate dataset. vignette’s examples, separate dataset called period/phase reference dataset depending data contains. Note periods, subperiods, phases can defined differently across datasets (instance, may different across safety efficacy analyses) case start/stop dates defined individual datasets, instead ADSL. However, vignette cover scenario.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/visits_periods.html","id":"a-note-on-study-specific-code","dir":"Articles","previous_headings":"Period, Subperiod, and Phase Variables","what":"A Note on Study Specific Code","title":"Visit and Period Variables","text":"sections showcase available tools admiral work period, subperiod phase variables. However, point study specific code always required. two options: Study specific code used first derive variables PxxSwSDT PxxSwEDT ADSL. create_period_dataset() derive_vars_joined() can used derive period/subperiod variables like ASPER ASPRSDT BDS OCCDS datasets. See example dataset . Study specific code used derive dataset one observation per patient, period, subperiod (see period reference dataset). derive_vars_period() can used derive PxxSwSDT PxxSwEDT ADSL derive_vars_joined() can used derive period/subperiod variables like ASPER ASPRSDT BDS OCCDS datasets. depends specific definition periods/subperiods option works best. definition based ADSL variables, first option work best. definition based vertically structured data like exposure data (EX dataset), second option used. vignette contains examples workflows.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/visits_periods.html","id":"reference","dir":"Articles","previous_headings":"Period, Subperiod, and Phase Variables","what":"The Period/Phase Reference Dataset","title":"Visit and Period Variables","text":"admiral functions expect separate reference datasets periods, subperiods, phases. periods numeric variable APERIOD expected, subperiods numeric variables APERIOD ASPER, phases numeric variable APHASEN. period/phase reference dataset created according design ADaM needs study question. contain one observation per subject period, subperiod, phase. See next section example dataset.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/visits_periods.html","id":"example_period","dir":"Articles","previous_headings":"Period, Subperiod, and Phase Variables","what":"Example Creation of the Period/Phase Reference Dataset","title":"Visit and Period Variables","text":"Consider simple crossover study following design:  Given design, option split study two periods: Period 1 (Day 1 Week 16); Period 2 (Week 16 End Study). Alternatively (additionally) one split two phases: Phase 1 - Treatment (Day 1 28 days Week 28); Phase 2 - Follow-(29 days Week 28 End Study). , present two example workflows: one period reference dataset created exposure dataset EX, phase reference dataset created using ADSL variables.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/visits_periods.html","id":"creating-the-periodphase-reference-dataset-from-ex","dir":"Articles","previous_headings":"Period, Subperiod, and Phase Variables > Example Creation of the Period/Phase Reference Dataset","what":"Creating the Period/Phase Reference Dataset from EX","title":"Visit and Period Variables","text":"create period reference dataset starting exposure dataset EX. Consider following exposure dataset: create period reference dataset, code like (similar) suffice: workflow populates Period End Date APEREDT periods except last one. value last period populated End Study Date (EOSDT) ADSL, obtain:","code":"ex <- tribble(   ~STUDYID, ~USUBJID,  ~VISIT,    ~EXTRT,   ~EXSTDTC,   \"xyz\",    \"1\",       \"Day 1\",   \"Drug X\", \"2022-01-02\",   \"xyz\",    \"1\",       \"Week 4\",  \"Drug X\", \"2022-02-05\",   \"xyz\",    \"1\",       \"Week 8\",  \"Drug X\", \"2022-03-01\",   \"xyz\",    \"1\",       \"Week 12\", \"Drug X\", \"2022-04-03\",   \"xyz\",    \"1\",       \"Week 16\", \"Drug Y\", \"2022-05-03\",   \"xyz\",    \"1\",       \"Week 20\", \"Drug Y\", \"2022-06-02\",   \"xyz\",    \"1\",       \"Week 24\", \"Drug Y\", \"2022-07-01\",   \"xyz\",    \"1\",       \"Week 28\", \"Drug Y\", \"2022-08-04\",   \"xyz\",    \"2\",       \"Day 1\",   \"Drug Y\", \"2023-10-20\",   \"xyz\",    \"2\",       \"Week 4\",  \"Drug Y\", \"2023-11-21\",   \"xyz\",    \"2\",       \"Week 8\",  \"Drug Y\", \"2023-12-19\",   \"xyz\",    \"2\",       \"Week 12\", \"Drug Y\", \"2024-01-19\",   \"xyz\",    \"2\",       \"Week 16\", \"Drug X\", \"2024-02-20\",   \"xyz\",    \"2\",       \"Week 20\", \"Drug X\", \"2024-03-17\",   \"xyz\",    \"2\",       \"Week 24\", \"Drug X\", \"2024-04-22\",   \"xyz\",    \"2\",       \"Week 28\", \"Drug X\", \"2024-05-21\" ) period_ref <- ex %>%   # Select visits marking the start of each period   filter(VISIT %in% c(\"Day 1\", \"Week 16\")) %>%   # Create APERIOD, APERSDT, TRTA based on SDTM counterparts   mutate(     APERIOD = case_when(       VISIT == \"Day 1\" ~ 1,       VISIT == \"Week 16\" ~ 2     ),     TRTA = EXTRT,     APERSDT = convert_dtc_to_dt(EXSTDTC)   ) %>%   # Create APEREDT based on start date of next period   arrange(USUBJID, APERSDT) %>%   group_by(USUBJID) %>%   mutate(     APEREDT = lead(APERSDT) - 1 # one day before start of next period   ) %>%   # Tidy up   ungroup() %>%   select(-starts_with(\"EX\"), -VISIT) adsl <- tribble(   ~STUDYID,  ~USUBJID,  ~TRTSDT,           ~TRTEDT,           ~EOSDT,   \"xyz\",     \"1\",       ymd(\"2022-01-02\"), ymd(\"2022-08-04\"), ymd(\"2022-09-10\"),   \"xyz\",     \"2\",       ymd(\"2023-10-20\"), ymd(\"2024-05-21\"), ymd(\"2024-06-30\") )  period_ref <- period_ref %>%   left_join(adsl, by = c(\"STUDYID\", \"USUBJID\")) %>%   mutate(APEREDT = case_when(     APERIOD == \"1\" ~ APEREDT,     APERIOD == \"2\" ~ EOSDT   )) %>%   select(-EOSDT, -TRTSDT, -TRTEDT)"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/visits_periods.html","id":"from_adsl","dir":"Articles","previous_headings":"Period, Subperiod, and Phase Variables > Example Creation of the Period/Phase Reference Dataset","what":"Creating the Period/Phase Reference Dataset from ADSL","title":"Visit and Period Variables","text":"treatment start end dates already included ADSL, can derive phase variables directly ADSL create phase reference dataset employing create_period_dataset(). example command achieve goal:","code":"adsl1 <- adsl %>%   mutate(     PH1SDT = TRTSDT,     PH1EDT = TRTEDT + 28,     APHASE1 = \"TREATMENT\",     PH2SDT = TRTEDT + 29,     PH2EDT = EOSDT,     APHASE2 = \"FUP\"   )  phase_ref <- create_period_dataset(   adsl1,   new_vars = exprs(PHSDT = PHwSDT, PHEDT = PHwEDT, APHASE = APHASEw) )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/visits_periods.html","id":"periods_adsl","dir":"Articles","previous_headings":"Period, Subperiod, and Phase Variables","what":"Creating ADSL Period, Subperiod, or Phase Variables","title":"Visit and Period Variables","text":"period/phase reference dataset available, ADSL variables periods, subperiods, phases can created dataset calling derive_vars_period(). example period reference dataset previous section can used add period variables (APxxSDT, APxxEDT) ADSL:","code":"adsl2 <- derive_vars_period(   adsl,   dataset_ref = period_ref,   new_vars = exprs(APxxSDT = APERSDT, APxxEDT = APEREDT) )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/visits_periods.html","id":"periods_bds","dir":"Articles","previous_headings":"Period, Subperiod, and Phase Variables","what":"Creating BDS and OCCDS Period, Subperiod, or Phase Variables","title":"Visit and Period Variables","text":"period/phase reference dataset available, BDS OCCDS variables periods, subperiods, phases can created calling derive_vars_joined(). example variables APHASEN, PHSDT, PHEDT, APHASE can derived phase reference dataset defined .","code":"adae <- tribble(   ~STUDYID, ~USUBJID, ~ASTDT,   \"xyz\",    \"1\",      \"2022-01-31\",   \"xyz\",    \"1\",      \"2022-05-02\",   \"xyz\",    \"1\",      \"2022-09-03\",   \"xyz\",    \"1\",      \"2022-09-09\",   \"xyz\",    \"2\",      \"2023-12-25\",   \"xyz\",    \"2\",      \"2024-06-19\", ) %>%   mutate(ASTDT = ymd(ASTDT))  adae1 <- adae %>%   derive_vars_joined(     dataset_add = phase_ref,     by_vars = exprs(STUDYID, USUBJID),     filter_join = PHSDT <= ASTDT & ASTDT <= PHEDT,     join_type = \"all\"   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/visits_periods.html","id":"treatment-variables-trtxxp-trtxxa-trtp-trta","dir":"Articles","previous_headings":"","what":"Treatment Variables (TRTxxP, TRTxxA, TRTP, TRTA, …)","title":"Visit and Period Variables","text":"studies multiple periods treatment can differ period, e.g. crossover trial - see previous section example design showcasing . CDISC defines variables planned actual treatments ADSL (TRTxxP, TRTxxA, TRxxPGy, TRxxAGy, …) corresponding variables BDS OCCDS datasets (TRTP, TRTA, TRTPGy, TRTAGy, …). can derived way (step) period, subperiod, phase variables.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/visits_periods.html","id":"treatment_adsl","dir":"Articles","previous_headings":"Treatment Variables (TRTxxP, TRTxxA, TRTP, TRTA, …)","what":"Creating ADSL Treatment Variables","title":"Visit and Period Variables","text":"treatment information included period/phase reference dataset, treatment ADSL variables can created calling derive_vars_period(). showcased using period reference dataset previous sections.","code":"adsl <- derive_vars_period(   adsl,   dataset_ref = period_ref,   new_vars = exprs(     APxxSDT = APERSDT,     APxxEDT = APEREDT,     TRTxxA = TRTA   ) )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/visits_periods.html","id":"treatment_bds","dir":"Articles","previous_headings":"Treatment Variables (TRTxxP, TRTxxA, TRTP, TRTA, …)","what":"Creating BDS and OCCDS Treatment Variables","title":"Visit and Period Variables","text":"period/phase reference dataset available, BDS OCCDS variables treatment can created calling derive_vars_joined(). example variables APERIOD TRTA can derived period reference dataset defined . period/phase reference dataset available period/phase variables ADSL, former can created ADSL calling create_period_dataset(), showcased . time, calling create_period_dataset() just need make sure include TRTA = TRTxxA part new_vars argument create treatment variables well.","code":"adae <- tribble(   ~STUDYID, ~USUBJID, ~ASTDT,   \"xyz\",    \"1\",      \"2022-01-31\",   \"xyz\",    \"1\",      \"2022-05-02\",   \"xyz\",    \"1\",      \"2022-08-24\",   \"xyz\",    \"1\",      \"2022-09-09\",   \"xyz\",    \"2\",      \"2023-12-25\",   \"xyz\",    \"2\",      \"2024-06-07\", ) %>%   mutate(ASTDT = ymd(ASTDT))  adae2 <- adae %>%   derive_vars_joined(     dataset_add = period_ref,     by_vars = exprs(STUDYID, USUBJID),     new_vars = exprs(APERIOD, TRTA),     join_vars = exprs(APERSDT, APEREDT),     join_type = \"all\",     filter_join = APERSDT <= ASTDT & ASTDT <= APEREDT   ) period_ref1 <- adsl %>%   create_period_dataset(     new_vars = exprs(APERSDT = APxxSDT, APEREDT = APxxEDT, TRTA = TRTxxA)   )"},{"path":"https://pharmaverse.github.io/admiral/dev/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Ben Straub. Author, maintainer. Stefan Bundfuss. Author. Jeffrey Dickinson. Author. Ross Farrugia. Author. Fanny Gautier. Author. G Gayatri. Author. Asha Chakma. Author. Dinakar Kulkarni. Author. Edoardo Mancini. Author. Sadchla Mascary. Author. Gordon Miller. Author. Lina Patil. Author. Jim Rothstein. Author. Sophie Shapcott. Author. Eric Simms. Author. Daniel Sjoberg. Author. Stefan Thoma. Author. Phillip Webster. Author. Kangjie Zhang. Author. Zelos Zhu. Author. F. Hoffmann-La Roche AG. Copyright holder, funder. GlaxoSmithKline LLC. Copyright holder, funder.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Straub B, Bundfuss S, Dickinson J, Farrugia R, Gautier F, Gayatri G, Chakma , Kulkarni D, Mancini E, Mascary S, Miller G, Patil L, Rothstein J, Shapcott S, Simms E, Sjoberg D, Thoma S, Webster P, Zhang K, Zhu Z (2024). admiral: ADaM R Asset Library. R package version 1.1.1.9045, https://github.com/pharmaverse/admiral, https://pharmaverse.github.io/admiral/.","code":"@Manual{,   title = {admiral: ADaM in R Asset Library},   author = {Ben Straub and Stefan Bundfuss and Jeffrey Dickinson and Ross Farrugia and Fanny Gautier and G Gayatri and Asha Chakma and Dinakar Kulkarni and Edoardo Mancini and Sadchla Mascary and Gordon Miller and Lina Patil and Jim Rothstein and Sophie Shapcott and Eric Simms and Daniel Sjoberg and Stefan Thoma and Phillip Webster and Kangjie Zhang and Zelos Zhu},   year = {2024},   note = {R package version 1.1.1.9045,     https://github.com/pharmaverse/admiral},   url = {https://pharmaverse.github.io/admiral/}, }"},{"path":"https://pharmaverse.github.io/admiral/dev/index.html","id":"admiral-","dir":"","previous_headings":"","what":"ADaM in R Asset Library","title":"ADaM in R Asset Library","text":"ADaM R Asset Library","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/index.html","id":"purpose","dir":"","previous_headings":"","what":"Purpose","title":"ADaM in R Asset Library","text":"provide open source, modularized toolbox enables pharmaceutical programming community develop ADaM datasets R.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"ADaM in R Asset Library","text":"package available CRAN can installed : install development version package GitHub run:","code":"install.packages(\"admiral\") # install.packages(\"devtools\") devtools::install_github(\"pharmaverse/admiral\")"},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/index.html","id":"release-schedule","dir":"","previous_headings":"","what":"Release Schedule","title":"ADaM in R Asset Library","text":"admiral family several downstream upstream dependencies releases done two Phases: Phase 1 release {admiraldev}, {pharmaversesdtm}, {admiral} core Phase 2 release extension packages, e.g. {admiralonco}, {admiralophtha}, {admiralvaccine} {pharmaverseadam}.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/index.html","id":"main-goal","dir":"","previous_headings":"","what":"Main Goal","title":"ADaM in R Asset Library","text":"Provide users open source, modularized toolbox create ADaM datasets R. opposed “run 1 line ADaM appears” black-box solution attempt automate ADaM. One key aspects admiral development users users. gives entry point collaborate, co-create contribute harmonized approach developing ADaMs R across pharmaceutical industry.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/index.html","id":"scope","dir":"","previous_headings":"","what":"Scope","title":"ADaM in R Asset Library","text":"set expectations: target admiral ever provide possible solutions ADaM datasets outside study specific needs. depends user’s collaboration contribution help grow time asset library robust, easy use across-industry focus. see coverage 100% ADaM derivations ever achievable—ADaM endless. provide: toolbox re-usable functions utilities create ADaM datasets using R scripts modular manner (“opinionated” design strategy). Pharmaceutical communities companies encouraged contribute admiral following provided programming strategy modular approach Functions comprehensively documented tested, including example calls—listed Reference section. Vignettes create ADSL, BDS OCCDS datasets, including example scripts. Vignettes ADaM dataset specific functionality (.e. dictionary coding, date imputation, SMQs …).","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/index.html","id":"the-admiral-family-of-packages","dir":"","previous_headings":"","what":"The {admiral} Family of Packages","title":"ADaM in R Asset Library","text":"three types packages admiral family: Core package—one package containing core functions required create ADaMs, usable company (.e. general derivations, utility functions checks ADSL, OCCDS BDS). TA (Therapeutic Area) package extensions—one package per TA functions specific algorithms requirements particular TA (e.g. {admiralonco}). Company package extensions—specific needs plug-ins company, access metadata (e.g. {admiralroche} {admiralgsk}).","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/index.html","id":"related-packages","dir":"","previous_headings":"","what":"Related Packages","title":"ADaM in R Asset Library","text":"Related data packages include: {pharmaversesdtm}—contains test SDTM data sourced CDISC pilot project constructed ad-hoc admiral team. prerequisite package admiral. {pharmaverseadam}—contains test ADaM data automatically generated running ADaM admiral TA package extensions templates {pharmaversesdtm} data. packages developed admiral team, can used across pharmaverse common, open-source test SDTM ADaM data. following packages also useful working ADaM datasets: {metacore} {metatools}—enable users manipulate work dataset metadata. {xportr}—provides functionality get xpt files ready transport.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/index.html","id":"admiral-manifesto","dir":"","previous_headings":"","what":"Admiral Manifesto","title":"ADaM in R Asset Library","text":"admiral extension packages, prioritize providing users simple adopt toolkit enables produce readable easily constructible ADaM programs. following explains philosophy, try adhere across admiral family packages. isn’t always clear single, straightforward rule, guiding principles adhere admiral. manifesto helps show considerations developers making decisions. four design principles achieve main goal:","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/index.html","id":"usability","dir":"","previous_headings":"Admiral Manifesto","what":"Usability","title":"ADaM in R Asset Library","text":"admiral functions easy use. Documentation absolute priority. function reference page cover purpose, descriptions argument permitted values, expected input output, clear real-life examples—users don’t need dig code find answers. Vignettes complement functional documentation help users see best functions can applied achieve ADaM requirements. Functions written structured way users able read, re-use extend study specific purposes needed (see Readability ).","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/index.html","id":"simplicity","dir":"","previous_headings":"Admiral Manifesto","what":"Simplicity","title":"ADaM in R Asset Library","text":"admiral functions clear purpose. try ever design single functions achieve numerous different derivations. example user pick function >10 different arguments chances going difficult understand function applied specific need. intention arguments/parameters can influence output function calculated, change purpose function. try combine similar tasks algorithms one function applicable reduce amount repetitive functions similar algorithms group together similar functionality increase usability (e.g. one study day calculation rather function per variable). strive design functions general trying fulfill multiple, complex purposes. Functions allow expressions arguments used code snippets function calls. recommend avoid copy paste complex computational algorithms repetitive code like checks advise wrap function. However also like avoid multi-layered functional nesting, needs considered carefully keep nesting 3-4 functions exception rather rule.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/index.html","id":"findability","dir":"","previous_headings":"Admiral Manifesto","what":"Findability","title":"ADaM in R Asset Library","text":"admiral functions easily findable. growing code base, across family packages, make every effort make functions easily findable. use consistent naming conventions across functions, provide vignettes ADaM templates help users get started build familiarity. admiral family package website searchable. avoid repetitive functions similar tasks (explained study day example). package extension kept focused specific scope, e.g. features relevant across multiple extension packages moved core admiral package.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/index.html","id":"readability","dir":"","previous_headings":"Admiral Manifesto","what":"Readability","title":"ADaM in R Asset Library","text":"admiral functions follow Programming Strategy developers contributors must follow, code high degree consistency readability. encourage use tidyverse (e.g. dplyr) similar functionality existing base R. sections code perform actual derivations (e.g. besides assertions basic utilities), try limit nesting many dependencies functions. Modularity focus—don’t try achieve many steps one. code well commented. recognize user Health Authority reviewer may wish delve code base (especially given open source setting), users may need extend/adapt code study specific needs. therefore want module understandable , admiral developers.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/index.html","id":"references-and-documentation","dir":"","previous_headings":"","what":"References and Documentation","title":"ADaM in R Asset Library","text":"Please go Get Started section start using admiral. Please see pharmaverse YouTube channel videos related admiral. Please see Programming Strategy understand functions created. Please see FAQ frequent questions. Please see Contribution Model get involved making contributions. Please see FAQ: R Package Versions develop certain R package versions.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/index.html","id":"pharmaverse-blog","dir":"","previous_headings":"","what":"Pharmaverse Blog","title":"ADaM in R Asset Library","text":"interested R Clinical Reporting, visit pharmaverse blog. contains regular, bite-sized posts showcasing admiral packages pharmaverse can used realize vision full end--end Clinical Reporting R. also always looking keen admiral users publish blog posts use package. , feel free make issue GitHub repo get started!","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/index.html","id":"recent-conference-presentations","dir":"","previous_headings":"","what":"Recent Conference Presentations","title":"ADaM in R Asset Library","text":"full collection admiral conference presentations years, please travel Presentation Archive.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/index.html","id":"contact","dir":"","previous_headings":"","what":"Contact","title":"ADaM in R Asset Library","text":"use following support communications user developer community: Slack—informal discussions, Q&building user community. don’t access, use link join pharmaverse Slack workspace. GitHub Issues—direct feedback, enhancement requests raising bugs.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/index.html","id":"acknowledgments","dir":"","previous_headings":"","what":"Acknowledgments","title":"ADaM in R Asset Library","text":"Along authors contributors, thanks following people work package: Jaxon Abercrombie, Mahdi , Teckla Akinyi, James Black, Claudia Carlucci, Asha Chakma, Bill Denney, Kamila Duniec, Alice Ehmann, Romain Francois, Ania Golab, Alana Harris, Declan Hodges, Anthony Howard, Shimeng Huang, Samia Kabi, James Kim, John Kirkpatrick, Leena Khatri, Robin Koeger, Konstantina Koukourikou, Pavan Kumar, Pooja Kumari, Shan Lee, Wenyi Liu, Iain McCay, Jack McGavigan, Jordanna Morrish, Syed Mubasheer, Thomas Neitmann, Yohann Omnes, Barbara O’Reilly, Hamza Rahal, Nick Ramirez, Tom Ratford, Jim Rothstein, Sukalpo Saha, Tamara Senior, Sophie Shapcott, Ondrej Slama, Andrew Smith, Daniil Stefonishin, Vignesh Thanikachalam, Michael Thorpe, Annie Yang, Ojesh Upadhyay, Franciszek Walkowiak Vladyslav Shuliar.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/admiral-package.html","id":null,"dir":"Reference","previous_headings":"","what":"admiral: ADaM in R Asset Library — admiral-package","title":"admiral: ADaM in R Asset Library — admiral-package","text":"toolbox programming Clinical Data Interchange Standards Consortium (CDISC) compliant Analysis Data Model (ADaM) datasets R. ADaM datasets mandatory part New Drug Biologics License Application submitted United States Food Drug Administration (FDA). Analysis derivations implemented accordance \"Analysis Data Model Implementation Guide\" (CDISC Analysis Data Model Team, 2021, https://www.cdisc.org/standards/foundational/adam).","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/admiral-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"admiral: ADaM in R Asset Library — admiral-package","text":"Maintainer: Ben Straub ben.x.straub@gsk.com Authors: Stefan Bundfuss (ORCID) Jeffrey Dickinson Ross Farrugia Fanny Gautier G Gayatri Asha Chakma Dinakar Kulkarni Edoardo Mancini (ORCID) Sadchla Mascary Gordon Miller Lina Patil Jim Rothstein (ORCID) Sophie Shapcott Eric Simms Daniel Sjoberg (ORCID) Stefan Thoma (ORCID) Phillip Webster Kangjie Zhang Zelos Zhu contributors: F. Hoffmann-La Roche AG [copyright holder, funder] GlaxoSmithKline LLC [copyright holder, funder]","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/admiral_adlb.html","id":null,"dir":"Reference","previous_headings":"","what":"Lab Analysis Dataset — admiral_adlb","title":"Lab Analysis Dataset — admiral_adlb","text":"example lab analysis dataset","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/admiral_adlb.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Lab Analysis Dataset — admiral_adlb","text":"","code":"admiral_adlb"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/admiral_adlb.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Lab Analysis Dataset — admiral_adlb","text":"object class tbl_df (inherits tbl, data.frame) 3779 rows 111 columns.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/admiral_adlb.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Lab Analysis Dataset — admiral_adlb","text":"Derived adlb template, filtered due dataset size following USUBJIDs: 01-701-1015, 01-701-1023, 01-701-1028, 01-701-1033, 01-701-1034, 01-701-1047, 01-701-1097, 01-705-1186, 01-705-1292, 01-705-1310, 01-708-1286","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/admiral_adsl.html","id":null,"dir":"Reference","previous_headings":"","what":"Subject Level Analysis Dataset — admiral_adsl","title":"Subject Level Analysis Dataset — admiral_adsl","text":"example subject level analysis dataset","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/admiral_adsl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Subject Level Analysis Dataset — admiral_adsl","text":"","code":"admiral_adsl"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/admiral_adsl.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Subject Level Analysis Dataset — admiral_adsl","text":"object class tbl_df (inherits tbl, data.frame) 306 rows 54 columns.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/admiral_adsl.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Subject Level Analysis Dataset — admiral_adsl","text":"Derived dm ds datasets using {admiral} (https://github.com/pharmaverse/admiral/blob/main/inst/templates/ad_adsl.R)","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/assert_parameters_argument.html","id":null,"dir":"Reference","previous_headings":"","what":"Asserts parameters Argument and Converts to List of Expressions — assert_parameters_argument","title":"Asserts parameters Argument and Converts to List of Expressions — assert_parameters_argument","text":"function asserts argument character vector list expressions. character vector, converts list symbols.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/assert_parameters_argument.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Asserts parameters Argument and Converts to List of Expressions — assert_parameters_argument","text":"","code":"assert_parameters_argument(parameters, optional = TRUE)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/assert_parameters_argument.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Asserts parameters Argument and Converts to List of Expressions — assert_parameters_argument","text":"parameters argument check optional checked argument optional? set FALSE parameters NULL error thrown.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/assert_parameters_argument.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Asserts parameters Argument and Converts to List of Expressions — assert_parameters_argument","text":"parameters argument (converted list symbol, character vector)","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/atoxgr_criteria_ctcv4.html","id":null,"dir":"Reference","previous_headings":"","what":"Metadata Holding Grading Criteria for NCI-CTCAEv4 — atoxgr_criteria_ctcv4","title":"Metadata Holding Grading Criteria for NCI-CTCAEv4 — atoxgr_criteria_ctcv4","text":"Metadata Holding Grading Criteria NCI-CTCAEv4","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/atoxgr_criteria_ctcv4.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Metadata Holding Grading Criteria for NCI-CTCAEv4 — atoxgr_criteria_ctcv4","text":"","code":"atoxgr_criteria_ctcv4"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/atoxgr_criteria_ctcv4.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Metadata Holding Grading Criteria for NCI-CTCAEv4 — atoxgr_criteria_ctcv4","text":"object class tbl_df (inherits tbl, data.frame) 40 rows 13 columns.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/atoxgr_criteria_ctcv4.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Metadata Holding Grading Criteria for NCI-CTCAEv4 — atoxgr_criteria_ctcv4","text":"metadata origin ADLB Grading Spec Excel file ships {admiral} can accessed using system.file(\"adlb_grading/adlb_grading_spec.xlsx\", package = \"admiral\") sheet = \"NCICTCAEv4\". dataset contained following columns: SOC: variable hold SOC lab test criteria. TERM: variable hold term describing criteria applied particular lab test, eg. 'Anemia' 'INR Increased'. Note: variable case insensitive. Grade 1: Criteria defining lab value Grade 1. Grade 2: Criteria defining lab value Grade 2. Grade 3: Criteria defining lab value Grade 3. Grade 4: Criteria defining lab value Grade 4. Grade 5: Criteria defining lab value Grade 5. Definition: Holds definition lab test abnormality. GRADE_CRITERIA_CODE: variable hold code creates grade based defined criteria. SI_UNIT_CHECK: variable hold unit particular lab test. Used check input data criteria based absolute values. VAR_CHECK: List variables required implement lab grade criteria. Use check input data. DIRECTION: variable hold direction abnormality particular lab test value. 'L' LOW values, 'H' HIGH values. Note: variable case insensitive. COMMENT: Holds information regarding rationale behind implementation grading criteria. Note: Variables SOC, TERM, Grade 1, Grade 2,Grade 3,Grade 4,Grade 5, Definition source document NCI-CTC website defining grading criteria. Common Terminology Criteria Adverse Events (CTCAE)v4.0 variables 'TERM' used {admiral} code, rest information traceability .","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/atoxgr_criteria_ctcv5.html","id":null,"dir":"Reference","previous_headings":"","what":"Metadata Holding Grading Criteria for NCI-CTCAEv5 — atoxgr_criteria_ctcv5","title":"Metadata Holding Grading Criteria for NCI-CTCAEv5 — atoxgr_criteria_ctcv5","text":"Metadata Holding Grading Criteria NCI-CTCAEv5","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/atoxgr_criteria_ctcv5.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Metadata Holding Grading Criteria for NCI-CTCAEv5 — atoxgr_criteria_ctcv5","text":"","code":"atoxgr_criteria_ctcv5"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/atoxgr_criteria_ctcv5.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Metadata Holding Grading Criteria for NCI-CTCAEv5 — atoxgr_criteria_ctcv5","text":"object class tbl_df (inherits tbl, data.frame) 37 rows 13 columns.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/atoxgr_criteria_ctcv5.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Metadata Holding Grading Criteria for NCI-CTCAEv5 — atoxgr_criteria_ctcv5","text":"metadata origin ADLB Grading Spec Excel file ships {admiral} can accessed using system.file(\"adlb_grading/adlb_grading_spec.xlsx\", package = \"admiral\") sheet = \"NCICTCAEv5\". dataset contained following columns: SOC: variable hold SOC lab test criteria. TERM: variable hold term describing criteria applied particular lab test, eg. 'Anemia' 'INR Increased'. Note: variable case insensitive. Grade 1: Criteria defining lab value Grade 1. Grade 2: Criteria defining lab value Grade 2. Grade 3: Criteria defining lab value Grade 3. Grade 4: Criteria defining lab value Grade 4. Grade 5: Criteria defining lab value Grade 5. Definition: Holds definition lab test abnormality. GRADE_CRITERIA_CODE: variable hold code creates grade based defined criteria. SI_UNIT_CHECK: variable hold unit particular lab test. Used check input data criteria based absolute values. VAR_CHECK: List variables required implement lab grade criteria. Use check input data. DIRECTION: variable hold direction abnormality particular lab test value. 'L' LOW values, 'H' HIGH values. Note: variable case insensitive. COMMENT: Holds information regarding rationale behind implementation grading criteria. Note: Variables SOC, TERM, Grade 1, Grade 2,Grade 3,Grade 4,Grade 5, Definition source document NCI-CTC website defining grading criteria. Common Terminology Criteria Adverse Events (CTCAE)v5.0 variables 'TERM' used {admiral} code, rest information traceability .","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/atoxgr_criteria_daids.html","id":null,"dir":"Reference","previous_headings":"","what":"Metadata Holding Grading Criteria for DAIDs — atoxgr_criteria_daids","title":"Metadata Holding Grading Criteria for DAIDs — atoxgr_criteria_daids","text":"Metadata Holding Grading Criteria DAIDs","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/atoxgr_criteria_daids.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Metadata Holding Grading Criteria for DAIDs — atoxgr_criteria_daids","text":"","code":"atoxgr_criteria_daids"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/atoxgr_criteria_daids.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Metadata Holding Grading Criteria for DAIDs — atoxgr_criteria_daids","text":"object class tbl_df (inherits tbl, data.frame) 63 rows 15 columns.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/atoxgr_criteria_daids.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Metadata Holding Grading Criteria for DAIDs — atoxgr_criteria_daids","text":"metadata origin ADLB Grading Spec Excel file ships {admiral} can accessed using system.file(\"adlb_grading/adlb_grading_spec.xlsx\", package = \"admiral\") sheet = \"DAIDS\". dataset contained following columns: SOC: variable hold SOC lab test criteria. TERM: variable hold term describing criteria applied particular lab test, eg. 'Anemia' 'INR Increased'. Note: variable case insensitive. SUBGROUP : Description sub-group subjects grading applied (.e. >= 18 years) Grade 1: Criteria defining lab value Grade 1. Grade 2: Criteria defining lab value Grade 2. Grade 3: Criteria defining lab value Grade 3. Grade 4: Criteria defining lab value Grade 4. Grade 5: Criteria defining lab value Grade 5. Definition: Holds definition lab test abnormality. FILTER : admiral code apply filter based SUBGROUP column. GRADE_CRITERIA_CODE: variable hold code creates grade based defined criteria. SI_UNIT_CHECK: variable hold unit particular lab test. Used check input data criteria based absolute values. VAR_CHECK: List variables required implement lab grade criteria. Use check input data. DIRECTION: variable hold direction abnormality particular lab test value. 'L' LOW values, 'H' HIGH values. Note: variable case insensitive. COMMENT: Holds information regarding rationale behind implementation grading criteria. Note: Variables SOC, TERM, SUBGROUP, Grade 1, Grade 2,Grade 3,Grade 4,Grade 5, Definition source document DAIDS website defining grading criteria. [Division AIDS (DAIDS) Table Grading Severity Adult Pediatric Adverse Events variables 'TERM' used {admiral} code, rest information traceability .","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/basket_select.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a basket_select object — basket_select","title":"Create a basket_select object — basket_select","text":"Create basket_select object","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/basket_select.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a basket_select object — basket_select","text":"","code":"basket_select(name = NULL, id = NULL, scope = NULL, type, ...)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/basket_select.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a basket_select object — basket_select","text":"name Name query used select definition query company database. id Identifier query used select definition query company database. scope Scope query used select definition query company database. Permitted Values: \"BROAD\", \"NARROW\", NA_character_ type type argument expects character scalar. passed company specific get_terms() function function can determine sort basket requested ... number named function arguments. Can used pass company specific conditions flags used user-defined function passed argument get_terms_fun function create_query_data().","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/basket_select.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a basket_select object — basket_select","text":"object class basket_select.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/basket_select.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create a basket_select object — basket_select","text":"Exactly one name id must specified.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/call_derivation.html","id":null,"dir":"Reference","previous_headings":"","what":"Call a Single Derivation Multiple Times — call_derivation","title":"Call a Single Derivation Multiple Times — call_derivation","text":"Call single derivation multiple times parameters/arguments fixed across iterations others varying.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/call_derivation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Call a Single Derivation Multiple Times — call_derivation","text":"","code":"call_derivation(dataset = NULL, derivation, variable_params, ...)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/call_derivation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Call a Single Derivation Multiple Times — call_derivation","text":"dataset Input dataset derivation derivation function call function performs specific derivation expected. derivation adds variables observations dataset. first argument derivation must expect dataset derivation must return dataset. function must provide dataset argument arguments specified params() objects passed variable_params ... argument. Please note possible specify {dplyr} functions like mutate() summarize(). variable_params list function arguments different across iterations. set function arguments must created using params(). ... number named function arguments stay across iterations. function argument specified inside variable_params ... value variable_params overwrites one ...","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/call_derivation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Call a Single Derivation Multiple Times — call_derivation","text":"input dataset additional records/variables added depending derivation used.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/call_derivation.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Call a Single Derivation Multiple Times — call_derivation","text":"","code":"library(dplyr, warn.conflicts = FALSE) adsl <- tribble(   ~STUDYID,   ~USUBJID,      ~TRTSDT,      ~TRTEDT,   \"PILOT01\", \"01-1307\",           NA,           NA,   \"PILOT01\", \"05-1377\", \"2014-01-04\", \"2014-01-25\",   \"PILOT01\", \"06-1384\", \"2012-09-15\", \"2012-09-24\",   \"PILOT01\", \"15-1085\", \"2013-02-16\", \"2013-08-18\",   \"PILOT01\", \"16-1298\", \"2013-04-08\", \"2013-06-28\" ) %>%   mutate(     across(TRTSDT:TRTEDT, as.Date)   )  ae <- tribble(   ~STUDYID,  ~DOMAIN,  ~USUBJID,     ~AESTDTC,     ~AEENDTC,   \"PILOT01\",    \"AE\", \"06-1384\", \"2012-09-15\", \"2012-09-29\",   \"PILOT01\",    \"AE\", \"06-1384\", \"2012-09-15\", \"2012-09-29\",   \"PILOT01\",    \"AE\", \"06-1384\", \"2012-09-23\", \"2012-09-29\",   \"PILOT01\",    \"AE\", \"06-1384\", \"2012-09-23\", \"2012-09-29\",   \"PILOT01\",    \"AE\", \"06-1384\", \"2012-09-15\", \"2012-09-29\",   \"PILOT01\",    \"AE\", \"06-1384\", \"2012-09-15\", \"2012-09-29\",   \"PILOT01\",    \"AE\", \"06-1384\", \"2012-09-15\", \"2012-09-29\",   \"PILOT01\",    \"AE\", \"06-1384\", \"2012-09-15\", \"2012-09-29\",   \"PILOT01\",    \"AE\", \"06-1384\", \"2012-09-23\", \"2012-09-29\",   \"PILOT01\",    \"AE\", \"06-1384\", \"2012-09-23\", \"2012-09-29\",   \"PILOT01\",    \"AE\", \"16-1298\", \"2013-06-08\", \"2013-07-06\",   \"PILOT01\",    \"AE\", \"16-1298\", \"2013-06-08\", \"2013-07-06\",   \"PILOT01\",    \"AE\", \"16-1298\", \"2013-04-22\", \"2013-07-06\",   \"PILOT01\",    \"AE\", \"16-1298\", \"2013-04-22\", \"2013-07-06\",   \"PILOT01\",    \"AE\", \"16-1298\", \"2013-04-22\", \"2013-07-06\",   \"PILOT01\",    \"AE\", \"16-1298\", \"2013-04-22\", \"2013-07-06\" )  adae <- ae %>%   derive_vars_merged(     dataset_add = adsl,     new_vars = exprs(TRTSDT, TRTEDT),     by_vars = exprs(USUBJID)   )  ## While `derive_vars_dt()` can only add one variable at a time, using `call_derivation()` ## one can add multiple variables in one go call_derivation(   dataset = adae,   derivation = derive_vars_dt,   variable_params = list(     params(dtc = AESTDTC, date_imputation = \"first\", new_vars_prefix = \"AST\"),     params(dtc = AEENDTC, date_imputation = \"last\", new_vars_prefix = \"AEN\")   ),   min_dates = exprs(TRTSDT),   max_dates = exprs(TRTEDT) ) #> # A tibble: 16 × 9 #>    STUDYID DOMAIN USUBJID AESTDTC    AEENDTC    TRTSDT     TRTEDT     ASTDT      #>    <chr>   <chr>  <chr>   <chr>      <chr>      <date>     <date>     <date>     #>  1 PILOT01 AE     06-1384 2012-09-15 2012-09-29 2012-09-15 2012-09-24 2012-09-15 #>  2 PILOT01 AE     06-1384 2012-09-15 2012-09-29 2012-09-15 2012-09-24 2012-09-15 #>  3 PILOT01 AE     06-1384 2012-09-23 2012-09-29 2012-09-15 2012-09-24 2012-09-23 #>  4 PILOT01 AE     06-1384 2012-09-23 2012-09-29 2012-09-15 2012-09-24 2012-09-23 #>  5 PILOT01 AE     06-1384 2012-09-15 2012-09-29 2012-09-15 2012-09-24 2012-09-15 #>  6 PILOT01 AE     06-1384 2012-09-15 2012-09-29 2012-09-15 2012-09-24 2012-09-15 #>  7 PILOT01 AE     06-1384 2012-09-15 2012-09-29 2012-09-15 2012-09-24 2012-09-15 #>  8 PILOT01 AE     06-1384 2012-09-15 2012-09-29 2012-09-15 2012-09-24 2012-09-15 #>  9 PILOT01 AE     06-1384 2012-09-23 2012-09-29 2012-09-15 2012-09-24 2012-09-23 #> 10 PILOT01 AE     06-1384 2012-09-23 2012-09-29 2012-09-15 2012-09-24 2012-09-23 #> 11 PILOT01 AE     16-1298 2013-06-08 2013-07-06 2013-04-08 2013-06-28 2013-06-08 #> 12 PILOT01 AE     16-1298 2013-06-08 2013-07-06 2013-04-08 2013-06-28 2013-06-08 #> 13 PILOT01 AE     16-1298 2013-04-22 2013-07-06 2013-04-08 2013-06-28 2013-04-22 #> 14 PILOT01 AE     16-1298 2013-04-22 2013-07-06 2013-04-08 2013-06-28 2013-04-22 #> 15 PILOT01 AE     16-1298 2013-04-22 2013-07-06 2013-04-08 2013-06-28 2013-04-22 #> 16 PILOT01 AE     16-1298 2013-04-22 2013-07-06 2013-04-08 2013-06-28 2013-04-22 #> # ℹ 1 more variable: AENDT <date>  ## The above call using `call_derivation()` is equivalent to the following adae %>%   derive_vars_dt(     new_vars_prefix = \"AST\",     dtc = AESTDTC,     date_imputation = \"first\",     min_dates = exprs(TRTSDT),     max_dates = exprs(TRTEDT)   ) %>%   derive_vars_dt(     new_vars_prefix = \"AEN\",     dtc = AEENDTC,     date_imputation = \"last\",     min_dates = exprs(TRTSDT),     max_dates = exprs(TRTEDT)   ) #> # A tibble: 16 × 9 #>    STUDYID DOMAIN USUBJID AESTDTC    AEENDTC    TRTSDT     TRTEDT     ASTDT      #>    <chr>   <chr>  <chr>   <chr>      <chr>      <date>     <date>     <date>     #>  1 PILOT01 AE     06-1384 2012-09-15 2012-09-29 2012-09-15 2012-09-24 2012-09-15 #>  2 PILOT01 AE     06-1384 2012-09-15 2012-09-29 2012-09-15 2012-09-24 2012-09-15 #>  3 PILOT01 AE     06-1384 2012-09-23 2012-09-29 2012-09-15 2012-09-24 2012-09-23 #>  4 PILOT01 AE     06-1384 2012-09-23 2012-09-29 2012-09-15 2012-09-24 2012-09-23 #>  5 PILOT01 AE     06-1384 2012-09-15 2012-09-29 2012-09-15 2012-09-24 2012-09-15 #>  6 PILOT01 AE     06-1384 2012-09-15 2012-09-29 2012-09-15 2012-09-24 2012-09-15 #>  7 PILOT01 AE     06-1384 2012-09-15 2012-09-29 2012-09-15 2012-09-24 2012-09-15 #>  8 PILOT01 AE     06-1384 2012-09-15 2012-09-29 2012-09-15 2012-09-24 2012-09-15 #>  9 PILOT01 AE     06-1384 2012-09-23 2012-09-29 2012-09-15 2012-09-24 2012-09-23 #> 10 PILOT01 AE     06-1384 2012-09-23 2012-09-29 2012-09-15 2012-09-24 2012-09-23 #> 11 PILOT01 AE     16-1298 2013-06-08 2013-07-06 2013-04-08 2013-06-28 2013-06-08 #> 12 PILOT01 AE     16-1298 2013-06-08 2013-07-06 2013-04-08 2013-06-28 2013-06-08 #> 13 PILOT01 AE     16-1298 2013-04-22 2013-07-06 2013-04-08 2013-06-28 2013-04-22 #> 14 PILOT01 AE     16-1298 2013-04-22 2013-07-06 2013-04-08 2013-06-28 2013-04-22 #> 15 PILOT01 AE     16-1298 2013-04-22 2013-07-06 2013-04-08 2013-06-28 2013-04-22 #> 16 PILOT01 AE     16-1298 2013-04-22 2013-07-06 2013-04-08 2013-06-28 2013-04-22 #> # ℹ 1 more variable: AENDT <date>"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/call_user_fun.html","id":null,"dir":"Reference","previous_headings":"","what":"Calls a Function Provided by the User — call_user_fun","title":"Calls a Function Provided by the User — call_user_fun","text":"Calls function provided user adds function call error message call fails.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/call_user_fun.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calls a Function Provided by the User — call_user_fun","text":"","code":"call_user_fun(call)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/call_user_fun.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calls a Function Provided by the User — call_user_fun","text":"call Call executed","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/call_user_fun.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calls a Function Provided by the User — call_user_fun","text":"return value function call","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/call_user_fun.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calls a Function Provided by the User — call_user_fun","text":"","code":"call_user_fun(compute_bmi(   height = 172,   weight = 60 )) #> [1] 20.28123  try(call_user_fun(compute_bmi(   height = 172,   weight = \"hallo\" ))) #> Error in call_user_fun(compute_bmi(height = 172, weight = \"hallo\")) :  #>   Calling `compute_bmi(height = 172, weight = \"hallo\")` caused the #> following error: #> Argument `weight` must be a numeric vector, but it is a string."},{"path":"https://pharmaverse.github.io/admiral/dev/reference/censor_source.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a censor_source Object — censor_source","title":"Create a censor_source Object — censor_source","text":"censor_source objects used define censorings input derive_param_tte() function. Note: wrapper function generic tte_source().","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/censor_source.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a censor_source Object — censor_source","text":"","code":"censor_source(   dataset_name,   filter = NULL,   date,   censor = 1,   set_values_to = NULL )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/censor_source.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a censor_source Object — censor_source","text":"dataset_name name source dataset name refers dataset provided source_datasets parameter derive_param_tte(). filter unquoted condition selecting observations dataset events possible censoring time points. date variable expression providing date event censoring. date, datetime can specified. unquoted symbol expression expected. Refer derive_vars_dt() convert_dtc_to_dt() impute derive date date character vector date object. censor Censoring value CDISC strongly recommends using 0 events positive integers censoring. set_values_to named list returned exprs() defining variables set event censoring, e.g. exprs(EVENTDESC = \"DEATH\", SRCDOM = \"ADSL\", SRCVAR = \"DTHDT\"). values must symbol, character string, numeric value, expression, NA.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/censor_source.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a censor_source Object — censor_source","text":"object class censor_source, inheriting class tte_source","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/censor_source.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a censor_source Object — censor_source","text":"","code":"# Last study date known alive censor  censor_source(   dataset_name = \"adsl\",   date = LSTALVDT,   set_values_to = exprs(     EVNTDESC = \"ALIVE\",     SRCDOM = \"ADSL\",     SRCVAR = \"LSTALVDT\"   ) ) #> <censor_source> object #> dataset_name: \"adsl\" #> filter: NULL #> date: LSTALVDT #> censor: 1 #> set_values_to: #>   EVNTDESC: \"ALIVE\" #>   SRCDOM: \"ADSL\" #>   SRCVAR: \"LSTALVDT\""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/chr2vars.html","id":null,"dir":"Reference","previous_headings":"","what":"Turn a Character Vector into a List of Expressions — chr2vars","title":"Turn a Character Vector into a List of Expressions — chr2vars","text":"Turn character vector list expressions","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/chr2vars.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Turn a Character Vector into a List of Expressions — chr2vars","text":"","code":"chr2vars(chr)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/chr2vars.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Turn a Character Vector into a List of Expressions — chr2vars","text":"chr character vector","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/chr2vars.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Turn a Character Vector into a List of Expressions — chr2vars","text":"list expressions returned exprs()","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/chr2vars.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Turn a Character Vector into a List of Expressions — chr2vars","text":"","code":"chr2vars(c(\"USUBJID\", \"AVAL\")) #> [[1]] #> USUBJID #>  #> [[2]] #> AVAL #>"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_age_years.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Age in Years — compute_age_years","title":"Compute Age in Years — compute_age_years","text":"Converts set age values specified time unit years.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_age_years.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Age in Years — compute_age_years","text":"","code":"compute_age_years(age, age_unit)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_age_years.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Age in Years — compute_age_years","text":"age ages convert. numeric vector expected. age_unit Age unit. Either string containing time unit ages age character vector containing time units age age expected. Note permitted values cases insensitive (e.g. \"YEARS\" treated \"years\" \"Years\"). Permitted Values: \"years\", \"months\", \"weeks\", \"days\", \"hours\", \"minutes\", \"seconds\", NA_character_.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_age_years.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute Age in Years — compute_age_years","text":"ages contained age converted years.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_age_years.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute Age in Years — compute_age_years","text":"Returns numeric vector ages years doubles. Note passing NA_character_ unit result NA value outputted age. Also note, underlying computations assume equal number days year (365.25).","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_age_years.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute Age in Years — compute_age_years","text":"","code":"compute_age_years(   age = c(240, 360, 480),   age_unit = \"MONTHS\" ) #> [1] 20 30 40  compute_age_years(   age = c(10, 520, 3650, 1000),   age_unit = c(\"YEARS\", \"WEEKS\", \"DAYS\", NA_character_) ) #> [1] 10.000000  9.965777  9.993155        NA"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_bmi.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Body Mass Index (BMI) — compute_bmi","title":"Compute Body Mass Index (BMI) — compute_bmi","text":"Computes BMI height weight","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_bmi.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Body Mass Index (BMI) — compute_bmi","text":"","code":"compute_bmi(height, weight)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_bmi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Body Mass Index (BMI) — compute_bmi","text":"height HEIGHT value expected HEIGHT cm. Permitted Values: numeric vector weight WEIGHT value expected WEIGHT kg. Permitted Values: numeric vector","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_bmi.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute Body Mass Index (BMI) — compute_bmi","text":"BMI (Body Mass Index Area) kg/m^2.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_bmi.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute Body Mass Index (BMI) — compute_bmi","text":"Usually computation function can used %>%.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_bmi.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute Body Mass Index (BMI) — compute_bmi","text":"","code":"compute_bmi(height = 170, weight = 75) #> [1] 25.95156"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_bsa.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Body Surface Area (BSA) — compute_bsa","title":"Compute Body Surface Area (BSA) — compute_bsa","text":"Computes BSA height weight making use specified derivation method","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_bsa.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Body Surface Area (BSA) — compute_bsa","text":"","code":"compute_bsa(height = height, weight = weight, method)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_bsa.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Body Surface Area (BSA) — compute_bsa","text":"height HEIGHT value expected HEIGHT cm. Permitted Values: numeric vector weight WEIGHT value expected WEIGHT kg. Permitted Values: numeric vector method Derivation method use: Mosteller: sqrt(height * weight / 3600) DuBois-DuBois: 0.007184 * height ^ 0.725 * weight ^ 0.425 Haycock: 0.024265 * height ^ 0.3964 * weight ^ 0.5378 Gehan-George: 0.0235 * height ^ 0.42246 * weight ^ 0.51456 Boyd: 0.0003207 * (height ^ 0.3) * (1000 * weight) ^ (0.7285 - (0.0188 * log10(1000 * weight))) Fujimoto: 0.008883 * height ^ 0.663 * weight ^ 0.444 Takahira: 0.007241 * height ^ 0.725 * weight ^ 0.425 Permitted Values: character value","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_bsa.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute Body Surface Area (BSA) — compute_bsa","text":"BSA (Body Surface Area) m^2.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_bsa.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute Body Surface Area (BSA) — compute_bsa","text":"Usually computation function can used %>%.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_bsa.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute Body Surface Area (BSA) — compute_bsa","text":"","code":"# Derive BSA by the Mosteller method compute_bsa(   height = 170,   weight = 75,   method = \"Mosteller\" ) #> [1] 1.881932  # Derive BSA by the DuBois & DuBois method compute_bsa(   height = c(170, 185),   weight = c(75, 90),   method = \"DuBois-DuBois\" ) #> [1] 1.863558 2.141011"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_dtf.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive the Date Imputation Flag — compute_dtf","title":"Derive the Date Imputation Flag — compute_dtf","text":"Derive date imputation flag ('--DTF') comparing date character vector ('--DTC') Date vector ('--DT').","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_dtf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive the Date Imputation Flag — compute_dtf","text":"","code":"compute_dtf(dtc, dt)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_dtf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive the Date Imputation Flag — compute_dtf","text":"dtc date character vector ('--DTC'). character date expected format like yyyy-mm-ddThh:mm:ss (partial complete). dt  Date vector compare. date object expected.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_dtf.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive the Date Imputation Flag — compute_dtf","text":"date imputation flag ('--DTF') (character value 'D', 'M' , 'Y' NA)","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_dtf.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Derive the Date Imputation Flag — compute_dtf","text":"Usually computation function can used %>%.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_dtf.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive the Date Imputation Flag — compute_dtf","text":"","code":"compute_dtf(dtc = \"2019-07\", dt = as.Date(\"2019-07-18\")) #> [1] \"D\" compute_dtf(dtc = \"2019\", dt = as.Date(\"2019-07-18\")) #> [1] \"M\" compute_dtf(dtc = \"--06-01T00:00\", dt = as.Date(\"2022-06-01\")) #> [1] \"Y\" compute_dtf(dtc = \"2022-06--T00:00\", dt = as.Date(\"2022-06-01\")) #> [1] \"D\" compute_dtf(dtc = \"2022---01T00:00\", dt = as.Date(\"2022-06-01\")) #> [1] \"M\" compute_dtf(dtc = \"2022----T00:00\", dt = as.Date(\"2022-06-01\")) #> [1] \"M\""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_duration.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Duration — compute_duration","title":"Compute Duration — compute_duration","text":"Compute duration two dates, e.g., duration adverse event, relative day, age, ...","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_duration.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Duration — compute_duration","text":"","code":"compute_duration(   start_date,   end_date,   in_unit = \"days\",   out_unit = \"days\",   floor_in = TRUE,   add_one = TRUE,   trunc_out = FALSE,   type = \"duration\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_duration.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Duration — compute_duration","text":"start_date start date date date-time object expected. Refer derive_vars_dt() impute derive date date character vector date object. Refer convert_dtc_to_dt() obtain vector imputed dates. end_date end date date date-time object expected. Refer derive_vars_dt() impute derive date date character vector date object. Refer convert_dtc_to_dt() obtain vector imputed dates. in_unit Input unit See floor_in add_one parameter details. Permitted Values (case-insensitive): years: \"year\", \"years\", \"yr\", \"yrs\", \"y\" months: \"month\", \"months\", \"mo\", \"mos\" days: \"day\", \"days\", \"d\" hours: \"hour\", \"hours\", \"hr\", \"hrs\", \"h\" minutes: \"minute\", \"minutes\", \"min\", \"mins\" seconds: \"second\", \"seconds\", \"sec\", \"secs\", \"s\" out_unit Output unit duration derived specified unit Permitted Values (case-insensitive): years: \"year\", \"years\", \"yr\", \"yrs\", \"y\" months: \"month\", \"months\", \"mo\", \"mos\" weeks: \"week\", \"weeks\", \"wk\", \"wks\", \"w\" days: \"day\", \"days\", \"d\" hours: \"hour\", \"hours\", \"hr\", \"hrs\", \"h\" minutes: \"minute\", \"minutes\", \"min\", \"mins\" seconds: \"second\", \"seconds\", \"sec\", \"secs\", \"s\" floor_in Round input dates? input dates round respect input unit, e.g., input unit 'days', time input dates ignored. Default: TRUE Permitted Values: TRUE, FALSE add_one Add one input unit? duration non-negative, one input unit added. .e., duration can zero. Default: TRUE Permitted Values: TRUE, FALSE trunc_out Return integer part fractional part duration (output unit) removed, .e., integer part returned. Default: FALSE Permitted Values: TRUE, FALSE type lubridate duration type. See details. Default: \"duration\" Permitted Values: \"duration\", \"interval\"","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_duration.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute Duration — compute_duration","text":"duration two date specified unit","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_duration.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute Duration — compute_duration","text":"output numeric vector providing duration time start end date specified unit. end date start date, duration negative.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_duration.html","id":"duration-type","dir":"Reference","previous_headings":"","what":"Duration Type","title":"Compute Duration — compute_duration","text":"lubridate package calculates two types spans two dates: duration interval. calculations largely , unit time period month year result can slightly different. difference arises ambiguity length \"1 month\" \"1 year\". Months may 31, 30, 28, 29 days, years 365 days 366 leap years. Durations intervals help solve ambiguity measures. interval 2000-02-01 2000-03-01 1 (.e. one month). duration two dates 0.95, accounts fact year 2000 leap year, February 29 days, average month length 30.4375, .e. 29 / 30.4375 = 0.95. additional details, review lubridate time span reference page.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_duration.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute Duration — compute_duration","text":"","code":"library(lubridate) #>  #> Attaching package: ‘lubridate’ #> The following objects are masked from ‘package:base’: #>  #>     date, intersect, setdiff, union  # Derive duration in days (integer), i.e., relative day compute_duration(   start_date = ymd_hms(\"2020-12-06T15:00:00\"),   end_date = ymd_hms(\"2020-12-24T08:15:00\") ) #> [1] 19  # Derive duration in days (float) compute_duration(   start_date = ymd_hms(\"2020-12-06T15:00:00\"),   end_date = ymd_hms(\"2020-12-24T08:15:00\"),   floor_in = FALSE,   add_one = FALSE ) #> [1] 17.71875  # Derive age in years compute_duration(   start_date = ymd(\"1984-09-06\"),   end_date = ymd(\"2020-02-24\"),   trunc_out = TRUE,   out_unit = \"years\",   add_one = FALSE ) #> [1] 35  # Derive duration in hours compute_duration(   start_date = ymd_hms(\"2020-12-06T9:00:00\"),   end_date = ymd_hms(\"2020-12-06T13:30:00\"),   out_unit = \"hours\",   floor_in = FALSE,   add_one = FALSE, ) #> [1] 4.5"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_egfr.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Estimated Glomerular Filtration Rate (eGFR) for Kidney Function — compute_egfr","title":"Compute Estimated Glomerular Filtration Rate (eGFR) for Kidney Function — compute_egfr","text":"Compute Kidney Function Tests: Estimated Creatinine Clearance (CRCL) Cockcroft-Gault equation Estimated Glomerular Filtration Rate (eGFR) CKD-EPI MDRD equations","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_egfr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Estimated Glomerular Filtration Rate (eGFR) for Kidney Function — compute_egfr","text":"","code":"compute_egfr(creat, creatu = \"SI\", age, weight, sex, race = NULL, method)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_egfr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Estimated Glomerular Filtration Rate (eGFR) for Kidney Function — compute_egfr","text":"creat Creatinine numeric vector expected. creatu Creatinine Units character vector expected. Default: \"SI\" Expected Values: \"SI\", \"CV\", \"umol/L\", \"mg/dL\" age Age (years) numeric vector expected. weight Weight (kg) numeric vector expected method = \"CRCL\" sex Gender character vector expected. Expected Values: \"M\", \"F\" race Race character vector expected method = \"MDRD\" Expected Values: \"BLACK AFRICAN AMERICAN\" others method Method character vector expected. Expected Values: \"CRCL\", \"CKD-EPI\", \"MDRD\"","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_egfr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute Estimated Glomerular Filtration Rate (eGFR) for Kidney Function — compute_egfr","text":"numeric vector egfr values","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_egfr.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute Estimated Glomerular Filtration Rate (eGFR) for Kidney Function — compute_egfr","text":"Calculates estimate Glomerular Filtration Rate (eGFR) CRCL Creatinine Clearance (Cockcroft-Gault) Creatinine umol/L: $$\\frac{(140 - age) \\times weight(kg) \\times constant}{Serum\\:Creatinine(\\mu mol/L)}$$ $$Constant = 1.04\\:\\:females, 1.23\\:\\:males$$ Creatinine mg/dL: $$\\frac{(140 - age) \\times weight(kg) \\times (0.85\\:\\:female)}{72 \\times Serum\\:Creatinine(mg/dL)}$$ units = mL/min CKD-EPI Chronic Kidney Disease Epidemiology Collaboration formula $$eGFR = 142 \\times min(SCr/{\\kappa}, 1)^{\\alpha} \\times max(SCr/{\\kappa}, 1)^{-1.200} \\times 0.9938^{Age} \\times 1.012 [\\:female]$$ SCr = standardized serum creatinine mg/dL (Note SCr(mg/dL) = Creat(umol/L) / 88.42) $$\\kappa$$ = 0.7 (females) 0.9 (males) $$\\alpha$$ = -0.241 (female) -0.302 (male) units = mL/min/1.73 m2 MDRD Modification Diet Renal Disease formula $$eGFR = 175 \\times (SCr)^{-1.154} \\times (age)^{-0.203} \\times 0.742 [\\:female] \\times 1.212 [\\:Black]$$ SCr = standardized serum creatinine mg/dL (Note SCr(mg/dL) = Creat(umol/L) / 88.42) units = mL/min/1.73 m2","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_egfr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute Estimated Glomerular Filtration Rate (eGFR) for Kidney Function — compute_egfr","text":"","code":"compute_egfr(   creat = 90, creatu = \"umol/L\", age = 53, weight = 85, sex = \"M\", method = \"CRCL\" ) #> [1] 101.065  compute_egfr(   creat = 90, creatu = \"umol/L\", age = 53, sex = \"M\", race = \"ASIAN\", method = \"MDRD\" ) #> [1] 76.58319  compute_egfr(   creat = 70, creatu = \"umol/L\", age = 52, sex = \"F\", race = \"BLACK OR AFRICAN AMERICAN\",   method = \"MDRD\" ) #> [1] 92.40002  compute_egfr(   creat = 90, creatu = \"umol/L\", age = 53, sex = \"M\", method = \"CKD-EPI\" ) #> [1] 88.10399   base <- tibble::tribble(   ~STUDYID, ~USUBJID, ~AGE, ~SEX, ~RACE, ~WTBL, ~CREATBL, ~CREATBLU,   \"P01\", \"P01-1001\", 55, \"M\", \"WHITE\", 90.7, 96.3, \"umol/L\",   \"P01\", \"P01-1002\", 52, \"F\", \"BLACK OR AFRICAN AMERICAN\", 68.5, 70, \"umol/L\",   \"P01\", \"P01-1003\", 67, \"M\", \"BLACK OR AFRICAN AMERICAN\", 85.0, 77, \"umol/L\",   \"P01\", \"P01-1004\", 76, \"F\", \"ASIAN\", 60.7, 65, \"umol/L\", )  base %>%   dplyr::mutate(     CRCL_CG = compute_egfr(       creat = CREATBL, creatu = CREATBLU, age = AGE, weight = WTBL, sex = SEX,       method = \"CRCL\"     ),     EGFR_EPI = compute_egfr(       creat = CREATBL, creatu = CREATBLU, age = AGE, weight = WTBL, sex = SEX,       method = \"CKD-EPI\"     ),     EGFR_MDRD = compute_egfr(       creat = CREATBL, creatu = CREATBLU, age = AGE, weight = WTBL, sex = SEX,       race = RACE, method = \"MDRD\"     ),   ) #> # A tibble: 4 × 11 #>   STUDYID USUBJID    AGE SEX   RACE       WTBL CREATBL CREATBLU CRCL_CG EGFR_EPI #>   <chr>   <chr>    <dbl> <chr> <chr>     <dbl>   <dbl> <chr>      <dbl>    <dbl> #> 1 P01     P01-1001    55 M     WHITE      90.7    96.3 umol/L      98.5     80.2 #> 2 P01     P01-1002    52 F     BLACK OR…  68.5    70   umol/L      89.6     89.7 #> 3 P01     P01-1003    67 M     BLACK OR…  85      77   umol/L      99.1     94.5 #> 4 P01     P01-1004    76 F     ASIAN      60.7    65   umol/L      62.2     84.5 #> # ℹ 1 more variable: EGFR_MDRD <dbl>"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_framingham.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Framingham Heart Study Cardiovascular Disease 10-Year Risk Score — compute_framingham","title":"Compute Framingham Heart Study Cardiovascular Disease 10-Year Risk Score — compute_framingham","text":"Computes Framingham Heart Study Cardiovascular Disease 10-Year Risk Score (FCVD101) based systolic blood pressure, total serum cholesterol (mg/dL), HDL serum cholesterol (mg/dL), sex, smoking status, diabetic status, treated hypertension flag.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_framingham.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Framingham Heart Study Cardiovascular Disease 10-Year Risk Score — compute_framingham","text":"","code":"compute_framingham(sysbp, chol, cholhdl, age, sex, smokefl, diabetfl, trthypfl)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_framingham.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Framingham Heart Study Cardiovascular Disease 10-Year Risk Score — compute_framingham","text":"sysbp Systolic blood pressure numeric vector expected. chol Total serum cholesterol (mg/dL) numeric vector expected. cholhdl HDL serum cholesterol (mg/dL) numeric vector expected. age Age (years) numeric vector expected. sex Gender character vector expected. Expected Values: 'M' 'F' smokefl Smoking Status character vector  expected. Expected Values: 'Y' 'N' diabetfl Diabetic Status character vector expected. Expected Values: 'Y' 'N' trthypfl Treated hypertension status character vector expected. Expected Values: 'Y' 'N'","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_framingham.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute Framingham Heart Study Cardiovascular Disease 10-Year Risk Score — compute_framingham","text":"numeric vector Framingham values","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_framingham.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute Framingham Heart Study Cardiovascular Disease 10-Year Risk Score — compute_framingham","text":"predicted probability cardiovascular disease (CVD) within 10-years according Framingham formula.  See AHA Journal article General Cardiovascular Risk Profile Use Primary Care reference. Women: Men: equation calculating risk: $$RiskFactors = (log(Age) * AgeFactor) + (log(TotalChol) * TotalCholFactor) + (log(CholHDL) * CholHDLFactor) \\\\ + (log(SysBP) * SysBPFactor) + Smoker + Diabetes Present - AvgRisk$$ $$Risk = 100 * (1 - RiskPeriodFactor ^ exp(RiskFactors))$$","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_framingham.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute Framingham Heart Study Cardiovascular Disease 10-Year Risk Score — compute_framingham","text":"","code":"compute_framingham(   sysbp = 133, chol = 216.16, cholhdl = 54.91, age = 53,   sex = \"M\", smokefl = \"N\", diabetfl = \"N\", trthypfl = \"N\" ) #> [1] 10.37514  compute_framingham(   sysbp = 161, chol = 186.39, cholhdl = 64.19, age = 52,   sex = \"F\", smokefl = \"Y\", diabetfl = \"N\", trthypfl = \"Y\" ) #> [1] 16.40353"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_map.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Mean Arterial Pressure (MAP) — compute_map","title":"Compute Mean Arterial Pressure (MAP) — compute_map","text":"Computes mean arterial pressure (MAP) based diastolic systolic blood pressure. Optionally heart rate can used well.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_map.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Mean Arterial Pressure (MAP) — compute_map","text":"","code":"compute_map(diabp, sysbp, hr = NULL)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_map.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Mean Arterial Pressure (MAP) — compute_map","text":"diabp Diastolic blood pressure numeric vector expected. sysbp Systolic blood pressure numeric vector expected. hr Heart rate numeric vector NULL expected.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_map.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute Mean Arterial Pressure (MAP) — compute_map","text":"numeric vector MAP values","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_map.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute Mean Arterial Pressure (MAP) — compute_map","text":"$$\\frac{2DIABP + SYSBP}{3}$$ based diastolic systolic blood pressure $$DIABP + 0.01 e^{4.14 - \\frac{40.74}{HR}} (SYSBP - DIABP)$$ based diastolic, systolic blood pressure, heart rate. Usually computation function can used %>%.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_map.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute Mean Arterial Pressure (MAP) — compute_map","text":"","code":"# Compute MAP based on diastolic and systolic blood pressure compute_map(diabp = 51, sysbp = 121) #> [1] 74.33333  # Compute MAP based on diastolic and systolic blood pressure and heart rate compute_map(diabp = 51, sysbp = 121, hr = 59) #> [1] 73.03907"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_qtc.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Corrected QT — compute_qtc","title":"Compute Corrected QT — compute_qtc","text":"Computes corrected QT using Bazett's, Fridericia's Sagie's formula.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_qtc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Corrected QT — compute_qtc","text":"","code":"compute_qtc(qt, rr, method)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_qtc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Corrected QT — compute_qtc","text":"qt QT interval numeric vector expected. expected QT measured ms msec. rr RR interval numeric vector expected. expected RR measured ms msec. method Method used QT correction Permitted Values: \"Bazett\", \"Fridericia\", \"Sagie\"","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_qtc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute Corrected QT — compute_qtc","text":"QT interval ms","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_qtc.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute Corrected QT — compute_qtc","text":"Depending chosen method one following formulae used. Bazett: $$\\frac{QT}{\\sqrt{\\frac{RR}{1000}}}$$ Fridericia: $$\\frac{QT}{\\sqrt[3]{\\frac{RR}{1000}}}$$ Sagie: $$1000\\left(\\frac{QT}{1000} + 0.154\\left(1 - \\frac{RR}{1000}\\right)\\right)$$ Usually computation function can used %>%.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_qtc.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute Corrected QT — compute_qtc","text":"","code":"compute_qtc(qt = 350, rr = 857, method = \"Bazett\") #> [1] 378.0747  compute_qtc(qt = 350, rr = 857, method = \"Fridericia\") #> [1] 368.4748  compute_qtc(qt = 350, rr = 857, method = \"Sagie\") #> [1] 372.022"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_qual_imputation.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to Impute Values When Qualifier Exists in Character Result — compute_qual_imputation","title":"Function to Impute Values When Qualifier Exists in Character Result — compute_qual_imputation","text":"Derive imputed value","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_qual_imputation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to Impute Values When Qualifier Exists in Character Result — compute_qual_imputation","text":"","code":"compute_qual_imputation(character_value, imputation_type = 1, factor = 0)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_qual_imputation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to Impute Values When Qualifier Exists in Character Result — compute_qual_imputation","text":"character_value Character version value imputed imputation_type (default value=1) Valid Values: 1: Strip <, >, = convert numeric. 2: imputation_type=1 character value contains < >, number decimals associated character value found factor 1/10^(number decimals + 1) added/subtracted numeric value. decimals exists, factor 1/10 added/subtracted value. factor Numeric value (default=0), using imputation_type = 1, value can added subtracted qualifier removed.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_qual_imputation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to Impute Values When Qualifier Exists in Character Result — compute_qual_imputation","text":"imputed value","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_qual_imputation.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to Impute Values When Qualifier Exists in Character Result — compute_qual_imputation","text":"","code":"compute_qual_imputation(\"<40\") #> [1] 40"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_qual_imputation_dec.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Factor for Value Imputations When Character Value Contains < or > — compute_qual_imputation_dec","title":"Compute Factor for Value Imputations When Character Value Contains < or > — compute_qual_imputation_dec","text":"Function compute factor value imputation character value contains < >.  factor calculated using number decimals. decimals, factor 1, otherwise factor = 1/10^decimal place.  example, factor 100 = 1, factor 5.4 = 1/10^1, factor 5.44 = 1/10^2.  results additional false precision added value.  intermediate function.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_qual_imputation_dec.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Factor for Value Imputations When Character Value Contains < or > — compute_qual_imputation_dec","text":"","code":"compute_qual_imputation_dec(character_value_decimal)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_qual_imputation_dec.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Factor for Value Imputations When Character Value Contains < or > — compute_qual_imputation_dec","text":"character_value_decimal Character value determine decimal precision","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_qual_imputation_dec.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute Factor for Value Imputations When Character Value Contains < or > — compute_qual_imputation_dec","text":"Decimal precision value add subtract","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_qual_imputation_dec.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute Factor for Value Imputations When Character Value Contains < or > — compute_qual_imputation_dec","text":"Derive imputed value","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_qual_imputation_dec.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute Factor for Value Imputations When Character Value Contains < or > — compute_qual_imputation_dec","text":"","code":"compute_qual_imputation_dec(\"<40.1\") #> [1] 0.1"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_rr.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute RR Interval From Heart Rate — compute_rr","title":"Compute RR Interval From Heart Rate — compute_rr","text":"Computes RR interval heart rate.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_rr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute RR Interval From Heart Rate — compute_rr","text":"","code":"compute_rr(hr)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_rr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute RR Interval From Heart Rate — compute_rr","text":"hr Heart rate numeric vector expected. expected heart rate measured beats/min.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_rr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute RR Interval From Heart Rate — compute_rr","text":"RR interval ms: $$\\frac{60000}{HR}$$","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_rr.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute RR Interval From Heart Rate — compute_rr","text":"Usually computation function can used %>%.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_rr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute RR Interval From Heart Rate — compute_rr","text":"","code":"compute_rr(hr = 70.14) #> [1] 855.432"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_scale.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Scale Parameters — compute_scale","title":"Compute Scale Parameters — compute_scale","text":"Computes average set source values transforms result source range target range. example, calculating average set questionnaire response scores re-coding average response obtain subscale score.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_scale.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Scale Parameters — compute_scale","text":"","code":"compute_scale(   source,   source_range = NULL,   target_range = NULL,   flip_direction = FALSE,   min_n = 1 )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_scale.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Scale Parameters — compute_scale","text":"source vector values scaled numeric vector expected. source_range permitted source range numeric vector containing two elements expected, representing lower upper bounds permitted source range. Alternatively, argument specified source_range target_range, transformation performed. target_range target range numeric vector containing two elements expected, representing lower upper bounds target range. Alternatively, argument specified source_range target_range, transformation performed. flip_direction Flip direction scale? transformed values reversed within target range, e.g. within range 0 100, 25 reversed 75. argument ignored source_range target_range specified. Default: FALSE Permitted Values: TRUE, FALSE min_n Minimum number values computation minimum number non-missing values source computation carried . number non-missing values min_n, result set missing, .e. NA. positive integer expected. Default: 1","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_scale.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute Scale Parameters — compute_scale","text":"average source transformed target range NA source contain min_n values.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_scale.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute Scale Parameters — compute_scale","text":"Returns numeric value. source contains less min_n values, result set NA. source_range target_range specified, mean computed without transformation performed.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_scale.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute Scale Parameters — compute_scale","text":"","code":"compute_scale(   source = c(1, 4, 3, 5),   source_range = c(1, 5),   target_range = c(0, 100),   flip_direction = TRUE,   min_n = 3 ) #> [1] 43.75"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_tmf.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive the Time Imputation Flag — compute_tmf","title":"Derive the Time Imputation Flag — compute_tmf","text":"Derive time imputation flag ('--TMF') comparing date character vector ('--DTC') Datetime vector ('--DTM').","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_tmf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive the Time Imputation Flag — compute_tmf","text":"","code":"compute_tmf(dtc, dtm, ignore_seconds_flag = FALSE)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_tmf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive the Time Imputation Flag — compute_tmf","text":"dtc date character vector ('--DTC'). character date expected format like yyyy-mm-ddThh:mm:ss (partial complete). dtm Date vector compare ('--DTM'). datetime object expected. ignore_seconds_flag ADaM IG states given SDTM ('--DTC') variable, hours minutes ever collected, seconds imputed ('--DTM') 00, necessary set ('--TMF') 'S'. user can set TRUE 'S' Flag dropped ('--TMF'). Permitted Values: logical value","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_tmf.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive the Time Imputation Flag — compute_tmf","text":"time imputation flag ('--TMF') (character value 'H', 'M' , 'S' NA)","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_tmf.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Derive the Time Imputation Flag — compute_tmf","text":"Usually computation function can used %>%.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_tmf.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive the Time Imputation Flag — compute_tmf","text":"","code":"library(lubridate)  compute_tmf(dtc = \"2019-07-18T15:25\", dtm = ymd_hms(\"2019-07-18T15:25:00\")) #> [1] \"S\" compute_tmf(dtc = \"2019-07-18T15\", dtm = ymd_hms(\"2019-07-18T15:25:00\")) #> [1] \"M\" compute_tmf(dtc = \"2019-07-18\", dtm = ymd(\"2019-07-18\")) #> [1] \"H\" compute_tmf(dtc = \"2022-05--T00:00\", dtm = ymd_hms(\"2022-05-15T23:59:59\")) #> [1] \"H\" compute_tmf(dtc = \"2022-05--T23:00\", dtm = ymd_hms(\"2022-05-15T23:59:59\")) #> [1] \"M\" compute_tmf(dtc = \"2022-05--T23:59:00\", dtm = ymd_hms(\"2022-05-15T23:59:59\")) #> [1] \"S\""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/consolidate_metadata.html","id":null,"dir":"Reference","previous_headings":"","what":"Consolidate Multiple Meta Datasets Into a Single One — consolidate_metadata","title":"Consolidate Multiple Meta Datasets Into a Single One — consolidate_metadata","text":"purpose function consolidate multiple meta datasets single one. example, global project specific parameter mappings single lookup table can created.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/consolidate_metadata.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Consolidate Multiple Meta Datasets Into a Single One — consolidate_metadata","text":"","code":"consolidate_metadata(   datasets,   key_vars,   source_var = SOURCE,   check_vars = \"warning\",   check_type = \"error\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/consolidate_metadata.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Consolidate Multiple Meta Datasets Into a Single One — consolidate_metadata","text":"datasets List datasets consolidate Permitted Values: named list datasets key_vars Key variables specified variables must unique input datasets. Permitted Values: list variables created exprs() source_var Source variable specified variable added output dataset. set name dataset observation originating . Permitted Values: symbol check_vars Check variables? \"message\", \"warning\", \"error\" specified, message issued variable names differ across input datasets (datasets). Permitted Values: \"none\", \"message\", \"warning\", \"error\" check_type Check uniqueness? \"warning\" \"error\" specified, message issued key variables (key_vars) unique key input datasets (datasets). Permitted Values: \"none\", \"warning\", \"error\"","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/consolidate_metadata.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Consolidate Multiple Meta Datasets Into a Single One — consolidate_metadata","text":"dataset contains one row group occurring input datasets.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/consolidate_metadata.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Consolidate Multiple Meta Datasets Into a Single One — consolidate_metadata","text":"observations input datasets put together single dataset. group (defined key_vars) exists one input datasets, observation last dataset selected.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/consolidate_metadata.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Consolidate Multiple Meta Datasets Into a Single One — consolidate_metadata","text":"","code":"library(tibble) glob_ranges <- tribble(   ~PARAMCD, ~ANRLO, ~ANRHI,   \"PULSE\",      60,    100,   \"SYSBP\",      90,    130,   \"DIABP\",      60,     80 ) proj_ranges <- tribble(   ~PARAMCD, ~ANRLO, ~ANRHI,   \"SYSBP\",     100,    140,   \"DIABP\",      70,     90 ) stud_ranges <- tribble(   ~PARAMCD, ~ANRLO, ~ANRHI,   \"BMI\",        18,     25 )  consolidate_metadata(   datasets = list(     global = glob_ranges,     project = proj_ranges,     study = stud_ranges   ),   key_vars = exprs(PARAMCD) ) #> # A tibble: 4 × 4 #>   SOURCE  PARAMCD ANRLO ANRHI #>   <chr>   <chr>   <dbl> <dbl> #> 1 study   BMI        18    25 #> 2 project DIABP      70    90 #> 3 global  PULSE      60   100 #> 4 project SYSBP     100   140"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/convert_blanks_to_na.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert Blank Strings Into NAs — convert_blanks_to_na","title":"Convert Blank Strings Into NAs — convert_blanks_to_na","text":"Turn SAS blank strings proper R NAs.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/convert_blanks_to_na.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert Blank Strings Into NAs — convert_blanks_to_na","text":"","code":"convert_blanks_to_na(x)  # Default S3 method convert_blanks_to_na(x)  # S3 method for class 'character' convert_blanks_to_na(x)  # S3 method for class 'list' convert_blanks_to_na(x)  # S3 method for class 'data.frame' convert_blanks_to_na(x)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/convert_blanks_to_na.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert Blank Strings Into NAs — convert_blanks_to_na","text":"x R object","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/convert_blanks_to_na.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert Blank Strings Into NAs — convert_blanks_to_na","text":"object class input","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/convert_blanks_to_na.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Convert Blank Strings Into NAs — convert_blanks_to_na","text":"default methods simply returns input unchanged. character method turns every instance \"\" NA_character_ preserving attributes. given data frame input function keeps non-character columns applies just described logic character columns. attributes labels preserved.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/convert_blanks_to_na.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert Blank Strings Into NAs — convert_blanks_to_na","text":"","code":"library(tibble)  convert_blanks_to_na(c(\"a\", \"b\", \"\", \"d\", \"\")) #> [1] \"a\" \"b\" NA  \"d\" NA   df <- tribble(   ~USUBJID,   ~RFICDTC,   \"1001\", \"2000-01-01\",   \"1002\", \"2001-01-01\",   \"1003\",           \"\" ) print(df) #> # A tibble: 3 × 2 #>   USUBJID RFICDTC      #>   <chr>   <chr>        #> 1 1001    \"2000-01-01\" #> 2 1002    \"2001-01-01\" #> 3 1003    \"\"           convert_blanks_to_na(df) #> # A tibble: 3 × 2 #>   USUBJID RFICDTC    #>   <chr>   <chr>      #> 1 1001    2000-01-01 #> 2 1002    2001-01-01 #> 3 1003    NA"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/convert_date_to_dtm.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert a Date into a Datetime Object — convert_date_to_dtm","title":"Convert a Date into a Datetime Object — convert_date_to_dtm","text":"Convert date (datetime, date, date character) Date vector (usually '--DTM'). Note: wrapper function function convert_dtc_to_dtm().","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/convert_date_to_dtm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert a Date into a Datetime Object — convert_date_to_dtm","text":"","code":"convert_date_to_dtm(   dt,   highest_imputation = \"h\",   date_imputation = \"first\",   time_imputation = \"first\",   min_dates = NULL,   max_dates = NULL,   preserve = FALSE )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/convert_date_to_dtm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert a Date into a Datetime Object — convert_date_to_dtm","text":"dt date convert. date character date expected format like yyyy-mm-ddThh:mm:ss. highest_imputation Highest imputation level highest_imputation argument controls components DTC value imputed missing. components specified level imputed. component higher level highest imputation level missing, NA_character_ returned. example, highest_imputation = \"D\" \"2020\" results NA_character_ month missing. \"n\" specified, imputation performed, .e., component missing, NA_character_ returned. \"Y\" specified, date_imputation \"first\" \"last\" min_dates max_dates specified respectively. Otherwise, NA_character_ returned year component missing. Permitted Values: \"Y\" (year, highest level), \"M\" (month), \"D\" (day), \"h\" (hour), \"m\" (minute), \"s\" (second), \"n\" (none, lowest level) date_imputation value impute day/month datepart missing. character value expected, either format month day specified \"mm-dd\": e.g. \"06-15\" 15th June (year can specified; imputing year \"first\" \"last\" together min_dates max_dates argument can used (see examples).), keyword: \"first\", \"mid\", \"last\" impute first/mid/last day/month. \"mid\" specified, missing components imputed middle possible range: month day missing, imputed \"06-30\" (middle year). day missing, imputed \"15\" (middle month). argument ignored highest_imputation less \"D\". time_imputation value impute time timepart missing. character value expected, either format hour, min sec specified \"hh:mm:ss\": e.g. \"00:00:00\" start day, keyword: \"first\",\"last\" impute start/end day. argument ignored highest_imputation = \"n\". min_dates Minimum dates list dates expected. ensured imputed date specified dates, e.g., imputed adverse event start date first treatment date. dates range possible dates dtc value considered. possible dates defined missing parts dtc date (see example ). ensures non-missing parts dtc date changed. date date-time object expected. example   returns \"2020-11-11T11:11:11\" possible dates \"2020-11\" range \"2020-11-01T00:00:00\" \"2020-11-30T23:59:59\". Therefore \"2020-12-06T12:12:12\" ignored. Returning \"2020-12-06T12:12:12\" changed month although missing (dtc date). date variables (datetime) list time imputed \"00:00:00\". Specifying date variables makes sense date imputed. time imputed, date variables affect result. max_dates Maximum dates list dates expected. ensured imputed date specified dates, e.g., imputed date data cut date. dates range possible dates considered. date date-time object expected. date variables (datetime) list time imputed \"23:59:59\". Specifying date variables makes sense date imputed. time imputed, date variables affect result. preserve Preserve lower level date/time part higher order part missing, e.g. preserve day month missing preserve minute hour missing. example \"2019---07\" return \"2019-06-07 preserve = TRUE (date_imputation = \"mid\"). Permitted Values: TRUE, FALSE","code":"impute_dtc_dtm(   \"2020-11\",   min_dates = list(    ymd_hms(\"2020-12-06T12:12:12\"),    ymd_hms(\"2020-11-11T11:11:11\")   ),   highest_imputation = \"M\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/convert_date_to_dtm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert a Date into a Datetime Object — convert_date_to_dtm","text":"datetime object","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/convert_date_to_dtm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Convert a Date into a Datetime Object — convert_date_to_dtm","text":"Usually computation function can used %>%.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/convert_date_to_dtm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert a Date into a Datetime Object — convert_date_to_dtm","text":"","code":"convert_date_to_dtm(\"2019-07-18T15:25:00\") #> [1] \"2019-07-18 15:25:00 UTC\" convert_date_to_dtm(Sys.time()) #> [1] \"2024-12-20 19:57:32 UTC\" convert_date_to_dtm(as.Date(\"2019-07-18\"), time_imputation = \"23:59:59\") #> [1] \"2019-07-18 23:59:59 UTC\" convert_date_to_dtm(\"2019-07-18\", time_imputation = \"23:59:59\") #> [1] \"2019-07-18 23:59:59 UTC\" convert_date_to_dtm(\"2019-07-18\") #> [1] \"2019-07-18 UTC\""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/convert_dtc_to_dt.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert a Date Character Vector into a Date Object — convert_dtc_to_dt","title":"Convert a Date Character Vector into a Date Object — convert_dtc_to_dt","text":"Convert date character vector (usually '–DTC') Date vector (usually '–DT').","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/convert_dtc_to_dt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert a Date Character Vector into a Date Object — convert_dtc_to_dt","text":"","code":"convert_dtc_to_dt(   dtc,   highest_imputation = \"n\",   date_imputation = \"first\",   min_dates = NULL,   max_dates = NULL,   preserve = FALSE )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/convert_dtc_to_dt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert a Date Character Vector into a Date Object — convert_dtc_to_dt","text":"dtc –DTC date convert. highest_imputation Highest imputation level highest_imputation argument controls components DTC value imputed missing. components specified level imputed. component higher level highest imputation level missing, NA_character_ returned. example, highest_imputation = \"D\" \"2020\" results NA_character_ month missing. \"n\" specified imputation performed, .e., component missing, NA_character_ returned. \"Y\" specified, date_imputation \"first\" \"last\" min_dates max_dates specified respectively. Otherwise, NA_character_ returned year component missing. Permitted Values: \"Y\" (year, highest level), \"M\" (month), \"D\" (day), \"n\" (none, lowest level) date_imputation value impute day/month datepart missing. character value expected, either format month day specified \"mm-dd\": e.g. \"06-15\" 15th June (year can specified; imputing year \"first\" \"last\" together min_dates max_dates argument can used (see examples).), keyword: \"first\", \"mid\", \"last\" impute first/mid/last day/month. \"mid\" specified, missing components imputed middle possible range: month day missing, imputed \"06-30\" (middle year). day missing, imputed \"15\" (middle month). argument ignored highest_imputation less \"D\". min_dates Minimum dates list dates expected. ensured imputed date specified dates, e.g., imputed adverse event start date first treatment date. dates range possible dates dtc value considered. possible dates defined missing parts dtc date (see example ). ensures non-missing parts dtc date changed. date date-time object expected. example   returns \"2020-11-11T11:11:11\" possible dates \"2020-11\" range \"2020-11-01T00:00:00\" \"2020-11-30T23:59:59\". Therefore \"2020-12-06T12:12:12\" ignored. Returning \"2020-12-06T12:12:12\" changed month although missing (dtc date). max_dates Maximum dates list dates expected. ensured imputed date specified dates, e.g., imputed date data cut date. dates range possible dates considered. date date-time object expected. preserve Preserve day month missing day present example \"2019---07\" return \"2019-06-07 preserve = TRUE (date_imputation = \"MID\"). Permitted Values: TRUE, FALSE","code":"impute_dtc_dtm(   \"2020-11\",   min_dates = list(    ymd_hms(\"2020-12-06T12:12:12\"),    ymd_hms(\"2020-11-11T11:11:11\")   ),   highest_imputation = \"M\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/convert_dtc_to_dt.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert a Date Character Vector into a Date Object — convert_dtc_to_dt","text":"date object","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/convert_dtc_to_dt.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Convert a Date Character Vector into a Date Object — convert_dtc_to_dt","text":"Usually computation function can used %>%.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/convert_dtc_to_dt.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert a Date Character Vector into a Date Object — convert_dtc_to_dt","text":"","code":"convert_dtc_to_dt(\"2019-07-18\") #> [1] \"2019-07-18\" convert_dtc_to_dt(\"2019-07\") #> [1] NA"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/convert_dtc_to_dtm.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert a Date Character Vector into a Datetime Object — convert_dtc_to_dtm","title":"Convert a Date Character Vector into a Datetime Object — convert_dtc_to_dtm","text":"Convert date character vector (usually '--DTC') Date vector (usually '--DTM').","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/convert_dtc_to_dtm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert a Date Character Vector into a Datetime Object — convert_dtc_to_dtm","text":"","code":"convert_dtc_to_dtm(   dtc,   highest_imputation = \"h\",   date_imputation = \"first\",   time_imputation = \"first\",   min_dates = NULL,   max_dates = NULL,   preserve = FALSE )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/convert_dtc_to_dtm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert a Date Character Vector into a Datetime Object — convert_dtc_to_dtm","text":"dtc '--DTC' date convert. highest_imputation Highest imputation level highest_imputation argument controls components DTC value imputed missing. components specified level imputed. component higher level highest imputation level missing, NA_character_ returned. example, highest_imputation = \"D\" \"2020\" results NA_character_ month missing. \"n\" specified, imputation performed, .e., component missing, NA_character_ returned. \"Y\" specified, date_imputation \"first\" \"last\" min_dates max_dates specified respectively. Otherwise, NA_character_ returned year component missing. Permitted Values: \"Y\" (year, highest level), \"M\" (month), \"D\" (day), \"h\" (hour), \"m\" (minute), \"s\" (second), \"n\" (none, lowest level) date_imputation value impute day/month datepart missing. character value expected, either format month day specified \"mm-dd\": e.g. \"06-15\" 15th June (year can specified; imputing year \"first\" \"last\" together min_dates max_dates argument can used (see examples).), keyword: \"first\", \"mid\", \"last\" impute first/mid/last day/month. \"mid\" specified, missing components imputed middle possible range: month day missing, imputed \"06-30\" (middle year). day missing, imputed \"15\" (middle month). argument ignored highest_imputation less \"D\". time_imputation value impute time timepart missing. character value expected, either format hour, min sec specified \"hh:mm:ss\": e.g. \"00:00:00\" start day, keyword: \"first\",\"last\" impute start/end day. argument ignored highest_imputation = \"n\". min_dates Minimum dates list dates expected. ensured imputed date specified dates, e.g., imputed adverse event start date first treatment date. dates range possible dates dtc value considered. possible dates defined missing parts dtc date (see example ). ensures non-missing parts dtc date changed. date date-time object expected. example   returns \"2020-11-11T11:11:11\" possible dates \"2020-11\" range \"2020-11-01T00:00:00\" \"2020-11-30T23:59:59\". Therefore \"2020-12-06T12:12:12\" ignored. Returning \"2020-12-06T12:12:12\" changed month although missing (dtc date). date variables (datetime) list time imputed \"00:00:00\". Specifying date variables makes sense date imputed. time imputed, date variables affect result. max_dates Maximum dates list dates expected. ensured imputed date specified dates, e.g., imputed date data cut date. dates range possible dates considered. date date-time object expected. date variables (datetime) list time imputed \"23:59:59\". Specifying date variables makes sense date imputed. time imputed, date variables affect result. preserve Preserve lower level date/time part higher order part missing, e.g. preserve day month missing preserve minute hour missing. example \"2019---07\" return \"2019-06-07 preserve = TRUE (date_imputation = \"mid\"). Permitted Values: TRUE, FALSE","code":"impute_dtc_dtm(   \"2020-11\",   min_dates = list(    ymd_hms(\"2020-12-06T12:12:12\"),    ymd_hms(\"2020-11-11T11:11:11\")   ),   highest_imputation = \"M\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/convert_dtc_to_dtm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert a Date Character Vector into a Datetime Object — convert_dtc_to_dtm","text":"datetime object","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/convert_dtc_to_dtm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Convert a Date Character Vector into a Datetime Object — convert_dtc_to_dtm","text":"Usually computation function can used %>%.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/convert_dtc_to_dtm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert a Date Character Vector into a Datetime Object — convert_dtc_to_dtm","text":"","code":"convert_dtc_to_dtm(\"2019-07-18T15:25:00\") #> [1] \"2019-07-18 15:25:00 UTC\" convert_dtc_to_dtm(\"2019-07-18T00:00:00\") # note Time = 00:00:00 is not printed #> [1] \"2019-07-18 UTC\" convert_dtc_to_dtm(\"2019-07-18\") #> [1] \"2019-07-18 UTC\""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/convert_na_to_blanks.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert NAs Into Blank Strings — convert_na_to_blanks","title":"Convert NAs Into Blank Strings — convert_na_to_blanks","text":"Turn NAs blank strings .","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/convert_na_to_blanks.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert NAs Into Blank Strings — convert_na_to_blanks","text":"","code":"convert_na_to_blanks(x)  # Default S3 method convert_na_to_blanks(x)  # S3 method for class 'character' convert_na_to_blanks(x)  # S3 method for class 'list' convert_na_to_blanks(x)  # S3 method for class 'data.frame' convert_na_to_blanks(x)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/convert_na_to_blanks.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert NAs Into Blank Strings — convert_na_to_blanks","text":"x R object","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/convert_na_to_blanks.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert NAs Into Blank Strings — convert_na_to_blanks","text":"object class input","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/convert_na_to_blanks.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Convert NAs Into Blank Strings — convert_na_to_blanks","text":"default methods simply returns input unchanged. character method turns every instance NA_character_ NA \"\" preserving attributes. given data frame input function keeps non-character columns applies just described logic character attributes labels preserved.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/convert_na_to_blanks.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert NAs Into Blank Strings — convert_na_to_blanks","text":"","code":"library(tibble)  convert_na_to_blanks(c(\"a\", \"b\", NA, \"d\", NA)) #> [1] \"a\" \"b\" \"\"  \"d\" \"\"   df <- tribble(   ~USUBJID,   ~RFICDTC,   \"1001\", \"2000-01-01\",   \"1002\", \"2001-01-01\",   \"1003\",           NA ) print(df) #> # A tibble: 3 × 2 #>   USUBJID RFICDTC    #>   <chr>   <chr>      #> 1 1001    2000-01-01 #> 2 1002    2001-01-01 #> 3 1003    NA         convert_na_to_blanks(df) #> # A tibble: 3 × 2 #>   USUBJID RFICDTC      #>   <chr>   <chr>        #> 1 1001    \"2000-01-01\" #> 2 1002    \"2001-01-01\" #> 3 1003    \"\""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/count_vals.html","id":null,"dir":"Reference","previous_headings":"","what":"Count Number of Observations Where a Variable Equals a Value — count_vals","title":"Count Number of Observations Where a Variable Equals a Value — count_vals","text":"Count number observations variable equals value.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/count_vals.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count Number of Observations Where a Variable Equals a Value — count_vals","text":"","code":"count_vals(var, val)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/count_vals.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count Number of Observations Where a Variable Equals a Value — count_vals","text":"var vector val value","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/count_vals.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Count Number of Observations Where a Variable Equals a Value — count_vals","text":"","code":"library(tibble) library(dplyr, warn.conflicts = FALSE) library(admiral) data <- tribble(   ~USUBJID, ~AVISITN, ~AVALC,   \"1\",      1,        \"PR\",   \"1\",      2,        \"CR\",   \"1\",      3,        \"NE\",   \"1\",      4,        \"CR\",   \"1\",      5,        \"NE\",   \"2\",      1,        \"CR\",   \"2\",      2,        \"PR\",   \"2\",      3,        \"CR\",   \"3\",      1,        \"CR\",   \"4\",      1,        \"CR\",   \"4\",      2,        \"NE\",   \"4\",      3,        \"NE\",   \"4\",      4,        \"CR\",   \"4\",      5,        \"PR\" )  # add variable providing the number of NEs for each subject group_by(data, USUBJID) %>%   mutate(nr_nes = count_vals(var = AVALC, val = \"NE\")) #> # A tibble: 14 × 4 #> # Groups:   USUBJID [4] #>    USUBJID AVISITN AVALC nr_nes #>    <chr>     <dbl> <chr>  <int> #>  1 1             1 PR         2 #>  2 1             2 CR         2 #>  3 1             3 NE         2 #>  4 1             4 CR         2 #>  5 1             5 NE         2 #>  6 2             1 CR         0 #>  7 2             2 PR         0 #>  8 2             3 CR         0 #>  9 3             1 CR         0 #> 10 4             1 CR         2 #> 11 4             2 NE         2 #> 12 4             3 NE         2 #> 13 4             4 CR         2 #> 14 4             5 PR         2"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/country_code_lookup.html","id":null,"dir":"Reference","previous_headings":"","what":"Country Code Lookup — country_code_lookup","title":"Country Code Lookup — country_code_lookup","text":"pre-defined country codes sourced ISO 3166 Standards. See also Wikipedia.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/country_code_lookup.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Country Code Lookup — country_code_lookup","text":"","code":"country_code_lookup"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/country_code_lookup.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Country Code Lookup — country_code_lookup","text":"object class tbl_df (inherits tbl, data.frame) 249 rows 3 columns.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/country_code_lookup.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Country Code Lookup — country_code_lookup","text":"country_code 3-letter ISO 3166-1 county code commonly found ADSL COUNTRY variable. country_name country long name corresponding 3-letter code. country_number numeric code corresponding alphabetic sorting 3-letter codes. see entire table console, run print(country_code_lookup).","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/country_code_lookup.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Country Code Lookup — country_code_lookup","text":"","code":"library(tibble) library(dplyr, warn.conflicts = FALSE) library(lubridate)  # Create reference dataset for periods adsl <- tribble(   ~USUBJID, ~SEX, ~COUNTRY,   \"ST01-01\", \"F\", \"AUT\",   \"ST01-02\", \"M\", \"MWI\",   \"ST01-03\", \"F\", \"GBR\",   \"ST01-04\", \"M\", \"CHE\",   \"ST01-05\", \"M\", \"NOR\",   \"ST01-06\", \"F\", \"JPN\",   \"ST01-07\", \"F\", \"USA\" )  covar <- adsl %>%   derive_vars_merged(     dataset_add = country_code_lookup,     new_vars = exprs(COUNTRYN = country_number, COUNTRYL = country_name),     by_vars = exprs(COUNTRY = country_code)   ) covar #> # A tibble: 7 × 5 #>   USUBJID SEX   COUNTRY COUNTRYN COUNTRYL                                        #>   <chr>   <chr> <chr>      <dbl> <chr>                                           #> 1 ST01-01 F     AUT           16 Austria                                         #> 2 ST01-02 M     MWI          157 Malawi                                          #> 3 ST01-03 F     GBR           80 United Kingdom of Great Britain and Northern I… #> 4 ST01-04 M     CHE           42 Switzerland                                     #> 5 ST01-05 M     NOR          168 Norway                                          #> 6 ST01-06 F     JPN          116 Japan                                           #> 7 ST01-07 F     USA          235 United States of America"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/create_period_dataset.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a Reference Dataset for Subperiods, Periods, or Phases — create_period_dataset","title":"Create a Reference Dataset for Subperiods, Periods, or Phases — create_period_dataset","text":"function creates reference dataset subperiods, periods, phases ADSL dataset. reference dataset can used derive subperiod, period, phase variables like ASPER, ASPRSDT, ASPREDT, APERIOD, APERSDT, APEREDT, TRTA, APHASEN, PHSDTM, PHEDTM, ... OCCDS BDS datasets.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/create_period_dataset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a Reference Dataset for Subperiods, Periods, or Phases — create_period_dataset","text":"","code":"create_period_dataset(   dataset,   new_vars,   subject_keys = get_admiral_option(\"subject_keys\") )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/create_period_dataset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a Reference Dataset for Subperiods, Periods, or Phases — create_period_dataset","text":"dataset Input dataset variables specified new_vars subject_keys arguments expected dataset. element new_vars least one variable form right hand side value must available dataset. new_vars New variables named list variables like exprs(PHSDT = PHwSDT, PHEDT = PHwEDT, APHASE = APHASEw) expected. left hand side elements defines variable output dataset, right hand side defines source variables ADSL dataset CDISC notation. lower case letter \"w\"  used refers phase variable, lower case letters \"xx\" used refers period variable, \"xx\" \"w\" used refers subperiod variable. one type must used, e.g., right hand side values must refer period variables. allowed mix example period subperiod variables. period subperiod variables required, separate reference datasets must created. subject_keys Variables uniquely identify subject list expressions expressions symbols returned exprs() expected.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/create_period_dataset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a Reference Dataset for Subperiods, Periods, or Phases — create_period_dataset","text":"period reference dataset (see \"Details\" section)","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/create_period_dataset.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create a Reference Dataset for Subperiods, Periods, or Phases — create_period_dataset","text":"subject subperiod/period/phase least one source variable NA observation added output dataset. Depending type source variable (subperiod, period, phase) variable ASPER, APERIOD, APHASEN added set number subperiod, period, phase. variables specified new_vars (left hand side) added output dataset set value source variable (right hand side).","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/create_period_dataset.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a Reference Dataset for Subperiods, Periods, or Phases — create_period_dataset","text":"","code":"library(tibble) library(dplyr, warn.conflicts = FALSE) library(lubridate)  # Create reference dataset for periods adsl <- tribble(   ~USUBJID, ~AP01SDT,     ~AP01EDT,     ~AP02SDT,     ~AP02EDT,     ~TRT01A, ~TRT02A,   \"1\",      \"2021-01-04\", \"2021-02-06\", \"2021-02-07\", \"2021-03-07\", \"A\",     \"B\",   \"2\",      \"2021-02-02\", \"2021-03-02\", \"2021-03-03\", \"2021-04-01\", \"B\",     \"A\", ) %>%   mutate(     across(matches(\"AP\\\\d\\\\d[ES]DT\"), ymd)   ) %>%   mutate(     STUDYID = \"xyz\"   )  create_period_dataset(   adsl,   new_vars = exprs(APERSDT = APxxSDT, APEREDT = APxxEDT, TRTA = TRTxxA) ) #> # A tibble: 4 × 6 #>   STUDYID USUBJID APERIOD APERSDT    APEREDT    TRTA  #>   <chr>   <chr>     <int> <date>     <date>     <chr> #> 1 xyz     1             1 2021-01-04 2021-02-06 A     #> 2 xyz     1             2 2021-02-07 2021-03-07 B     #> 3 xyz     2             1 2021-02-02 2021-03-02 B     #> 4 xyz     2             2 2021-03-03 2021-04-01 A      # Create reference dataset for phases adsl <- tribble(   ~USUBJID, ~PH1SDT,      ~PH1EDT,      ~PH2SDT,      ~PH2EDT,      ~APHASE1,    ~APHASE2,   \"1\",      \"2021-01-04\", \"2021-02-06\", \"2021-02-07\", \"2021-03-07\", \"TREATMENT\", \"FUP\",   \"2\",      \"2021-02-02\", \"2021-03-02\", NA,           NA,           \"TREATMENT\", NA ) %>%   mutate(     across(matches(\"PH\\\\d[ES]DT\"), ymd)   ) %>%   mutate(     STUDYID = \"xyz\"   )  create_period_dataset(   adsl,   new_vars = exprs(PHSDT = PHwSDT, PHEDT = PHwEDT, APHASE = APHASEw) ) #> # A tibble: 3 × 6 #>   STUDYID USUBJID APHASEN PHSDT      PHEDT      APHASE    #>   <chr>   <chr>     <int> <date>     <date>     <chr>     #> 1 xyz     1             1 2021-01-04 2021-02-06 TREATMENT #> 2 xyz     1             2 2021-02-07 2021-03-07 FUP       #> 3 xyz     2             1 2021-02-02 2021-03-02 TREATMENT  # Create reference datasets for subperiods adsl <- tribble(   ~USUBJID, ~P01S1SDT,    ~P01S1EDT,    ~P01S2SDT,    ~P01S2EDT,    ~P02S1SDT,    ~P02S1EDT,   \"1\",      \"2021-01-04\", \"2021-01-19\", \"2021-01-20\", \"2021-02-06\", \"2021-02-07\", \"2021-03-07\",   \"2\",      \"2021-02-02\", \"2021-03-02\", NA,           NA,           \"2021-03-03\", \"2021-04-01\" ) %>%   mutate(     across(matches(\"P\\\\d\\\\dS\\\\d[ES]DT\"), ymd)   ) %>%   mutate(     STUDYID = \"xyz\"   )  create_period_dataset(   adsl,   new_vars = exprs(ASPRSDT = PxxSwSDT, ASPREDT = PxxSwEDT) ) #> # A tibble: 5 × 6 #>   STUDYID USUBJID APERIOD ASPER ASPRSDT    ASPREDT    #>   <chr>   <chr>     <int> <int> <date>     <date>     #> 1 xyz     1             1     1 2021-01-04 2021-01-19 #> 2 xyz     1             1     2 2021-01-20 2021-02-06 #> 3 xyz     1             2     1 2021-02-07 2021-03-07 #> 4 xyz     2             1     1 2021-02-02 2021-03-02 #> 5 xyz     2             2     1 2021-03-03 2021-04-01"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/create_query_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a queries dataset as input dataset to the dataset_queries argument in derive_vars_query() — create_query_data","title":"Creates a queries dataset as input dataset to the dataset_queries argument in derive_vars_query() — create_query_data","text":"Creates queries dataset input dataset dataset_queries argument derive_vars_query() function defined Queries Dataset Documentation.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/create_query_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a queries dataset as input dataset to the dataset_queries argument in derive_vars_query() — create_query_data","text":"","code":"create_query_data(queries, version = NULL, get_terms_fun = NULL)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/create_query_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a queries dataset as input dataset to the dataset_queries argument in derive_vars_query() — create_query_data","text":"queries List queries list query() objects expected. version Dictionary version dictionary version used coding terms specified. queries basket (SMQ, SDG, ....) customized query including basket, parameter needs specified. Permitted Values: character string (expected format company-specific) get_terms_fun Function returns terms query specified queries parameter referring basket (.e., definition field set basket_select() object list contains least one basket_select() object) specified function called retrieve terms defining query. function provided admiral company specific, .e., implemented company level. function must return dataset terms defining basket. output dataset must contain following variables. SRCVAR: variable used defining term basket, e.g., AEDECOD TERMCHAR: name term variable SRCVAR referring character TERMNUM numeric id term variable SRCVAR referring numeric GRPNAME: name basket. values must observations. function must provide following parameters basket_select: basket_select() object. version: dictionary version. value specified version create_query_data() call passed parameter. keep_id: set TRUE, output dataset must contain GRPID variable. variable must set numeric id basket. temp_env: temporary environment passed parameter. can used store data used baskets create_query_data() call. example SMQs need read database SMQs can read stored environment first SMQ handled. SMQs terms can retrieved environment instead accessing database .","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/create_query_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Creates a queries dataset as input dataset to the dataset_queries argument in derive_vars_query() — create_query_data","text":"dataset used input dataset dataset_queries argument derive_vars_query()","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/create_query_data.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Creates a queries dataset as input dataset to the dataset_queries argument in derive_vars_query() — create_query_data","text":"query() object listed queries argument, terms belonging query (SRCVAR, TERMCHAR, TERMNUM) determined respect definition field query: definition field query() object basket_select() object, terms read basket database calling function specified get_terms_fun parameter. data frame, terms stored data frame used. list data frames basket_select() objects, terms data frames terms read basket database referenced basket_select() objects collated. following variables (described Queries Dataset Documentation) created: PREFIX: Prefix variables created derive_vars_query() specified prefix element. GRPNAME: Name query specified name element. GRPID: Id query specified id element. id element specified query, variable set NA. id element specified query, variable created. SCOPE: scope query specified scope element basket_select() object. queries defined basket_select() object, variable set NA. none queries defined basket_select() object, variable created. SCOPEN: numeric scope query. set 1 scope broad. Otherwise set 2. add_scope_num element equals FALSE, variable set NA. add_scope_num element equals FALSE baskets none queries basket , variable created. SRCVAR: Name variable used identify terms. TERMCHAR: Value term variable character variable. TERMNUM: Value term variable numeric variable. VERSION: Set value version argument. specified, variable created.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/create_query_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Creates a queries dataset as input dataset to the dataset_queries argument in derive_vars_query() — create_query_data","text":"","code":"library(tibble) library(dplyr, warn.conflicts = FALSE) library(pharmaversesdtm) library(admiral)  # creating a query dataset for a customized query cqterms <- tribble(   ~TERMCHAR, ~TERMNUM,   \"APPLICATION SITE ERYTHEMA\", 10003041L,   \"APPLICATION SITE PRURITUS\", 10003053L ) %>%   mutate(SRCVAR = \"AEDECOD\")  cq <- query(   prefix = \"CQ01\",   name = \"Application Site Issues\",   definition = cqterms )  create_query_data(queries = list(cq)) #> # A tibble: 2 × 5 #>   TERMCHAR                   TERMNUM SRCVAR  PREFIX GRPNAME                 #>   <chr>                        <int> <chr>   <chr>  <chr>                   #> 1 APPLICATION SITE ERYTHEMA 10003041 AEDECOD CQ01   Application Site Issues #> 2 APPLICATION SITE PRURITUS 10003053 AEDECOD CQ01   Application Site Issues  # create a query dataset for SMQs pregsmq <- query(   prefix = \"SMQ02\",   id = auto,   definition = basket_select(     name = \"Pregnancy and neonatal topics (SMQ)\",     scope = \"NARROW\",     type = \"smq\"   ) )  bilismq <- query(   prefix = \"SMQ04\",   definition = basket_select(     id = 20000121L,     scope = \"BROAD\",     type = \"smq\"   ) )  # The get_terms function from pharmaversesdtm is used for this example. # In a real application a company-specific function must be used. create_query_data(   queries = list(pregsmq, bilismq),   get_terms_fun = pharmaversesdtm:::get_terms,   version = \"20.1\" ) #> # A tibble: 43 × 7 #>    TERMCHAR                           SRCVAR GRPNAME  GRPID SCOPE PREFIX VERSION #>    <chr>                              <chr>  <chr>    <int> <chr> <chr>  <chr>   #>  1 Achromotrichia congenital          AEDEC… Pregna… 2.00e7 NARR… SMQ02  20.1    #>  2 Craniosynostosis                   AEDEC… Pregna… 2.00e7 NARR… SMQ02  20.1    #>  3 Hypophosphatasia                   AEDEC… Pregna… 2.00e7 NARR… SMQ02  20.1    #>  4 Congenital pyelocaliectasis        AEDEC… Pregna… 2.00e7 NARR… SMQ02  20.1    #>  5 Uterine contractions during pregn… AEDEC… Pregna… 2.00e7 NARR… SMQ02  20.1    #>  6 Ductus arteriosus premature closu… AEDEC… Pregna… 2.00e7 NARR… SMQ02  20.1    #>  7 Pseudotruncus arteriosus           AEDEC… Pregna… 2.00e7 NARR… SMQ02  20.1    #>  8 Lipomeningocele                    AEDEC… Pregna… 2.00e7 NARR… SMQ02  20.1    #>  9 Macrocephaly                       AEDEC… Pregna… 2.00e7 NARR… SMQ02  20.1    #> 10 Carnitine palmitoyltransferase de… AEDEC… Pregna… 2.00e7 NARR… SMQ02  20.1    #> # ℹ 33 more rows  # create a query dataset for SDGs sdg <- query(   prefix = \"SDG01\",   id = auto,   definition = basket_select(     name = \"5-aminosalicylates for ulcerative colitis\",     scope = NA_character_,     type = \"sdg\"   ) )  # The get_terms function from pharmaversesdtm is used for this example. # In a real application a company-specific function must be used. create_query_data(   queries = list(sdg),   get_terms_fun = pharmaversesdtm:::get_terms,   version = \"2019-09\" ) #> # A tibble: 16 × 7 #>    TERMCHAR                            SRCVAR GRPNAME GRPID SCOPE PREFIX VERSION #>    <chr>                               <chr>  <chr>   <int> <chr> <chr>  <chr>   #>  1 AMINOSALICYLIC ACID                 CMDEC… 5-amin…   220 NA    SDG01  2019-09 #>  2 AMINOSALICYLATE CALCIUM             CMDEC… 5-amin…   220 NA    SDG01  2019-09 #>  3 AMINOSALICYLATE CALCIUM ALUMINIUM   CMDEC… 5-amin…   220 NA    SDG01  2019-09 #>  4 AMINOSALICYLATE SODIUM              CMDEC… 5-amin…   220 NA    SDG01  2019-09 #>  5 SODIUM AMINOSALICYLATE DIHYDRATE    CMDEC… 5-amin…   220 NA    SDG01  2019-09 #>  6 AMINOSALICYLATE SODIUM;AMINOSALICY… CMDEC… 5-amin…   220 NA    SDG01  2019-09 #>  7 SULFASALAZINE                       CMDEC… 5-amin…   220 NA    SDG01  2019-09 #>  8 CALCIUM BENZAMIDOSALICYLATE         CMDEC… 5-amin…   220 NA    SDG01  2019-09 #>  9 OLSALAZINE                          CMDEC… 5-amin…   220 NA    SDG01  2019-09 #> 10 OLSALAZINE SODIUM                   CMDEC… 5-amin…   220 NA    SDG01  2019-09 #> 11 MESALAZINE                          CMDEC… 5-amin…   220 NA    SDG01  2019-09 #> 12 BALSALAZIDE                         CMDEC… 5-amin…   220 NA    SDG01  2019-09 #> 13 BALSALAZIDE SODIUM                  CMDEC… 5-amin…   220 NA    SDG01  2019-09 #> 14 BALSALAZIDE DISODIUM DIHYDRATE      CMDEC… 5-amin…   220 NA    SDG01  2019-09 #> 15 DERSALAZINE                         CMDEC… 5-amin…   220 NA    SDG01  2019-09 #> 16 DERSALAZINE SODIUM                  CMDEC… 5-amin…   220 NA    SDG01  2019-09  # creating a query dataset for a customized query including SMQs # The get_terms function from pharmaversesdtm is used for this example. # In a real application a company-specific function must be used. create_query_data(   queries = list(     query(       prefix = \"CQ03\",       name = \"Special issues of interest\",       definition = list(         basket_select(           name = \"Pregnancy and neonatal topics (SMQ)\",           scope = \"NARROW\",           type = \"smq\"         ),         cqterms       )     )   ),   get_terms_fun = pharmaversesdtm:::get_terms,   version = \"20.1\" ) #> # A tibble: 23 × 6 #>    TERMCHAR                                SRCVAR GRPNAME TERMNUM PREFIX VERSION #>    <chr>                                   <chr>  <chr>     <int> <chr>  <chr>   #>  1 Achromotrichia congenital               AEDEC… Specia…      NA CQ03   20.1    #>  2 Craniosynostosis                        AEDEC… Specia…      NA CQ03   20.1    #>  3 Hypophosphatasia                        AEDEC… Specia…      NA CQ03   20.1    #>  4 Congenital pyelocaliectasis             AEDEC… Specia…      NA CQ03   20.1    #>  5 Uterine contractions during pregnancy   AEDEC… Specia…      NA CQ03   20.1    #>  6 Ductus arteriosus premature closure     AEDEC… Specia…      NA CQ03   20.1    #>  7 Pseudotruncus arteriosus                AEDEC… Specia…      NA CQ03   20.1    #>  8 Lipomeningocele                         AEDEC… Specia…      NA CQ03   20.1    #>  9 Macrocephaly                            AEDEC… Specia…      NA CQ03   20.1    #> 10 Carnitine palmitoyltransferase deficie… AEDEC… Specia…      NA CQ03   20.1    #> # ℹ 13 more rows"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/create_single_dose_dataset.html","id":null,"dir":"Reference","previous_headings":"","what":"Create dataset of single doses — create_single_dose_dataset","title":"Create dataset of single doses — create_single_dose_dataset","text":"Derives dataset single dose aggregate dose information. may necessary e.g. calculating last dose adverse event ADAE deriving total dose parameter ADEX EXDOSFRQ != .","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/create_single_dose_dataset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create dataset of single doses — create_single_dose_dataset","text":"","code":"create_single_dose_dataset(   dataset,   dose_freq = EXDOSFRQ,   start_date = ASTDT,   start_datetime = NULL,   end_date = AENDT,   end_datetime = NULL,   lookup_table = dose_freq_lookup,   lookup_column = CDISC_VALUE,   nominal_time = NULL,   keep_source_vars = expr_c(get_admiral_option(\"subject_keys\"), dose_freq, start_date,     start_datetime, end_date, end_datetime) )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/create_single_dose_dataset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create dataset of single doses — create_single_dose_dataset","text":"dataset Input dataset variables specified dose_freq, start_date, end_date arguments expected dataset. dose_freq dose frequency aggregate dosing frequency used multiple doses row. Permitted Values: defined lookup table. start_date start date date object expected. object contain NA values. Refer derive_vars_dt() impute derive date date character vector date object. start_datetime start date-time date-time object expected. object contain NA values. Refer derive_vars_dtm() impute derive date-time date character vector date object. input dataset contains frequencies refer DOSE_WINDOW equals \"HOUR\" \"MINUTE\", parameter must specified. end_date end date date date-time object expected. object contain NA values. Refer derive_vars_dt() impute derive date date character vector date object. end_datetime end date-time date-time object expected. object contain NA values. Refer derive_vars_dtm() impute derive date-time date character vector date object. input dataset contains frequencies refer DOSE_WINDOW equals \"HOUR\" \"MINUTE\", parameter must specified. lookup_table dose frequency value lookup table table used look dose_freq values determine appropriate multiplier used row generation. lookup table default used, must columns DOSE_WINDOW, DOSE_COUNT, CONVERSION_FACTOR. default table dose_freq_lookup described detail . Permitted Values DOSE_WINDOW: \"MINUTE\", \"HOUR\", \"DAY\", \"WEEK\", \"MONTH\", \"YEAR\" lookup_column dose frequency value column lookup table column lookup_table. nominal_time nominal relative time first dose (NFRLT) Used PK analysis, hours 0 first dose.  can derived (VISITDY - 1) * 24 example. expanded single dose dataset created.  example EXDOFRQ \"QD\" result nominal_time incremented 24 hours expanded record. value can NULL needed. keep_source_vars List variables retained source dataset parameter can specified additional information required output dataset. example EXTRT studies one drug.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/create_single_dose_dataset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create dataset of single doses — create_single_dose_dataset","text":"input dataset single dose per row.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/create_single_dose_dataset.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create dataset of single doses — create_single_dose_dataset","text":"aggregate dose row split multiple rows represent single dose.number completed dose periods start_date start_datetime end_date end_datetime calculated compute_duration multiplied DOSE_COUNT. DOSE_WINDOW values \"WEEK\", \"MONTH\", \"YEAR\", CONVERSION_FACTOR used convert days time object added start_date. Observations dose frequency \"\" copied output dataset unchanged.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/create_single_dose_dataset.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create dataset of single doses — create_single_dose_dataset","text":"","code":"# Example with default lookup  library(lubridate) library(stringr) library(tibble) library(dplyr)  data <- tribble(   ~STUDYID, ~USUBJID, ~EXDOSFRQ, ~ASTDT, ~ASTDTM, ~AENDT, ~AENDTM,   \"STUDY01\", \"P01\", \"Q2D\", ymd(\"2021-01-01\"), ymd_hms(\"2021-01-01 10:30:00\"),   ymd(\"2021-01-07\"), ymd_hms(\"2021-01-07 11:30:00\"),   \"STUDY01\", \"P01\", \"Q3D\", ymd(\"2021-01-08\"), ymd_hms(\"2021-01-08 12:00:00\"),   ymd(\"2021-01-14\"), ymd_hms(\"2021-01-14 14:00:00\"),   \"STUDY01\", \"P01\", \"EVERY 2 WEEKS\", ymd(\"2021-01-15\"), ymd_hms(\"2021-01-15 09:57:00\"),   ymd(\"2021-01-29\"), ymd_hms(\"2021-01-29 10:57:00\") )  create_single_dose_dataset(data) #> # A tibble: 9 × 5 #>   STUDYID USUBJID EXDOSFRQ ASTDT      AENDT      #>   <chr>   <chr>   <chr>    <date>     <date>     #> 1 STUDY01 P01     ONCE     2021-01-01 2021-01-01 #> 2 STUDY01 P01     ONCE     2021-01-03 2021-01-03 #> 3 STUDY01 P01     ONCE     2021-01-05 2021-01-05 #> 4 STUDY01 P01     ONCE     2021-01-07 2021-01-07 #> 5 STUDY01 P01     ONCE     2021-01-08 2021-01-08 #> 6 STUDY01 P01     ONCE     2021-01-11 2021-01-11 #> 7 STUDY01 P01     ONCE     2021-01-14 2021-01-14 #> 8 STUDY01 P01     ONCE     2021-01-15 2021-01-15 #> 9 STUDY01 P01     ONCE     2021-01-29 2021-01-29  # Example with custom lookup  custom_lookup <- tribble(   ~Value,   ~DOSE_COUNT, ~DOSE_WINDOW, ~CONVERSION_FACTOR,   \"Q30MIN\", (1 / 30),    \"MINUTE\",                      1,   \"Q90MIN\", (1 / 90),    \"MINUTE\",                      1 )  data <- tribble(   ~STUDYID, ~USUBJID, ~EXDOSFRQ, ~ASTDT, ~ASTDTM, ~AENDT, ~AENDTM,   \"STUDY01\", \"P01\", \"Q30MIN\", ymd(\"2021-01-01\"), ymd_hms(\"2021-01-01T06:00:00\"),   ymd(\"2021-01-01\"), ymd_hms(\"2021-01-01T07:00:00\"),   \"STUDY02\", \"P02\", \"Q90MIN\", ymd(\"2021-01-01\"), ymd_hms(\"2021-01-01T06:00:00\"),   ymd(\"2021-01-01\"), ymd_hms(\"2021-01-01T09:00:00\") )  create_single_dose_dataset(data,   lookup_table = custom_lookup,   lookup_column = Value,   start_datetime = ASTDTM,   end_datetime = AENDTM ) #> # A tibble: 6 × 7 #>   STUDYID USUBJID EXDOSFRQ ASTDT      ASTDTM              AENDT      #>   <chr>   <chr>   <chr>    <date>     <dttm>              <date>     #> 1 STUDY01 P01     ONCE     2021-01-01 2021-01-01 06:00:00 2021-01-01 #> 2 STUDY01 P01     ONCE     2021-01-01 2021-01-01 06:30:00 2021-01-01 #> 3 STUDY01 P01     ONCE     2021-01-01 2021-01-01 07:00:00 2021-01-01 #> 4 STUDY02 P02     ONCE     2021-01-01 2021-01-01 06:00:00 2021-01-01 #> 5 STUDY02 P02     ONCE     2021-01-01 2021-01-01 07:30:00 2021-01-01 #> 6 STUDY02 P02     ONCE     2021-01-01 2021-01-01 09:00:00 2021-01-01 #> # ℹ 1 more variable: AENDTM <dttm> # Example with nominal time  data <- tribble(   ~STUDYID, ~USUBJID, ~EXDOSFRQ, ~NFRLT, ~ASTDT, ~ASTDTM, ~AENDT, ~AENDTM,   \"STUDY01\", \"P01\", \"BID\", 0, ymd(\"2021-01-01\"), ymd_hms(\"2021-01-01 08:00:00\"),   ymd(\"2021-01-07\"), ymd_hms(\"2021-01-07 20:00:00\"),   \"STUDY01\", \"P01\", \"BID\", 168, ymd(\"2021-01-08\"), ymd_hms(\"2021-01-08 08:00:00\"),   ymd(\"2021-01-14\"), ymd_hms(\"2021-01-14 20:00:00\"),   \"STUDY01\", \"P01\", \"BID\", 336, ymd(\"2021-01-15\"), ymd_hms(\"2021-01-15 08:00:00\"),   ymd(\"2021-01-29\"), ymd_hms(\"2021-01-29 20:00:00\") )  create_single_dose_dataset(data,   dose_freq = EXDOSFRQ,   start_date = ASTDT,   start_datetime = ASTDTM,   end_date = AENDT,   end_datetime = AENDTM,   lookup_table = dose_freq_lookup,   lookup_column = CDISC_VALUE,   nominal_time = NFRLT,   keep_source_vars = exprs(     USUBJID, EXDOSFRQ, ASTDT, ASTDTM, AENDT, AENDTM, NFRLT   ) ) #> # A tibble: 58 × 7 #>    USUBJID EXDOSFRQ ASTDT      ASTDTM              AENDT      #>    <chr>   <chr>    <date>     <dttm>              <date>     #>  1 P01     ONCE     2021-01-01 2021-01-01 08:00:00 2021-01-01 #>  2 P01     ONCE     2021-01-01 2021-01-01 20:00:00 2021-01-01 #>  3 P01     ONCE     2021-01-02 2021-01-02 08:00:00 2021-01-02 #>  4 P01     ONCE     2021-01-02 2021-01-02 20:00:00 2021-01-02 #>  5 P01     ONCE     2021-01-03 2021-01-03 08:00:00 2021-01-03 #>  6 P01     ONCE     2021-01-03 2021-01-03 20:00:00 2021-01-03 #>  7 P01     ONCE     2021-01-04 2021-01-04 08:00:00 2021-01-04 #>  8 P01     ONCE     2021-01-04 2021-01-04 20:00:00 2021-01-04 #>  9 P01     ONCE     2021-01-05 2021-01-05 08:00:00 2021-01-05 #> 10 P01     ONCE     2021-01-05 2021-01-05 20:00:00 2021-01-05 #> # ℹ 48 more rows #> # ℹ 2 more variables: AENDTM <dttm>, NFRLT <dbl>  # Example - derive a single dose dataset with imputations  # For either single drug administration records, or multiple drug administration # records covering a range of dates, fill-in of missing treatment end datetime # `EXENDTC` by substitution with an acceptable alternate, for example date of # death, date of datacut may be required. This example shows the # maximum possible number of single dose records to be derived. The example # requires the date of datacut `DCUTDT` to be specified correctly, or # if not appropriate to use `DCUTDT` as missing treatment end data and missing # treatment end datetime could set equal to treatment start date and treatment # start datetime. ADSL variables `DTHDT` and `DCUTDT` are preferred for # imputation use. # # All available trial treatments are included, allowing multiple different # last dose variables to be created in for example `use_ad_template(\"ADAE\")` # if required.  adsl <- tribble(   ~STUDYID, ~USUBJID, ~DTHDT,   \"01\", \"1211\", ymd(\"2013-01-14\"),   \"01\", \"1083\", ymd(\"2013-08-02\"),   \"01\", \"1445\", ymd(\"2014-11-01\"),   \"01\", \"1015\", NA,   \"01\", \"1023\", NA )  ex <- tribble(   ~STUDYID, ~USUBJID, ~EXSEQ, ~EXTRT, ~EXDOSE, ~EXDOSU, ~EXDOSFRQ, ~EXSTDTC, ~EXENDTC,   \"01\", \"1015\", 1, \"PLAC\", 0, \"mg\", \"QD\", \"2014-01-02\", \"2014-01-16\",   \"01\", \"1015\", 2, \"PLAC\", 0, \"mg\", \"QD\", \"2014-06-17\", \"2014-06-18\",   \"01\", \"1015\", 3, \"PLAC\", 0, \"mg\", \"QD\", \"2014-06-19\", NA_character_,   \"01\", \"1023\", 1, \"PLAC\", 0, \"mg\", \"QD\", \"2012-08-05\", \"2012-08-27\",   \"01\", \"1023\", 2, \"PLAC\", 0, \"mg\", \"QD\", \"2012-08-28\", \"2012-09-01\",   \"01\", \"1211\", 1, \"XANO\", 54, \"mg\", \"QD\", \"2012-11-15\", \"2012-11-28\",   \"01\", \"1211\", 2, \"XANO\", 54, \"mg\", \"QD\", \"2012-11-29\", NA_character_,   \"01\", \"1445\", 1, \"PLAC\", 0, \"mg\", \"QD\", \"2014-05-11\", \"2014-05-25\",   \"01\", \"1445\", 2, \"PLAC\", 0, \"mg\", \"QD\", \"2014-05-26\", \"2014-11-01\",   \"01\", \"1083\", 1, \"PLAC\", 0, \"mg\", \"QD\", \"2013-07-22\", \"2013-08-01\" )  adsl_death <- adsl %>%   mutate(     DTHDTM = convert_date_to_dtm(DTHDT),     # Remove `DCUT` setup line below if ADSL `DCUTDT` is populated.     DCUTDT = convert_dtc_to_dt(\"2015-03-06\"), # Example only, enter date.     DCUTDTM = convert_date_to_dtm(DCUTDT)   )  # Select valid dose records, non-missing `EXSTDTC` and `EXDOSE`. ex_mod <- ex %>%   filter(!is.na(EXSTDTC) & !is.na(EXDOSE)) %>%   derive_vars_merged(adsl_death, by_vars = get_admiral_option(\"subject_keys\")) %>%   # Example, set up missing `EXDOSFRQ` as QD daily dosing regime.   # Replace with study dosing regime per trial treatment.   mutate(EXDOSFRQ = if_else(is.na(EXDOSFRQ), \"QD\", EXDOSFRQ)) %>%   # Create EXxxDTM variables and replace missing `EXENDTM`.   derive_vars_dtm(     dtc = EXSTDTC,     new_vars_prefix = \"EXST\",     date_imputation = \"first\",     time_imputation = \"first\",     flag_imputation = \"none\",   ) %>%   derive_vars_dtm_to_dt(exprs(EXSTDTM)) %>%   derive_vars_dtm(     dtc = EXENDTC,     new_vars_prefix = \"EXEN\",     # Maximum imputed treatment end date must not be not greater than     # date of death or after the datacut date.     max_dates = exprs(DTHDTM, DCUTDTM),     date_imputation = \"last\",     time_imputation = \"last\",     flag_imputation = \"none\",     highest_imputation = \"Y\",   ) %>%   derive_vars_dtm_to_dt(exprs(EXENDTM)) %>%   # Select only unique values.   # Removes duplicated records before final step.   distinct(     STUDYID, USUBJID, EXTRT, EXDOSE, EXDOSFRQ, DCUTDT, DTHDT, EXSTDT,     EXSTDTM, EXENDT, EXENDTM, EXSTDTC, EXENDTC   )  create_single_dose_dataset(   ex_mod,   start_date = EXSTDT,   start_datetime = EXSTDTM,   end_date = EXENDT,   end_datetime = EXENDTM,   keep_source_vars = exprs(     STUDYID, USUBJID, EXTRT, EXDOSE, EXDOSFRQ,     DCUTDT, EXSTDT, EXSTDTM, EXENDT, EXENDTM, EXSTDTC, EXENDTC   ) ) #> # A tibble: 553 × 12 #>    STUDYID USUBJID EXTRT EXDOSE EXDOSFRQ DCUTDT     EXSTDT     #>    <chr>   <chr>   <chr>  <dbl> <chr>    <date>     <date>     #>  1 01      1015    PLAC       0 ONCE     2015-03-06 2014-01-02 #>  2 01      1015    PLAC       0 ONCE     2015-03-06 2014-01-03 #>  3 01      1015    PLAC       0 ONCE     2015-03-06 2014-01-04 #>  4 01      1015    PLAC       0 ONCE     2015-03-06 2014-01-05 #>  5 01      1015    PLAC       0 ONCE     2015-03-06 2014-01-06 #>  6 01      1015    PLAC       0 ONCE     2015-03-06 2014-01-07 #>  7 01      1015    PLAC       0 ONCE     2015-03-06 2014-01-08 #>  8 01      1015    PLAC       0 ONCE     2015-03-06 2014-01-09 #>  9 01      1015    PLAC       0 ONCE     2015-03-06 2014-01-10 #> 10 01      1015    PLAC       0 ONCE     2015-03-06 2014-01-11 #> # ℹ 543 more rows #> # ℹ 5 more variables: EXSTDTM <dttm>, EXENDT <date>, EXENDTM <dttm>, #> #   EXSTDTC <chr>, EXENDTC <chr>"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/date_source.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a date_source object — date_source","title":"Create a date_source object — date_source","text":"date_source() function superseded favor derive_vars_extreme_event(). Create date_source object input derive_var_extreme_dt() derive_var_extreme_dtm().","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/date_source.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a date_source object — date_source","text":"","code":"date_source(dataset_name, filter = NULL, date, set_values_to = NULL)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/date_source.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a date_source object — date_source","text":"dataset_name name dataset, .e. string, used search date. filter unquoted condition filtering dataset. date variable expression providing date. date datetime can specified. unquoted symbol expression expected. set_values_to Variables set","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/date_source.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a date_source object — date_source","text":"object class date_source.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/date_source.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a date_source object — date_source","text":"","code":"# treatment end date from ADSL trt_end_date <- date_source(   dataset_name = \"adsl\",   date = TRTEDT )  # lab date from LB where assessment was taken, i.e. not \"NOT DONE\" lb_date <- date_source(   dataset_name = \"lb\",   filter = LBSTAT != \"NOT DONE\" | is.na(LBSTAT),   date = convert_dtc_to_dt(LBDTC) )  # death date from ADSL including traceability variables death_date <- date_source(   dataset_name = \"adsl\",   date = DTHDT,   set_values_to = exprs(     LALVDOM = \"ADSL\",     LALVVAR = \"DTHDT\"   ) )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/default_qtc_paramcd.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Default Parameter Code for Corrected QT — default_qtc_paramcd","title":"Get Default Parameter Code for Corrected QT — default_qtc_paramcd","text":"Get Default Parameter Code Corrected QT","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/default_qtc_paramcd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Default Parameter Code for Corrected QT — default_qtc_paramcd","text":"","code":"default_qtc_paramcd(method)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/default_qtc_paramcd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Default Parameter Code for Corrected QT — default_qtc_paramcd","text":"method Method used QT correction Permitted Values: \"Bazett\", \"Fridericia\", \"Sagie\"","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/default_qtc_paramcd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Default Parameter Code for Corrected QT — default_qtc_paramcd","text":"\"QTCBR\" method \"Bazett\", \"QTCFR\" \"Fridericia\" \"QTLCR\" \"Sagie\". error otherwise.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/default_qtc_paramcd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get Default Parameter Code for Corrected QT — default_qtc_paramcd","text":"","code":"default_qtc_paramcd(\"Sagie\") #> $PARAMCD #> [1] \"QTLCR\" #>"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derivation_slice.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a derivation_slice Object — derivation_slice","title":"Create a derivation_slice Object — derivation_slice","text":"Create derivation_slice object input slice_derivation().","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derivation_slice.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a derivation_slice Object — derivation_slice","text":"","code":"derivation_slice(filter, args = NULL)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derivation_slice.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a derivation_slice Object — derivation_slice","text":"filter unquoted condition defining observations slice args Arguments derivation used slice params() object expected.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derivation_slice.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a derivation_slice Object — derivation_slice","text":"object class derivation_slice","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_basetype_records.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive Basetype Variable — derive_basetype_records","title":"Derive Basetype Variable — derive_basetype_records","text":"Baseline Type BASETYPE needed one definition baseline given Analysis Parameter PARAM dataset.  given parameter, Baseline Value BASE populated, one definition baseline, BASETYPE must non-null records type parameter. value BASETYPE refers definition baseline characterizes value BASE row.  Please see section 4.2.1.6 ADaM Implementation Guide, version 1.3 background.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_basetype_records.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive Basetype Variable — derive_basetype_records","text":"","code":"derive_basetype_records(dataset, basetypes)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_basetype_records.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive Basetype Variable — derive_basetype_records","text":"dataset Input dataset variables specified basetypes argument expected dataset. basetypes named list expressions created using rlang::exprs() function names corresponds values newly created BASETYPE variables expressions used subset input dataset.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_basetype_records.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive Basetype Variable — derive_basetype_records","text":"input dataset variable BASETYPE added","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_basetype_records.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Derive Basetype Variable — derive_basetype_records","text":"Adds BASETYPE variable dataset duplicates records based upon provided conditions. element basetypes input dataset subset based upon provided expression BASETYPE variable set name expression. , subsets stacked. Records match condition kept BASETYPE set NA.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_basetype_records.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive Basetype Variable — derive_basetype_records","text":"","code":"library(tibble) library(dplyr, warn.conflicts = FALSE)  bds <- tribble(   ~USUBJID, ~EPOCH,         ~PARAMCD,  ~ASEQ, ~AVAL,   \"P01\",    \"RUN-IN\",       \"PARAM01\",     1,  10.0,   \"P01\",    \"RUN-IN\",       \"PARAM01\",     2,   9.8,   \"P01\",    \"DOUBLE-BLIND\", \"PARAM01\",     3,   9.2,   \"P01\",    \"DOUBLE-BLIND\", \"PARAM01\",     4,  10.1,   \"P01\",    \"OPEN-LABEL\",   \"PARAM01\",     5,  10.4,   \"P01\",    \"OPEN-LABEL\",   \"PARAM01\",     6,   9.9,   \"P02\",    \"RUN-IN\",       \"PARAM01\",     1,  12.1,   \"P02\",    \"DOUBLE-BLIND\", \"PARAM01\",     2,  10.2,   \"P02\",    \"DOUBLE-BLIND\", \"PARAM01\",     3,  10.8,   \"P02\",    \"OPEN-LABEL\",   \"PARAM01\",     4,  11.4,   \"P02\",    \"OPEN-LABEL\",   \"PARAM01\",     5,  10.8 )  bds_with_basetype <- derive_basetype_records(   dataset = bds,   basetypes = exprs(     \"RUN-IN\" = EPOCH %in% c(\"RUN-IN\", \"STABILIZATION\", \"DOUBLE-BLIND\", \"OPEN-LABEL\"),     \"DOUBLE-BLIND\" = EPOCH %in% c(\"DOUBLE-BLIND\", \"OPEN-LABEL\"),     \"OPEN-LABEL\" = EPOCH == \"OPEN-LABEL\"   ) )   # Below print statement will print all 23 records in the data frame # bds_with_basetype print(bds_with_basetype, n = Inf) #> # A tibble: 23 × 6 #>    USUBJID EPOCH        PARAMCD  ASEQ  AVAL BASETYPE     #>    <chr>   <chr>        <chr>   <dbl> <dbl> <chr>        #>  1 P01     RUN-IN       PARAM01     1  10   RUN-IN       #>  2 P01     RUN-IN       PARAM01     2   9.8 RUN-IN       #>  3 P01     DOUBLE-BLIND PARAM01     3   9.2 RUN-IN       #>  4 P01     DOUBLE-BLIND PARAM01     4  10.1 RUN-IN       #>  5 P01     OPEN-LABEL   PARAM01     5  10.4 RUN-IN       #>  6 P01     OPEN-LABEL   PARAM01     6   9.9 RUN-IN       #>  7 P02     RUN-IN       PARAM01     1  12.1 RUN-IN       #>  8 P02     DOUBLE-BLIND PARAM01     2  10.2 RUN-IN       #>  9 P02     DOUBLE-BLIND PARAM01     3  10.8 RUN-IN       #> 10 P02     OPEN-LABEL   PARAM01     4  11.4 RUN-IN       #> 11 P02     OPEN-LABEL   PARAM01     5  10.8 RUN-IN       #> 12 P01     DOUBLE-BLIND PARAM01     3   9.2 DOUBLE-BLIND #> 13 P01     DOUBLE-BLIND PARAM01     4  10.1 DOUBLE-BLIND #> 14 P01     OPEN-LABEL   PARAM01     5  10.4 DOUBLE-BLIND #> 15 P01     OPEN-LABEL   PARAM01     6   9.9 DOUBLE-BLIND #> 16 P02     DOUBLE-BLIND PARAM01     2  10.2 DOUBLE-BLIND #> 17 P02     DOUBLE-BLIND PARAM01     3  10.8 DOUBLE-BLIND #> 18 P02     OPEN-LABEL   PARAM01     4  11.4 DOUBLE-BLIND #> 19 P02     OPEN-LABEL   PARAM01     5  10.8 DOUBLE-BLIND #> 20 P01     OPEN-LABEL   PARAM01     5  10.4 OPEN-LABEL   #> 21 P01     OPEN-LABEL   PARAM01     6   9.9 OPEN-LABEL   #> 22 P02     OPEN-LABEL   PARAM01     4  11.4 OPEN-LABEL   #> 23 P02     OPEN-LABEL   PARAM01     5  10.8 OPEN-LABEL    count(bds_with_basetype, BASETYPE, name = \"Number of Records\") #> # A tibble: 3 × 2 #>   BASETYPE     `Number of Records` #>   <chr>                      <int> #> 1 DOUBLE-BLIND                   8 #> 2 OPEN-LABEL                     4 #> 3 RUN-IN                        11  # An example where all parameter records need to be included for 2 different # baseline type derivations (such as LAST and WORST) bds <- tribble(   ~USUBJID, ~EPOCH,         ~PARAMCD,  ~ASEQ, ~AVAL,   \"P01\",    \"RUN-IN\",       \"PARAM01\",     1,  10.0,   \"P01\",    \"RUN-IN\",       \"PARAM01\",     2,   9.8,   \"P01\",    \"DOUBLE-BLIND\", \"PARAM01\",     3,   9.2,   \"P01\",    \"DOUBLE-BLIND\", \"PARAM01\",     4,  10.1 )  bds_with_basetype <- derive_basetype_records(   dataset = bds,   basetypes = exprs(     \"LAST\" = TRUE,     \"WORST\" = TRUE   ) )  print(bds_with_basetype, n = Inf) #> # A tibble: 8 × 6 #>   USUBJID EPOCH        PARAMCD  ASEQ  AVAL BASETYPE #>   <chr>   <chr>        <chr>   <dbl> <dbl> <chr>    #> 1 P01     RUN-IN       PARAM01     1  10   LAST     #> 2 P01     RUN-IN       PARAM01     2   9.8 LAST     #> 3 P01     DOUBLE-BLIND PARAM01     3   9.2 LAST     #> 4 P01     DOUBLE-BLIND PARAM01     4  10.1 LAST     #> 5 P01     RUN-IN       PARAM01     1  10   WORST    #> 6 P01     RUN-IN       PARAM01     2   9.8 WORST    #> 7 P01     DOUBLE-BLIND PARAM01     3   9.2 WORST    #> 8 P01     DOUBLE-BLIND PARAM01     4  10.1 WORST     count(bds_with_basetype, BASETYPE, name = \"Number of Records\") #> # A tibble: 2 × 2 #>   BASETYPE `Number of Records` #>   <chr>                  <int> #> 1 LAST                       4 #> 2 WORST                      4"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_expected_records.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive Expected Records — derive_expected_records","title":"Derive Expected Records — derive_expected_records","text":"Add expected records new observations 'group' dataset contains missing observations.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_expected_records.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive Expected Records — derive_expected_records","text":"","code":"derive_expected_records(   dataset,   dataset_ref,   by_vars = NULL,   set_values_to = NULL )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_expected_records.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive Expected Records — derive_expected_records","text":"dataset Input dataset variables specified dataset_ref by_vars arguments expected dataset. dataset_ref Expected observations dataset Data frame expected observations, e.g., expected combinations PARAMCD, PARAM, AVISIT, AVISITN, ... by_vars Grouping variables group defined by_vars observations dataset_ref added output dataset corresponding observation input dataset. Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) set_values_to Variables set specified variables set specified values new observations. list variable name-value pairs expected. LHS refers variable. RHS refers values set variable. can string, symbol, numeric value, NA, expressions, e.g., exprs(PARAMCD =   \"TDOSE\", PARCAT1 = \"OVERALL\").","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_expected_records.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive Expected Records — derive_expected_records","text":"input dataset missed expected observations added by_vars. Note, variable populated new parameter rows specified by_vars set_values_to.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_expected_records.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Derive Expected Records — derive_expected_records","text":"group (variables specified by_vars parameter), records dataset_ref missing input dataset added output dataset.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_expected_records.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive Expected Records — derive_expected_records","text":"","code":"library(tibble)  adqs <- tribble(   ~USUBJID, ~PARAMCD, ~AVISITN, ~AVISIT, ~AVAL,   \"1\",      \"a\",             1, \"WEEK 1\",   10,   \"1\",      \"b\",             1, \"WEEK 1\",   11,   \"2\",      \"a\",             2, \"WEEK 2\",   12,   \"2\",      \"b\",             2, \"WEEK 2\",   14 )  # Example 1. visit variables are parameter independent parm_visit_ref <- tribble(   ~AVISITN, ~AVISIT,   1,        \"WEEK 1\",   2,        \"WEEK 2\" )  derive_expected_records(   dataset = adqs,   dataset_ref = parm_visit_ref,   by_vars = exprs(USUBJID, PARAMCD),   set_values_to = exprs(DTYPE = \"DERIVED\") ) #> # A tibble: 8 × 6 #>   USUBJID PARAMCD AVISITN AVISIT  AVAL DTYPE   #>   <chr>   <chr>     <dbl> <chr>  <dbl> <chr>   #> 1 1       a             1 WEEK 1    10 NA      #> 2 1       a             2 WEEK 2    NA DERIVED #> 3 1       b             1 WEEK 1    11 NA      #> 4 1       b             2 WEEK 2    NA DERIVED #> 5 2       a             1 WEEK 1    NA DERIVED #> 6 2       a             2 WEEK 2    12 NA      #> 7 2       b             1 WEEK 1    NA DERIVED #> 8 2       b             2 WEEK 2    14 NA       # Example 2. visit variables are parameter dependent parm_visit_ref <- tribble(   ~PARAMCD, ~AVISITN, ~AVISIT,   \"a\",             1, \"WEEK 1\",   \"a\",             2, \"WEEK 2\",   \"b\",             1, \"WEEK 1\" )  derive_expected_records(   dataset = adqs,   dataset_ref = parm_visit_ref,   by_vars = exprs(USUBJID, PARAMCD),   set_values_to = exprs(DTYPE = \"DERIVED\") ) #> # A tibble: 7 × 6 #>   USUBJID PARAMCD AVISITN AVISIT  AVAL DTYPE   #>   <chr>   <chr>     <dbl> <chr>  <dbl> <chr>   #> 1 1       a             1 WEEK 1    10 NA      #> 2 1       a             2 WEEK 2    NA DERIVED #> 3 1       b             1 WEEK 1    11 NA      #> 4 2       a             1 WEEK 1    NA DERIVED #> 5 2       a             2 WEEK 2    12 NA      #> 6 2       b             1 WEEK 1    NA DERIVED #> 7 2       b             2 WEEK 2    14 NA"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_extreme_event.html","id":null,"dir":"Reference","previous_headings":"","what":"Add the Worst or Best Observation for Each By Group as New Records — derive_extreme_event","title":"Add the Worst or Best Observation for Each By Group as New Records — derive_extreme_event","text":"Add first available record events group new records, variables selected observation kept. can used selecting extreme observation series user-defined events. distinguishes derive_extreme_event() derive_extreme_records(), extreme records derived based certain order existing variables.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_extreme_event.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add the Worst or Best Observation for Each By Group as New Records — derive_extreme_event","text":"","code":"derive_extreme_event(   dataset = NULL,   by_vars,   events,   tmp_event_nr_var = NULL,   order,   mode,   source_datasets = NULL,   check_type = \"warning\",   set_values_to = NULL,   keep_source_vars = exprs(everything()) )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_extreme_event.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add the Worst or Best Observation for Each By Group as New Records — derive_extreme_event","text":"dataset Input dataset variables specified by_vars order arguments expected dataset. by_vars Grouping variables Default: NULL Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) events Conditions new values defining events list event() event_joined() objects expected. observations listed events considered deriving extreme event. multiple records meet filter condition, take first record sorted order. data grouped by_vars, .e., summary functions like () () can used condition. event_joined() events observations selected calling filter_joined(). condition field passed filter_join argument. tmp_event_nr_var Temporary event number variable specified variable added source datasets set number event selecting records event. can used order determine record used records one event selected. variable included output dataset. order Sort order particular event events one observation, within event group, records ordered specified order. handling NAs sorting variables see Sort Order. Permitted Values: list expressions created exprs(), e.g., exprs(ADT, desc(AVAL)) mode Selection mode (first last) particular event events one observation, \"first\"/\"last\" used select first/last record type event sorting order. Permitted Values: \"first\", \"last\" source_datasets Source datasets named list datasets expected. dataset_name field event() event_joined() refers dataset provided list. check_type Check uniqueness? \"warning\" \"error\" specified, specified message issued observations input dataset unique respect variables order. Default: \"warning\" Permitted Values: \"none\", \"warning\", \"error\" set_values_to Variables set specified variables set specified values new observations. Set list variables specified value new records LHS refer variable. RHS refers values set variable. can string, symbol, numeric value, expression NA. summary functions used, values summarized variables specified by_vars. example:   keep_source_vars Variables keep source dataset event specified variables kept selected observations. variables specified by_vars created set_values_to always kept. keep_source_vars field event take precedence value keep_source_vars argument. Permitted Values: list expressions element symbol tidyselect expression, e.g., exprs(VISIT, VISITNUM, starts_with(\"RS\")).","code":"set_values_to = exprs(     AVAL = sum(AVAL),     DTYPE = \"AVERAGE\",   )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_extreme_event.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add the Worst or Best Observation for Each By Group as New Records — derive_extreme_event","text":"input dataset best worst observation group added new observations.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_extreme_event.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Add the Worst or Best Observation for Each By Group as New Records — derive_extreme_event","text":"event select observations consider: event class event, observations source dataset restricted condition first last (mode) observation per group (by_vars) selected. event class event_joined, filter_joined() called select observations. variables specified set_values_to field event added selected observations. variable specified tmp_event_nr_var added set number event. variables specified keep_source_vars field event, variables (by_vars) variables created set_values_to kept. keep_source_vars = NULL used event derive_extreme_event() value keep_source_vars argument derive_extreme_event() used. selected observations bound together. group (respect variables specified by_vars parameter) first last observation (respect order specified order parameter mode specified mode parameter) selected. variables specified set_values_to parameter added selected observations. observations added input dataset.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_extreme_event.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add the Worst or Best Observation for Each By Group as New Records — derive_extreme_event","text":"","code":"library(tibble) library(dplyr) library(lubridate)  adqs <- tribble(   ~USUBJID, ~PARAMCD,       ~AVALC,        ~ADY,   \"1\",      \"NO SLEEP\",     \"N\",              1,   \"1\",      \"WAKE UP\",      \"N\",              2,   \"1\",      \"FALL ASLEEP\",  \"N\",              3,   \"2\",      \"NO SLEEP\",     \"N\",              1,   \"2\",      \"WAKE UP\",      \"Y\",              2,   \"2\",      \"WAKE UP\",      \"Y\",              3,   \"2\",      \"FALL ASLEEP\",  \"N\",              4,   \"3\",      \"NO SLEEP\",     NA_character_,    1 )  # Add a new record for each USUBJID storing the the worst sleeping problem. derive_extreme_event(   adqs,   by_vars = exprs(USUBJID),   events = list(     event(       condition = PARAMCD == \"NO SLEEP\" & AVALC == \"Y\",       set_values_to = exprs(AVALC = \"No sleep\", AVAL = 1)     ),     event(       condition = PARAMCD == \"WAKE UP\" & AVALC == \"Y\",       set_values_to = exprs(AVALC = \"Waking up more than three times\", AVAL = 2)     ),     event(       condition = PARAMCD == \"FALL ASLEEP\" & AVALC == \"Y\",       set_values_to = exprs(AVALC = \"More than 30 mins to fall asleep\", AVAL = 3)     ),     event(       condition = all(AVALC == \"N\"),       set_values_to = exprs(         AVALC = \"No sleeping problems\", AVAL = 4       )     ),     event(       condition = TRUE,       set_values_to = exprs(AVALC = \"Missing\", AVAL = 99)     )   ),   tmp_event_nr_var = event_nr,   order = exprs(event_nr, desc(ADY)),   mode = \"first\",   set_values_to = exprs(     PARAMCD = \"WSP\",     PARAM = \"Worst Sleeping Problems\"   ) ) #> # A tibble: 11 × 6 #>    USUBJID PARAMCD     AVALC                             ADY  AVAL PARAM         #>    <chr>   <chr>       <chr>                           <dbl> <dbl> <chr>         #>  1 1       NO SLEEP    N                                   1    NA NA            #>  2 1       WAKE UP     N                                   2    NA NA            #>  3 1       FALL ASLEEP N                                   3    NA NA            #>  4 2       NO SLEEP    N                                   1    NA NA            #>  5 2       WAKE UP     Y                                   2    NA NA            #>  6 2       WAKE UP     Y                                   3    NA NA            #>  7 2       FALL ASLEEP N                                   4    NA NA            #>  8 3       NO SLEEP    NA                                  1    NA NA            #>  9 1       WSP         No sleeping problems                3     4 Worst Sleepi… #> 10 2       WSP         Waking up more than three times     3     2 Worst Sleepi… #> 11 3       WSP         Missing                             1    99 Worst Sleepi…  # Use different mode by event adhy <- tribble(   ~USUBJID, ~AVISITN, ~CRIT1FL,   \"1\",             1, \"Y\",   \"1\",             2, \"Y\",   \"2\",             1, \"Y\",   \"2\",             2, NA_character_,   \"2\",             3, \"Y\",   \"2\",             4, NA_character_ ) %>%   mutate(     PARAMCD = \"ALKPH\",     PARAM = \"Alkaline Phosphatase (U/L)\"   )  derive_extreme_event(   adhy,   by_vars = exprs(USUBJID),   events = list(     event(       condition = is.na(CRIT1FL),       set_values_to = exprs(AVALC = \"N\")     ),     event(       condition = CRIT1FL == \"Y\",       mode = \"last\",       set_values_to = exprs(AVALC = \"Y\")     )   ),   tmp_event_nr_var = event_nr,   order = exprs(event_nr, AVISITN),   mode = \"first\",   keep_source_vars = exprs(AVISITN),   set_values_to = exprs(     PARAMCD = \"ALK2\",     PARAM = \"ALKPH <= 2 times ULN\"   ) ) #> # A tibble: 8 × 6 #>   USUBJID AVISITN CRIT1FL PARAMCD PARAM                      AVALC #>   <chr>     <dbl> <chr>   <chr>   <chr>                      <chr> #> 1 1             1 Y       ALKPH   Alkaline Phosphatase (U/L) NA    #> 2 1             2 Y       ALKPH   Alkaline Phosphatase (U/L) NA    #> 3 2             1 Y       ALKPH   Alkaline Phosphatase (U/L) NA    #> 4 2             2 NA      ALKPH   Alkaline Phosphatase (U/L) NA    #> 5 2             3 Y       ALKPH   Alkaline Phosphatase (U/L) NA    #> 6 2             4 NA      ALKPH   Alkaline Phosphatase (U/L) NA    #> 7 1             2 NA      ALK2    ALKPH <= 2 times ULN       Y     #> 8 2             2 NA      ALK2    ALKPH <= 2 times ULN       N      # Derive confirmed best overall response (using event_joined()) # CR - complete response, PR - partial response, SD - stable disease # NE - not evaluable, PD - progressive disease adsl <- tribble(   ~USUBJID, ~TRTSDTC,   \"1\",      \"2020-01-01\",   \"2\",      \"2019-12-12\",   \"3\",      \"2019-11-11\",   \"4\",      \"2019-12-30\",   \"5\",      \"2020-01-01\",   \"6\",      \"2020-02-02\",   \"7\",      \"2020-02-02\",   \"8\",      \"2020-02-01\" ) %>%   mutate(TRTSDT = ymd(TRTSDTC))  adrs <- tribble(   ~USUBJID, ~ADTC,        ~AVALC,   \"1\",      \"2020-01-01\", \"PR\",   \"1\",      \"2020-02-01\", \"CR\",   \"1\",      \"2020-02-16\", \"NE\",   \"1\",      \"2020-03-01\", \"CR\",   \"1\",      \"2020-04-01\", \"SD\",   \"2\",      \"2020-01-01\", \"SD\",   \"2\",      \"2020-02-01\", \"PR\",   \"2\",      \"2020-03-01\", \"SD\",   \"2\",      \"2020-03-13\", \"CR\",   \"4\",      \"2020-01-01\", \"PR\",   \"4\",      \"2020-03-01\", \"NE\",   \"4\",      \"2020-04-01\", \"NE\",   \"4\",      \"2020-05-01\", \"PR\",   \"5\",      \"2020-01-01\", \"PR\",   \"5\",      \"2020-01-10\", \"PR\",   \"5\",      \"2020-01-20\", \"PR\",   \"6\",      \"2020-02-06\", \"PR\",   \"6\",      \"2020-02-16\", \"CR\",   \"6\",      \"2020-03-30\", \"PR\",   \"7\",      \"2020-02-06\", \"PR\",   \"7\",      \"2020-02-16\", \"CR\",   \"7\",      \"2020-04-01\", \"NE\",   \"8\",      \"2020-02-16\", \"PD\" ) %>%   mutate(     ADT = ymd(ADTC),     PARAMCD = \"OVR\",     PARAM = \"Overall Response by Investigator\"   ) %>%   derive_vars_merged(     dataset_add = adsl,     by_vars = exprs(USUBJID),     new_vars = exprs(TRTSDT)   )  derive_extreme_event(   adrs,   by_vars = exprs(USUBJID),   tmp_event_nr_var = event_nr,   order = exprs(event_nr, ADT),   mode = \"first\",   source_datasets = list(adsl = adsl),   events = list(     event_joined(       description = paste(         \"CR needs to be confirmed by a second CR at least 28 days later\",         \"at most one NE is acceptable between the two assessments\"       ),       join_vars = exprs(AVALC, ADT),       join_type = \"after\",       first_cond_upper = AVALC.join == \"CR\" &         ADT.join >= ADT + 28,       condition = AVALC == \"CR\" &         all(AVALC.join %in% c(\"CR\", \"NE\")) &         count_vals(var = AVALC.join, val = \"NE\") <= 1,       set_values_to = exprs(         AVALC = \"CR\"       )     ),     event_joined(       description = paste(         \"PR needs to be confirmed by a second CR or PR at least 28 days later,\",         \"at most one NE is acceptable between the two assessments\"       ),       join_vars = exprs(AVALC, ADT),       join_type = \"after\",       first_cond_upper = AVALC.join %in% c(\"CR\", \"PR\") &         ADT.join >= ADT + 28,       condition = AVALC == \"PR\" &         all(AVALC.join %in% c(\"CR\", \"PR\", \"NE\")) &         count_vals(var = AVALC.join, val = \"NE\") <= 1,       set_values_to = exprs(         AVALC = \"PR\"       )     ),     event(       description = paste(         \"CR, PR, or SD are considered as SD if occurring at least 28\",         \"after treatment start\"       ),       condition = AVALC %in% c(\"CR\", \"PR\", \"SD\") & ADT >= TRTSDT + 28,       set_values_to = exprs(         AVALC = \"SD\"       )     ),     event(       condition = AVALC == \"PD\",       set_values_to = exprs(         AVALC = \"PD\"       )     ),     event(       condition = AVALC %in% c(\"CR\", \"PR\", \"SD\", \"NE\"),       set_values_to = exprs(         AVALC = \"NE\"       )     ),     event(       description = \"set response to MISSING for patients without records in ADRS\",       dataset_name = \"adsl\",       condition = TRUE,       set_values_to = exprs(         AVALC = \"MISSING\"       ),       keep_source_vars = exprs(TRTSDT)     )   ),   set_values_to = exprs(     PARAMCD = \"CBOR\",     PARAM = \"Best Confirmed Overall Response by Investigator\"   ) ) %>%   filter(PARAMCD == \"CBOR\") #> # A tibble: 8 × 7 #>   USUBJID ADTC       AVALC   ADT        PARAMCD PARAM                 TRTSDT     #>   <chr>   <chr>      <chr>   <date>     <chr>   <chr>                 <date>     #> 1 1       2020-02-01 CR      2020-02-01 CBOR    Best Confirmed Overa… 2020-01-01 #> 2 2       2020-02-01 SD      2020-02-01 CBOR    Best Confirmed Overa… 2019-12-12 #> 3 3       NA         MISSING NA         CBOR    Best Confirmed Overa… 2019-11-11 #> 4 4       2020-05-01 SD      2020-05-01 CBOR    Best Confirmed Overa… 2019-12-30 #> 5 5       2020-01-01 NE      2020-01-01 CBOR    Best Confirmed Overa… 2020-01-01 #> 6 6       2020-02-06 PR      2020-02-06 CBOR    Best Confirmed Overa… 2020-02-02 #> 7 7       2020-02-06 NE      2020-02-06 CBOR    Best Confirmed Overa… 2020-02-02 #> 8 8       2020-02-16 PD      2020-02-16 CBOR    Best Confirmed Overa… 2020-02-01"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_extreme_records.html","id":null,"dir":"Reference","previous_headings":"","what":"Add the First or Last Observation for Each By Group as New Records — derive_extreme_records","title":"Add the First or Last Observation for Each By Group as New Records — derive_extreme_records","text":"Add first last observation group new observations. new observations can selected additional dataset. function can used adding maximum minimum value separate visit. variables selected observation kept. distinguishes derive_extreme_records() derive_summary_records(), variables populated new records.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_extreme_records.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add the First or Last Observation for Each By Group as New Records — derive_extreme_records","text":"","code":"derive_extreme_records(   dataset = NULL,   dataset_add,   dataset_ref = NULL,   by_vars = NULL,   order = NULL,   mode = NULL,   filter_add = NULL,   check_type = \"warning\",   exist_flag = NULL,   true_value = \"Y\",   false_value = NA_character_,   keep_source_vars = exprs(everything()),   set_values_to )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_extreme_records.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add the First or Last Observation for Each By Group as New Records — derive_extreme_records","text":"dataset Input dataset dataset_add Additional dataset additional dataset, determines groups returned input dataset, based groups exist dataset subset filter_add. variables specified by_vars filter_add parameters expected dataset. mode order specified, first last observation within group, defined by_vars, selected. dataset_ref Reference dataset variables specified by_vars expected. observation specified dataset new observation added input dataset. by_vars Grouping variables dataset_ref specified, argument must specified. Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) order Sort order Within group observations ordered specified order. Permitted Values: list expressions created exprs(), e.g., exprs(ADT, desc(AVAL)) mode Selection mode (first last) \"first\" specified, first observation group added input dataset. \"last\" specified, last observation group added input dataset. Permitted Values: \"first\", \"last\" filter_add Filter additional dataset (dataset_add) observations dataset_add fulfilling specified condition considered. check_type Check uniqueness? \"warning\" \"error\" specified, specified message issued observations (restricted) additional dataset unique respect variables order. Permitted Values: \"none\", \"warning\", \"error\" exist_flag Existence flag specified variable added output dataset. groups least one observation additional dataset (dataset_add) exist_flag set value specified true_value argument. groups exist_flag set value specified false_value argument. Permitted Values: Variable name true_value True value new observations selected additional dataset (dataset_add), exist_flag set specified value. false_value False value new observations selected additional dataset (dataset_add), exist_flag set specified value. keep_source_vars Variables kept new records named list tidyselect expressions created exprs() defining variables kept new records. variables specified by_vars set_values_to need specified kept automatically. set_values_to Variables set specified variables set specified values new observations. Set list variables specified value new records LHS refer variable. RHS refers values set variable. can string, symbol, numeric value, expression NA. summary functions used, values summarized variables specified by_vars. example:","code":"set_values_to = exprs(     AVAL = sum(AVAL),     DTYPE = \"AVERAGE\",   )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_extreme_records.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add the First or Last Observation for Each By Group as New Records — derive_extreme_records","text":"input dataset first last observation group added new observations.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_extreme_records.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Add the First or Last Observation for Each By Group as New Records — derive_extreme_records","text":"additional dataset (dataset_add) restricted specified filter_add argument. group (respect variables specified by_vars argument) first last observation (respect order specified order argument mode specified mode argument) selected. dataset_ref specified, observations dataset_ref selected records added. variables specified set_values_to argument added selected observations. variables specified keep_source_vars argument selected along variables specified by_vars set_values_to arguments. observations added input dataset.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_extreme_records.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add the First or Last Observation for Each By Group as New Records — derive_extreme_records","text":"","code":"library(tibble) library(dplyr, warn.conflicts = FALSE) library(lubridate)  adlb <- tribble(   ~USUBJID, ~AVISITN, ~AVAL, ~LBSEQ,   \"1\",      1,          113,      1,   \"1\",      2,          113,      2,   \"1\",      3,          117,      3,   \"2\",      1,          101,      1,   \"2\",      2,          101,      2,   \"2\",      3,           95,      3 )  # Add a new record for each USUBJID storing the minimum value (first AVAL). # If multiple records meet the minimum criterion, take the first value by # AVISITN. Set AVISITN = 97 and DTYPE = MINIMUM for these new records. # Specify the variables that need to be kept in the new records. derive_extreme_records(   adlb,   dataset_add = adlb,   by_vars = exprs(USUBJID),   order = exprs(AVAL, AVISITN),   mode = \"first\",   filter_add = !is.na(AVAL),   keep_source_vars = exprs(AVAL),   set_values_to = exprs(     AVISITN = 97,     DTYPE = \"MINIMUM\"   ) ) #> # A tibble: 8 × 5 #>   USUBJID AVISITN  AVAL LBSEQ DTYPE   #>   <chr>     <dbl> <dbl> <dbl> <chr>   #> 1 1             1   113     1 NA      #> 2 1             2   113     2 NA      #> 3 1             3   117     3 NA      #> 4 2             1   101     1 NA      #> 5 2             2   101     2 NA      #> 6 2             3    95     3 NA      #> 7 1            97   113    NA MINIMUM #> 8 2            97    95    NA MINIMUM  # Add a new record for each USUBJID storing the maximum value (last AVAL). # If multiple records meet the maximum criterion, take the first value by # AVISITN. Set AVISITN = 98 and DTYPE = MAXIMUM for these new records. derive_extreme_records(   adlb,   dataset_add = adlb,   by_vars = exprs(USUBJID),   order = exprs(desc(AVAL), AVISITN),   mode = \"first\",   filter_add = !is.na(AVAL),   set_values_to = exprs(     AVISITN = 98,     DTYPE = \"MAXIMUM\"   ) ) #> # A tibble: 8 × 5 #>   USUBJID AVISITN  AVAL LBSEQ DTYPE   #>   <chr>     <dbl> <dbl> <dbl> <chr>   #> 1 1             1   113     1 NA      #> 2 1             2   113     2 NA      #> 3 1             3   117     3 NA      #> 4 2             1   101     1 NA      #> 5 2             2   101     2 NA      #> 6 2             3    95     3 NA      #> 7 1            98   117     3 MAXIMUM #> 8 2            98   101     1 MAXIMUM  # Add a new record for each USUBJID storing for the last value. # Set AVISITN = 99 and DTYPE = LOV for these new records. derive_extreme_records(   adlb,   dataset_add = adlb,   by_vars = exprs(USUBJID),   order = exprs(AVISITN),   mode = \"last\",   set_values_to = exprs(     AVISITN = 99,     DTYPE = \"LOV\"   ) ) #> # A tibble: 8 × 5 #>   USUBJID AVISITN  AVAL LBSEQ DTYPE #>   <chr>     <dbl> <dbl> <dbl> <chr> #> 1 1             1   113     1 NA    #> 2 1             2   113     2 NA    #> 3 1             3   117     3 NA    #> 4 2             1   101     1 NA    #> 5 2             2   101     2 NA    #> 6 2             3    95     3 NA    #> 7 1            99   117     3 LOV   #> 8 2            99    95     3 LOV    # Derive a new parameter for the first disease progression (PD) adsl <- tribble(   ~USUBJID, ~DTHDT,   \"1\",      ymd(\"2022-05-13\"),   \"2\",      ymd(\"\"),   \"3\",      ymd(\"\") ) %>%   mutate(STUDYID = \"XX1234\")  adrs <- tribble(   ~USUBJID, ~ADTC,        ~AVALC,   \"1\",      \"2020-01-02\", \"PR\",   \"1\",      \"2020-02-01\", \"CR\",   \"1\",      \"2020-03-01\", \"CR\",   \"1\",      \"2020-04-01\", \"SD\",   \"2\",      \"2021-06-15\", \"SD\",   \"2\",      \"2021-07-16\", \"PD\",   \"2\",      \"2021-09-14\", \"PD\" ) %>%   mutate(     STUDYID = \"XX1234\",     ADT = ymd(ADTC),     PARAMCD = \"OVR\",     PARAM = \"Overall Response\",     ANL01FL = \"Y\"   ) %>%   select(-ADTC)  derive_extreme_records(   adrs,   dataset_ref = adsl,   dataset_add = adrs,   by_vars = exprs(STUDYID, USUBJID),   filter_add = PARAMCD == \"OVR\" & AVALC == \"PD\",   order = exprs(ADT),   exist_flag = AVALC,   true_value = \"Y\",   false_value = \"N\",   mode = \"first\",   set_values_to = exprs(     PARAMCD = \"PD\",     PARAM = \"Disease Progression\",     AVAL = yn_to_numeric(AVALC),     ANL01FL = \"Y\",     ADT = ADT   ) ) #> # A tibble: 10 × 8 #>    USUBJID AVALC STUDYID ADT        PARAMCD PARAM               ANL01FL  AVAL #>    <chr>   <chr> <chr>   <date>     <chr>   <chr>               <chr>   <dbl> #>  1 1       PR    XX1234  2020-01-02 OVR     Overall Response    Y          NA #>  2 1       CR    XX1234  2020-02-01 OVR     Overall Response    Y          NA #>  3 1       CR    XX1234  2020-03-01 OVR     Overall Response    Y          NA #>  4 1       SD    XX1234  2020-04-01 OVR     Overall Response    Y          NA #>  5 2       SD    XX1234  2021-06-15 OVR     Overall Response    Y          NA #>  6 2       PD    XX1234  2021-07-16 OVR     Overall Response    Y          NA #>  7 2       PD    XX1234  2021-09-14 OVR     Overall Response    Y          NA #>  8 2       Y     XX1234  2021-07-16 PD      Disease Progression Y           1 #>  9 1       N     XX1234  NA         PD      Disease Progression Y           0 #> 10 3       N     XX1234  NA         PD      Disease Progression Y           0  # derive parameter indicating death derive_extreme_records(   dataset_ref = adsl,   dataset_add = adsl,   by_vars = exprs(STUDYID, USUBJID),   filter_add = !is.na(DTHDT),   exist_flag = AVALC,   true_value = \"Y\",   false_value = \"N\",   mode = \"first\",   set_values_to = exprs(     PARAMCD = \"DEATH\",     PARAM = \"Death\",     ANL01FL = \"Y\",     ADT = DTHDT   ) ) #> # A tibble: 3 × 8 #>   STUDYID USUBJID PARAMCD PARAM ANL01FL ADT        DTHDT      AVALC #>   <chr>   <chr>   <chr>   <chr> <chr>   <date>     <date>     <chr> #> 1 XX1234  1       DEATH   Death Y       2022-05-13 2022-05-13 Y     #> 2 XX1234  2       DEATH   Death Y       NA         NA         N     #> 3 XX1234  3       DEATH   Death Y       NA         NA         N"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_locf_records.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive LOCF (Last Observation Carried Forward) Records — derive_locf_records","title":"Derive LOCF (Last Observation Carried Forward) Records — derive_locf_records","text":"Adds LOCF records new observations 'group' dataset contain observations missed visits/time points.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_locf_records.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive LOCF (Last Observation Carried Forward) Records — derive_locf_records","text":"","code":"derive_locf_records(   dataset,   dataset_ref,   by_vars,   analysis_var = AVAL,   order,   keep_vars = NULL )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_locf_records.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive LOCF (Last Observation Carried Forward) Records — derive_locf_records","text":"dataset Input dataset variables specified by_vars, analysis_var, order, keep_vars arguments expected dataset. dataset_ref Expected observations dataset Data frame combinations PARAMCD, PARAM, AVISIT, AVISITN, ... expected dataset expected. by_vars Grouping variables group defined by_vars observations dataset_ref added output dataset corresponding observation input dataset analysis_var NA corresponding observation input dataset. Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) analysis_var Analysis variable. Default: AVAL Permitted Values: variable order Sort order dataset sorted order carrying last observation forward (e.g. AVAL) within by_vars. handling NAs sorting variables see Sort Order. keep_vars Variables need carrying last observation forward Keep variables need carrying last observation forward analysis_var (e.g., PARAMN, VISITNUM). default NULL, variables specified by_vars analysis_var populated newly created records.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_locf_records.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive LOCF (Last Observation Carried Forward) Records — derive_locf_records","text":"input dataset new \"LOCF\" observations added by_vars. Note, variable populated new parameter rows specified by_vars.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_locf_records.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Derive LOCF (Last Observation Carried Forward) Records — derive_locf_records","text":"group (respect variables specified by_vars parameter) observations dataset_ref added output dataset corresponding observation input dataset analysis_var NA corresponding observation input dataset. new observations, analysis_var set non-missing analysis_var previous observation input dataset (sorted order) DTYPE set \"LOCF\".","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_locf_records.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Derive LOCF (Last Observation Carried Forward) Records — derive_locf_records","text":"G Gayatri","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_locf_records.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive LOCF (Last Observation Carried Forward) Records — derive_locf_records","text":"","code":"library(dplyr) library(tibble)  advs <- tribble(   ~STUDYID,  ~USUBJID,      ~PARAMCD, ~PARAMN, ~AVAL, ~AVISITN, ~AVISIT,   \"CDISC01\", \"01-701-1015\", \"PULSE\",        1,    65,        0, \"BASELINE\",   \"CDISC01\", \"01-701-1015\", \"DIABP\",        2,    79,        0, \"BASELINE\",   \"CDISC01\", \"01-701-1015\", \"DIABP\",        2,    80,        2, \"WEEK 2\",   \"CDISC01\", \"01-701-1015\", \"DIABP\",        2,    NA,        4, \"WEEK 4\",   \"CDISC01\", \"01-701-1015\", \"DIABP\",        2,    NA,        6, \"WEEK 6\",   \"CDISC01\", \"01-701-1015\", \"SYSBP\",        3,   130,        0, \"BASELINE\",   \"CDISC01\", \"01-701-1015\", \"SYSBP\",        3,   132,        2, \"WEEK 2\",   \"CDISC01\", \"01-701-1028\", \"PULSE\",        1,    61,        0, \"BASELINE\",   \"CDISC01\", \"01-701-1028\", \"PULSE\",        1,    60,        6, \"WEEK 6\",   \"CDISC01\", \"01-701-1028\", \"DIABP\",        2,    51,        0, \"BASELINE\",   \"CDISC01\", \"01-701-1028\", \"DIABP\",        2,    50,        2, \"WEEK 2\",   \"CDISC01\", \"01-701-1028\", \"DIABP\",        2,    51,        4, \"WEEK 4\",   \"CDISC01\", \"01-701-1028\", \"DIABP\",        2,    50,        6, \"WEEK 6\",   \"CDISC01\", \"01-701-1028\", \"SYSBP\",        3,   121,        0, \"BASELINE\",   \"CDISC01\", \"01-701-1028\", \"SYSBP\",        3,   121,        2, \"WEEK 2\",   \"CDISC01\", \"01-701-1028\", \"SYSBP\",        3,   121,        4, \"WEEK 4\",   \"CDISC01\", \"01-701-1028\", \"SYSBP\",        3,   121,        6, \"WEEK 6\" )  # A dataset with all the combinations of PARAMCD, PARAM, AVISIT, AVISITN, ... which are expected. advs_expected_obsv <- tribble(   ~PARAMCD, ~AVISITN, ~AVISIT,   \"PULSE\",         0, \"BASELINE\",   \"PULSE\",         6, \"WEEK 6\",   \"DIABP\",         0, \"BASELINE\",   \"DIABP\",         2, \"WEEK 2\",   \"DIABP\",         4, \"WEEK 4\",   \"DIABP\",         6, \"WEEK 6\",   \"SYSBP\",         0, \"BASELINE\",   \"SYSBP\",         2, \"WEEK 2\",   \"SYSBP\",         4, \"WEEK 4\",   \"SYSBP\",         6, \"WEEK 6\" )  derive_locf_records(   dataset = advs,   dataset_ref = advs_expected_obsv,   by_vars = exprs(STUDYID, USUBJID, PARAMCD),   order = exprs(AVISITN, AVISIT),   keep_vars = exprs(PARAMN) ) |>   arrange(USUBJID, PARAMCD, AVISIT) #> # A tibble: 22 × 8 #>    STUDYID USUBJID     PARAMCD PARAMN  AVAL AVISITN AVISIT   DTYPE #>    <chr>   <chr>       <chr>    <dbl> <dbl>   <dbl> <chr>    <chr> #>  1 CDISC01 01-701-1015 DIABP        2    79       0 BASELINE NA    #>  2 CDISC01 01-701-1015 DIABP        2    80       2 WEEK 2   NA    #>  3 CDISC01 01-701-1015 DIABP        2    80       4 WEEK 4   LOCF  #>  4 CDISC01 01-701-1015 DIABP        2    NA       4 WEEK 4   NA    #>  5 CDISC01 01-701-1015 DIABP        2    80       6 WEEK 6   LOCF  #>  6 CDISC01 01-701-1015 DIABP        2    NA       6 WEEK 6   NA    #>  7 CDISC01 01-701-1015 PULSE        1    65       0 BASELINE NA    #>  8 CDISC01 01-701-1015 PULSE        1    65       6 WEEK 6   LOCF  #>  9 CDISC01 01-701-1015 SYSBP        3   130       0 BASELINE NA    #> 10 CDISC01 01-701-1015 SYSBP        3   132       2 WEEK 2   NA    #> # ℹ 12 more rows"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_bmi.html","id":null,"dir":"Reference","previous_headings":"","what":"Adds a Parameter for BMI — derive_param_bmi","title":"Adds a Parameter for BMI — derive_param_bmi","text":"Adds record BMI/Body Mass Index using Weight Height group (e.g., subject visit) source parameters available. Note: wrapper function generic derive_param_computed().","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_bmi.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adds a Parameter for BMI — derive_param_bmi","text":"","code":"derive_param_bmi(   dataset,   by_vars,   set_values_to = exprs(PARAMCD = \"BMI\"),   weight_code = \"WEIGHT\",   height_code = \"HEIGHT\",   get_unit_expr,   filter = NULL,   constant_by_vars = NULL )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_bmi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adds a Parameter for BMI — derive_param_bmi","text":"dataset Input dataset variables specified by_vars argument expected dataset. PARAMCD, AVAL expected well. variable specified by_vars PARAMCD must unique key input dataset restricting filter condition (filter parameter) parameters specified weight_code height_code. by_vars Grouping variables group defined by_vars observation added output dataset. variables specified by_vars populated newly created records. Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) set_values_to Variables set specified variables set specified values new observations. example exprs(PARAMCD = \"MAP\") defines parameter code new parameter. Permitted Values: List variable-value pairs weight_code WEIGHT parameter code observations PARAMCD equals specified value considered WEIGHT. expected WEIGHT measured kg Permitted Values: character value height_code HEIGHT parameter code observations PARAMCD equals specified value considered HEIGHT. expected HEIGHT measured cm Permitted Values: character value Permitted Values: logical scalar get_unit_expr expression providing unit parameter result used check units input parameters. Permitted Values: expression evaluable input dataset results character value filter Filter condition specified condition applied input dataset deriving new parameter, .e., observations fulfilling condition taken account. Permitted Values: condition constant_by_vars variables HEIGHT constant HEIGHT constant, HEIGHT parameters (measured ) merged parameters using specified variables. height constant (e.g. measured screening baseline) use constant_by_vars select subject-level variable merge (e.g. USUBJID). produce BMI visits weight measured.  Otherwise calculated visits height weight collected. Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT)","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_bmi.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adds a Parameter for BMI — derive_param_bmi","text":"input dataset new parameter added. Note, variable populated new parameter rows specified by_vars.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_bmi.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Adds a Parameter for BMI — derive_param_bmi","text":"analysis value new parameter derived $$BMI = \\frac{WEIGHT}{HEIGHT^2}$$","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_bmi.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Adds a Parameter for BMI — derive_param_bmi","text":"","code":"# Example 1: Derive BMI where height is measured only once using constant_by_vars advs <- tibble::tribble(   ~USUBJID, ~PARAMCD, ~PARAM, ~AVAL, ~AVISIT,   \"01-701-1015\", \"HEIGHT\", \"Height (cm)\", 147, \"SCREENING\",   \"01-701-1015\", \"WEIGHT\", \"Weight (kg)\", 54.0, \"SCREENING\",   \"01-701-1015\", \"WEIGHT\", \"Weight (kg)\", 54.4, \"BASELINE\",   \"01-701-1015\", \"WEIGHT\", \"Weight (kg)\", 53.1, \"WEEK 2\",   \"01-701-1028\", \"HEIGHT\", \"Height (cm)\", 163, \"SCREENING\",   \"01-701-1028\", \"WEIGHT\", \"Weight (kg)\", 78.5, \"SCREENING\",   \"01-701-1028\", \"WEIGHT\", \"Weight (kg)\", 80.3, \"BASELINE\",   \"01-701-1028\", \"WEIGHT\", \"Weight (kg)\", 80.7, \"WEEK 2\" )  derive_param_bmi(   advs,   by_vars = exprs(USUBJID, AVISIT),   weight_code = \"WEIGHT\",   height_code = \"HEIGHT\",   set_values_to = exprs(     PARAMCD = \"BMI\",     PARAM = \"Body Mass Index (kg/m^2)\"   ),   get_unit_expr = extract_unit(PARAM),   constant_by_vars = exprs(USUBJID) ) #> # A tibble: 14 × 5 #>    USUBJID     PARAMCD PARAM                     AVAL AVISIT    #>    <chr>       <chr>   <chr>                    <dbl> <chr>     #>  1 01-701-1015 HEIGHT  Height (cm)              147   SCREENING #>  2 01-701-1015 WEIGHT  Weight (kg)               54   SCREENING #>  3 01-701-1015 WEIGHT  Weight (kg)               54.4 BASELINE  #>  4 01-701-1015 WEIGHT  Weight (kg)               53.1 WEEK 2    #>  5 01-701-1028 HEIGHT  Height (cm)              163   SCREENING #>  6 01-701-1028 WEIGHT  Weight (kg)               78.5 SCREENING #>  7 01-701-1028 WEIGHT  Weight (kg)               80.3 BASELINE  #>  8 01-701-1028 WEIGHT  Weight (kg)               80.7 WEEK 2    #>  9 01-701-1015 BMI     Body Mass Index (kg/m^2)  25.0 SCREENING #> 10 01-701-1015 BMI     Body Mass Index (kg/m^2)  25.2 BASELINE  #> 11 01-701-1015 BMI     Body Mass Index (kg/m^2)  24.6 WEEK 2    #> 12 01-701-1028 BMI     Body Mass Index (kg/m^2)  29.5 SCREENING #> 13 01-701-1028 BMI     Body Mass Index (kg/m^2)  30.2 BASELINE  #> 14 01-701-1028 BMI     Body Mass Index (kg/m^2)  30.4 WEEK 2     # Example 2: Derive BMI where height is measured only once and keep only one record # where both height and weight are measured. derive_param_bmi(   advs,   by_vars = exprs(USUBJID, AVISIT),   weight_code = \"WEIGHT\",   height_code = \"HEIGHT\",   set_values_to = exprs(     PARAMCD = \"BMI\",     PARAM = \"Body Mass Index (kg/m^2)\"   ),   get_unit_expr = extract_unit(PARAM) ) #> # A tibble: 10 × 5 #>    USUBJID     PARAMCD PARAM                     AVAL AVISIT    #>    <chr>       <chr>   <chr>                    <dbl> <chr>     #>  1 01-701-1015 HEIGHT  Height (cm)              147   SCREENING #>  2 01-701-1015 WEIGHT  Weight (kg)               54   SCREENING #>  3 01-701-1015 WEIGHT  Weight (kg)               54.4 BASELINE  #>  4 01-701-1015 WEIGHT  Weight (kg)               53.1 WEEK 2    #>  5 01-701-1028 HEIGHT  Height (cm)              163   SCREENING #>  6 01-701-1028 WEIGHT  Weight (kg)               78.5 SCREENING #>  7 01-701-1028 WEIGHT  Weight (kg)               80.3 BASELINE  #>  8 01-701-1028 WEIGHT  Weight (kg)               80.7 WEEK 2    #>  9 01-701-1015 BMI     Body Mass Index (kg/m^2)  25.0 SCREENING #> 10 01-701-1028 BMI     Body Mass Index (kg/m^2)  29.5 SCREENING  # Example 3: Pediatric study where height and weight are measured multiple times advs <- tibble::tribble(   ~USUBJID, ~PARAMCD, ~PARAM, ~AVAL, ~VISIT,   \"01-101-1001\", \"HEIGHT\", \"Height (cm)\", 47.1, \"BASELINE\",   \"01-101-1001\", \"HEIGHT\", \"Height (cm)\", 59.1, \"WEEK 12\",   \"01-101-1001\", \"HEIGHT\", \"Height (cm)\", 64.7, \"WEEK 24\",   \"01-101-1001\", \"HEIGHT\", \"Height (cm)\", 68.2, \"WEEK 48\",   \"01-101-1001\", \"WEIGHT\", \"Weight (kg)\", 2.6, \"BASELINE\",   \"01-101-1001\", \"WEIGHT\", \"Weight (kg)\", 5.3, \"WEEK 12\",   \"01-101-1001\", \"WEIGHT\", \"Weight (kg)\", 6.7, \"WEEK 24\",   \"01-101-1001\", \"WEIGHT\", \"Weight (kg)\", 7.4, \"WEEK 48\", )  derive_param_bmi(   advs,   by_vars = exprs(USUBJID, VISIT),   weight_code = \"WEIGHT\",   height_code = \"HEIGHT\",   set_values_to = exprs(     PARAMCD = \"BMI\",     PARAM = \"Body Mass Index (kg/m^2)\"   ),   get_unit_expr = extract_unit(PARAM) ) #> # A tibble: 12 × 5 #>    USUBJID     PARAMCD PARAM                     AVAL VISIT    #>    <chr>       <chr>   <chr>                    <dbl> <chr>    #>  1 01-101-1001 HEIGHT  Height (cm)               47.1 BASELINE #>  2 01-101-1001 HEIGHT  Height (cm)               59.1 WEEK 12  #>  3 01-101-1001 HEIGHT  Height (cm)               64.7 WEEK 24  #>  4 01-101-1001 HEIGHT  Height (cm)               68.2 WEEK 48  #>  5 01-101-1001 WEIGHT  Weight (kg)                2.6 BASELINE #>  6 01-101-1001 WEIGHT  Weight (kg)                5.3 WEEK 12  #>  7 01-101-1001 WEIGHT  Weight (kg)                6.7 WEEK 24  #>  8 01-101-1001 WEIGHT  Weight (kg)                7.4 WEEK 48  #>  9 01-101-1001 BMI     Body Mass Index (kg/m^2)  11.7 BASELINE #> 10 01-101-1001 BMI     Body Mass Index (kg/m^2)  15.2 WEEK 12  #> 11 01-101-1001 BMI     Body Mass Index (kg/m^2)  16.0 WEEK 24  #> 12 01-101-1001 BMI     Body Mass Index (kg/m^2)  15.9 WEEK 48"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_bsa.html","id":null,"dir":"Reference","previous_headings":"","what":"Adds a Parameter for BSA (Body Surface Area) Using the Specified Method — derive_param_bsa","title":"Adds a Parameter for BSA (Body Surface Area) Using the Specified Method — derive_param_bsa","text":"Adds record BSA (Body Surface Area) using specified derivation method group (e.g., subject visit) source parameters available. Note: wrapper function generic derive_param_computed().","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_bsa.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adds a Parameter for BSA (Body Surface Area) Using the Specified Method — derive_param_bsa","text":"","code":"derive_param_bsa(   dataset,   by_vars,   method,   set_values_to = exprs(PARAMCD = \"BSA\"),   height_code = \"HEIGHT\",   weight_code = \"WEIGHT\",   get_unit_expr,   filter = NULL,   constant_by_vars = NULL )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_bsa.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adds a Parameter for BSA (Body Surface Area) Using the Specified Method — derive_param_bsa","text":"dataset Input dataset variables specified by_vars argument expected dataset. PARAMCD, AVAL expected well. variable specified by_vars PARAMCD must unique key input dataset restricting filter condition (filter parameter) parameters specified HEIGHT WEIGHT. by_vars Grouping variables group defined by_vars observation added output dataset. variables specified by_vars populated newly created records. Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) method Derivation method use. Note HEIGHT expected cm WEIGHT expected kg: Mosteller: sqrt(height * weight / 3600) DuBois-DuBois: 0.20247 * (height/100) ^ 0.725 * weight ^ 0.425 Haycock: 0.024265 * height ^ 0.3964 * weight ^ 0.5378 Gehan-George: 0.0235 * height ^ 0.42246 * weight ^ 0.51456 Boyd: 0.0003207 * (height ^ 0.3) * (1000 * weight) ^ (0.7285 - (0.0188 * log10(1000 * weight))) Fujimoto: 0.008883 * height ^ 0.663 * weight ^ 0.444 Takahira: 0.007241 * height ^ 0.725 * weight ^ 0.425 Permitted Values: character value set_values_to Variables set specified variables set specified values new observations. example exprs(PARAMCD = \"MAP\") defines parameter code new parameter. Permitted Values: List variable-value pairs height_code HEIGHT parameter code observations PARAMCD equals specified value considered HEIGHT assessments. expected HEIGHT measured cm. Permitted Values: character value weight_code WEIGHT parameter code observations PARAMCD equals specified value considered WEIGHT assessments. expected WEIGHT measured kg. Permitted Values: character value get_unit_expr expression providing unit parameter result used check units input parameters. Permitted Values: expression evaluable input dataset results character value filter Filter condition specified condition applied input dataset deriving new parameter, .e., observations fulfilling condition taken account. Permitted Values: condition constant_by_vars variables HEIGHT constant HEIGHT constant, HEIGHT parameters (measured ) merged parameters using specified variables. height constant (e.g. measured screening baseline) use constant_by_vars select subject-level variable merge (e.g. USUBJID). produce BSA visits weight measured.  Otherwise calculated visits height weight collected. Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT)","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_bsa.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adds a Parameter for BSA (Body Surface Area) Using the Specified Method — derive_param_bsa","text":"input dataset new parameter added. Note, variable populated new parameter rows specified by_vars.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_bsa.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Adds a Parameter for BSA (Body Surface Area) Using the Specified Method — derive_param_bsa","text":"","code":"library(tibble)  # Example 1: Derive BSA where height is measured only once using constant_by_vars advs <- tibble::tribble(   ~USUBJID, ~PARAMCD, ~PARAM, ~AVAL, ~VISIT,   \"01-701-1015\", \"HEIGHT\", \"Height (cm)\", 170, \"BASELINE\",   \"01-701-1015\", \"WEIGHT\", \"Weight (kg)\", 75, \"BASELINE\",   \"01-701-1015\", \"WEIGHT\", \"Weight (kg)\", 78, \"MONTH 1\",   \"01-701-1015\", \"WEIGHT\", \"Weight (kg)\", 80, \"MONTH 2\",   \"01-701-1028\", \"HEIGHT\", \"Height (cm)\", 185, \"BASELINE\",   \"01-701-1028\", \"WEIGHT\", \"Weight (kg)\", 90, \"BASELINE\",   \"01-701-1028\", \"WEIGHT\", \"Weight (kg)\", 88, \"MONTH 1\",   \"01-701-1028\", \"WEIGHT\", \"Weight (kg)\", 85, \"MONTH 2\", )  derive_param_bsa(   advs,   by_vars = exprs(USUBJID, VISIT),   method = \"Mosteller\",   set_values_to = exprs(     PARAMCD = \"BSA\",     PARAM = \"Body Surface Area (m^2)\"   ),   get_unit_expr = extract_unit(PARAM),   constant_by_vars = exprs(USUBJID) ) #> # A tibble: 14 × 5 #>    USUBJID     PARAMCD PARAM                     AVAL VISIT    #>    <chr>       <chr>   <chr>                    <dbl> <chr>    #>  1 01-701-1015 HEIGHT  Height (cm)             170    BASELINE #>  2 01-701-1015 WEIGHT  Weight (kg)              75    BASELINE #>  3 01-701-1015 WEIGHT  Weight (kg)              78    MONTH 1  #>  4 01-701-1015 WEIGHT  Weight (kg)              80    MONTH 2  #>  5 01-701-1028 HEIGHT  Height (cm)             185    BASELINE #>  6 01-701-1028 WEIGHT  Weight (kg)              90    BASELINE #>  7 01-701-1028 WEIGHT  Weight (kg)              88    MONTH 1  #>  8 01-701-1028 WEIGHT  Weight (kg)              85    MONTH 2  #>  9 01-701-1015 BSA     Body Surface Area (m^2)   1.88 BASELINE #> 10 01-701-1015 BSA     Body Surface Area (m^2)   1.92 MONTH 1  #> 11 01-701-1015 BSA     Body Surface Area (m^2)   1.94 MONTH 2  #> 12 01-701-1028 BSA     Body Surface Area (m^2)   2.15 BASELINE #> 13 01-701-1028 BSA     Body Surface Area (m^2)   2.13 MONTH 1  #> 14 01-701-1028 BSA     Body Surface Area (m^2)   2.09 MONTH 2   derive_param_bsa(   advs,   by_vars = exprs(USUBJID, VISIT),   method = \"Fujimoto\",   set_values_to = exprs(     PARAMCD = \"BSA\",     PARAM = \"Body Surface Area (m^2)\"   ),   get_unit_expr = extract_unit(PARAM),   constant_by_vars = exprs(USUBJID) ) #> # A tibble: 14 × 5 #>    USUBJID     PARAMCD PARAM                     AVAL VISIT    #>    <chr>       <chr>   <chr>                    <dbl> <chr>    #>  1 01-701-1015 HEIGHT  Height (cm)             170    BASELINE #>  2 01-701-1015 WEIGHT  Weight (kg)              75    BASELINE #>  3 01-701-1015 WEIGHT  Weight (kg)              78    MONTH 1  #>  4 01-701-1015 WEIGHT  Weight (kg)              80    MONTH 2  #>  5 01-701-1028 HEIGHT  Height (cm)             185    BASELINE #>  6 01-701-1028 WEIGHT  Weight (kg)              90    BASELINE #>  7 01-701-1028 WEIGHT  Weight (kg)              88    MONTH 1  #>  8 01-701-1028 WEIGHT  Weight (kg)              85    MONTH 2  #>  9 01-701-1015 BSA     Body Surface Area (m^2)   1.82 BASELINE #> 10 01-701-1015 BSA     Body Surface Area (m^2)   1.85 MONTH 1  #> 11 01-701-1015 BSA     Body Surface Area (m^2)   1.87 MONTH 2  #> 12 01-701-1028 BSA     Body Surface Area (m^2)   2.09 BASELINE #> 13 01-701-1028 BSA     Body Surface Area (m^2)   2.07 MONTH 1  #> 14 01-701-1028 BSA     Body Surface Area (m^2)   2.03 MONTH 2   # Example 2: Derive BSA where height is measured only once and keep only one record # where both height and weight are measured.  derive_param_bsa(   advs,   by_vars = exprs(USUBJID, VISIT),   method = \"Mosteller\",   set_values_to = exprs(     PARAMCD = \"BSA\",     PARAM = \"Body Surface Area (m^2)\"   ),   get_unit_expr = extract_unit(PARAM) ) #> # A tibble: 10 × 5 #>    USUBJID     PARAMCD PARAM                     AVAL VISIT    #>    <chr>       <chr>   <chr>                    <dbl> <chr>    #>  1 01-701-1015 HEIGHT  Height (cm)             170    BASELINE #>  2 01-701-1015 WEIGHT  Weight (kg)              75    BASELINE #>  3 01-701-1015 WEIGHT  Weight (kg)              78    MONTH 1  #>  4 01-701-1015 WEIGHT  Weight (kg)              80    MONTH 2  #>  5 01-701-1028 HEIGHT  Height (cm)             185    BASELINE #>  6 01-701-1028 WEIGHT  Weight (kg)              90    BASELINE #>  7 01-701-1028 WEIGHT  Weight (kg)              88    MONTH 1  #>  8 01-701-1028 WEIGHT  Weight (kg)              85    MONTH 2  #>  9 01-701-1015 BSA     Body Surface Area (m^2)   1.88 BASELINE #> 10 01-701-1028 BSA     Body Surface Area (m^2)   2.15 BASELINE  # Example 3: Pediatric study where height and weight are measured multiple times advs <- tibble::tribble(   ~USUBJID, ~PARAMCD, ~PARAM, ~AVAL, ~VISIT,   \"01-101-1001\", \"HEIGHT\", \"Height (cm)\", 47.1, \"BASELINE\",   \"01-101-1001\", \"HEIGHT\", \"Height (cm)\", 59.1, \"WEEK 12\",   \"01-101-1001\", \"HEIGHT\", \"Height (cm)\", 64.7, \"WEEK 24\",   \"01-101-1001\", \"HEIGHT\", \"Height (cm)\", 68.2, \"WEEK 48\",   \"01-101-1001\", \"WEIGHT\", \"Weight (kg)\", 2.6, \"BASELINE\",   \"01-101-1001\", \"WEIGHT\", \"Weight (kg)\", 5.3, \"WEEK 12\",   \"01-101-1001\", \"WEIGHT\", \"Weight (kg)\", 6.7, \"WEEK 24\",   \"01-101-1001\", \"WEIGHT\", \"Weight (kg)\", 7.4, \"WEEK 48\", ) derive_param_bsa(   advs,   by_vars = exprs(USUBJID, VISIT),   method = \"Mosteller\",   set_values_to = exprs(     PARAMCD = \"BSA\",     PARAM = \"Body Surface Area (m^2)\"   ),   get_unit_expr = extract_unit(PARAM) ) #> # A tibble: 12 × 5 #>    USUBJID     PARAMCD PARAM                     AVAL VISIT    #>    <chr>       <chr>   <chr>                    <dbl> <chr>    #>  1 01-101-1001 HEIGHT  Height (cm)             47.1   BASELINE #>  2 01-101-1001 HEIGHT  Height (cm)             59.1   WEEK 12  #>  3 01-101-1001 HEIGHT  Height (cm)             64.7   WEEK 24  #>  4 01-101-1001 HEIGHT  Height (cm)             68.2   WEEK 48  #>  5 01-101-1001 WEIGHT  Weight (kg)              2.6   BASELINE #>  6 01-101-1001 WEIGHT  Weight (kg)              5.3   WEEK 12  #>  7 01-101-1001 WEIGHT  Weight (kg)              6.7   WEEK 24  #>  8 01-101-1001 WEIGHT  Weight (kg)              7.4   WEEK 48  #>  9 01-101-1001 BSA     Body Surface Area (m^2)  0.184 BASELINE #> 10 01-101-1001 BSA     Body Surface Area (m^2)  0.295 WEEK 12  #> 11 01-101-1001 BSA     Body Surface Area (m^2)  0.347 WEEK 24  #> 12 01-101-1001 BSA     Body Surface Area (m^2)  0.374 WEEK 48"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_computed.html","id":null,"dir":"Reference","previous_headings":"","what":"Adds a Parameter Computed from the Analysis Value of Other Parameters — derive_param_computed","title":"Adds a Parameter Computed from the Analysis Value of Other Parameters — derive_param_computed","text":"Adds parameter computed analysis value parameters. expected analysis value new parameter defined expression using analysis values parameters, addition/sum, subtraction/difference, multiplication/product, division/ratio, exponentiation/logarithm, formula.  example mean arterial pressure (MAP) can derived systolic (SYSBP) diastolic blood pressure (DIABP) formula $$MAP = \\frac{SYSBP + 2 DIABP}{3}$$","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_computed.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adds a Parameter Computed from the Analysis Value of Other Parameters — derive_param_computed","text":"","code":"derive_param_computed(   dataset = NULL,   dataset_add = NULL,   by_vars,   parameters,   set_values_to,   filter = NULL,   constant_by_vars = NULL,   constant_parameters = NULL,   keep_nas = FALSE )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_computed.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adds a Parameter Computed from the Analysis Value of Other Parameters — derive_param_computed","text":"dataset Input dataset variables specified by_vars argument expected dataset. PARAMCD expected well. variable specified by_vars PARAMCD must unique key input dataset restricting filter condition (filter parameter) parameters specified parameters. dataset_add Additional dataset variables specified by_vars parameter expected. variable specified by_vars PARAMCD must unique key additional dataset restricting parameters specified parameters. argument specified, observations additional dataset considered addition observations input dataset (dataset restricted filter). by_vars Grouping variables group defined by_vars observation added output dataset. variables specified by_vars populated newly created records. Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) parameters Required parameter codes expected parameter codes (PARAMCD) required derive new parameter specified parameter constant_parameters parameter. observations considered parameter code, e.g., SDTM dataset used, temporary parameter codes can derived specifying list expressions. name element defines temporary parameter code expression condition selecting records. example parameters = exprs(HGHT = VSTESTCD == \"HEIGHT\") selects observations VSTESTCD == \"HEIGHT\" input data (dataset dataset_add), sets PARAMCD = \"HGHT\" observations, adds observations consider. Unnamed elements list expressions considered parameter codes. example, parameters = exprs(WEIGHT, HGHT = VSTESTCD == \"HEIGHT\") uses parameter code \"WEIGHT\" creates temporary parameter code \"HGHT\". Permitted Values: character vector PARAMCD values list expressions set_values_to Variables set specified variables set specified values new observations. values variables parameters specified parameters can accessed using <variable name>.<parameter code>. example   defines analysis value parameter code new parameter. Variable names expression must contain one dot. Permitted Values: List variable-value pairs filter Filter condition specified condition applied input dataset deriving new parameter, .e., observations fulfilling condition taken account. Permitted Values: condition constant_by_vars variables constant parameters constant parameters (parameters measured ) merged parameters using specified variables. (Refer Example 2) Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) constant_parameters Required constant parameter codes expected parameter codes (PARAMCD) required derive new parameter measured specified . example BMI derived height measured weight measured visit. Height specified constant_parameters parameter. (Refer Example 2) observations considered parameter code, e.g., SDTM dataset used, temporary parameter codes can derived specifying list expressions. name element defines temporary parameter code expression condition selecting records. example constant_parameters = exprs(HGHT = VSTESTCD == \"HEIGHT\") selects observations VSTESTCD == \"HEIGHT\" input data (dataset dataset_add), sets PARAMCD = \"HGHT\" observations, adds observations consider. Unnamed elements list expressions considered parameter codes. example, constant_parameters = exprs(WEIGHT, HGHT = VSTESTCD == \"HEIGHT\") uses parameter code \"WEIGHT\" creates temporary parameter code \"HGHT\". Permitted Values: character vector PARAMCD values list expressions keep_nas Keep observations NAs argument set TRUE, observations added even values contributing computed value NA (see Example 1b). argument set list variables, observations added even specified variables NA (see Example 1c). Permitted Values: TRUE, FALSE, list variables created exprs() e.g. exprs(ADTF, ATMF)","code":"exprs(   AVAL = (AVAL.SYSBP + 2 * AVAL.DIABP) / 3,   PARAMCD = \"MAP\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_computed.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adds a Parameter Computed from the Analysis Value of Other Parameters — derive_param_computed","text":"input dataset new parameter added. Note, variable populated new parameter rows specified by_vars.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_computed.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Adds a Parameter Computed from the Analysis Value of Other Parameters — derive_param_computed","text":"group (respect variables specified by_vars parameter) observation added output dataset filtered input dataset (dataset) additional dataset (dataset_add) contains exactly one observation parameter code specified parameters contributing values like AVAL.SYSBP NA. keep_nas can used specify variables NAs acceptable. See also Example 1b 1c. new observations variables specified set_values_to set provided values. values variables input dataset set NA.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_computed.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Adds a Parameter Computed from the Analysis Value of Other Parameters — derive_param_computed","text":"","code":"library(tibble) library(dplyr) library(lubridate)  # Example 1a: Derive MAP advs <- tribble(   ~USUBJID,      ~PARAMCD, ~PARAM,                            ~AVAL, ~VISIT,   \"01-701-1015\", \"DIABP\",  \"Diastolic Blood Pressure (mmHg)\",    51, \"BASELINE\",   \"01-701-1015\", \"DIABP\",  \"Diastolic Blood Pressure (mmHg)\",    50, \"WEEK 2\",   \"01-701-1015\", \"SYSBP\",  \"Systolic Blood Pressure (mmHg)\",    121, \"BASELINE\",   \"01-701-1015\", \"SYSBP\",  \"Systolic Blood Pressure (mmHg)\",    121, \"WEEK 2\",   \"01-701-1028\", \"DIABP\",  \"Diastolic Blood Pressure (mmHg)\",    79, \"BASELINE\",   \"01-701-1028\", \"DIABP\",  \"Diastolic Blood Pressure (mmHg)\",    80, \"WEEK 2\",   \"01-701-1028\", \"SYSBP\",  \"Systolic Blood Pressure (mmHg)\",    130, \"BASELINE\",   \"01-701-1028\", \"SYSBP\",  \"Systolic Blood Pressure (mmHg)\",     NA, \"WEEK 2\" ) %>%   mutate(     AVALU = \"mmHg\",     ADT = case_when(       VISIT == \"BASELINE\" ~ as.Date(\"2024-01-10\"),       VISIT == \"WEEK 2\" ~ as.Date(\"2024-01-24\")     ),     ADTF = NA_character_   )  derive_param_computed(   advs,   by_vars = exprs(USUBJID, VISIT),   parameters = c(\"SYSBP\", \"DIABP\"),   set_values_to = exprs(     AVAL = (AVAL.SYSBP + 2 * AVAL.DIABP) / 3,     PARAMCD = \"MAP\",     PARAM = \"Mean Arterial Pressure (mmHg)\",     AVALU = \"mmHg\",     ADT = ADT.SYSBP   ) ) #> # A tibble: 11 × 8 #>    USUBJID     PARAMCD PARAM                   AVAL VISIT AVALU ADT        ADTF  #>    <chr>       <chr>   <chr>                  <dbl> <chr> <chr> <date>     <chr> #>  1 01-701-1015 DIABP   Diastolic Blood Press…  51   BASE… mmHg  2024-01-10 NA    #>  2 01-701-1015 DIABP   Diastolic Blood Press…  50   WEEK… mmHg  2024-01-24 NA    #>  3 01-701-1015 SYSBP   Systolic Blood Pressu… 121   BASE… mmHg  2024-01-10 NA    #>  4 01-701-1015 SYSBP   Systolic Blood Pressu… 121   WEEK… mmHg  2024-01-24 NA    #>  5 01-701-1028 DIABP   Diastolic Blood Press…  79   BASE… mmHg  2024-01-10 NA    #>  6 01-701-1028 DIABP   Diastolic Blood Press…  80   WEEK… mmHg  2024-01-24 NA    #>  7 01-701-1028 SYSBP   Systolic Blood Pressu… 130   BASE… mmHg  2024-01-10 NA    #>  8 01-701-1028 SYSBP   Systolic Blood Pressu…  NA   WEEK… mmHg  2024-01-24 NA    #>  9 01-701-1015 MAP     Mean Arterial Pressur…  74.3 BASE… mmHg  2024-01-10 NA    #> 10 01-701-1015 MAP     Mean Arterial Pressur…  73.7 WEEK… mmHg  2024-01-24 NA    #> 11 01-701-1028 MAP     Mean Arterial Pressur…  96   BASE… mmHg  2024-01-10 NA     # Example 1b: Using option `keep_nas = TRUE` to derive MAP in the case where some/all # values of a variable used in the computation are missing  derive_param_computed(   advs,   by_vars = exprs(USUBJID, VISIT),   parameters = c(\"SYSBP\", \"DIABP\"),   set_values_to = exprs(     AVAL = (AVAL.SYSBP + 2 * AVAL.DIABP) / 3,     PARAMCD = \"MAP\",     PARAM = \"Mean Arterial Pressure (mmHg)\",     AVALU = \"mmHg\",     ADT = ADT.SYSBP,     ADTF = ADTF.SYSBP   ),   keep_nas = TRUE ) #> # A tibble: 12 × 8 #>    USUBJID     PARAMCD PARAM                   AVAL VISIT AVALU ADT        ADTF  #>    <chr>       <chr>   <chr>                  <dbl> <chr> <chr> <date>     <chr> #>  1 01-701-1015 DIABP   Diastolic Blood Press…  51   BASE… mmHg  2024-01-10 NA    #>  2 01-701-1015 DIABP   Diastolic Blood Press…  50   WEEK… mmHg  2024-01-24 NA    #>  3 01-701-1015 SYSBP   Systolic Blood Pressu… 121   BASE… mmHg  2024-01-10 NA    #>  4 01-701-1015 SYSBP   Systolic Blood Pressu… 121   WEEK… mmHg  2024-01-24 NA    #>  5 01-701-1028 DIABP   Diastolic Blood Press…  79   BASE… mmHg  2024-01-10 NA    #>  6 01-701-1028 DIABP   Diastolic Blood Press…  80   WEEK… mmHg  2024-01-24 NA    #>  7 01-701-1028 SYSBP   Systolic Blood Pressu… 130   BASE… mmHg  2024-01-10 NA    #>  8 01-701-1028 SYSBP   Systolic Blood Pressu…  NA   WEEK… mmHg  2024-01-24 NA    #>  9 01-701-1015 MAP     Mean Arterial Pressur…  74.3 BASE… mmHg  2024-01-10 NA    #> 10 01-701-1015 MAP     Mean Arterial Pressur…  73.7 WEEK… mmHg  2024-01-24 NA    #> 11 01-701-1028 MAP     Mean Arterial Pressur…  96   BASE… mmHg  2024-01-10 NA    #> 12 01-701-1028 MAP     Mean Arterial Pressur…  NA   WEEK… mmHg  2024-01-24 NA     # Example 1c: Using option `keep_nas = exprs(ADTF)` to derive MAP in the case where # some/all values of a variable used in the computation are missing but ignoring ADTF  derive_param_computed(   advs,   by_vars = exprs(USUBJID, VISIT),   parameters = c(\"SYSBP\", \"DIABP\"),   set_values_to = exprs(     AVAL = (AVAL.SYSBP + 2 * AVAL.DIABP) / 3,     PARAMCD = \"MAP\",     PARAM = \"Mean Arterial Pressure (mmHg)\",     AVALU = \"mmHg\",     ADT = ADT.SYSBP,     ADTF = ADTF.SYSBP   ),   keep_nas = exprs(ADTF) ) #> # A tibble: 11 × 8 #>    USUBJID     PARAMCD PARAM                   AVAL VISIT AVALU ADT        ADTF  #>    <chr>       <chr>   <chr>                  <dbl> <chr> <chr> <date>     <chr> #>  1 01-701-1015 DIABP   Diastolic Blood Press…  51   BASE… mmHg  2024-01-10 NA    #>  2 01-701-1015 DIABP   Diastolic Blood Press…  50   WEEK… mmHg  2024-01-24 NA    #>  3 01-701-1015 SYSBP   Systolic Blood Pressu… 121   BASE… mmHg  2024-01-10 NA    #>  4 01-701-1015 SYSBP   Systolic Blood Pressu… 121   WEEK… mmHg  2024-01-24 NA    #>  5 01-701-1028 DIABP   Diastolic Blood Press…  79   BASE… mmHg  2024-01-10 NA    #>  6 01-701-1028 DIABP   Diastolic Blood Press…  80   WEEK… mmHg  2024-01-24 NA    #>  7 01-701-1028 SYSBP   Systolic Blood Pressu… 130   BASE… mmHg  2024-01-10 NA    #>  8 01-701-1028 SYSBP   Systolic Blood Pressu…  NA   WEEK… mmHg  2024-01-24 NA    #>  9 01-701-1015 MAP     Mean Arterial Pressur…  74.3 BASE… mmHg  2024-01-10 NA    #> 10 01-701-1015 MAP     Mean Arterial Pressur…  73.7 WEEK… mmHg  2024-01-24 NA    #> 11 01-701-1028 MAP     Mean Arterial Pressur…  96   BASE… mmHg  2024-01-10 NA     # Example 2: Derive BMI where height is measured only once advs <- tribble(   ~USUBJID,      ~PARAMCD, ~PARAM,        ~AVAL, ~AVALU, ~VISIT,   \"01-701-1015\", \"HEIGHT\", \"Height (cm)\", 147.0, \"cm\",   \"SCREENING\",   \"01-701-1015\", \"WEIGHT\", \"Weight (kg)\",  54.0, \"kg\",   \"SCREENING\",   \"01-701-1015\", \"WEIGHT\", \"Weight (kg)\",  54.4, \"kg\",   \"BASELINE\",   \"01-701-1015\", \"WEIGHT\", \"Weight (kg)\",  53.1, \"kg\",   \"WEEK 2\",   \"01-701-1028\", \"HEIGHT\", \"Height (cm)\", 163.0, \"cm\",   \"SCREENING\",   \"01-701-1028\", \"WEIGHT\", \"Weight (kg)\",  78.5, \"kg\",   \"SCREENING\",   \"01-701-1028\", \"WEIGHT\", \"Weight (kg)\",  80.3, \"kg\",   \"BASELINE\",   \"01-701-1028\", \"WEIGHT\", \"Weight (kg)\",  80.7, \"kg\",   \"WEEK 2\" )  derive_param_computed(   advs,   by_vars = exprs(USUBJID, VISIT),   parameters = \"WEIGHT\",   set_values_to = exprs(     AVAL = AVAL.WEIGHT / (AVAL.HEIGHT / 100)^2,     PARAMCD = \"BMI\",     PARAM = \"Body Mass Index (kg/m^2)\",     AVALU = \"kg/m^2\"   ),   constant_parameters = c(\"HEIGHT\"),   constant_by_vars = exprs(USUBJID) ) #> # A tibble: 14 × 6 #>    USUBJID     PARAMCD PARAM                     AVAL AVALU  VISIT     #>    <chr>       <chr>   <chr>                    <dbl> <chr>  <chr>     #>  1 01-701-1015 HEIGHT  Height (cm)              147   cm     SCREENING #>  2 01-701-1015 WEIGHT  Weight (kg)               54   kg     SCREENING #>  3 01-701-1015 WEIGHT  Weight (kg)               54.4 kg     BASELINE  #>  4 01-701-1015 WEIGHT  Weight (kg)               53.1 kg     WEEK 2    #>  5 01-701-1028 HEIGHT  Height (cm)              163   cm     SCREENING #>  6 01-701-1028 WEIGHT  Weight (kg)               78.5 kg     SCREENING #>  7 01-701-1028 WEIGHT  Weight (kg)               80.3 kg     BASELINE  #>  8 01-701-1028 WEIGHT  Weight (kg)               80.7 kg     WEEK 2    #>  9 01-701-1015 BMI     Body Mass Index (kg/m^2)  25.0 kg/m^2 SCREENING #> 10 01-701-1015 BMI     Body Mass Index (kg/m^2)  25.2 kg/m^2 BASELINE  #> 11 01-701-1015 BMI     Body Mass Index (kg/m^2)  24.6 kg/m^2 WEEK 2    #> 12 01-701-1028 BMI     Body Mass Index (kg/m^2)  29.5 kg/m^2 SCREENING #> 13 01-701-1028 BMI     Body Mass Index (kg/m^2)  30.2 kg/m^2 BASELINE  #> 14 01-701-1028 BMI     Body Mass Index (kg/m^2)  30.4 kg/m^2 WEEK 2     # Example 3: Using data from an additional dataset and other variables than AVAL qs <- tribble(   ~USUBJID, ~AVISIT,   ~QSTESTCD, ~QSORRES, ~QSSTRESN,   \"1\",      \"WEEK 2\",  \"CHSF112\", NA,               1,   \"1\",      \"WEEK 2\",  \"CHSF113\", \"Yes\",           NA,   \"1\",      \"WEEK 2\",  \"CHSF114\", NA,               1,   \"1\",      \"WEEK 4\",  \"CHSF112\", NA,               2,   \"1\",      \"WEEK 4\",  \"CHSF113\", \"No\",            NA,   \"1\",      \"WEEK 4\",  \"CHSF114\", NA,               1 )  adchsf <- tribble(   ~USUBJID, ~AVISIT,  ~PARAMCD, ~QSSTRESN, ~AVAL,   \"1\",      \"WEEK 2\", \"CHSF12\", 1,             6,   \"1\",      \"WEEK 2\", \"CHSF14\", 1,             6,   \"1\",      \"WEEK 4\", \"CHSF12\", 2,            12,   \"1\",      \"WEEK 4\", \"CHSF14\", 1,             6 ) %>%   mutate(QSORRES = NA_character_)  derive_param_computed(   adchsf,   dataset_add = qs,   by_vars = exprs(USUBJID, AVISIT),   parameters = exprs(CHSF12, CHSF13 = QSTESTCD %in% c(\"CHSF113\", \"CHSF213\"), CHSF14),   set_values_to = exprs(     AVAL = case_when(       QSORRES.CHSF13 == \"Not applicable\" ~ 0,       QSORRES.CHSF13 == \"Yes\" ~ 38,       QSORRES.CHSF13 == \"No\" ~ if_else(         QSSTRESN.CHSF12 > QSSTRESN.CHSF14,         25,         0       )     ),     PARAMCD = \"CHSF13\"   ) ) #> # A tibble: 6 × 6 #>   USUBJID AVISIT PARAMCD QSSTRESN  AVAL QSORRES #>   <chr>   <chr>  <chr>      <dbl> <dbl> <chr>   #> 1 1       WEEK 2 CHSF12         1     6 NA      #> 2 1       WEEK 2 CHSF14         1     6 NA      #> 3 1       WEEK 4 CHSF12         2    12 NA      #> 4 1       WEEK 4 CHSF14         1     6 NA      #> 5 1       WEEK 2 CHSF13        NA    38 NA      #> 6 1       WEEK 4 CHSF13        NA    25 NA       # Example 4: Computing more than one variable adlb_tbilialk <- tribble(   ~USUBJID, ~PARAMCD, ~AVALC, ~ADTM,        ~ADTF,   \"1\",      \"ALK2\",   \"Y\",    \"2021-05-13\", NA_character_,   \"1\",      \"TBILI2\", \"Y\",    \"2021-06-30\", \"D\",   \"2\",      \"ALK2\",   \"Y\",    \"2021-12-31\", \"M\",   \"2\",      \"TBILI2\", \"N\",    \"2021-11-11\", NA_character_,   \"3\",      \"ALK2\",   \"N\",    \"2021-04-03\", NA_character_,   \"3\",      \"TBILI2\", \"N\",    \"2021-04-04\", NA_character_ ) %>%   mutate(ADTM = ymd(ADTM))  derive_param_computed(   dataset_add = adlb_tbilialk,   by_vars = exprs(USUBJID),   parameters = c(\"ALK2\", \"TBILI2\"),   set_values_to = exprs(     AVALC = if_else(AVALC.TBILI2 == \"Y\" & AVALC.ALK2 == \"Y\", \"Y\", \"N\"),     ADTM = pmax(ADTM.TBILI2, ADTM.ALK2),     ADTF = if_else(ADTM == ADTM.TBILI2, ADTF.TBILI2, ADTF.ALK2),     PARAMCD = \"TB2AK2\",     PARAM = \"TBILI > 2 times ULN and ALKPH <= 2 times ULN\"   ),   keep_nas = TRUE ) #> # A tibble: 3 × 6 #>   USUBJID AVALC ADTM       ADTF  PARAMCD PARAM                                   #>   <chr>   <chr> <date>     <chr> <chr>   <chr>                                   #> 1 1       Y     2021-06-30 D     TB2AK2  TBILI > 2 times ULN and ALKPH <= 2 tim… #> 2 2       N     2021-12-31 M     TB2AK2  TBILI > 2 times ULN and ALKPH <= 2 tim… #> 3 3       N     2021-04-04 NA    TB2AK2  TBILI > 2 times ULN and ALKPH <= 2 tim…"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_doseint.html","id":null,"dir":"Reference","previous_headings":"","what":"Adds a Parameter for Dose Intensity — derive_param_doseint","title":"Adds a Parameter for Dose Intensity — derive_param_doseint","text":"Adds record dose intensity group (e.g., subject visit) source parameters available. Note: wrapper function generic derive_param_computed(). analysis value new parameter derived Total Dose / Planned Dose * 100","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_doseint.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adds a Parameter for Dose Intensity — derive_param_doseint","text":"","code":"derive_param_doseint(   dataset,   by_vars,   set_values_to = exprs(PARAMCD = \"TNDOSINT\"),   tadm_code = \"TNDOSE\",   tpadm_code = \"TSNDOSE\",   zero_doses = \"Inf\",   filter = NULL )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_doseint.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adds a Parameter for Dose Intensity — derive_param_doseint","text":"dataset Input dataset variables specified by_vars argument expected dataset. PARAMCD, AVAL expected well. variable specified by_vars PARAMCD must unique key input dataset restricting filter condition (filter parameter) parameters specified tadm_code padm_code. by_vars Grouping variables variables specified by_vars populated newly created records. Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) set_values_to Variables set specified variables set specified values new observations. example exprs(PARAMCD = \"MAP\") defines parameter code new parameter. Permitted Values: List variable-value pairs tadm_code Total Doses Administered parameter code observations PARAMCD equals specified value considered total dose administered.  AVAL associated PARAMCD numerator dose intensity calculation. Permitted Values: character value tpadm_code Total Doses Planned parameter code observations PARAMCD equals specified value considered total planned dose.  AVAL associated PARAMCD denominator dose intensity calculation. Permitted Values: character value zero_doses Flag indicating logic handling 0 planned administered doses by_vars group Default: Inf Permitted Values: Inf, 100 record returned either planned (tpadm_code) administered (tadm_code) AVAL NA.  record returned record exist tadm_code tpadm_code specified by_var. zero_doses = Inf: planned dose (tpadm_code) 0 administered dose (tadm_code) 0, NaN returned. planned dose (tpadm_code) 0 administered dose (tadm_code) > 0, Inf returned. zero_doses = 100 : planned dose (tpadm_code) 0 administered dose (tadm_code) 0, 0 returned. planned dose (tpadm_code) 0 administered dose (tadm_code) > 0, 100 returned. filter Filter condition specified condition applied input dataset deriving new parameter, .e., observations fulfilling condition taken account. Permitted Values: condition","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_doseint.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adds a Parameter for Dose Intensity — derive_param_doseint","text":"input dataset new parameter rows added. Note, variable populated new parameter rows specified by_vars.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_doseint.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Adds a Parameter for Dose Intensity — derive_param_doseint","text":"","code":"library(tibble) library(lubridate, warn.conflicts = FALSE)  adex <- tribble(   ~USUBJID, ~PARAMCD, ~VISIT, ~ANL01FL, ~ASTDT, ~AENDT, ~AVAL,   \"P001\", \"TNDOSE\", \"V1\", \"Y\", ymd(\"2020-01-01\"), ymd(\"2020-01-30\"), 59,   \"P001\", \"TSNDOSE\", \"V1\", \"Y\", ymd(\"2020-01-01\"), ymd(\"2020-02-01\"), 96,   \"P001\", \"TNDOSE\", \"V2\", \"Y\", ymd(\"2020-02-01\"), ymd(\"2020-03-15\"), 88,   \"P001\", \"TSNDOSE\", \"V2\", \"Y\", ymd(\"2020-02-05\"), ymd(\"2020-03-01\"), 88,   \"P002\", \"TNDOSE\", \"V1\", \"Y\", ymd(\"2021-01-01\"), ymd(\"2021-01-30\"), 0,   \"P002\", \"TSNDOSE\", \"V1\", \"Y\", ymd(\"2021-01-01\"), ymd(\"2021-02-01\"), 0,   \"P002\", \"TNDOSE\", \"V2\", \"Y\", ymd(\"2021-02-01\"), ymd(\"2021-03-15\"), 52,   \"P002\", \"TSNDOSE\", \"V2\", \"Y\", ymd(\"2021-02-05\"), ymd(\"2021-03-01\"), 0 )  derive_param_doseint(   adex,   by_vars = exprs(USUBJID, VISIT),   set_values_to = exprs(PARAMCD = \"TNDOSINT\"),   tadm_code = \"TNDOSE\",   tpadm_code = \"TSNDOSE\" ) #> # A tibble: 12 × 7 #>    USUBJID PARAMCD  VISIT ANL01FL ASTDT      AENDT       AVAL #>    <chr>   <chr>    <chr> <chr>   <date>     <date>     <dbl> #>  1 P001    TNDOSE   V1    Y       2020-01-01 2020-01-30  59   #>  2 P001    TSNDOSE  V1    Y       2020-01-01 2020-02-01  96   #>  3 P001    TNDOSE   V2    Y       2020-02-01 2020-03-15  88   #>  4 P001    TSNDOSE  V2    Y       2020-02-05 2020-03-01  88   #>  5 P002    TNDOSE   V1    Y       2021-01-01 2021-01-30   0   #>  6 P002    TSNDOSE  V1    Y       2021-01-01 2021-02-01   0   #>  7 P002    TNDOSE   V2    Y       2021-02-01 2021-03-15  52   #>  8 P002    TSNDOSE  V2    Y       2021-02-05 2021-03-01   0   #>  9 P001    TNDOSINT V1    NA      NA         NA          61.5 #> 10 P001    TNDOSINT V2    NA      NA         NA         100   #> 11 P002    TNDOSINT V1    NA      NA         NA         NaN   #> 12 P002    TNDOSINT V2    NA      NA         NA         Inf    derive_param_doseint(   adex,   by_vars = exprs(USUBJID, VISIT),   set_values_to = exprs(PARAMCD = \"TDOSINT2\"),   tadm_code = \"TNDOSE\",   tpadm_code = \"TSNDOSE\",   zero_doses = \"100\" ) #> # A tibble: 12 × 7 #>    USUBJID PARAMCD  VISIT ANL01FL ASTDT      AENDT       AVAL #>    <chr>   <chr>    <chr> <chr>   <date>     <date>     <dbl> #>  1 P001    TNDOSE   V1    Y       2020-01-01 2020-01-30  59   #>  2 P001    TSNDOSE  V1    Y       2020-01-01 2020-02-01  96   #>  3 P001    TNDOSE   V2    Y       2020-02-01 2020-03-15  88   #>  4 P001    TSNDOSE  V2    Y       2020-02-05 2020-03-01  88   #>  5 P002    TNDOSE   V1    Y       2021-01-01 2021-01-30   0   #>  6 P002    TSNDOSE  V1    Y       2021-01-01 2021-02-01   0   #>  7 P002    TNDOSE   V2    Y       2021-02-01 2021-03-15  52   #>  8 P002    TSNDOSE  V2    Y       2021-02-05 2021-03-01   0   #>  9 P001    TDOSINT2 V1    NA      NA         NA          61.5 #> 10 P001    TDOSINT2 V2    NA      NA         NA         100   #> 11 P002    TDOSINT2 V1    NA      NA         NA           0   #> 12 P002    TDOSINT2 V2    NA      NA         NA         100"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_exist_flag.html","id":null,"dir":"Reference","previous_headings":"","what":"Add an Existence Flag Parameter — derive_param_exist_flag","title":"Add an Existence Flag Parameter — derive_param_exist_flag","text":"Add new parameter indicating certain event exists dataset. AVALC AVAL indicate event occurred . example, function can derive parameter indicating measurable disease baseline.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_exist_flag.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add an Existence Flag Parameter — derive_param_exist_flag","text":"","code":"derive_param_exist_flag(   dataset = NULL,   dataset_ref,   dataset_add,   condition,   true_value = \"Y\",   false_value = NA_character_,   missing_value = NA_character_,   filter_add = NULL,   by_vars = get_admiral_option(\"subject_keys\"),   set_values_to )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_exist_flag.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add an Existence Flag Parameter — derive_param_exist_flag","text":"dataset Input dataset variables specified by_vars argument expected dataset. PARAMCD expected well. dataset_ref Reference dataset, e.g., ADSL variables specified by_vars expected. group (defined by_vars) specified dataset (dataset_ref), existence flag calculated added new observation input datasets (dataset). dataset_add Additional dataset variables specified by_vars parameter expected. dataset used check event occurred . observation dataset fulfilling event condition (condition) considered event. condition Event condition condition evaluated additional dataset (dataset_add). groups evaluates TRUE least AVALC set true value (true_value) new observations. groups evaluates FALSE NA observations AVALC set false value (false_value). groups present additional dataset AVALC set missing value (missing_value). true_value True value groups least one observations additional dataset (dataset_add) fulfilling event condition (condition), AVALC set specified value (true_value). Default: \"Y\" Permitted Value: character scalar false_value False value groups least one observations additional dataset (dataset_add) none fulfilling event condition (condition), AVALC set specified value (false_value). Default: NA_character_ Permitted Value: character scalar missing_value Values used missing information groups without observation additional dataset (dataset_add), AVALC set specified value (missing_value). Default: NA_character_ Permitted Value: character scalar filter_add Filter additional data observations fulfilling specified condition taken account flagging. parameter specified, observations considered. Permitted Values: condition by_vars Grouping variables Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) set_values_to Variables set named list returned exprs() defining variables set new parameter, e.g. exprs(PARAMCD = \"MDIS\", PARAM = \"Measurable Disease Baseline\") expected. values must symbols, character strings, numeric values, NA, expressions.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_exist_flag.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add an Existence Flag Parameter — derive_param_exist_flag","text":"input dataset new parameter indicating event occurred (AVALC variables specified by_vars set_value_to populated new parameter).","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_exist_flag.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Add an Existence Flag Parameter — derive_param_exist_flag","text":"additional dataset (dataset_add) restricted observations matching filter_add condition. group dataset_ref new observation created. AVALC variable added set true value (true_value) group least one observation exists (restricted) additional dataset condition evaluates TRUE. set false value (false_value) group least one observation exists observations condition evaluates FALSE NA. Otherwise, set missing value (missing_value), .e., groups dataset_add. variables specified set_values_to parameter added new observations. new observations added input dataset.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_exist_flag.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add an Existence Flag Parameter — derive_param_exist_flag","text":"","code":"library(tibble) library(dplyr, warn.conflicts = FALSE) library(lubridate)  # Derive a new parameter for measurable disease at baseline adsl <- tribble(   ~USUBJID,   \"1\",   \"2\",   \"3\" ) %>%   mutate(STUDYID = \"XX1234\")  tu <- tribble(   ~USUBJID, ~VISIT,      ~TUSTRESC,   \"1\",      \"SCREENING\", \"TARGET\",   \"1\",      \"WEEK 1\",    \"TARGET\",   \"1\",      \"WEEK 5\",    \"TARGET\",   \"1\",      \"WEEK 9\",    \"NON-TARGET\",   \"2\",      \"SCREENING\", \"NON-TARGET\",   \"2\",      \"SCREENING\", \"NON-TARGET\" ) %>%   mutate(     STUDYID = \"XX1234\",     TUTESTCD = \"TUMIDENT\"   )  derive_param_exist_flag(   dataset_ref = adsl,   dataset_add = tu,   filter_add = TUTESTCD == \"TUMIDENT\" & VISIT == \"SCREENING\",   condition = TUSTRESC == \"TARGET\",   false_value = \"N\",   missing_value = \"N\",   set_values_to = exprs(     AVAL = yn_to_numeric(AVALC),     PARAMCD = \"MDIS\",     PARAM = \"Measurable Disease at Baseline\"   ) ) #> # A tibble: 3 × 6 #>   USUBJID STUDYID AVALC  AVAL PARAMCD PARAM                          #>   <chr>   <chr>   <chr> <dbl> <chr>   <chr>                          #> 1 1       XX1234  Y         1 MDIS    Measurable Disease at Baseline #> 2 2       XX1234  N         0 MDIS    Measurable Disease at Baseline #> 3 3       XX1234  N         0 MDIS    Measurable Disease at Baseline"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_exposure.html","id":null,"dir":"Reference","previous_headings":"","what":"Add an Aggregated Parameter and Derive the Associated Start and End Dates — derive_param_exposure","title":"Add an Aggregated Parameter and Derive the Associated Start and End Dates — derive_param_exposure","text":"Add record computed aggregated analysis value another parameter compute start (ASTDT(M))end date (AENDT(M)) minimum maximum date by_vars.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_exposure.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add an Aggregated Parameter and Derive the Associated Start and End Dates — derive_param_exposure","text":"","code":"derive_param_exposure(   dataset = NULL,   dataset_add,   by_vars,   input_code,   filter_add = NULL,   set_values_to = NULL )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_exposure.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add an Aggregated Parameter and Derive the Associated Start and End Dates — derive_param_exposure","text":"dataset Input dataset variables specified by_vars argument expected dataset. dataset_add Additional dataset variables specified by_vars, analysis_var, PARAMCD, alongside either ASTDTM AENDTM ASTDT AENDT also expected. Observations specified dataset going used calculate added new records input dataset (dataset). by_vars Grouping variables group defined by_vars observation added output dataset. variables specified by_vars populated newly created records. Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) input_code Required parameter code observations PARAMCD equals specified value considered compute summary record. Permitted Values: character PARAMCD value filter_add Filter condition logical expression apply summary calculation. default, filtering expressions computed within by_vars help aggregating, lagging, ranking function involved. example, filter_add = (AVAL > mean(AVAL, na.rm = TRUE)) filter AVAL values greater mean AVAL by_vars. filter_add = (dplyr::n() > 2) filter n count by_vars greater 2. set_values_to Variable-value pairs Set list variables specified value new observation(s) LHS refer variable. expected least PARAMCD defined. RHS refers values set variable. can string, symbol, numeric value, NA, expression. (e.g.  exprs(PARAMCD = \"TDOSE\",PARCAT1 = \"OVERALL\")). Permitted Values: List variable-value pairs","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_exposure.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add an Aggregated Parameter and Derive the Associated Start and End Dates — derive_param_exposure","text":"input dataset new record added group (defined by_vars parameter). , variable populated new record specified by_vars. new record, set_values_to lists specified variable computes value, variable(s) specified LHS set_values_to set paired value (RHS). addition, start end date computed minimum/maximum dates by_vars. input datasets contains AxxDTM AxxDT ASTDTM,AENDTM, ASTDT, AENDT computed AxxDTM ASTDTM,AENDTM computed AxxDT ASTDT,AENDT computed.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_exposure.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Add an Aggregated Parameter and Derive the Associated Start and End Dates — derive_param_exposure","text":"group (respect variables specified by_vars parameter), observation added output dataset defined values set defined variables","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_exposure.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add an Aggregated Parameter and Derive the Associated Start and End Dates — derive_param_exposure","text":"","code":"library(tibble) library(dplyr, warn.conflicts = FALSE) library(lubridate, warn.conflicts = FALSE) library(stringr, warn.conflicts = FALSE) adex <- tribble(   ~USUBJID, ~PARAMCD, ~AVAL, ~AVALC, ~VISIT, ~ASTDT, ~AENDT,   \"1015\", \"DOSE\", 80, NA_character_, \"BASELINE\", ymd(\"2014-01-02\"), ymd(\"2014-01-16\"),   \"1015\", \"DOSE\", 85, NA_character_, \"WEEK 2\", ymd(\"2014-01-17\"), ymd(\"2014-06-18\"),   \"1015\", \"DOSE\", 82, NA_character_, \"WEEK 24\", ymd(\"2014-06-19\"), ymd(\"2014-07-02\"),   \"1015\", \"ADJ\", NA, NA_character_, \"BASELINE\", ymd(\"2014-01-02\"), ymd(\"2014-01-16\"),   \"1015\", \"ADJ\", NA, NA_character_, \"WEEK 2\", ymd(\"2014-01-17\"), ymd(\"2014-06-18\"),   \"1015\", \"ADJ\", NA, NA_character_, \"WEEK 24\", ymd(\"2014-06-19\"), ymd(\"2014-07-02\"),   \"1017\", \"DOSE\", 80, NA_character_, \"BASELINE\", ymd(\"2014-01-05\"), ymd(\"2014-01-19\"),   \"1017\", \"DOSE\", 50, NA_character_, \"WEEK 2\", ymd(\"2014-01-20\"), ymd(\"2014-05-10\"),   \"1017\", \"DOSE\", 65, NA_character_, \"WEEK 24\", ymd(\"2014-05-10\"), ymd(\"2014-07-02\"),   \"1017\", \"ADJ\", NA, NA_character_, \"BASELINE\", ymd(\"2014-01-05\"), ymd(\"2014-01-19\"),   \"1017\", \"ADJ\", NA, \"ADVERSE EVENT\", \"WEEK 2\", ymd(\"2014-01-20\"), ymd(\"2014-05-10\"),   \"1017\", \"ADJ\", NA, NA_character_, \"WEEK 24\", ymd(\"2014-05-10\"), ymd(\"2014-07-02\") ) %>%   mutate(ASTDTM = ymd_hms(paste(ASTDT, \"00:00:00\")), AENDTM = ymd_hms(paste(AENDT, \"00:00:00\")))  # Cumulative dose adex %>%   derive_param_exposure(     dataset_add = adex,     by_vars = exprs(USUBJID),     set_values_to = exprs(       PARAMCD = \"TDOSE\",       PARCAT1 = \"OVERALL\",       AVAL = sum(AVAL, na.rm = TRUE)     ),     input_code = \"DOSE\"   ) %>%   select(-ASTDTM, -AENDTM) #> # A tibble: 14 × 8 #>    USUBJID PARAMCD  AVAL AVALC         VISIT    ASTDT      AENDT      PARCAT1 #>    <chr>   <chr>   <dbl> <chr>         <chr>    <date>     <date>     <chr>   #>  1 1015    DOSE       80 NA            BASELINE 2014-01-02 2014-01-16 NA      #>  2 1015    DOSE       85 NA            WEEK 2   2014-01-17 2014-06-18 NA      #>  3 1015    DOSE       82 NA            WEEK 24  2014-06-19 2014-07-02 NA      #>  4 1015    ADJ        NA NA            BASELINE 2014-01-02 2014-01-16 NA      #>  5 1015    ADJ        NA NA            WEEK 2   2014-01-17 2014-06-18 NA      #>  6 1015    ADJ        NA NA            WEEK 24  2014-06-19 2014-07-02 NA      #>  7 1017    DOSE       80 NA            BASELINE 2014-01-05 2014-01-19 NA      #>  8 1017    DOSE       50 NA            WEEK 2   2014-01-20 2014-05-10 NA      #>  9 1017    DOSE       65 NA            WEEK 24  2014-05-10 2014-07-02 NA      #> 10 1017    ADJ        NA NA            BASELINE 2014-01-05 2014-01-19 NA      #> 11 1017    ADJ        NA ADVERSE EVENT WEEK 2   2014-01-20 2014-05-10 NA      #> 12 1017    ADJ        NA NA            WEEK 24  2014-05-10 2014-07-02 NA      #> 13 1015    TDOSE     247 NA            NA       2014-01-02 2014-07-02 OVERALL #> 14 1017    TDOSE     195 NA            NA       2014-01-05 2014-07-02 OVERALL  # average dose in w2-24 adex %>%   derive_param_exposure(     dataset_add = adex,     by_vars = exprs(USUBJID),     filter_add = VISIT %in% c(\"WEEK 2\", \"WEEK 24\"),     set_values_to = exprs(       PARAMCD = \"AVDW224\",       PARCAT1 = \"WEEK2-24\",       AVAL = mean(AVAL, na.rm = TRUE)     ),     input_code = \"DOSE\"   ) %>%   select(-ASTDTM, -AENDTM) #> # A tibble: 14 × 8 #>    USUBJID PARAMCD  AVAL AVALC         VISIT    ASTDT      AENDT      PARCAT1  #>    <chr>   <chr>   <dbl> <chr>         <chr>    <date>     <date>     <chr>    #>  1 1015    DOSE     80   NA            BASELINE 2014-01-02 2014-01-16 NA       #>  2 1015    DOSE     85   NA            WEEK 2   2014-01-17 2014-06-18 NA       #>  3 1015    DOSE     82   NA            WEEK 24  2014-06-19 2014-07-02 NA       #>  4 1015    ADJ      NA   NA            BASELINE 2014-01-02 2014-01-16 NA       #>  5 1015    ADJ      NA   NA            WEEK 2   2014-01-17 2014-06-18 NA       #>  6 1015    ADJ      NA   NA            WEEK 24  2014-06-19 2014-07-02 NA       #>  7 1017    DOSE     80   NA            BASELINE 2014-01-05 2014-01-19 NA       #>  8 1017    DOSE     50   NA            WEEK 2   2014-01-20 2014-05-10 NA       #>  9 1017    DOSE     65   NA            WEEK 24  2014-05-10 2014-07-02 NA       #> 10 1017    ADJ      NA   NA            BASELINE 2014-01-05 2014-01-19 NA       #> 11 1017    ADJ      NA   ADVERSE EVENT WEEK 2   2014-01-20 2014-05-10 NA       #> 12 1017    ADJ      NA   NA            WEEK 24  2014-05-10 2014-07-02 NA       #> 13 1015    AVDW224  83.5 NA            NA       2014-01-17 2014-07-02 WEEK2-24 #> 14 1017    AVDW224  57.5 NA            NA       2014-01-20 2014-07-02 WEEK2-24  # Any dose adjustment? adex %>%   derive_param_exposure(     dataset_add = adex,     by_vars = exprs(USUBJID),     set_values_to = exprs(       PARAMCD = \"TADJ\",       PARCAT1 = \"OVERALL\",       AVALC = if_else(sum(!is.na(AVALC)) > 0, \"Y\", NA_character_)     ),     input_code = \"ADJ\"   ) %>%   select(-ASTDTM, -AENDTM) #> # A tibble: 14 × 8 #>    USUBJID PARAMCD  AVAL AVALC         VISIT    ASTDT      AENDT      PARCAT1 #>    <chr>   <chr>   <dbl> <chr>         <chr>    <date>     <date>     <chr>   #>  1 1015    DOSE       80 NA            BASELINE 2014-01-02 2014-01-16 NA      #>  2 1015    DOSE       85 NA            WEEK 2   2014-01-17 2014-06-18 NA      #>  3 1015    DOSE       82 NA            WEEK 24  2014-06-19 2014-07-02 NA      #>  4 1015    ADJ        NA NA            BASELINE 2014-01-02 2014-01-16 NA      #>  5 1015    ADJ        NA NA            WEEK 2   2014-01-17 2014-06-18 NA      #>  6 1015    ADJ        NA NA            WEEK 24  2014-06-19 2014-07-02 NA      #>  7 1017    DOSE       80 NA            BASELINE 2014-01-05 2014-01-19 NA      #>  8 1017    DOSE       50 NA            WEEK 2   2014-01-20 2014-05-10 NA      #>  9 1017    DOSE       65 NA            WEEK 24  2014-05-10 2014-07-02 NA      #> 10 1017    ADJ        NA NA            BASELINE 2014-01-05 2014-01-19 NA      #> 11 1017    ADJ        NA ADVERSE EVENT WEEK 2   2014-01-20 2014-05-10 NA      #> 12 1017    ADJ        NA NA            WEEK 24  2014-05-10 2014-07-02 NA      #> 13 1015    TADJ       NA NA            NA       2014-01-02 2014-07-02 OVERALL #> 14 1017    TADJ       NA Y             NA       2014-01-05 2014-07-02 OVERALL"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_extreme_record.html","id":null,"dir":"Reference","previous_headings":"","what":"Adds a Parameter Based on First or Last Record from Multiple Sources — derive_param_extreme_record","title":"Adds a Parameter Based on First or Last Record from Multiple Sources — derive_param_extreme_record","text":"derive_param_extreme_record() function superseded favor derive_extreme_event(). Generates parameter based first last observation multiple source datasets, based user-defined filter, order group criteria. variables selected observation kept.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_extreme_record.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adds a Parameter Based on First or Last Record from Multiple Sources — derive_param_extreme_record","text":"","code":"derive_param_extreme_record(   dataset = NULL,   sources,   source_datasets,   by_vars = NULL,   order,   mode,   set_values_to )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_extreme_record.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adds a Parameter Based on First or Last Record from Multiple Sources — derive_param_extreme_record","text":"dataset Input dataset sources Sources list records_source() objects expected. source_datasets Source datasets named list datasets expected. dataset_name field records_source() refers dataset provided list. variables specified order by_vars arguments expected applying new_vars. by_vars Grouping variables argument specified, group observations selected separately. Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) order Sort order argument set non-null value, group first last observation source datasets selected respect specified order. Variables created via new_vars e.g., imputed date variables, can specified well (see examples ). Please note NA considered last value. .e., order variable NA mode = \"last\", observation chosen mode = \"first\" observation chosen observations variable NA. Permitted Values: list expressions created exprs(), e.g., exprs(ADT, desc(AVAL)) mode Selection mode (first last) \"first\" specified, group first observation respect order included output dataset. \"last\" specified, last observation included output dataset. Permitted Values:  \"first\", \"last\" set_values_to Variables set specified variables set specified values new observations. list variable name-value pairs expected. LHS refers variable. RHS refers values set variable. can string, symbol, numeric value NA, e.g., exprs(PARAMCD = \"PD\", PARAM =   \"First Progressive Disease\").","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_extreme_record.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adds a Parameter Based on First or Last Record from Multiple Sources — derive_param_extreme_record","text":"input dataset first last observation group added new observations.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_extreme_record.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Adds a Parameter Based on First or Last Record from Multiple Sources — derive_param_extreme_record","text":"following steps performed create output dataset: source dataset observations specified filter element selected. Variables specified new_vars created source dataset. first last observation (respect order variable) group (specified by_vars) multiple sources selected added input dataset.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_extreme_record.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Adds a Parameter Based on First or Last Record from Multiple Sources — derive_param_extreme_record","text":"","code":"aevent_samp <- tibble::tribble(   ~USUBJID, ~PARAMCD,                       ~PARAM,     ~RSSTDTC,   \"1\",          \"PD\",  \"First Progressive Disease\", \"2022-04-01\",   \"2\",          \"PD\",  \"First Progressive Disease\", \"2021-04-01\",   \"3\",          \"PD\",  \"First Progressive Disease\", \"2023-04-01\" )  cm <- tibble::tribble(   ~STUDYID, ~USUBJID, ~CMDECOD,     ~CMSTDTC,   \"1001\",        \"1\",    \"ACT\", \"2021-12-25\" )  pr <- tibble::tribble(   ~STUDYID, ~USUBJID, ~PRDECOD,     ~PRSTDTC,   \"1001\",        \"1\",    \"ACS\", \"2021-12-27\",   \"1001\",        \"2\",    \"ACS\", \"2020-12-25\",   \"1001\",        \"3\",    \"ACS\", \"2022-12-25\", ) derive_param_extreme_record(   dataset = aevent_samp,   sources = list(     records_source(       dataset_name = \"cm\",       filter = CMDECOD == \"ACT\",       new_vars = exprs(         ADT = convert_dtc_to_dt(CMSTDTC),         AVALC = CMDECOD       )     ),     records_source(       dataset_name = \"pr\",       filter = PRDECOD == \"ACS\",       new_vars = exprs(         ADT = convert_dtc_to_dt(PRSTDTC),         AVALC = PRDECOD       )     )   ),   source_datasets = list(cm = cm, pr = pr),   by_vars = exprs(USUBJID),   order = exprs(ADT),   mode = \"first\",   set_values_to = exprs(     PARAMCD = \"FIRSTACT\",     PARAM = \"First Anti-Cancer Therapy\"   ) ) #> # A tibble: 6 × 6 #>   USUBJID PARAMCD  PARAM                     RSSTDTC    ADT        AVALC #>   <chr>   <chr>    <chr>                     <chr>      <date>     <chr> #> 1 1       PD       First Progressive Disease 2022-04-01 NA         NA    #> 2 2       PD       First Progressive Disease 2021-04-01 NA         NA    #> 3 3       PD       First Progressive Disease 2023-04-01 NA         NA    #> 4 1       FIRSTACT First Anti-Cancer Therapy NA         2021-12-25 ACT   #> 5 2       FIRSTACT First Anti-Cancer Therapy NA         2020-12-25 ACS   #> 6 3       FIRSTACT First Anti-Cancer Therapy NA         2022-12-25 ACS"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_framingham.html","id":null,"dir":"Reference","previous_headings":"","what":"Adds a Parameter for Framingham Heart Study Cardiovascular Disease 10-Year Risk Score — derive_param_framingham","title":"Adds a Parameter for Framingham Heart Study Cardiovascular Disease 10-Year Risk Score — derive_param_framingham","text":"Adds record framingham score (FCVD101) group (e.g., subject visit) source parameters available.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_framingham.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adds a Parameter for Framingham Heart Study Cardiovascular Disease 10-Year Risk Score — derive_param_framingham","text":"","code":"derive_param_framingham(   dataset,   by_vars,   set_values_to = exprs(PARAMCD = \"FCVD101\"),   sysbp_code = \"SYSBP\",   chol_code = \"CHOL\",   cholhdl_code = \"CHOLHDL\",   age = AGE,   sex = SEX,   smokefl = SMOKEFL,   diabetfl = DIABETFL,   trthypfl = TRTHYPFL,   get_unit_expr,   filter = NULL )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_framingham.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adds a Parameter for Framingham Heart Study Cardiovascular Disease 10-Year Risk Score — derive_param_framingham","text":"dataset Input dataset variables specified by_vars argument expected dataset. PARAMCD, AVAL expected well. variable specified by_vars PARAMCD must unique key input dataset restricting filter condition (filter parameter) parameters specified sysbp_code, chol_code hdl_code. by_vars Grouping variables variables specified by_vars populated newly created records. Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) set_values_to Variables set specified variables set specified values new observations. example exprs(PARAMCD = \"MAP\") defines parameter code new parameter. Permitted Values: List variable-value pairs sysbp_code Systolic blood pressure parameter code observations PARAMCD equals specified value considered systolic blood pressure assessments. Permitted Values: character value chol_code Total serum cholesterol code observations PARAMCD equals specified value considered total cholesterol assessments. must measured mg/dL. Permitted Values: character value cholhdl_code HDL serum cholesterol code observations PARAMCD equals specified value considered HDL cholesterol assessments. must measured mg/dL. Permitted Values: character value age Subject age variable containing subject's age. Permitted Values: numeric variable name refers subject age column input dataset sex Subject sex variable containing subject's sex. Permitted Values: character variable name refers subject sex column input dataset smokefl Smoking status flag flag indicating smoking status. Permitted Values: character variable name refers smoking status column input dataset. diabetfl Diabetic flag flag indicating diabetic status. Permitted Values: character variable name refers diabetic status column input dataset trthypfl Treated hypertension medication flag flag indicating subject treated hypertension medication. Permitted Values: character variable name refers column indicates whether subject treated high blood pressure get_unit_expr expression providing unit parameter result used check units input parameters. Permitted Values: expression evaluable input dataset results character value filter Filter condition specified condition applied input dataset deriving new parameter, .e., observations fulfilling condition taken account. Permitted Values: condition","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_framingham.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adds a Parameter for Framingham Heart Study Cardiovascular Disease 10-Year Risk Score — derive_param_framingham","text":"input dataset new parameter added","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_framingham.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Adds a Parameter for Framingham Heart Study Cardiovascular Disease 10-Year Risk Score — derive_param_framingham","text":"values age, sex, smokefl, diabetfl trthypfl added by_vars list. predicted probability cardiovascular disease (CVD) within 10-years according Framingham formula. See AHA Journal article General Cardiovascular Risk Profile Use Primary Care reference. Women: Men: equation calculating risk: $$RiskFactors = (log(Age) * AgeFactor) + (log(TotalChol) * TotalCholFactor) + (log(CholHDL) * CholHDLFactor) \\\\ + (log(SysBP) * SysBPFactor) + Smoker + Diabetes Present - AvgRisk$$ $$Risk = 100 * (1 - RiskPeriodFactor^{RiskFactors})$$","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_framingham.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Adds a Parameter for Framingham Heart Study Cardiovascular Disease 10-Year Risk Score — derive_param_framingham","text":"","code":"library(tibble)  adcvrisk <- tribble(   ~USUBJID, ~PARAMCD, ~PARAM, ~AVAL, ~AVALU,   ~VISIT, ~AGE, ~SEX, ~SMOKEFL, ~DIABETFL, ~TRTHYPFL,   \"01-701-1015\", \"SYSBP\", \"Systolic Blood Pressure (mmHg)\", 121,   \"mmHg\", \"BASELINE\", 44, \"F\", \"N\", \"N\", \"N\",   \"01-701-1015\", \"SYSBP\", \"Systolic Blood Pressure (mmHg)\", 115,   \"mmHg\", \"WEEK 2\", 44, \"F\", \"N\", \"N\", \"Y\",   \"01-701-1015\", \"CHOL\", \"Total Cholesterol (mg/dL)\", 216.16,   \"mg/dL\", \"BASELINE\", 44, \"F\", \"N\", \"N\", \"N\",   \"01-701-1015\", \"CHOL\", \"Total Cholesterol (mg/dL)\", 210.78,   \"mg/dL\", \"WEEK 2\", 44, \"F\", \"N\", \"N\", \"Y\",   \"01-701-1015\", \"CHOLHDL\", \"Cholesterol/HDL-Cholesterol (mg/dL)\", 54.91,   \"mg/dL\", \"BASELINE\", 44, \"F\", \"N\", \"N\", \"N\",   \"01-701-1015\", \"CHOLHDL\", \"Cholesterol/HDL-Cholesterol (mg/dL)\", 26.72,   \"mg/dL\", \"WEEK 2\", 44, \"F\", \"N\", \"N\", \"Y\",   \"01-701-1028\", \"SYSBP\", \"Systolic Blood Pressure (mmHg)\", 119,   \"mmHg\", \"BASELINE\", 55, \"M\", \"Y\", \"Y\", \"Y\",   \"01-701-1028\", \"SYSBP\", \"Systolic Blood Pressure (mmHg)\", 101,   \"mmHg\", \"WEEK 2\", 55, \"M\", \"Y\", \"Y\", \"Y\",   \"01-701-1028\", \"CHOL\", \"Total Cholesterol (mg/dL)\", 292.01,   \"mg/dL\", \"BASELINE\", 55, \"M\", \"Y\", \"Y\", \"Y\",   \"01-701-1028\", \"CHOL\", \"Total Cholesterol (mg/dL)\", 246.73,   \"mg/dL\", \"WEEK 2\", 55, \"M\", \"Y\", \"Y\", \"Y\",   \"01-701-1028\", \"CHOLHDL\", \"Cholesterol/HDL-Cholesterol (mg/dL)\", 65.55,   \"mg/dL\", \"BASELINE\", 55, \"M\", \"Y\", \"Y\", \"Y\",   \"01-701-1028\", \"CHOLHDL\", \"Cholesterol/HDL-Cholesterol (mg/dL)\", 44.62,   \"mg/dL\", \"WEEK 2\", 55, \"M\", \"Y\", \"Y\", \"Y\" )   adcvrisk %>%   derive_param_framingham(     by_vars = exprs(USUBJID, VISIT),     set_values_to = exprs(       PARAMCD = \"FCVD101\",       PARAM = \"FCVD1-Framingham CVD 10-Year Risk Score (%)\"     ),     get_unit_expr = AVALU   ) #> # A tibble: 16 × 11 #>    USUBJID     PARAMCD PARAM       AVAL AVALU VISIT   AGE SEX   SMOKEFL DIABETFL #>    <chr>       <chr>   <chr>      <dbl> <chr> <chr> <dbl> <chr> <chr>   <chr>    #>  1 01-701-1015 SYSBP   Systolic… 121    mmHg  BASE…    44 F     N       N        #>  2 01-701-1015 SYSBP   Systolic… 115    mmHg  WEEK…    44 F     N       N        #>  3 01-701-1015 CHOL    Total Ch… 216.   mg/dL BASE…    44 F     N       N        #>  4 01-701-1015 CHOL    Total Ch… 211.   mg/dL WEEK…    44 F     N       N        #>  5 01-701-1015 CHOLHDL Choleste…  54.9  mg/dL BASE…    44 F     N       N        #>  6 01-701-1015 CHOLHDL Choleste…  26.7  mg/dL WEEK…    44 F     N       N        #>  7 01-701-1028 SYSBP   Systolic… 119    mmHg  BASE…    55 M     Y       Y        #>  8 01-701-1028 SYSBP   Systolic… 101    mmHg  WEEK…    55 M     Y       Y        #>  9 01-701-1028 CHOL    Total Ch… 292.   mg/dL BASE…    55 M     Y       Y        #> 10 01-701-1028 CHOL    Total Ch… 247.   mg/dL WEEK…    55 M     Y       Y        #> 11 01-701-1028 CHOLHDL Choleste…  65.6  mg/dL BASE…    55 M     Y       Y        #> 12 01-701-1028 CHOLHDL Choleste…  44.6  mg/dL WEEK…    55 M     Y       Y        #> 13 01-701-1015 FCVD101 FCVD1-Fr…   3.14 NA    BASE…    44 F     N       N        #> 14 01-701-1015 FCVD101 FCVD1-Fr…   5.80 NA    WEEK…    44 F     N       N        #> 15 01-701-1028 FCVD101 FCVD1-Fr…  42.3  NA    BASE…    55 M     Y       Y        #> 16 01-701-1028 FCVD101 FCVD1-Fr…  37.5  NA    WEEK…    55 M     Y       Y        #> # ℹ 1 more variable: TRTHYPFL <chr>  derive_param_framingham(   adcvrisk,   by_vars = exprs(USUBJID, VISIT),   set_values_to = exprs(     PARAMCD = \"FCVD101\",     PARAM = \"FCVD1-Framingham CVD 10-Year Risk Score (%)\"   ),   get_unit_expr = extract_unit(PARAM) ) #> # A tibble: 16 × 11 #>    USUBJID     PARAMCD PARAM       AVAL AVALU VISIT   AGE SEX   SMOKEFL DIABETFL #>    <chr>       <chr>   <chr>      <dbl> <chr> <chr> <dbl> <chr> <chr>   <chr>    #>  1 01-701-1015 SYSBP   Systolic… 121    mmHg  BASE…    44 F     N       N        #>  2 01-701-1015 SYSBP   Systolic… 115    mmHg  WEEK…    44 F     N       N        #>  3 01-701-1015 CHOL    Total Ch… 216.   mg/dL BASE…    44 F     N       N        #>  4 01-701-1015 CHOL    Total Ch… 211.   mg/dL WEEK…    44 F     N       N        #>  5 01-701-1015 CHOLHDL Choleste…  54.9  mg/dL BASE…    44 F     N       N        #>  6 01-701-1015 CHOLHDL Choleste…  26.7  mg/dL WEEK…    44 F     N       N        #>  7 01-701-1028 SYSBP   Systolic… 119    mmHg  BASE…    55 M     Y       Y        #>  8 01-701-1028 SYSBP   Systolic… 101    mmHg  WEEK…    55 M     Y       Y        #>  9 01-701-1028 CHOL    Total Ch… 292.   mg/dL BASE…    55 M     Y       Y        #> 10 01-701-1028 CHOL    Total Ch… 247.   mg/dL WEEK…    55 M     Y       Y        #> 11 01-701-1028 CHOLHDL Choleste…  65.6  mg/dL BASE…    55 M     Y       Y        #> 12 01-701-1028 CHOLHDL Choleste…  44.6  mg/dL WEEK…    55 M     Y       Y        #> 13 01-701-1015 FCVD101 FCVD1-Fr…   3.14 NA    BASE…    44 F     N       N        #> 14 01-701-1015 FCVD101 FCVD1-Fr…   5.80 NA    WEEK…    44 F     N       N        #> 15 01-701-1028 FCVD101 FCVD1-Fr…  42.3  NA    BASE…    55 M     Y       Y        #> 16 01-701-1028 FCVD101 FCVD1-Fr…  37.5  NA    WEEK…    55 M     Y       Y        #> # ℹ 1 more variable: TRTHYPFL <chr>"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_map.html","id":null,"dir":"Reference","previous_headings":"","what":"Adds a Parameter for Mean Arterial Pressure — derive_param_map","title":"Adds a Parameter for Mean Arterial Pressure — derive_param_map","text":"Adds record mean arterial pressure (MAP) group (e.g., subject visit) source parameters available. Note: wrapper function generic derive_param_computed().","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_map.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adds a Parameter for Mean Arterial Pressure — derive_param_map","text":"","code":"derive_param_map(   dataset,   by_vars,   set_values_to = exprs(PARAMCD = \"MAP\"),   sysbp_code = \"SYSBP\",   diabp_code = \"DIABP\",   hr_code = NULL,   get_unit_expr,   filter = NULL )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_map.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adds a Parameter for Mean Arterial Pressure — derive_param_map","text":"dataset Input dataset variables specified by_vars argument expected dataset. PARAMCD, AVAL expected well. variable specified by_vars PARAMCD must unique key input dataset restricting filter condition (filter parameter) parameters specified sysbp_code, diabp_code hr_code. by_vars Grouping variables group defined by_vars observation added output dataset. variables specified by_vars populated newly created records. Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) set_values_to Variables set specified variables set specified values new observations. example exprs(PARAMCD = \"MAP\") defines parameter code new parameter. Permitted Values: List variable-value pairs sysbp_code Systolic blood pressure parameter code observations PARAMCD equals specified value considered systolic blood pressure assessments. Permitted Values: character value diabp_code Diastolic blood pressure parameter code observations PARAMCD equals specified value considered diastolic blood pressure assessments. Permitted Values: character value hr_code Heart rate parameter code observations PARAMCD equals specified value considered heart rate assessments. Permitted Values: character value get_unit_expr expression providing unit parameter result used check units input parameters. Permitted Values: expression evaluable input dataset results character value filter Filter condition specified condition applied input dataset deriving new parameter, .e., observations fulfilling condition taken account. Permitted Values: condition","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_map.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adds a Parameter for Mean Arterial Pressure — derive_param_map","text":"input dataset new parameter added. Note, variable populated new parameter rows specified by_vars.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_map.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Adds a Parameter for Mean Arterial Pressure — derive_param_map","text":"analysis value new parameter derived $$\\frac{2DIABP + SYSBP}{3}$$ based diastolic systolic blood pressure $$DIABP + 0.01 e^{4.14 - \\frac{40.74}{HR}} (SYSBP - DIABP)$$ based diastolic, systolic blood pressure, heart rate.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_map.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Adds a Parameter for Mean Arterial Pressure — derive_param_map","text":"","code":"library(tibble) library(dplyr, warn.conflicts = FALSE)  advs <- tibble::tribble(   ~USUBJID, ~PARAMCD, ~PARAM, ~AVAL, ~VISIT,   \"01-701-1015\", \"PULSE\", \"Pulse (beats/min)\", 59, \"BASELINE\",   \"01-701-1015\", \"PULSE\", \"Pulse (beats/min)\", 61, \"WEEK 2\",   \"01-701-1015\", \"DIABP\", \"Diastolic Blood Pressure (mmHg)\", 51, \"BASELINE\",   \"01-701-1015\", \"DIABP\", \"Diastolic Blood Pressure (mmHg)\", 50, \"WEEK 2\",   \"01-701-1015\", \"SYSBP\", \"Systolic Blood Pressure (mmHg)\", 121, \"BASELINE\",   \"01-701-1015\", \"SYSBP\", \"Systolic Blood Pressure (mmHg)\", 121, \"WEEK 2\",   \"01-701-1028\", \"PULSE\", \"Pulse (beats/min)\", 62, \"BASELINE\",   \"01-701-1028\", \"PULSE\", \"Pulse (beats/min)\", 77, \"WEEK 2\",   \"01-701-1028\", \"DIABP\", \"Diastolic Blood Pressure (mmHg)\", 79, \"BASELINE\",   \"01-701-1028\", \"DIABP\", \"Diastolic Blood Pressure (mmHg)\", 80, \"WEEK 2\",   \"01-701-1028\", \"SYSBP\", \"Systolic Blood Pressure (mmHg)\", 130, \"BASELINE\",   \"01-701-1028\", \"SYSBP\", \"Systolic Blood Pressure (mmHg)\", 132, \"WEEK 2\" )  # Derive MAP based on diastolic and systolic blood pressure advs %>%   derive_param_map(     by_vars = exprs(USUBJID, VISIT),     set_values_to = exprs(       PARAMCD = \"MAP\",       PARAM = \"Mean Arterial Pressure (mmHg)\"     ),     get_unit_expr = extract_unit(PARAM)   ) %>%   filter(PARAMCD != \"PULSE\") #> # A tibble: 12 × 5 #>    USUBJID     PARAMCD PARAM                            AVAL VISIT    #>    <chr>       <chr>   <chr>                           <dbl> <chr>    #>  1 01-701-1015 DIABP   Diastolic Blood Pressure (mmHg)  51   BASELINE #>  2 01-701-1015 DIABP   Diastolic Blood Pressure (mmHg)  50   WEEK 2   #>  3 01-701-1015 SYSBP   Systolic Blood Pressure (mmHg)  121   BASELINE #>  4 01-701-1015 SYSBP   Systolic Blood Pressure (mmHg)  121   WEEK 2   #>  5 01-701-1028 DIABP   Diastolic Blood Pressure (mmHg)  79   BASELINE #>  6 01-701-1028 DIABP   Diastolic Blood Pressure (mmHg)  80   WEEK 2   #>  7 01-701-1028 SYSBP   Systolic Blood Pressure (mmHg)  130   BASELINE #>  8 01-701-1028 SYSBP   Systolic Blood Pressure (mmHg)  132   WEEK 2   #>  9 01-701-1015 MAP     Mean Arterial Pressure (mmHg)    74.3 BASELINE #> 10 01-701-1015 MAP     Mean Arterial Pressure (mmHg)    73.7 WEEK 2   #> 11 01-701-1028 MAP     Mean Arterial Pressure (mmHg)    96   BASELINE #> 12 01-701-1028 MAP     Mean Arterial Pressure (mmHg)    97.3 WEEK 2    # Derive MAP based on diastolic and systolic blood pressure and heart rate derive_param_map(   advs,   by_vars = exprs(USUBJID, VISIT),   hr_code = \"PULSE\",   set_values_to = exprs(     PARAMCD = \"MAP\",     PARAM = \"Mean Arterial Pressure (mmHg)\"   ),   get_unit_expr = extract_unit(PARAM) ) #> # A tibble: 16 × 5 #>    USUBJID     PARAMCD PARAM                            AVAL VISIT    #>    <chr>       <chr>   <chr>                           <dbl> <chr>    #>  1 01-701-1015 PULSE   Pulse (beats/min)                59   BASELINE #>  2 01-701-1015 PULSE   Pulse (beats/min)                61   WEEK 2   #>  3 01-701-1015 DIABP   Diastolic Blood Pressure (mmHg)  51   BASELINE #>  4 01-701-1015 DIABP   Diastolic Blood Pressure (mmHg)  50   WEEK 2   #>  5 01-701-1015 SYSBP   Systolic Blood Pressure (mmHg)  121   BASELINE #>  6 01-701-1015 SYSBP   Systolic Blood Pressure (mmHg)  121   WEEK 2   #>  7 01-701-1028 PULSE   Pulse (beats/min)                62   BASELINE #>  8 01-701-1028 PULSE   Pulse (beats/min)                77   WEEK 2   #>  9 01-701-1028 DIABP   Diastolic Blood Pressure (mmHg)  79   BASELINE #> 10 01-701-1028 DIABP   Diastolic Blood Pressure (mmHg)  80   WEEK 2   #> 11 01-701-1028 SYSBP   Systolic Blood Pressure (mmHg)  130   BASELINE #> 12 01-701-1028 SYSBP   Systolic Blood Pressure (mmHg)  132   WEEK 2   #> 13 01-701-1015 MAP     Mean Arterial Pressure (mmHg)    73.0 BASELINE #> 14 01-701-1015 MAP     Mean Arterial Pressure (mmHg)    72.9 WEEK 2   #> 15 01-701-1028 MAP     Mean Arterial Pressure (mmHg)    95.6 BASELINE #> 16 01-701-1028 MAP     Mean Arterial Pressure (mmHg)    99.2 WEEK 2"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_qtc.html","id":null,"dir":"Reference","previous_headings":"","what":"Adds a Parameter for Corrected QT (an ECG measurement) — derive_param_qtc","title":"Adds a Parameter for Corrected QT (an ECG measurement) — derive_param_qtc","text":"Adds record corrected QT using either Bazett's, Fridericia's Sagie's formula group (e.g., subject visit) source parameters available. Note: wrapper function generic derive_param_computed().","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_qtc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adds a Parameter for Corrected QT (an ECG measurement) — derive_param_qtc","text":"","code":"derive_param_qtc(   dataset,   by_vars,   method,   set_values_to = default_qtc_paramcd(method),   qt_code = \"QT\",   rr_code = \"RR\",   get_unit_expr,   filter = NULL )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_qtc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adds a Parameter for Corrected QT (an ECG measurement) — derive_param_qtc","text":"dataset Input dataset variables specified by_vars get_unit_expr arguments expected dataset. PARAMCD, AVAL expected well. variable specified by_vars PARAMCD must unique key input dataset restricting filter condition (filter argument) parameters specified qt_code rr_code. by_vars Grouping variables variables specified by_vars populated newly created records. Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) method Method used QT correction See compute_qtc() details. Permitted Values: \"Bazett\", \"Fridericia\", \"Sagie\" set_values_to Variables set specified variables set specified values new observations. example exprs(PARAMCD = \"MAP\") defines parameter code new parameter. Permitted Values: List variable-value pairs qt_code QT parameter code observations PARAMCD equals specified value considered QT interval assessments. expected QT measured ms msec. Permitted Values: character value rr_code RR parameter code observations PARAMCD equals specified value considered RR interval assessments. expected RR measured ms msec. Permitted Values: character value get_unit_expr expression providing unit parameter result used check units input parameters. Permitted Values: expression evaluable input dataset results character value filter Filter condition specified condition applied input dataset deriving new parameter, .e., observations fulfilling condition taken account. Permitted Values: condition","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_qtc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adds a Parameter for Corrected QT (an ECG measurement) — derive_param_qtc","text":"input dataset new parameter added. Note, variable populated new parameter rows specified by_vars.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_qtc.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Adds a Parameter for Corrected QT (an ECG measurement) — derive_param_qtc","text":"","code":"library(tibble)  adeg <- tribble(   ~USUBJID,      ~PARAMCD, ~PARAM,                   ~AVAL, ~AVALU,      ~VISIT,   \"01-701-1015\", \"HR\",     \"Heart Rate (beats/min)\", 70.14, \"beats/min\", \"BASELINE\",   \"01-701-1015\", \"QT\",     \"QT Duration (ms)\",         370, \"ms\",        \"WEEK 2\",   \"01-701-1015\", \"HR\",     \"Heart Rate (beats/min)\", 62.66, \"beats/min\", \"WEEK 1\",   \"01-701-1015\", \"RR\",     \"RR Duration (ms)\",         710, \"ms\",        \"WEEK 2\",   \"01-701-1028\", \"HR\",     \"Heart Rate (beats/min)\", 85.45, \"beats/min\", \"BASELINE\",   \"01-701-1028\", \"QT\",     \"QT Duration (ms)\",         480, \"ms\",        \"WEEK 2\",   \"01-701-1028\", \"QT\",     \"QT Duration (ms)\",         350, \"ms\",        \"WEEK 3\",   \"01-701-1028\", \"HR\",     \"Heart Rate (beats/min)\", 56.54, \"beats/min\", \"WEEK 3\",   \"01-701-1028\", \"RR\",     \"RR Duration (ms)\",         842, \"ms\",        \"WEEK 2\" )  derive_param_qtc(   adeg,   by_vars = exprs(USUBJID, VISIT),   method = \"Bazett\",   set_values_to = exprs(     PARAMCD = \"QTCBR\",     PARAM = \"QTcB - Bazett's Correction Formula Rederived (ms)\",     AVALU = \"ms\"   ),   get_unit_expr = AVALU ) #> # A tibble: 11 × 6 #>    USUBJID     PARAMCD PARAM                                    AVAL AVALU VISIT #>    <chr>       <chr>   <chr>                                   <dbl> <chr> <chr> #>  1 01-701-1015 HR      Heart Rate (beats/min)                   70.1 beat… BASE… #>  2 01-701-1015 QT      QT Duration (ms)                        370   ms    WEEK… #>  3 01-701-1015 HR      Heart Rate (beats/min)                   62.7 beat… WEEK… #>  4 01-701-1015 RR      RR Duration (ms)                        710   ms    WEEK… #>  5 01-701-1028 HR      Heart Rate (beats/min)                   85.4 beat… BASE… #>  6 01-701-1028 QT      QT Duration (ms)                        480   ms    WEEK… #>  7 01-701-1028 QT      QT Duration (ms)                        350   ms    WEEK… #>  8 01-701-1028 HR      Heart Rate (beats/min)                   56.5 beat… WEEK… #>  9 01-701-1028 RR      RR Duration (ms)                        842   ms    WEEK… #> 10 01-701-1015 QTCBR   QTcB - Bazett's Correction Formula Red… 439.  ms    WEEK… #> 11 01-701-1028 QTCBR   QTcB - Bazett's Correction Formula Red… 523.  ms    WEEK…  derive_param_qtc(   adeg,   by_vars = exprs(USUBJID, VISIT),   method = \"Fridericia\",   set_values_to = exprs(     PARAMCD = \"QTCFR\",     PARAM = \"QTcF - Fridericia's Correction Formula Rederived (ms)\",     AVALU = \"ms\"   ),   get_unit_expr = extract_unit(PARAM) ) #> # A tibble: 11 × 6 #>    USUBJID     PARAMCD PARAM                                    AVAL AVALU VISIT #>    <chr>       <chr>   <chr>                                   <dbl> <chr> <chr> #>  1 01-701-1015 HR      Heart Rate (beats/min)                   70.1 beat… BASE… #>  2 01-701-1015 QT      QT Duration (ms)                        370   ms    WEEK… #>  3 01-701-1015 HR      Heart Rate (beats/min)                   62.7 beat… WEEK… #>  4 01-701-1015 RR      RR Duration (ms)                        710   ms    WEEK… #>  5 01-701-1028 HR      Heart Rate (beats/min)                   85.4 beat… BASE… #>  6 01-701-1028 QT      QT Duration (ms)                        480   ms    WEEK… #>  7 01-701-1028 QT      QT Duration (ms)                        350   ms    WEEK… #>  8 01-701-1028 HR      Heart Rate (beats/min)                   56.5 beat… WEEK… #>  9 01-701-1028 RR      RR Duration (ms)                        842   ms    WEEK… #> 10 01-701-1015 QTCFR   QTcF - Fridericia's Correction Formula… 415.  ms    WEEK… #> 11 01-701-1028 QTCFR   QTcF - Fridericia's Correction Formula… 508.  ms    WEEK…  derive_param_qtc(   adeg,   by_vars = exprs(USUBJID, VISIT),   method = \"Sagie\",   set_values_to = exprs(     PARAMCD = \"QTLCR\",     PARAM = \"QTlc - Sagie's Correction Formula Rederived (ms)\",     AVALU = \"ms\"   ),   get_unit_expr = extract_unit(PARAM) ) #> # A tibble: 11 × 6 #>    USUBJID     PARAMCD PARAM                                    AVAL AVALU VISIT #>    <chr>       <chr>   <chr>                                   <dbl> <chr> <chr> #>  1 01-701-1015 HR      Heart Rate (beats/min)                   70.1 beat… BASE… #>  2 01-701-1015 QT      QT Duration (ms)                        370   ms    WEEK… #>  3 01-701-1015 HR      Heart Rate (beats/min)                   62.7 beat… WEEK… #>  4 01-701-1015 RR      RR Duration (ms)                        710   ms    WEEK… #>  5 01-701-1028 HR      Heart Rate (beats/min)                   85.4 beat… BASE… #>  6 01-701-1028 QT      QT Duration (ms)                        480   ms    WEEK… #>  7 01-701-1028 QT      QT Duration (ms)                        350   ms    WEEK… #>  8 01-701-1028 HR      Heart Rate (beats/min)                   56.5 beat… WEEK… #>  9 01-701-1028 RR      RR Duration (ms)                        842   ms    WEEK… #> 10 01-701-1015 QTLCR   QTlc - Sagie's Correction Formula Rede… 415.  ms    WEEK… #> 11 01-701-1028 QTLCR   QTlc - Sagie's Correction Formula Rede… 504.  ms    WEEK…"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_rr.html","id":null,"dir":"Reference","previous_headings":"","what":"Adds a Parameter for Derived RR (an ECG measurement) — derive_param_rr","title":"Adds a Parameter for Derived RR (an ECG measurement) — derive_param_rr","text":"Adds record derived RR based heart rate group (e.g., subject visit) source parameters available. Note: wrapper function generic derive_param_computed(). analysis value new parameter derived $$\\frac{60000}{HR}$$","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_rr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adds a Parameter for Derived RR (an ECG measurement) — derive_param_rr","text":"","code":"derive_param_rr(   dataset,   by_vars,   set_values_to = exprs(PARAMCD = \"RRR\"),   hr_code = \"HR\",   get_unit_expr,   filter = NULL )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_rr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adds a Parameter for Derived RR (an ECG measurement) — derive_param_rr","text":"dataset Input dataset variables specified by_vars argument expected dataset. PARAMCD, AVAL expected well. variable specified by_vars PARAMCD must unique key input dataset restricting filter condition (filter argument) parameters specified hr_code. by_vars Grouping variables group defined by_vars observation added output dataset. variables specified by_vars populated newly created records. Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) set_values_to Variables set specified variables set specified values new observations. example exprs(PARAMCD = \"MAP\") defines parameter code new parameter. Permitted Values: List variable-value pairs hr_code HR parameter code observations PARAMCD equals specified value considered heart rate assessments. Permitted Values: character value get_unit_expr expression providing unit parameter result used check units input parameters. Permitted Values: expression evaluable input dataset results character value filter Filter condition specified condition applied input dataset deriving new parameter, .e., observations fulfilling condition taken account. Permitted Values: condition","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_rr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adds a Parameter for Derived RR (an ECG measurement) — derive_param_rr","text":"input dataset new parameter added. Note, variable populated new parameter rows specified by_vars.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_rr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Adds a Parameter for Derived RR (an ECG measurement) — derive_param_rr","text":"","code":"library(tibble)  adeg <- tribble(   ~USUBJID, ~PARAMCD, ~PARAM, ~AVAL, ~AVALU, ~VISIT,   \"01-701-1015\", \"HR\", \"Heart Rate\", 70.14, \"beats/min\", \"BASELINE\",   \"01-701-1015\", \"QT\", \"QT Duration\", 370, \"ms\", \"WEEK 2\",   \"01-701-1015\", \"HR\", \"Heart Rate\", 62.66, \"beats/min\", \"WEEK 1\",   \"01-701-1015\", \"RR\", \"RR Duration\", 710, \"ms\", \"WEEK 2\",   \"01-701-1028\", \"HR\", \"Heart Rate\", 85.45, \"beats/min\", \"BASELINE\",   \"01-701-1028\", \"QT\", \"QT Duration\", 480, \"ms\", \"WEEK 2\",   \"01-701-1028\", \"QT\", \"QT Duration\", 350, \"ms\", \"WEEK 3\",   \"01-701-1028\", \"HR\", \"Heart Rate\", 56.54, \"beats/min\", \"WEEK 3\",   \"01-701-1028\", \"RR\", \"RR Duration\", 842, \"ms\", \"WEEK 2\" )  derive_param_rr(   adeg,   by_vars = exprs(USUBJID, VISIT),   set_values_to = exprs(     PARAMCD = \"RRR\",     PARAM = \"RR Duration Rederived (ms)\",     AVALU = \"ms\"   ),   get_unit_expr = AVALU ) #> # A tibble: 13 × 6 #>    USUBJID     PARAMCD PARAM                        AVAL AVALU     VISIT    #>    <chr>       <chr>   <chr>                       <dbl> <chr>     <chr>    #>  1 01-701-1015 HR      Heart Rate                   70.1 beats/min BASELINE #>  2 01-701-1015 QT      QT Duration                 370   ms        WEEK 2   #>  3 01-701-1015 HR      Heart Rate                   62.7 beats/min WEEK 1   #>  4 01-701-1015 RR      RR Duration                 710   ms        WEEK 2   #>  5 01-701-1028 HR      Heart Rate                   85.4 beats/min BASELINE #>  6 01-701-1028 QT      QT Duration                 480   ms        WEEK 2   #>  7 01-701-1028 QT      QT Duration                 350   ms        WEEK 3   #>  8 01-701-1028 HR      Heart Rate                   56.5 beats/min WEEK 3   #>  9 01-701-1028 RR      RR Duration                 842   ms        WEEK 2   #> 10 01-701-1015 RRR     RR Duration Rederived (ms)  855.  ms        BASELINE #> 11 01-701-1015 RRR     RR Duration Rederived (ms)  958.  ms        WEEK 1   #> 12 01-701-1028 RRR     RR Duration Rederived (ms)  702.  ms        BASELINE #> 13 01-701-1028 RRR     RR Duration Rederived (ms) 1061.  ms        WEEK 3"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_tte.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive a Time-to-Event Parameter — derive_param_tte","title":"Derive a Time-to-Event Parameter — derive_param_tte","text":"Add time--event parameter input dataset.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_tte.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive a Time-to-Event Parameter — derive_param_tte","text":"","code":"derive_param_tte(   dataset = NULL,   dataset_adsl,   source_datasets,   by_vars = NULL,   start_date = TRTSDT,   event_conditions,   censor_conditions,   create_datetime = FALSE,   set_values_to,   subject_keys = get_admiral_option(\"subject_keys\") )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_tte.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive a Time-to-Event Parameter — derive_param_tte","text":"dataset Input dataset PARAMCD expected. dataset_adsl ADSL input dataset variables specified start_date, subject_keys expected. source_datasets Source datasets named list datasets expected. dataset_name field tte_source() refers dataset provided list. by_vars variables parameter specified, separate time event parameters derived group. variables must least one source datasets. source dataset must contain either variables none variables. variables included output dataset. Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) start_date Time event origin date variable STARTDT set specified date. value taken ADSL dataset. event censoring date origin date, ADT set origin date. event_conditions Sources conditions defining events list event_source() objects expected. censor_conditions Sources conditions defining censorings list censor_source() objects expected. create_datetime Create datetime variables? set TRUE, variables ADTM STARTDTM created. Otherwise, variables ADT STARTDT created. set_values_to Variables set named list returned exprs() defining variables set new parameter, e.g. exprs(PARAMCD = \"OS\", PARAM = \"Overall Survival\") expected. values must symbols, character strings, numeric values, expressions, NA. subject_keys Variables uniquely identify subject list symbols created using exprs() expected.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_tte.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive a Time-to-Event Parameter — derive_param_tte","text":"input dataset new parameter added","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_tte.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Derive a Time-to-Event Parameter — derive_param_tte","text":"following steps performed create observations new parameter: Deriving events: event source dataset observations specified filter element selected. patient first observation (respect date) selected. ADT variable set variable specified date element. date variable datetime variable, datepart copied. CNSR variable added set censor element. variables specified set_values_to element added. selected observations event source datasets combined single dataset. patient first observation (respect ADT variable) single dataset selected. Deriving censoring observations: censoring source dataset observations specified filter element selected. patient last observation (respect date) selected. ADT variable set variable specified date element. date variable datetime variable, datepart copied. CNSR variable added set censor element. variables specified set_values_to element added. selected observations censoring source datasets combined single dataset. patient last observation (respect ADT variable) single dataset selected. subject (defined subject_keys parameter) observation selected. event available, event observation selected. Otherwise censoring observation selected. Finally: variable specified start_date joined ADSL dataset. subjects datasets kept, .e., subjects event censoring observation dataset_adsl. variables defined set_values_to parameter added. ADT/ADTM variable set maximum ADT/ADTM STARTDT/STARTDTM (depending create_datetime parameter). new observations added output dataset.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_tte.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive a Time-to-Event Parameter — derive_param_tte","text":"","code":"library(tibble) library(dplyr, warn.conflicts = FALSE) library(lubridate) data(\"admiral_adsl\")  adsl <- admiral_adsl  # derive overall survival parameter death <- event_source(   dataset_name = \"adsl\",   filter = DTHFL == \"Y\",   date = DTHDT,   set_values_to = exprs(     EVNTDESC = \"DEATH\",     SRCDOM = \"ADSL\",     SRCVAR = \"DTHDT\"   ) )  last_alive_dt <- censor_source(   dataset_name = \"adsl\",   date = LSTALVDT,   set_values_to = exprs(     EVNTDESC = \"LAST DATE KNOWN ALIVE\",     SRCDOM = \"ADSL\",     SRCVAR = \"LSTALVDT\"   ) )  derive_param_tte(   dataset_adsl = adsl,   event_conditions = list(death),   censor_conditions = list(last_alive_dt),   source_datasets = list(adsl = adsl),   set_values_to = exprs(     PARAMCD = \"OS\",     PARAM = \"Overall Survival\"   ) ) %>%   select(-STUDYID) %>%   filter(row_number() %in% 20:30) #> # A tibble: 11 × 9 #>    USUBJID     EVNTDESC  SRCDOM SRCVAR  CNSR ADT        STARTDT    PARAMCD PARAM #>    <chr>       <chr>     <chr>  <chr>  <int> <date>     <date>     <chr>   <chr> #>  1 01-701-1203 LAST DAT… ADSL   LSTAL…     1 2013-08-03 2013-02-02 OS      Over… #>  2 01-701-1211 DEATH     ADSL   DTHDT      0 2013-01-14 2012-11-15 OS      Over… #>  3 01-701-1234 LAST DAT… ADSL   LSTAL…     1 2013-09-22 2013-03-30 OS      Over… #>  4 01-701-1239 LAST DAT… ADSL   LSTAL…     1 2014-07-11 2014-01-11 OS      Over… #>  5 01-701-1275 LAST DAT… ADSL   LSTAL…     1 2014-06-14 2014-02-07 OS      Over… #>  6 01-701-1287 LAST DAT… ADSL   LSTAL…     1 2014-07-26 2014-01-25 OS      Over… #>  7 01-701-1294 LAST DAT… ADSL   LSTAL…     1 2013-06-14 2013-03-24 OS      Over… #>  8 01-701-1302 LAST DAT… ADSL   LSTAL…     1 2013-11-05 2013-08-29 OS      Over… #>  9 01-701-1317 LAST DAT… ADSL   LSTAL…     1 2014-11-20 2014-05-22 OS      Over… #> 10 01-701-1324 LAST DAT… ADSL   LSTAL…     1 2013-04-02 2012-10-02 OS      Over… #> 11 01-701-1341 LAST DAT… ADSL   LSTAL…     1 2013-02-07 2013-01-05 OS      Over…  # derive duration of response # only observations for subjects in dataset_adsl are created adsl <- tribble(   ~USUBJID, ~DTHFL, ~DTHDT,            ~RSPDT,   \"01\",     \"Y\",    ymd(\"2021-06-12\"), ymd(\"2021-03-04\"),   \"02\",     \"N\",    NA,                NA,   \"03\",     \"Y\",    ymd(\"2021-08-21\"), NA,   \"04\",     \"N\",    NA,                ymd(\"2021-04-14\") ) %>%   mutate(STUDYID = \"AB42\")  adrs <- tribble(   ~USUBJID, ~AVALC, ~ADT,              ~ASEQ,   \"01\",     \"SD\",   ymd(\"2021-01-03\"), 1,   \"01\",     \"PR\",   ymd(\"2021-03-04\"), 2,   \"01\",     \"PD\",   ymd(\"2021-05-05\"), 3,   \"02\",     \"PD\",   ymd(\"2021-02-03\"), 1,   \"04\",     \"SD\",   ymd(\"2021-02-13\"), 1,   \"04\",     \"PR\",   ymd(\"2021-04-14\"), 2,   \"04\",     \"CR\",   ymd(\"2021-05-15\"), 3 ) %>%   mutate(STUDYID = \"AB42\", PARAMCD = \"OVR\")  pd <- event_source(   dataset_name = \"adrs\",   filter = AVALC == \"PD\",   date = ADT,   set_values_to = exprs(     EVENTDESC = \"PD\",     SRCDOM = \"ADRS\",     SRCVAR = \"ADTM\",     SRCSEQ = ASEQ   ) )  death <- event_source(   dataset_name = \"adsl\",   filter = DTHFL == \"Y\",   date = DTHDT,   set_values_to = exprs(     EVENTDESC = \"DEATH\",     SRCDOM = \"ADSL\",     SRCVAR = \"DTHDT\"   ) )  lastvisit <- censor_source(   dataset_name = \"adrs\",   date = ADT,   censor = 1,   set_values_to = exprs(     EVENTDESC = \"LAST TUMOR ASSESSMENT\",     SRCDOM = \"ADRS\",     SRCVAR = \"ADTM\",     SRCSEQ = ASEQ   ) )  first_response <- censor_source(   dataset_name = \"adsl\",   date = RSPDT,   censor = 1,   set_values_to = exprs(     EVENTDESC = \"FIRST RESPONSE\",     SRCDOM = \"ADSL\",     SRCVAR = \"RSPDT\"   ) )  derive_param_tte(   dataset_adsl = filter(adsl, !is.na(RSPDT)),   start_date = RSPDT,   event_conditions = list(pd, death),   censor_conditions = list(lastvisit, first_response),   source_datasets = list(adsl = adsl, adrs = adrs),   set_values_to = exprs(     PARAMCD = \"DURRSP\",     PARAM = \"Duration of Response\"   ) ) #> # A tibble: 2 × 11 #>   USUBJID ADT        STUDYID EVENTDESC     SRCDOM SRCVAR SRCSEQ  CNSR STARTDT    #>   <chr>   <date>     <chr>   <chr>         <chr>  <chr>   <dbl> <int> <date>     #> 1 01      2021-05-05 AB42    PD            ADRS   ADTM        3     0 2021-03-04 #> 2 04      2021-05-15 AB42    LAST TUMOR A… ADRS   ADTM        3     1 2021-04-14 #> # ℹ 2 more variables: PARAMCD <chr>, PARAM <chr>  # derive time to adverse event for each preferred term adsl <- tribble(   ~USUBJID, ~TRTSDT,           ~EOSDT,   \"01\",     ymd(\"2020-12-06\"), ymd(\"2021-03-06\"),   \"02\",     ymd(\"2021-01-16\"), ymd(\"2021-02-03\") ) %>%   mutate(STUDYID = \"AB42\")  ae <- tribble(   ~USUBJID, ~AESTDTC,           ~AESEQ, ~AEDECOD,   \"01\",     \"2021-01-03T10:56\", 1,      \"Flu\",   \"01\",     \"2021-03-04\",       2,      \"Cough\",   \"01\",     \"2021\",             3,      \"Flu\" ) %>%   mutate(STUDYID = \"AB42\")  ae_ext <- derive_vars_dt(   ae,   dtc = AESTDTC,   new_vars_prefix = \"AEST\",   highest_imputation = \"M\",   flag_imputation = \"none\" )  ttae <- event_source(   dataset_name = \"ae\",   date = AESTDT,   set_values_to = exprs(     EVNTDESC = \"AE\",     SRCDOM = \"AE\",     SRCVAR = \"AESTDTC\",     SRCSEQ = AESEQ   ) )  eos <- censor_source(   dataset_name = \"adsl\",   date = EOSDT,   set_values_to = exprs(     EVNTDESC = \"END OF STUDY\",     SRCDOM = \"ADSL\",     SRCVAR = \"EOSDT\"   ) )  derive_param_tte(   dataset_adsl = adsl,   by_vars = exprs(AEDECOD),   start_date = TRTSDT,   event_conditions = list(ttae),   censor_conditions = list(eos),   source_datasets = list(adsl = adsl, ae = ae_ext),   set_values_to = exprs(     PARAMCD = paste0(\"TTAE\", as.numeric(as.factor(AEDECOD))),     PARAM = paste(\"Time to First\", AEDECOD, \"Adverse Event\"),     PARCAT1 = \"TTAE\",     PARCAT2 = AEDECOD   ) ) %>%   select(USUBJID, STARTDT, PARAMCD, PARAM, ADT, CNSR, SRCSEQ) #> # A tibble: 4 × 7 #>   USUBJID STARTDT    PARAMCD PARAM                       ADT         CNSR SRCSEQ #>   <chr>   <date>     <chr>   <chr>                       <date>     <int>  <dbl> #> 1 01      2020-12-06 TTAE1   Time to First Cough Advers… 2021-03-04     0      2 #> 2 01      2020-12-06 TTAE2   Time to First Flu Adverse … 2021-01-01     0      3 #> 3 02      2021-01-16 TTAE1   Time to First Cough Advers… 2021-02-03     1     NA #> 4 02      2021-01-16 TTAE2   Time to First Flu Adverse … 2021-02-03     1     NA"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_wbc_abs.html","id":null,"dir":"Reference","previous_headings":"","what":"Add a parameter for lab differentials converted to absolute values — derive_param_wbc_abs","title":"Add a parameter for lab differentials converted to absolute values — derive_param_wbc_abs","text":"Add parameter converting lab differentials fraction percentage absolute values","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_wbc_abs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add a parameter for lab differentials converted to absolute values — derive_param_wbc_abs","text":"","code":"derive_param_wbc_abs(   dataset,   by_vars,   set_values_to,   get_unit_expr,   wbc_unit = \"10^9/L\",   wbc_code = \"WBC\",   diff_code,   diff_type = \"fraction\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_wbc_abs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add a parameter for lab differentials converted to absolute values — derive_param_wbc_abs","text":"dataset Input dataset variables specified by_vars argument expected dataset. PARAMCD, AVAL expected well. variable specified by_vars PARAMCD must unique key input dataset, parameters specified wbc_code diff_code. by_vars Grouping variables Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) set_values_to Variables set named list returned exprs() defining variables set new parameter, e.g. exprs(PARAMCD = \"LYMPH\", PARAM = \"Lymphocytes Abs (10^9/L)\") expected. get_unit_expr expression providing unit parameter result used check units input parameters. Permitted Values: variable containing unit input dataset, function call, example, get_unit_expr = extract_unit(PARAM). wbc_unit string containing required unit WBC parameter Default: \"10^9/L\" wbc_code White Blood Cell (WBC) parameter observations PARAMCD equals specified value considered WBC absolute results use converting differentials. Default: \"WBC\" Permitted Values: character value diff_code white blood differential parameter observations PARAMCD equals specified value considered white blood differential lab results fraction percentage value converted absolute value. diff_type string specifying type differential Permitted Values: \"percent\", \"fraction\" Default: fraction","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_wbc_abs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add a parameter for lab differentials converted to absolute values — derive_param_wbc_abs","text":"input dataset new parameter added","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_wbc_abs.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Add a parameter for lab differentials converted to absolute values — derive_param_wbc_abs","text":"diff_type \"percent\", analysis value new parameter derived $$\\frac{White Blood Cell Count  * Percentage Value}{100}$$ diff_type \"fraction\", analysis value new parameter derived $$White Blood Cell Count  * Fraction Value$$ New records created group records (grouped by_vars) 1) white blood cell component absolute value already available input dataset, 2) white blood cell absolute value (identified wbc_code) white blood cell differential (identified diff_code) present.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_wbc_abs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add a parameter for lab differentials converted to absolute values — derive_param_wbc_abs","text":"","code":"library(tibble)  test_lb <- tribble(   ~USUBJID, ~PARAMCD, ~AVAL, ~PARAM, ~VISIT,   \"P01\", \"WBC\", 33, \"Leukocyte Count (10^9/L)\", \"CYCLE 1 DAY 1\",   \"P01\", \"WBC\", 38, \"Leukocyte Count (10^9/L)\", \"CYCLE 2 DAY 1\",   \"P01\", \"LYMLE\", 0.90, \"Lymphocytes (fraction of 1)\", \"CYCLE 1 DAY 1\",   \"P01\", \"LYMLE\", 0.70, \"Lymphocytes (fraction of 1)\", \"CYCLE 2 DAY 1\",   \"P01\", \"ALB\", 36, \"Albumin (g/dL)\", \"CYCLE 2 DAY 1\",   \"P02\", \"WBC\", 33, \"Leukocyte Count (10^9/L)\", \"CYCLE 1 DAY 1\",   \"P02\", \"LYMPH\", 29, \"Lymphocytes Abs (10^9/L)\", \"CYCLE 1 DAY 1\",   \"P02\", \"LYMLE\", 0.87, \"Lymphocytes (fraction of 1)\", \"CYCLE 1 DAY 1\",   \"P03\", \"LYMLE\", 0.89, \"Lymphocytes (fraction of 1)\", \"CYCLE 1 DAY 1\" )  derive_param_wbc_abs(   dataset = test_lb,   by_vars = exprs(USUBJID, VISIT),   set_values_to = exprs(     PARAMCD = \"LYMPH\",     PARAM = \"Lymphocytes Abs (10^9/L)\",     DTYPE = \"CALCULATION\"   ),   get_unit_expr = extract_unit(PARAM),   wbc_code = \"WBC\",   diff_code = \"LYMLE\",   diff_type = \"fraction\" ) #> # A tibble: 11 × 6 #>    USUBJID PARAMCD  AVAL PARAM                       VISIT         DTYPE       #>    <chr>   <chr>   <dbl> <chr>                       <chr>         <chr>       #>  1 P01     WBC     33    Leukocyte Count (10^9/L)    CYCLE 1 DAY 1 NA          #>  2 P01     WBC     38    Leukocyte Count (10^9/L)    CYCLE 2 DAY 1 NA          #>  3 P01     LYMLE    0.9  Lymphocytes (fraction of 1) CYCLE 1 DAY 1 NA          #>  4 P01     LYMLE    0.7  Lymphocytes (fraction of 1) CYCLE 2 DAY 1 NA          #>  5 P01     ALB     36    Albumin (g/dL)              CYCLE 2 DAY 1 NA          #>  6 P02     WBC     33    Leukocyte Count (10^9/L)    CYCLE 1 DAY 1 NA          #>  7 P02     LYMPH   29    Lymphocytes Abs (10^9/L)    CYCLE 1 DAY 1 NA          #>  8 P02     LYMLE    0.87 Lymphocytes (fraction of 1) CYCLE 1 DAY 1 NA          #>  9 P03     LYMLE    0.89 Lymphocytes (fraction of 1) CYCLE 1 DAY 1 NA          #> 10 P01     LYMPH   29.7  Lymphocytes Abs (10^9/L)    CYCLE 1 DAY 1 CALCULATION #> 11 P01     LYMPH   26.6  Lymphocytes Abs (10^9/L)    CYCLE 2 DAY 1 CALCULATION"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_summary_records.html","id":null,"dir":"Reference","previous_headings":"","what":"Add New Records Within By Groups Using Aggregation Functions — derive_summary_records","title":"Add New Records Within By Groups Using Aggregation Functions — derive_summary_records","text":"uncommon analysis need whereby one needs derive analysis value (AVAL) multiple records. ADaM basic dataset structure variable DTYPE available indicate new derived records added dataset.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_summary_records.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add New Records Within By Groups Using Aggregation Functions — derive_summary_records","text":"","code":"derive_summary_records(   dataset = NULL,   dataset_add,   dataset_ref = NULL,   by_vars,   filter_add = NULL,   set_values_to,   missing_values = NULL )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_summary_records.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add New Records Within By Groups Using Aggregation Functions — derive_summary_records","text":"dataset Input dataset variables specified by_vars argument expected dataset. dataset_add Additional dataset variables specified by_vars expected. Observations specified dataset going used calculate added new records input dataset (dataset). dataset_ref Reference dataset variables specified by_vars expected. observation specified dataset new observation added input dataset. by_vars Grouping variables Variables consider generation groupwise summary records. Providing names variables exprs() create groupwise summary generate summary records specified groups. Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) filter_add Filter condition logical expression apply summary calculation. default, filtering expressions computed within by_vars help aggregating, lagging, ranking function involved. example, filter_add = (AVAL > mean(AVAL, na.rm = TRUE)) filter AVAL values greater mean AVAL by_vars. filter_add = (dplyr::n() > 2) filter n count by_vars greater 2. set_values_to Variables set specified variables set specified values new observations. Set list variables specified value new records LHS refer variable. RHS refers values set variable. can string, symbol, numeric value, expression NA. summary functions used, values summarized variables specified by_vars. example:   missing_values Values missing summary values observations reference dataset (dataset_ref) complete mapping defined summarization defined set_values_to.  variables specified set_values_to can specified missing_values. Permitted Values: named list expressions, e.g., exprs(AVAL = -9999)","code":"set_values_to = exprs(     AVAL = sum(AVAL),     DTYPE = \"AVERAGE\",   )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_summary_records.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add New Records Within By Groups Using Aggregation Functions — derive_summary_records","text":"data frame derived records appended original dataset.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_summary_records.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Add New Records Within By Groups Using Aggregation Functions — derive_summary_records","text":"records values within by_vars function retain common values newly derived records. Otherwise new value set NA.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_summary_records.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add New Records Within By Groups Using Aggregation Functions — derive_summary_records","text":"","code":"library(tibble) library(dplyr)  adeg <- tribble(   ~USUBJID,   ~EGSEQ, ~PARAM,             ~AVISIT,    ~EGDTC,             ~AVAL, ~TRTA,   \"XYZ-1001\", 1,      \"QTcF Int. (msec)\", \"Baseline\", \"2016-02-24T07:50\", 385,   NA_character_,   \"XYZ-1001\", 2,      \"QTcF Int. (msec)\", \"Baseline\", \"2016-02-24T07:52\", 399,   NA_character_,   \"XYZ-1001\", 3,      \"QTcF Int. (msec)\", \"Baseline\", \"2016-02-24T07:56\", 396,   NA_character_,   \"XYZ-1001\", 4,      \"QTcF Int. (msec)\", \"Visit 2\",  \"2016-03-08T09:45\", 384,   \"Placebo\",   \"XYZ-1001\", 5,      \"QTcF Int. (msec)\", \"Visit 2\",  \"2016-03-08T09:48\", 393,   \"Placebo\",   \"XYZ-1001\", 6,      \"QTcF Int. (msec)\", \"Visit 2\",  \"2016-03-08T09:51\", 388,   \"Placebo\",   \"XYZ-1001\", 7,      \"QTcF Int. (msec)\", \"Visit 3\",  \"2016-03-22T10:45\", 385,   \"Placebo\",   \"XYZ-1001\", 8,      \"QTcF Int. (msec)\", \"Visit 3\",  \"2016-03-22T10:48\", 394,   \"Placebo\",   \"XYZ-1001\", 9,      \"QTcF Int. (msec)\", \"Visit 3\",  \"2016-03-22T10:51\", 402,   \"Placebo\",   \"XYZ-1002\", 1,      \"QTcF Int. (msec)\", \"Baseline\", \"2016-02-22T07:58\", 399,   NA_character_,   \"XYZ-1002\", 2,      \"QTcF Int. (msec)\", \"Baseline\", \"2016-02-22T07:58\", 410,   NA_character_,   \"XYZ-1002\", 3,      \"QTcF Int. (msec)\", \"Baseline\", \"2016-02-22T08:01\", 392,   NA_character_,   \"XYZ-1002\", 4,      \"QTcF Int. (msec)\", \"Visit 2\",  \"2016-03-06T09:50\", 401,   \"Active 20mg\",   \"XYZ-1002\", 5,      \"QTcF Int. (msec)\", \"Visit 2\",  \"2016-03-06T09:53\", 407,   \"Active 20mg\",   \"XYZ-1002\", 6,      \"QTcF Int. (msec)\", \"Visit 2\",  \"2016-03-06T09:56\", 400,   \"Active 20mg\",   \"XYZ-1002\", 7,      \"QTcF Int. (msec)\", \"Visit 3\",  \"2016-03-24T10:50\", 412,   \"Active 20mg\",   \"XYZ-1002\", 8,      \"QTcF Int. (msec)\", \"Visit 3\",  \"2016-03-24T10:53\", 414,   \"Active 20mg\",   \"XYZ-1002\", 9,      \"QTcF Int. (msec)\", \"Visit 3\",  \"2016-03-24T10:56\", 402,   \"Active 20mg\" ) %>%   mutate(     ADTM = convert_dtc_to_dtm(EGDTC)   )  # Summarize the average of the triplicate ECG interval values (AVAL) derive_summary_records(   adeg,   dataset_add = adeg,   by_vars = exprs(USUBJID, PARAM, AVISIT),   set_values_to = exprs(     AVAL = mean(AVAL, na.rm = TRUE),     DTYPE = \"AVERAGE\"   ) ) %>%   arrange(USUBJID, AVISIT) #> # A tibble: 24 × 9 #>    USUBJID  EGSEQ PARAM       AVISIT EGDTC  AVAL TRTA  ADTM                DTYPE #>    <chr>    <dbl> <chr>       <chr>  <chr> <dbl> <chr> <dttm>              <chr> #>  1 XYZ-1001     1 QTcF Int. … Basel… 2016…  385  NA    2016-02-24 07:50:00 NA    #>  2 XYZ-1001     2 QTcF Int. … Basel… 2016…  399  NA    2016-02-24 07:52:00 NA    #>  3 XYZ-1001     3 QTcF Int. … Basel… 2016…  396  NA    2016-02-24 07:56:00 NA    #>  4 XYZ-1001    NA QTcF Int. … Basel… NA     393. NA    NA                  AVER… #>  5 XYZ-1001     4 QTcF Int. … Visit… 2016…  384  Plac… 2016-03-08 09:45:00 NA    #>  6 XYZ-1001     5 QTcF Int. … Visit… 2016…  393  Plac… 2016-03-08 09:48:00 NA    #>  7 XYZ-1001     6 QTcF Int. … Visit… 2016…  388  Plac… 2016-03-08 09:51:00 NA    #>  8 XYZ-1001    NA QTcF Int. … Visit… NA     388. NA    NA                  AVER… #>  9 XYZ-1001     7 QTcF Int. … Visit… 2016…  385  Plac… 2016-03-22 10:45:00 NA    #> 10 XYZ-1001     8 QTcF Int. … Visit… 2016…  394  Plac… 2016-03-22 10:48:00 NA    #> # ℹ 14 more rows  # Derive more than one summary variable derive_summary_records(   adeg,   dataset_add = adeg,   by_vars = exprs(USUBJID, PARAM, AVISIT),   set_values_to = exprs(     AVAL = mean(AVAL),     ADTM = max(ADTM),     DTYPE = \"AVERAGE\"   ) ) %>%   arrange(USUBJID, AVISIT) %>%   select(-EGSEQ, -TRTA) #> # A tibble: 24 × 7 #>    USUBJID  PARAM            AVISIT   EGDTC       AVAL ADTM                DTYPE #>    <chr>    <chr>            <chr>    <chr>      <dbl> <dttm>              <chr> #>  1 XYZ-1001 QTcF Int. (msec) Baseline 2016-02-2…  385  2016-02-24 07:50:00 NA    #>  2 XYZ-1001 QTcF Int. (msec) Baseline 2016-02-2…  399  2016-02-24 07:52:00 NA    #>  3 XYZ-1001 QTcF Int. (msec) Baseline 2016-02-2…  396  2016-02-24 07:56:00 NA    #>  4 XYZ-1001 QTcF Int. (msec) Baseline NA          393. 2016-02-24 07:56:00 AVER… #>  5 XYZ-1001 QTcF Int. (msec) Visit 2  2016-03-0…  384  2016-03-08 09:45:00 NA    #>  6 XYZ-1001 QTcF Int. (msec) Visit 2  2016-03-0…  393  2016-03-08 09:48:00 NA    #>  7 XYZ-1001 QTcF Int. (msec) Visit 2  2016-03-0…  388  2016-03-08 09:51:00 NA    #>  8 XYZ-1001 QTcF Int. (msec) Visit 2  NA          388. 2016-03-08 09:51:00 AVER… #>  9 XYZ-1001 QTcF Int. (msec) Visit 3  2016-03-2…  385  2016-03-22 10:45:00 NA    #> 10 XYZ-1001 QTcF Int. (msec) Visit 3  2016-03-2…  394  2016-03-22 10:48:00 NA    #> # ℹ 14 more rows  # Sample ADEG dataset with triplicate record for only AVISIT = 'Baseline' adeg <- tribble(   ~USUBJID,   ~EGSEQ, ~PARAM,             ~AVISIT,    ~EGDTC,             ~AVAL, ~TRTA,   \"XYZ-1001\", 1,      \"QTcF Int. (msec)\", \"Baseline\", \"2016-02-24T07:50\", 385,   NA_character_,   \"XYZ-1001\", 2,      \"QTcF Int. (msec)\", \"Baseline\", \"2016-02-24T07:52\", 399,   NA_character_,   \"XYZ-1001\", 3,      \"QTcF Int. (msec)\", \"Baseline\", \"2016-02-24T07:56\", 396,   NA_character_,   \"XYZ-1001\", 4,      \"QTcF Int. (msec)\", \"Visit 2\",  \"2016-03-08T09:48\", 393,   \"Placebo\",   \"XYZ-1001\", 5,      \"QTcF Int. (msec)\", \"Visit 2\",  \"2016-03-08T09:51\", 388,   \"Placebo\",   \"XYZ-1001\", 6,      \"QTcF Int. (msec)\", \"Visit 3\",  \"2016-03-22T10:48\", 394,   \"Placebo\",   \"XYZ-1001\", 7,      \"QTcF Int. (msec)\", \"Visit 3\",  \"2016-03-22T10:51\", 402,   \"Placebo\",   \"XYZ-1002\", 1,      \"QTcF Int. (msec)\", \"Baseline\", \"2016-02-22T07:58\", 399,   NA_character_,   \"XYZ-1002\", 2,      \"QTcF Int. (msec)\", \"Baseline\", \"2016-02-22T07:58\", 410,   NA_character_,   \"XYZ-1002\", 3,      \"QTcF Int. (msec)\", \"Baseline\", \"2016-02-22T08:01\", 392,   NA_character_,   \"XYZ-1002\", 4,      \"QTcF Int. (msec)\", \"Visit 2\",  \"2016-03-06T09:53\", 407,   \"Active 20mg\",   \"XYZ-1002\", 5,      \"QTcF Int. (msec)\", \"Visit 2\",  \"2016-03-06T09:56\", 400,   \"Active 20mg\",   \"XYZ-1002\", 6,      \"QTcF Int. (msec)\", \"Visit 3\",  \"2016-03-24T10:53\", 414,   \"Active 20mg\",   \"XYZ-1002\", 7,      \"QTcF Int. (msec)\", \"Visit 3\",  \"2016-03-24T10:56\", 402,   \"Active 20mg\" )  # Compute the average of AVAL only if there are more than 2 records within the # by group derive_summary_records(   adeg,   dataset_add = adeg,   by_vars = exprs(USUBJID, PARAM, AVISIT),   filter_add = n() > 2,   set_values_to = exprs(     AVAL = mean(AVAL, na.rm = TRUE),     DTYPE = \"AVERAGE\"   ) ) %>%   arrange(USUBJID, AVISIT) #> # A tibble: 16 × 8 #>    USUBJID  EGSEQ PARAM            AVISIT   EGDTC             AVAL TRTA    DTYPE #>    <chr>    <dbl> <chr>            <chr>    <chr>            <dbl> <chr>   <chr> #>  1 XYZ-1001     1 QTcF Int. (msec) Baseline 2016-02-24T07:50  385  NA      NA    #>  2 XYZ-1001     2 QTcF Int. (msec) Baseline 2016-02-24T07:52  399  NA      NA    #>  3 XYZ-1001     3 QTcF Int. (msec) Baseline 2016-02-24T07:56  396  NA      NA    #>  4 XYZ-1001    NA QTcF Int. (msec) Baseline NA                393. NA      AVER… #>  5 XYZ-1001     4 QTcF Int. (msec) Visit 2  2016-03-08T09:48  393  Placebo NA    #>  6 XYZ-1001     5 QTcF Int. (msec) Visit 2  2016-03-08T09:51  388  Placebo NA    #>  7 XYZ-1001     6 QTcF Int. (msec) Visit 3  2016-03-22T10:48  394  Placebo NA    #>  8 XYZ-1001     7 QTcF Int. (msec) Visit 3  2016-03-22T10:51  402  Placebo NA    #>  9 XYZ-1002     1 QTcF Int. (msec) Baseline 2016-02-22T07:58  399  NA      NA    #> 10 XYZ-1002     2 QTcF Int. (msec) Baseline 2016-02-22T07:58  410  NA      NA    #> 11 XYZ-1002     3 QTcF Int. (msec) Baseline 2016-02-22T08:01  392  NA      NA    #> 12 XYZ-1002    NA QTcF Int. (msec) Baseline NA                400. NA      AVER… #> 13 XYZ-1002     4 QTcF Int. (msec) Visit 2  2016-03-06T09:53  407  Active… NA    #> 14 XYZ-1002     5 QTcF Int. (msec) Visit 2  2016-03-06T09:56  400  Active… NA    #> 15 XYZ-1002     6 QTcF Int. (msec) Visit 3  2016-03-24T10:53  414  Active… NA    #> 16 XYZ-1002     7 QTcF Int. (msec) Visit 3  2016-03-24T10:56  402  Active… NA"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_age_years.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive Age in Years — derive_var_age_years","title":"Derive Age in Years — derive_var_age_years","text":"Converts given age variable (age_var) unit 'years' current units given age_var+U variable age_unit argument stores new variable (new_var).","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_age_years.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive Age in Years — derive_var_age_years","text":"","code":"derive_var_age_years(dataset, age_var, age_unit = NULL, new_var)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_age_years.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive Age in Years — derive_var_age_years","text":"dataset Input dataset variables specified age_var argument expected dataset. age_var Age variable. numeric object expected. age_unit Age unit. age_unit argument expected variable age_var+U dataset. gives unit age_var variable used convert AGE 'years' grouping can occur. Default: NULL Permitted Values: 'years', 'months', 'weeks', 'days', 'hours', 'minutes', 'seconds' new_var New age variable created years. returned values doubles integers. '","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_age_years.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive Age in Years — derive_var_age_years","text":"input dataset (dataset) new_var variable added years.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_age_years.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Derive Age in Years — derive_var_age_years","text":"function used convert age variable unit 'years' can used create age groups. resulting column contains equivalent years double. Note, underlying computations assume equal number days year (365.25).","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_age_years.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive Age in Years — derive_var_age_years","text":"","code":"library(tibble)  # Derive age with age units specified data <- tribble(   ~AGE, ~AGEU,   27, \"days\",   24, \"months\",   3, \"years\",   4, \"weeks\",   1, \"years\" )  derive_var_age_years(data, AGE, new_var = AAGE) #> # A tibble: 5 × 3 #>     AGE AGEU     AAGE #>   <dbl> <chr>   <dbl> #> 1    27 days   0.0739 #> 2    24 months 2      #> 3     3 years  3      #> 4     4 weeks  0.0767 #> 5     1 years  1       # Derive age without age units variable specified data <- tribble(   ~AGE,   12,   24,   36,   48 ) derive_var_age_years(data, AGE, age_unit = \"months\", new_var = AAGE) #> # A tibble: 4 × 2 #>     AGE  AAGE #>   <dbl> <dbl> #> 1    12     1 #> 2    24     2 #> 3    36     3 #> 4    48     4"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_analysis_ratio.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive Ratio Variable — derive_var_analysis_ratio","title":"Derive Ratio Variable — derive_var_analysis_ratio","text":"Derives ratio variable BDS dataset based user specified variables.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_analysis_ratio.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive Ratio Variable — derive_var_analysis_ratio","text":"","code":"derive_var_analysis_ratio(dataset, numer_var, denom_var, new_var = NULL)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_analysis_ratio.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive Ratio Variable — derive_var_analysis_ratio","text":"dataset Input dataset variables specified numer_var denom_var arguments expected dataset. numer_var Variable containing numeric values used numerator ratio calculation. denom_var Variable containing numeric values used denominator ratio calculation. new_var user-defined variable appended dataset. default behavior take denominator variable prefix R2 append dataset. Using argument override default behavior. Default NULL.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_analysis_ratio.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive Ratio Variable — derive_var_analysis_ratio","text":"input dataset ratio variable appended","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_analysis_ratio.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Derive Ratio Variable — derive_var_analysis_ratio","text":"user wishing calculate Ratio Baseline, AVAL / BASE returned new variable R2BASE appended input dataset. Ratio Analysis Range Lower Limit AVAL / ANRLO return new variable R2ANRLO, Ratio Analysis Range  Upper Limit AVAL / ANRHI return new variable R2ANRLO. Please note denominator variable prefix R2----. user can override default returned variables using new_var argument. Also, values 0 denominator return NA derivation. Reference CDISC ADaM Implementation Guide Version 1.1 Section 3.3.4 Analysis Parameter Variables BDS Datasets","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_analysis_ratio.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive Ratio Variable — derive_var_analysis_ratio","text":"","code":"library(tibble)  data <- tribble(   ~USUBJID, ~PARAMCD, ~SEQ, ~AVAL, ~BASE, ~ANRLO, ~ANRHI,   \"P01\", \"ALT\", 1, 27, 27, 6, 34,   \"P01\", \"ALT\", 2, 41, 27, 6, 34,   \"P01\", \"ALT\", 3, 17, 27, 6, 34,   \"P02\", \"ALB\", 1, 38, 38, 33, 49,   \"P02\", \"ALB\", 2, 39, 38, 33, 49,   \"P02\", \"ALB\", 3, 37, 38, 33, 49 )  # Returns \"R2\" prefixed variables data %>%   derive_var_analysis_ratio(numer_var = AVAL, denom_var = BASE) %>%   derive_var_analysis_ratio(numer_var = AVAL, denom_var = ANRLO) %>%   derive_var_analysis_ratio(numer_var = AVAL, denom_var = ANRHI) #> # A tibble: 6 × 10 #>   USUBJID PARAMCD   SEQ  AVAL  BASE ANRLO ANRHI R2BASE R2ANRLO R2ANRHI #>   <chr>   <chr>   <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl>   <dbl>   <dbl> #> 1 P01     ALT         1    27    27     6    34  1        4.5    0.794 #> 2 P01     ALT         2    41    27     6    34  1.52     6.83   1.21  #> 3 P01     ALT         3    17    27     6    34  0.630    2.83   0.5   #> 4 P02     ALB         1    38    38    33    49  1        1.15   0.776 #> 5 P02     ALB         2    39    38    33    49  1.03     1.18   0.796 #> 6 P02     ALB         3    37    38    33    49  0.974    1.12   0.755  # Returns user-defined variables data %>%   derive_var_analysis_ratio(numer_var = AVAL, denom_var = BASE, new_var = R01BASE) %>%   derive_var_analysis_ratio(numer_var = AVAL, denom_var = ANRLO, new_var = R01ANRLO) %>%   derive_var_analysis_ratio(numer_var = AVAL, denom_var = ANRHI, new_var = R01ANRHI) #> # A tibble: 6 × 10 #>   USUBJID PARAMCD   SEQ  AVAL  BASE ANRLO ANRHI R01BASE R01ANRLO R01ANRHI #>   <chr>   <chr>   <dbl> <dbl> <dbl> <dbl> <dbl>   <dbl>    <dbl>    <dbl> #> 1 P01     ALT         1    27    27     6    34   1         4.5     0.794 #> 2 P01     ALT         2    41    27     6    34   1.52      6.83    1.21  #> 3 P01     ALT         3    17    27     6    34   0.630     2.83    0.5   #> 4 P02     ALB         1    38    38    33    49   1         1.15    0.776 #> 5 P02     ALB         2    39    38    33    49   1.03      1.18    0.796 #> 6 P02     ALB         3    37    38    33    49   0.974     1.12    0.755"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_anrind.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive Reference Range Indicator — derive_var_anrind","title":"Derive Reference Range Indicator — derive_var_anrind","text":"Derive Reference Range Indicator","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_anrind.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive Reference Range Indicator — derive_var_anrind","text":"","code":"derive_var_anrind(   dataset,   signif_dig = get_admiral_option(\"signif_digits\"),   use_a1hia1lo = FALSE )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_anrind.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive Reference Range Indicator — derive_var_anrind","text":"dataset Input dataset ANRLO, ANRHI, AVAL expected use_a1hia1lo set TRUE, A1LO A1H1 expected well. signif_dig Number significant digits use comparing values. Significant digits used avoid floating point discrepancies comparing numeric values. See blog: admiral handles floating points use_a1hia1lo logical value indicating whether use A1H1 A1LO derivation ANRIND.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_anrind.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive Reference Range Indicator — derive_var_anrind","text":"input dataset additional column ANRIND","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_anrind.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Derive Reference Range Indicator — derive_var_anrind","text":"case A1H1 A1LO used, ANRIND set : \"NORMAL\" AVAL greater equal ANRLO less equal ANRHI; AVAL greater equal ANRLO ANRHI missing; AVAL less equal ANRHI ANRLO missing \"LOW\" AVAL less ANRLO either A1LO missing AVAL greater equal A1LO \"HIGH\" AVAL greater ANRHI either A1HI missing AVAL less equal A1HI \"LOW LOW\" AVAL less A1LO \"HIGH HIGH\" AVAL greater A1HI case A1H1 A1LO used, ANRIND set : \"NORMAL\" AVAL greater equal ANRLO less equal ANRHI; AVAL greater equal ANRLO ANRHI missing; AVAL less equal ANRHI ANRLO missing \"LOW\" AVAL less ANRLO \"HIGH\" AVAL greater ANRHI","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_anrind.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive Reference Range Indicator — derive_var_anrind","text":"","code":"library(tibble) library(dplyr, warn.conflicts = FALSE)  vs <- tibble::tribble(   ~USUBJID, ~PARAMCD, ~AVAL, ~ANRLO, ~ANRHI, ~A1LO, ~A1HI,   \"P01\",       \"PUL\",    70,     60,    100,    40,   110,   \"P01\",       \"PUL\",    57,     60,    100,    40,   110,   \"P01\",       \"PUL\",    60,     60,    100,    40,   110,   \"P01\",     \"DIABP\",   102,     60,     80,    40,    90,   \"P02\",       \"PUL\",   109,     60,    100,    40,   110,   \"P02\",       \"PUL\",   100,     60,    100,    40,   110,   \"P02\",     \"DIABP\",    80,     60,     80,    40,    90,   \"P03\",       \"PUL\",    39,     60,    100,    40,   110,   \"P03\",       \"PUL\",    40,     60,    100,    40,   110 )  vs %>% derive_var_anrind(use_a1hia1lo = TRUE) #> # A tibble: 9 × 8 #>   USUBJID PARAMCD  AVAL ANRLO ANRHI  A1LO  A1HI ANRIND    #>   <chr>   <chr>   <dbl> <dbl> <dbl> <dbl> <dbl> <chr>     #> 1 P01     PUL        70    60   100    40   110 NORMAL    #> 2 P01     PUL        57    60   100    40   110 LOW       #> 3 P01     PUL        60    60   100    40   110 NORMAL    #> 4 P01     DIABP     102    60    80    40    90 HIGH HIGH #> 5 P02     PUL       109    60   100    40   110 HIGH      #> 6 P02     PUL       100    60   100    40   110 NORMAL    #> 7 P02     DIABP      80    60    80    40    90 NORMAL    #> 8 P03     PUL        39    60   100    40   110 LOW LOW   #> 9 P03     PUL        40    60   100    40   110 LOW       vs %>% derive_var_anrind(use_a1hia1lo = FALSE) #> # A tibble: 9 × 8 #>   USUBJID PARAMCD  AVAL ANRLO ANRHI  A1LO  A1HI ANRIND #>   <chr>   <chr>   <dbl> <dbl> <dbl> <dbl> <dbl> <chr>  #> 1 P01     PUL        70    60   100    40   110 NORMAL #> 2 P01     PUL        57    60   100    40   110 LOW    #> 3 P01     PUL        60    60   100    40   110 NORMAL #> 4 P01     DIABP     102    60    80    40    90 HIGH   #> 5 P02     PUL       109    60   100    40   110 HIGH   #> 6 P02     PUL       100    60   100    40   110 NORMAL #> 7 P02     DIABP      80    60    80    40    90 NORMAL #> 8 P03     PUL        39    60   100    40   110 LOW    #> 9 P03     PUL        40    60   100    40   110 LOW"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_atoxgr.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive Lab High toxicity Grade 0 - 4 and Low Toxicity Grades 0 - (-4) — derive_var_atoxgr","title":"Derive Lab High toxicity Grade 0 - 4 and Low Toxicity Grades 0 - (-4) — derive_var_atoxgr","text":"Derives character lab grade based high low severity/toxicity grade(s).","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_atoxgr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive Lab High toxicity Grade 0 - 4 and Low Toxicity Grades 0 - (-4) — derive_var_atoxgr","text":"","code":"derive_var_atoxgr(   dataset,   lotox_description_var = ATOXDSCL,   hitox_description_var = ATOXDSCH )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_atoxgr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive Lab High toxicity Grade 0 - 4 and Low Toxicity Grades 0 - (-4) — derive_var_atoxgr","text":"dataset Input dataset variables specified lotox_description_var hitox_description_var arguments expected dataset. ATOXGRL, ATOXGRH expected well. lotox_description_var Variable containing toxicity grade description low values, eg. \"Anemia\" hitox_description_var Variable containing toxicity grade description high values, eg. \"Hemoglobin Increased\".","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_atoxgr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive Lab High toxicity Grade 0 - 4 and Low Toxicity Grades 0 - (-4) — derive_var_atoxgr","text":"input data set character variable added","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_atoxgr.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Derive Lab High toxicity Grade 0 - 4 and Low Toxicity Grades 0 - (-4) — derive_var_atoxgr","text":"Created variable ATOXGR contain values \"-4\", \"-3\", \"-2\", \"-1\" low values \"1\", \"2\", \"3\", \"4\" high values, contain \"0\" value gradable satisfy criteria high low values. ATOXGR set missing information available give grade. Function applies following rules: High low missing - overall missing Low grade missing > 0 - overall holds low grade High grade missing > 0 - overall holds high grade (high direction low direction NORMAL) high grade normal - overall NORMAL (low direction high direction NORMAL) low grade normal - overall NORMAL otherwise set missing","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_atoxgr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive Lab High toxicity Grade 0 - 4 and Low Toxicity Grades 0 - (-4) — derive_var_atoxgr","text":"","code":"library(tibble)  adlb <- tribble(   ~ATOXDSCL,          ~ATOXDSCH,        ~ATOXGRL,      ~ATOXGRH,   \"Hypoglycemia\",     \"Hyperglycemia\",  NA_character_, \"0\",   \"Hypoglycemia\",     \"Hyperglycemia\",  \"0\",           \"1\",   \"Hypoglycemia\",     \"Hyperglycemia\",  \"0\",           \"0\",   NA_character_,      \"INR Increased\",  NA_character_, \"0\",   \"Hypophosphatemia\", NA_character_,    \"1\",           NA_character_ )  derive_var_atoxgr(adlb) #> # A tibble: 5 × 5 #>   ATOXDSCL         ATOXDSCH      ATOXGRL ATOXGRH ATOXGR #>   <chr>            <chr>         <chr>   <chr>   <chr>  #> 1 Hypoglycemia     Hyperglycemia NA      0       NA     #> 2 Hypoglycemia     Hyperglycemia 0       1       1      #> 3 Hypoglycemia     Hyperglycemia 0       0       0      #> 4 NA               INR Increased NA      0       0      #> 5 Hypophosphatemia NA            1       NA      -1"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_atoxgr_dir.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive Lab Toxicity Grade 0 - 4 — derive_var_atoxgr_dir","title":"Derive Lab Toxicity Grade 0 - 4 — derive_var_atoxgr_dir","text":"Derives character lab grade based severity/toxicity criteria.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_atoxgr_dir.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive Lab Toxicity Grade 0 - 4 — derive_var_atoxgr_dir","text":"","code":"derive_var_atoxgr_dir(   dataset,   new_var,   tox_description_var,   meta_criteria,   criteria_direction,   abnormal_indicator = NULL,   get_unit_expr,   signif_dig = get_admiral_option(\"signif_digits\") )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_atoxgr_dir.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive Lab Toxicity Grade 0 - 4 — derive_var_atoxgr_dir","text":"dataset Input dataset variables specified tox_description_var argument expected dataset. new_var Name character grade variable create, example, ATOXGRH ATOXGRL. tox_description_var Variable containing description grading criteria. example: \"Anemia\" \"INR Increased\". meta_criteria Metadata data set holding criteria (normally case statement) Permitted Values: atoxgr_criteria_ctcv4, atoxgr_criteria_ctcv5, atoxgr_criteria_daids atoxgr_criteria_ctcv4 implements Common Terminology Criteria Adverse Events (CTCAE) v4.0 atoxgr_criteria_ctcv5 implements Common Terminology Criteria Adverse Events (CTCAE) v5.0 atoxgr_criteria_daids implements Division AIDS (DAIDS) Table Grading Severity Adult Pediatric Adverse Events metadata following variables: TERM: variable hold term describing criteria applied particular lab test, eg. \"Anemia\" \"INR Increased\". Note: variable case insensitive. DIRECTION: variable hold direction abnormality particular lab test value. \"L\" LOW values, \"H\" HIGH values. Note: variable case insensitive. SI_UNIT_CHECK: variable hold unit particular lab test. Used check input data criteria based absolute values. VAR_CHECK: variable hold comma separated list variables used criteria. Used check input data variables exist. GRADE_CRITERIA_CODE: variable hold code creates grade based defined criteria. FILTER: Required DAIDS grading, specifies admiral code filter lab data based subset subjects (e.g. AGE > 18 YEARS) criteria_direction Direction (L= Low, H = High) toxicity grade. Permitted Values: \"L\", \"H\" abnormal_indicator Value BNRIND derivation indicate abnormal value. Usually \"HIGH\" criteria_direction = \"H\" \"LOW\" criteria_direction = \"L\". required meta_criteria = atoxgr_criteria_ctcv5 BNRIND required variable. Currently terms \"Alanine aminotransferase increased\", \"Alkaline phosphatase increased\", \"Aspartate aminotransferase increased\", \"Blood bilirubin increased\" \"GGT increased\" get_unit_expr expression providing unit parameter result used check units input parameters. Compared SI_UNIT_CHECK metadata (see meta_criteria parameter). Permitted Values: variable containing unit input dataset, function call, example, get_unit_expr = extract_unit(PARAM). signif_dig Number significant digits use comparing lab value another value. Significant digits used avoid floating point discrepancies comparing numeric values. See blog: admiral handles floating points","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_atoxgr_dir.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive Lab Toxicity Grade 0 - 4 — derive_var_atoxgr_dir","text":"input dataset character variable added","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_atoxgr_dir.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Derive Lab Toxicity Grade 0 - 4 — derive_var_atoxgr_dir","text":"new_var derived values NA, \"0\", \"1\", \"2\", \"3\", \"4\", \"4\" severe grade \"4\" lab value satisfies criteria grade 4. \"3\" lab value satisfies criteria grade 3. \"2\" lab value satisfies criteria grade 2. \"1\" lab value satisfies criteria grade 1. \"0\" grade can derived grade \"1\", \"2\", \"3\" \"4\". NA grade derived.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_atoxgr_dir.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive Lab Toxicity Grade 0 - 4 — derive_var_atoxgr_dir","text":"","code":"library(tibble)  data <- tribble(   ~ATOXDSCL,                    ~AVAL, ~ANRLO, ~ANRHI, ~PARAM,   \"Hypoglycemia\",               119,   4,      7,      \"Glucose (mmol/L)\",   \"Lymphocyte count decreased\", 0.7,   1,      4,      \"Lymphocytes Abs (10^9/L)\",   \"Anemia\",                     129,   120,    180,    \"Hemoglobin (g/L)\",   \"White blood cell decreased\", 10,    5,      20,     \"White blood cell (10^9/L)\",   \"White blood cell decreased\", 15,    5,      20,     \"White blood cell (10^9/L)\",   \"Anemia\",                     140,   120,    180,    \"Hemoglobin (g/L)\" )  derive_var_atoxgr_dir(data,   new_var = ATOXGRL,   tox_description_var = ATOXDSCL,   meta_criteria = atoxgr_criteria_ctcv5,   criteria_direction = \"L\",   get_unit_expr = extract_unit(PARAM) ) #> # A tibble: 6 × 6 #>   ATOXDSCL                    AVAL ANRLO ANRHI PARAM                     ATOXGRL #>   <chr>                      <dbl> <dbl> <dbl> <chr>                     <chr>   #> 1 Anemia                     129     120   180 Hemoglobin (g/L)          0       #> 2 Anemia                     140     120   180 Hemoglobin (g/L)          0       #> 3 Hypoglycemia               119       4     7 Glucose (mmol/L)          0       #> 4 Lymphocyte count decreased   0.7     1     4 Lymphocytes Abs (10^9/L)  2       #> 5 White blood cell decreased  10       5    20 White blood cell (10^9/L) 0       #> 6 White blood cell decreased  15       5    20 White blood cell (10^9/L) 0        data <- tribble(   ~ATOXDSCH,                     ~AVAL,  ~ANRLO,   ~ANRHI, ~PARAM,   \"CPK increased\",               129,    0,        30,     \"Creatine Kinase (U/L)\",   \"Lymphocyte count increased\",  4,      1,        4,      \"Lymphocytes Abs (10^9/L)\",   \"Lymphocyte count increased\",  2,      1,        4,      \"Lymphocytes Abs (10^9/L)\",   \"CPK increased\",               140,    120,      180,    \"Creatine Kinase (U/L)\" )  derive_var_atoxgr_dir(data,   new_var = ATOXGRH,   tox_description_var = ATOXDSCH,   meta_criteria = atoxgr_criteria_ctcv5,   criteria_direction = \"H\",   get_unit_expr = extract_unit(PARAM) ) #> # A tibble: 4 × 6 #>   ATOXDSCH                    AVAL ANRLO ANRHI PARAM                    ATOXGRH #>   <chr>                      <dbl> <dbl> <dbl> <chr>                    <chr>   #> 1 CPK increased                129     0    30 Creatine Kinase (U/L)    2       #> 2 CPK increased                140   120   180 Creatine Kinase (U/L)    0       #> 3 Lymphocyte count increased     4     1     4 Lymphocytes Abs (10^9/L) 0       #> 4 Lymphocyte count increased     2     1     4 Lymphocytes Abs (10^9/L) 0"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_base.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive Baseline Variables — derive_var_base","title":"Derive Baseline Variables — derive_var_base","text":"Derive baseline variables, e.g. BASE BNRIND, BDS dataset. Note: wrapper function generic derive_vars_merged().","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_base.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive Baseline Variables — derive_var_base","text":"","code":"derive_var_base(   dataset,   by_vars,   source_var = AVAL,   new_var = BASE,   filter = ABLFL == \"Y\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_base.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive Baseline Variables — derive_var_base","text":"dataset Input dataset variables specified by_vars source_var arguments expected dataset. by_vars Grouping variables Grouping variables uniquely identifying set records calculate new_var. Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) source_var column extract baseline value, e.g. AVAL new_var name newly created baseline column, e.g. BASE filter condition used filter dataset baseline records. default ABLFL == \"Y\"","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_base.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive Baseline Variables — derive_var_base","text":"new data.frame containing records variables input dataset plus new_var variable","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_base.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Derive Baseline Variables — derive_var_base","text":"by_vars group, baseline record identified condition specified filter defaults ABLFL == \"Y\". Subsequently, every value new_var variable by_vars group set value source_var variable baseline record. case multiple baseline records within by_vars error issued.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_base.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive Baseline Variables — derive_var_base","text":"","code":"library(tibble)  dataset <- tribble(   ~STUDYID, ~USUBJID, ~PARAMCD, ~AVAL, ~AVALC,   ~AVISIT,    ~ABLFL, ~ANRIND,   \"TEST01\", \"PAT01\", \"PARAM01\", 10.12, NA,       \"Baseline\", \"Y\",    \"NORMAL\",   \"TEST01\", \"PAT01\", \"PARAM01\", 9.700, NA,       \"Day 7\",    NA,     \"LOW\",   \"TEST01\", \"PAT01\", \"PARAM01\", 15.01, NA,       \"Day 14\",   NA,     \"HIGH\",   \"TEST01\", \"PAT01\", \"PARAM02\", 8.350, NA,       \"Baseline\", \"Y\",    \"LOW\",   \"TEST01\", \"PAT01\", \"PARAM02\",    NA, NA,       \"Day 7\",    NA,     NA,   \"TEST01\", \"PAT01\", \"PARAM02\", 8.350, NA,       \"Day 14\",   NA,     \"LOW\",   \"TEST01\", \"PAT01\", \"PARAM03\",    NA, \"LOW\",    \"Baseline\", \"Y\",    NA,   \"TEST01\", \"PAT01\", \"PARAM03\",    NA, \"LOW\",    \"Day 7\",    NA,     NA,   \"TEST01\", \"PAT01\", \"PARAM03\",    NA, \"MEDIUM\", \"Day 14\",   NA,     NA,   \"TEST01\", \"PAT01\", \"PARAM04\",    NA, \"HIGH\",   \"Baseline\", \"Y\",    NA,   \"TEST01\", \"PAT01\", \"PARAM04\",    NA, \"HIGH\",   \"Day 7\",    NA,     NA,   \"TEST01\", \"PAT01\", \"PARAM04\",    NA, \"MEDIUM\", \"Day 14\",   NA,     NA )  ## Derive `BASE` variable from `AVAL` derive_var_base(   dataset,   by_vars = exprs(USUBJID, PARAMCD),   source_var = AVAL,   new_var = BASE ) #> # A tibble: 12 × 9 #>    STUDYID USUBJID PARAMCD  AVAL AVALC  AVISIT   ABLFL ANRIND  BASE #>    <chr>   <chr>   <chr>   <dbl> <chr>  <chr>    <chr> <chr>  <dbl> #>  1 TEST01  PAT01   PARAM01 10.1  NA     Baseline Y     NORMAL 10.1  #>  2 TEST01  PAT01   PARAM01  9.7  NA     Day 7    NA    LOW    10.1  #>  3 TEST01  PAT01   PARAM01 15.0  NA     Day 14   NA    HIGH   10.1  #>  4 TEST01  PAT01   PARAM02  8.35 NA     Baseline Y     LOW     8.35 #>  5 TEST01  PAT01   PARAM02 NA    NA     Day 7    NA    NA      8.35 #>  6 TEST01  PAT01   PARAM02  8.35 NA     Day 14   NA    LOW     8.35 #>  7 TEST01  PAT01   PARAM03 NA    LOW    Baseline Y     NA     NA    #>  8 TEST01  PAT01   PARAM03 NA    LOW    Day 7    NA    NA     NA    #>  9 TEST01  PAT01   PARAM03 NA    MEDIUM Day 14   NA    NA     NA    #> 10 TEST01  PAT01   PARAM04 NA    HIGH   Baseline Y     NA     NA    #> 11 TEST01  PAT01   PARAM04 NA    HIGH   Day 7    NA    NA     NA    #> 12 TEST01  PAT01   PARAM04 NA    MEDIUM Day 14   NA    NA     NA     ## Derive `BASEC` variable from `AVALC` derive_var_base(   dataset,   by_vars = exprs(USUBJID, PARAMCD),   source_var = AVALC,   new_var = BASEC ) #> # A tibble: 12 × 9 #>    STUDYID USUBJID PARAMCD  AVAL AVALC  AVISIT   ABLFL ANRIND BASEC #>    <chr>   <chr>   <chr>   <dbl> <chr>  <chr>    <chr> <chr>  <chr> #>  1 TEST01  PAT01   PARAM01 10.1  NA     Baseline Y     NORMAL NA    #>  2 TEST01  PAT01   PARAM01  9.7  NA     Day 7    NA    LOW    NA    #>  3 TEST01  PAT01   PARAM01 15.0  NA     Day 14   NA    HIGH   NA    #>  4 TEST01  PAT01   PARAM02  8.35 NA     Baseline Y     LOW    NA    #>  5 TEST01  PAT01   PARAM02 NA    NA     Day 7    NA    NA     NA    #>  6 TEST01  PAT01   PARAM02  8.35 NA     Day 14   NA    LOW    NA    #>  7 TEST01  PAT01   PARAM03 NA    LOW    Baseline Y     NA     LOW   #>  8 TEST01  PAT01   PARAM03 NA    LOW    Day 7    NA    NA     LOW   #>  9 TEST01  PAT01   PARAM03 NA    MEDIUM Day 14   NA    NA     LOW   #> 10 TEST01  PAT01   PARAM04 NA    HIGH   Baseline Y     NA     HIGH  #> 11 TEST01  PAT01   PARAM04 NA    HIGH   Day 7    NA    NA     HIGH  #> 12 TEST01  PAT01   PARAM04 NA    MEDIUM Day 14   NA    NA     HIGH   ## Derive `BNRIND` variable from `ANRIND` derive_var_base(   dataset,   by_vars = exprs(USUBJID, PARAMCD),   source_var = ANRIND,   new_var = BNRIND ) #> # A tibble: 12 × 9 #>    STUDYID USUBJID PARAMCD  AVAL AVALC  AVISIT   ABLFL ANRIND BNRIND #>    <chr>   <chr>   <chr>   <dbl> <chr>  <chr>    <chr> <chr>  <chr>  #>  1 TEST01  PAT01   PARAM01 10.1  NA     Baseline Y     NORMAL NORMAL #>  2 TEST01  PAT01   PARAM01  9.7  NA     Day 7    NA    LOW    NORMAL #>  3 TEST01  PAT01   PARAM01 15.0  NA     Day 14   NA    HIGH   NORMAL #>  4 TEST01  PAT01   PARAM02  8.35 NA     Baseline Y     LOW    LOW    #>  5 TEST01  PAT01   PARAM02 NA    NA     Day 7    NA    NA     LOW    #>  6 TEST01  PAT01   PARAM02  8.35 NA     Day 14   NA    LOW    LOW    #>  7 TEST01  PAT01   PARAM03 NA    LOW    Baseline Y     NA     NA     #>  8 TEST01  PAT01   PARAM03 NA    LOW    Day 7    NA    NA     NA     #>  9 TEST01  PAT01   PARAM03 NA    MEDIUM Day 14   NA    NA     NA     #> 10 TEST01  PAT01   PARAM04 NA    HIGH   Baseline Y     NA     NA     #> 11 TEST01  PAT01   PARAM04 NA    HIGH   Day 7    NA    NA     NA     #> 12 TEST01  PAT01   PARAM04 NA    MEDIUM Day 14   NA    NA     NA"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_chg.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive Change from Baseline — derive_var_chg","title":"Derive Change from Baseline — derive_var_chg","text":"Derive change baseline (CHG) BDS dataset","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_chg.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive Change from Baseline — derive_var_chg","text":"","code":"derive_var_chg(dataset)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_chg.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive Change from Baseline — derive_var_chg","text":"dataset Input dataset AVAL BASE expected.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_chg.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive Change from Baseline — derive_var_chg","text":"input dataset additional column named CHG","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_chg.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Derive Change from Baseline — derive_var_chg","text":"Change baseline calculated subtracting baseline value analysis value.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_chg.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive Change from Baseline — derive_var_chg","text":"","code":"library(tibble)  advs <- tribble(   ~USUBJID, ~PARAMCD, ~AVAL, ~ABLFL, ~BASE,   \"P01\",    \"WEIGHT\", 80,    \"Y\",    80,   \"P01\",    \"WEIGHT\", 80.8,  NA,     80,   \"P01\",    \"WEIGHT\", 81.4,  NA,     80,   \"P02\",    \"WEIGHT\", 75.3,  \"Y\",    75.3,   \"P02\",    \"WEIGHT\", 76,    NA,     75.3 ) derive_var_chg(advs) #> # A tibble: 5 × 6 #>   USUBJID PARAMCD  AVAL ABLFL  BASE   CHG #>   <chr>   <chr>   <dbl> <chr> <dbl> <dbl> #> 1 P01     WEIGHT   80   Y      80   0     #> 2 P01     WEIGHT   80.8 NA     80   0.800 #> 3 P01     WEIGHT   81.4 NA     80   1.40  #> 4 P02     WEIGHT   75.3 Y      75.3 0     #> 5 P02     WEIGHT   76   NA     75.3 0.700"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_dthcaus.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive Death Cause — derive_var_dthcaus","title":"Derive Death Cause — derive_var_dthcaus","text":"derive_var_dthcaus() function superseded favor derive_vars_extreme_event(). Derive death cause (DTHCAUS) add traceability variables required.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_dthcaus.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive Death Cause — derive_var_dthcaus","text":"","code":"derive_var_dthcaus(   dataset,   ...,   source_datasets,   subject_keys = get_admiral_option(\"subject_keys\") )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_dthcaus.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive Death Cause — derive_var_dthcaus","text":"dataset Input dataset variables specified subject_keys argument expected dataset. ... Objects class \"dthcaus_source\" created dthcaus_source(). source_datasets named list containing datasets search death cause subject_keys Variables uniquely identify subject list expressions expressions symbols returned exprs() expected.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_dthcaus.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive Death Cause — derive_var_dthcaus","text":"input dataset DTHCAUS variable added.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_dthcaus.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Derive Death Cause — derive_var_dthcaus","text":"function derives DTHCAUS along user-defined traceability variables, required. subject death info multiple sources, one source earliest death date used. dates equivalent, first source kept, user provide inputs preferred order.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_dthcaus.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive Death Cause — derive_var_dthcaus","text":"","code":"library(tibble) library(dplyr, warn.conflicts = FALSE)  adsl <- tribble(   ~STUDYID,  ~USUBJID,   \"STUDY01\", \"PAT01\",   \"STUDY01\", \"PAT02\",   \"STUDY01\", \"PAT03\" ) ae <- tribble(   ~STUDYID,  ~USUBJID, ~AESEQ, ~AEDECOD,       ~AEOUT,  ~AEDTHDTC,   \"STUDY01\", \"PAT01\",  12,     \"SUDDEN DEATH\", \"FATAL\", \"2021-04-04\" )  ds <- tribble(   ~STUDYID, ~USUBJID, ~DSSEQ, ~DSDECOD, ~DSTERM, ~DSSTDTC,   \"STUDY01\", \"PAT02\", 1, \"INFORMED CONSENT OBTAINED\", \"INFORMED CONSENT OBTAINED\", \"2021-04-03\",   \"STUDY01\", \"PAT02\", 2, \"RANDOMIZATION\", \"RANDOMIZATION\", \"2021-04-11\",   \"STUDY01\", \"PAT02\", 3, \"DEATH\", \"DEATH DUE TO PROGRESSION OF DISEASE\", \"2022-02-01\",   \"STUDY01\", \"PAT03\", 1, \"DEATH\", \"POST STUDY REPORTING OF DEATH\", \"2022-03-03\" )  # Derive `DTHCAUS` only - for on-study deaths only src_ae <- dthcaus_source(   dataset_name = \"ae\",   filter = AEOUT == \"FATAL\",   date = convert_dtc_to_dt(AEDTHDTC),   mode = \"first\",   dthcaus = AEDECOD )  src_ds <- dthcaus_source(   dataset_name = \"ds\",   filter = DSDECOD == \"DEATH\" & grepl(\"DEATH DUE TO\", DSTERM),   date = convert_dtc_to_dt(DSSTDTC),   mode = \"first\",   dthcaus = DSTERM )  derive_var_dthcaus(adsl, src_ae, src_ds, source_datasets = list(ae = ae, ds = ds)) #> # A tibble: 3 × 3 #>   STUDYID USUBJID DTHCAUS                             #>   <chr>   <chr>   <chr>                               #> 1 STUDY01 PAT01   SUDDEN DEATH                        #> 2 STUDY01 PAT02   DEATH DUE TO PROGRESSION OF DISEASE #> 3 STUDY01 PAT03   NA                                   # Derive `DTHCAUS` and add traceability variables - for on-study deaths only src_ae <- dthcaus_source(   dataset_name = \"ae\",   filter = AEOUT == \"FATAL\",   date = convert_dtc_to_dt(AEDTHDTC),   mode = \"first\",   dthcaus = AEDECOD,   set_values_to = exprs(DTHDOM = \"AE\", DTHSEQ = AESEQ) )  src_ds <- dthcaus_source(   dataset_name = \"ds\",   filter = DSDECOD == \"DEATH\" & grepl(\"DEATH DUE TO\", DSTERM),   date = convert_dtc_to_dt(DSSTDTC),   mode = \"first\",   dthcaus = DSTERM,   set_values_to = exprs(DTHDOM = \"DS\", DTHSEQ = DSSEQ) )  derive_var_dthcaus(adsl, src_ae, src_ds, source_datasets = list(ae = ae, ds = ds)) #> # A tibble: 3 × 5 #>   STUDYID USUBJID DTHCAUS                             DTHDOM DTHSEQ #>   <chr>   <chr>   <chr>                               <chr>   <dbl> #> 1 STUDY01 PAT01   SUDDEN DEATH                        AE         12 #> 2 STUDY01 PAT02   DEATH DUE TO PROGRESSION OF DISEASE DS          3 #> 3 STUDY01 PAT03   NA                                  NA         NA  # Derive `DTHCAUS` as above - now including post-study deaths with different `DTHCAUS` value src_ae <- dthcaus_source(   dataset_name = \"ae\",   filter = AEOUT == \"FATAL\",   date = convert_dtc_to_dt(AEDTHDTC),   mode = \"first\",   dthcaus = AEDECOD,   set_values_to = exprs(DTHDOM = \"AE\", DTHSEQ = AESEQ) )  ds <- mutate(   ds,   DSSTDT = convert_dtc_to_dt(DSSTDTC) )  src_ds <- dthcaus_source(   dataset_name = \"ds\",   filter = DSDECOD == \"DEATH\" & grepl(\"DEATH DUE TO\", DSTERM),   date = DSSTDT,   mode = \"first\",   dthcaus = DSTERM,   set_values_to = exprs(DTHDOM = \"DS\", DTHSEQ = DSSEQ) )  src_ds_post <- dthcaus_source(   dataset_name = \"ds\",   filter = DSDECOD == \"DEATH\" & DSTERM == \"POST STUDY REPORTING OF DEATH\",   date = DSSTDT,   mode = \"first\",   dthcaus = \"POST STUDY: UNKNOWN CAUSE\",   set_values_to = exprs(DTHDOM = \"DS\", DTHSEQ = DSSEQ) )  derive_var_dthcaus(   adsl,   src_ae, src_ds, src_ds_post,   source_datasets = list(ae = ae, ds = ds) ) #> # A tibble: 3 × 5 #>   STUDYID USUBJID DTHCAUS                             DTHDOM DTHSEQ #>   <chr>   <chr>   <chr>                               <chr>   <dbl> #> 1 STUDY01 PAT01   SUDDEN DEATH                        AE         12 #> 2 STUDY01 PAT02   DEATH DUE TO PROGRESSION OF DISEASE DS          3 #> 3 STUDY01 PAT03   POST STUDY: UNKNOWN CAUSE           DS          1"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_extreme_dt.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive First or Last Date from Multiple Sources — derive_var_extreme_dt","title":"Derive First or Last Date from Multiple Sources — derive_var_extreme_dt","text":"derive_var_extreme_dt() function superseded favor derive_vars_extreme_event(). Add first last date multiple sources dataset, e.g., last known alive date (LSTALVDT). Note: wrapper function function derive_var_extreme_dtm().","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_extreme_dt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive First or Last Date from Multiple Sources — derive_var_extreme_dt","text":"","code":"derive_var_extreme_dt(   dataset,   new_var,   ...,   source_datasets,   mode,   subject_keys = get_admiral_option(\"subject_keys\") )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_extreme_dt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive First or Last Date from Multiple Sources — derive_var_extreme_dt","text":"dataset Input dataset variables specified subject_keys argument expected dataset. new_var Name variable create ... Source(s) dates. One date_source() objects expected. source_datasets named list containing datasets search first last date mode Selection mode (first last) \"first\" specified, first date subject selected. \"last\" specified, last date subject selected. Permitted Values:  \"first\", \"last\" subject_keys Variables uniquely identify subject list expressions expressions symbols returned exprs() expected.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_extreme_dt.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive First or Last Date from Multiple Sources — derive_var_extreme_dt","text":"input dataset new variable added.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_extreme_dt.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Derive First or Last Date from Multiple Sources — derive_var_extreme_dt","text":"following steps performed create output dataset: source dataset observations specified filter element selected observations date NA removed. patient first last observation (respect date mode) selected. new variable set variable expression specified date element. variables specified set_values_to element added. selected observations source datasets combined single dataset. patient first last observation (respect new variable mode) single dataset selected new variable merged input dataset. time part removed new variable.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_extreme_dt.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive First or Last Date from Multiple Sources — derive_var_extreme_dt","text":"","code":"library(dplyr, warn.conflicts = FALSE) ae <- tribble(   ~STUDYID,  ~DOMAIN,  ~USUBJID, ~AESEQ,     ~AESTDTC,     ~AEENDTC,   \"PILOT01\",    \"AE\", \"01-1130\",      5, \"2014-05-09\", \"2014-05-09\",   \"PILOT01\",    \"AE\", \"01-1130\",      6, \"2014-05-22\",           NA,   \"PILOT01\",    \"AE\", \"01-1130\",      4, \"2014-05-09\", \"2014-05-09\",   \"PILOT01\",    \"AE\", \"01-1130\",      8, \"2014-05-22\",           NA,   \"PILOT01\",    \"AE\", \"01-1130\",      7, \"2014-05-22\",           NA,   \"PILOT01\",    \"AE\", \"01-1130\",      2, \"2014-03-09\", \"2014-03-09\",   \"PILOT01\",    \"AE\", \"01-1130\",      1, \"2014-03-09\", \"2014-03-16\",   \"PILOT01\",    \"AE\", \"01-1130\",      3, \"2014-03-09\", \"2014-03-16\",   \"PILOT01\",    \"AE\", \"01-1133\",      1, \"2012-12-27\",           NA,   \"PILOT01\",    \"AE\", \"01-1133\",      3, \"2012-12-27\",           NA,   \"PILOT01\",    \"AE\", \"01-1133\",      2, \"2012-12-27\",           NA,   \"PILOT01\",    \"AE\", \"01-1133\",      4, \"2012-12-27\",           NA,   \"PILOT01\",    \"AE\", \"01-1211\",      5, \"2012-11-29\",           NA,   \"PILOT01\",    \"AE\", \"01-1211\",      1, \"2012-11-16\",           NA,   \"PILOT01\",    \"AE\", \"01-1211\",      7, \"2013-01-11\",           NA,   \"PILOT01\",    \"AE\", \"01-1211\",      8, \"2013-01-11\",           NA,   \"PILOT01\",    \"AE\", \"01-1211\",      4, \"2012-11-22\",           NA,   \"PILOT01\",    \"AE\", \"01-1211\",      2, \"2012-11-21\", \"2012-11-21\",   \"PILOT01\",    \"AE\", \"01-1211\",      3, \"2012-11-21\",           NA,   \"PILOT01\",    \"AE\", \"01-1211\",      6, \"2012-12-09\",           NA,   \"PILOT01\",    \"AE\", \"01-1211\",      9, \"2013-01-14\", \"2013-01-14\",   \"PILOT01\",    \"AE\", \"09-1081\",      2, \"2014-05-01\",           NA,   \"PILOT01\",    \"AE\", \"09-1081\",      1, \"2014-04-07\",           NA,   \"PILOT01\",    \"AE\", \"09-1088\",      1, \"2014-05-08\",           NA,   \"PILOT01\",    \"AE\", \"09-1088\",      2, \"2014-08-02\",           NA )  adsl <- tribble(   ~STUDYID,   ~USUBJID,              ~TRTEDTM,      ~TRTEDT,   \"PILOT01\", \"01-1130\", \"2014-08-16 23:59:59\", \"2014-08-16\",   \"PILOT01\", \"01-1133\", \"2013-04-28 23:59:59\", \"2013-04-28\",   \"PILOT01\", \"01-1211\", \"2013-01-12 23:59:59\", \"2013-01-12\",   \"PILOT01\", \"09-1081\", \"2014-04-27 23:59:59\", \"2014-04-27\",   \"PILOT01\", \"09-1088\", \"2014-10-09 23:59:59\", \"2014-10-09\" ) %>%   mutate(     across(TRTEDTM:TRTEDT, as.Date)   )   lb <- tribble(   ~STUDYID,  ~DOMAIN,  ~USUBJID, ~LBSEQ,             ~LBDTC,   \"PILOT01\",    \"LB\", \"01-1130\",    219, \"2014-06-07T13:20\",   \"PILOT01\",    \"LB\", \"01-1130\",    322, \"2014-08-16T13:10\",   \"PILOT01\",    \"LB\", \"01-1133\",    268, \"2013-04-18T15:30\",   \"PILOT01\",    \"LB\", \"01-1133\",    304, \"2013-04-29T10:13\",   \"PILOT01\",    \"LB\", \"01-1211\",      8, \"2012-10-30T14:26\",   \"PILOT01\",    \"LB\", \"01-1211\",    162, \"2013-01-08T12:13\",   \"PILOT01\",    \"LB\", \"09-1081\",     47, \"2014-02-01T10:55\",   \"PILOT01\",    \"LB\", \"09-1081\",    219, \"2014-05-10T11:15\",   \"PILOT01\",    \"LB\", \"09-1088\",    283, \"2014-09-27T12:13\",   \"PILOT01\",    \"LB\", \"09-1088\",    322, \"2014-10-09T13:25\" )  dm <- tribble(   ~STUDYID,  ~DOMAIN,  ~USUBJID, ~AGE,   ~AGEU,   \"PILOT01\",    \"DM\", \"01-1130\",   84, \"YEARS\",   \"PILOT01\",    \"DM\", \"01-1133\",   81, \"YEARS\",   \"PILOT01\",    \"DM\", \"01-1211\",   76, \"YEARS\",   \"PILOT01\",    \"DM\", \"09-1081\",   86, \"YEARS\",   \"PILOT01\",    \"DM\", \"09-1088\",   69, \"YEARS\" )  ae_start <- date_source(   dataset_name = \"ae\",   date = convert_dtc_to_dt(AESTDTC, highest_imputation = \"M\") ) ae_end <- date_source(   dataset_name = \"ae\",   date = convert_dtc_to_dt(AEENDTC, highest_imputation = \"M\") )  ae_ext <- ae %>%   derive_vars_dt(     dtc = AESTDTC,     new_vars_prefix = \"AEST\",     highest_imputation = \"M\"   ) %>%   derive_vars_dt(     dtc = AEENDTC,     new_vars_prefix = \"AEEN\",     highest_imputation = \"M\"   )  lb_date <- date_source(   dataset_name = \"lb\",   date = convert_dtc_to_dt(LBDTC) )  lb_ext <- derive_vars_dt(   lb,   dtc = LBDTC,   new_vars_prefix = \"LB\" )  adsl_date <- date_source(dataset_name = \"adsl\", date = TRTEDT)  dm %>%   derive_var_extreme_dt(     new_var = LSTALVDT,     ae_start, ae_end, lb_date, adsl_date,     source_datasets = list(       adsl = adsl,       ae = ae_ext,       lb = lb_ext     ),     mode = \"last\"   ) %>%   select(USUBJID, LSTALVDT) #> # A tibble: 5 × 2 #>   USUBJID LSTALVDT   #>   <chr>   <date>     #> 1 01-1130 2014-08-16 #> 2 01-1133 2013-04-29 #> 3 01-1211 2013-01-14 #> 4 09-1081 2014-05-10 #> 5 09-1088 2014-10-09  # derive last alive date and traceability variables ae_start <- date_source(   dataset_name = \"ae\",   date = convert_dtc_to_dt(AESTDTC, highest_imputation = \"M\"),   set_values_to = exprs(     LALVDOM = \"AE\",     LALVSEQ = AESEQ,     LALVVAR = \"AESTDTC\"   ) )  ae_end <- date_source(   dataset_name = \"ae\",   date = convert_dtc_to_dt(AEENDTC, highest_imputation = \"M\"),   set_values_to = exprs(     LALVDOM = \"AE\",     LALVSEQ = AESEQ,     LALVVAR = \"AEENDTC\"   ) )  lb_date <- date_source(   dataset_name = \"lb\",   date = convert_dtc_to_dt(LBDTC),   set_values_to = exprs(     LALVDOM = \"LB\",     LALVSEQ = LBSEQ,     LALVVAR = \"LBDTC\"   ) )  adsl_date <- date_source(   dataset_name = \"adsl\",   date = TRTEDT,   set_values_to = exprs(     LALVDOM = \"ADSL\",     LALVSEQ = NA_integer_,     LALVVAR = \"TRTEDT\"   ) )  dm %>%   derive_var_extreme_dt(     new_var = LSTALVDT,     ae_start, ae_end, lb_date, adsl_date,     source_datasets = list(       adsl = adsl,       ae = ae_ext,       lb = lb_ext     ),     mode = \"last\"   ) %>%   select(USUBJID, LSTALVDT, LALVDOM, LALVSEQ, LALVVAR) #> # A tibble: 5 × 5 #>   USUBJID LSTALVDT   LALVDOM LALVSEQ LALVVAR #>   <chr>   <date>     <chr>     <dbl> <chr>   #> 1 01-1130 2014-08-16 ADSL         NA TRTEDT  #> 2 01-1133 2013-04-29 LB          304 LBDTC   #> 3 01-1211 2013-01-14 AE            9 AEENDTC #> 4 09-1081 2014-05-10 LB          219 LBDTC   #> 5 09-1088 2014-10-09 ADSL         NA TRTEDT"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_extreme_dtm.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive First or Last Datetime from Multiple Sources — derive_var_extreme_dtm","title":"Derive First or Last Datetime from Multiple Sources — derive_var_extreme_dtm","text":"derive_var_extreme_dtm() function superseded favor derive_vars_extreme_event(). Add first last datetime multiple sources dataset, e.g., last known alive datetime (LSTALVDTM).","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_extreme_dtm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive First or Last Datetime from Multiple Sources — derive_var_extreme_dtm","text":"","code":"derive_var_extreme_dtm(   dataset,   new_var,   ...,   source_datasets,   mode,   subject_keys = get_admiral_option(\"subject_keys\") )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_extreme_dtm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive First or Last Datetime from Multiple Sources — derive_var_extreme_dtm","text":"dataset Input dataset variables specified subject_keys argument expected dataset. new_var Name variable create ... Source(s) dates. One date_source() objects expected. source_datasets named list containing datasets search first last date mode Selection mode (first last) \"first\" specified, first date subject selected. \"last\" specified, last date subject selected. Permitted Values:  \"first\", \"last\" subject_keys Variables uniquely identify subject list expressions expressions symbols returned exprs() expected.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_extreme_dtm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive First or Last Datetime from Multiple Sources — derive_var_extreme_dtm","text":"input dataset new variable added.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_extreme_dtm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Derive First or Last Datetime from Multiple Sources — derive_var_extreme_dtm","text":"following steps performed create output dataset: source dataset observations specified filter element selected observations date NA removed. patient first last observation (respect date mode) selected. new variable set variable expression specified date element. date variable (rather datetime), time imputed \"00:00:00\". variables specified set_values_to element added. selected observations source datasets combined single dataset. patient first last observation (respect new variable mode) single dataset selected new variable merged input dataset.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_extreme_dtm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive First or Last Datetime from Multiple Sources — derive_var_extreme_dtm","text":"","code":"library(dplyr, warn.conflicts = FALSE) library(lubridate) dm <- tribble(   ~STUDYID,  ~DOMAIN,  ~USUBJID, ~AGE,   ~AGEU,   \"PILOT01\",    \"DM\", \"01-1130\",   84, \"YEARS\",   \"PILOT01\",    \"DM\", \"01-1133\",   81, \"YEARS\",   \"PILOT01\",    \"DM\", \"01-1211\",   76, \"YEARS\",   \"PILOT01\",    \"DM\", \"09-1081\",   86, \"YEARS\",   \"PILOT01\",    \"DM\", \"09-1088\",   69, \"YEARS\" ) ae <- tribble(   ~STUDYID,  ~DOMAIN,  ~USUBJID, ~AESEQ,     ~AESTDTC,     ~AEENDTC,   \"PILOT01\",    \"AE\", \"01-1130\",      5, \"2014-05-09\", \"2014-05-09\",   \"PILOT01\",    \"AE\", \"01-1130\",      6, \"2014-05-22\",           NA,   \"PILOT01\",    \"AE\", \"01-1130\",      4, \"2014-05-09\", \"2014-05-09\",   \"PILOT01\",    \"AE\", \"01-1130\",      8, \"2014-05-22\",           NA,   \"PILOT01\",    \"AE\", \"01-1130\",      7, \"2014-05-22\",           NA,   \"PILOT01\",    \"AE\", \"01-1130\",      2, \"2014-03-09\", \"2014-03-09\",   \"PILOT01\",    \"AE\", \"01-1130\",      1, \"2014-03-09\", \"2014-03-16\",   \"PILOT01\",    \"AE\", \"01-1130\",      3, \"2014-03-09\", \"2014-03-16\",   \"PILOT01\",    \"AE\", \"01-1133\",      1, \"2012-12-27\",           NA,   \"PILOT01\",    \"AE\", \"01-1133\",      3, \"2012-12-27\",           NA,   \"PILOT01\",    \"AE\", \"01-1133\",      2, \"2012-12-27\",           NA,   \"PILOT01\",    \"AE\", \"01-1133\",      4, \"2012-12-27\",           NA,   \"PILOT01\",    \"AE\", \"01-1211\",      5, \"2012-11-29\",           NA,   \"PILOT01\",    \"AE\", \"01-1211\",      1, \"2012-11-16\",           NA,   \"PILOT01\",    \"AE\", \"01-1211\",      7, \"2013-01-11\",           NA,   \"PILOT01\",    \"AE\", \"01-1211\",      8, \"2013-01-11\",           NA,   \"PILOT01\",    \"AE\", \"01-1211\",      4, \"2012-11-22\",           NA,   \"PILOT01\",    \"AE\", \"01-1211\",      2, \"2012-11-21\", \"2012-11-21\",   \"PILOT01\",    \"AE\", \"01-1211\",      3, \"2012-11-21\",           NA,   \"PILOT01\",    \"AE\", \"01-1211\",      6, \"2012-12-09\",           NA,   \"PILOT01\",    \"AE\", \"01-1211\",      9, \"2013-01-14\", \"2013-01-14\",   \"PILOT01\",    \"AE\", \"09-1081\",      2, \"2014-05-01\",           NA,   \"PILOT01\",    \"AE\", \"09-1081\",      1, \"2014-04-07\",           NA,   \"PILOT01\",    \"AE\", \"09-1088\",      1, \"2014-05-08\",           NA,   \"PILOT01\",    \"AE\", \"09-1088\",      2, \"2014-08-02\",           NA ) lb <- tribble(   ~STUDYID,  ~DOMAIN,  ~USUBJID, ~LBSEQ,             ~LBDTC,   \"PILOT01\",    \"LB\", \"01-1130\",    219, \"2014-06-07T13:20\",   \"PILOT01\",    \"LB\", \"01-1130\",    322, \"2014-08-16T13:10\",   \"PILOT01\",    \"LB\", \"01-1133\",    268, \"2013-04-18T15:30\",   \"PILOT01\",    \"LB\", \"01-1133\",    304, \"2013-04-29T10:13\",   \"PILOT01\",    \"LB\", \"01-1211\",      8, \"2012-10-30T14:26\",   \"PILOT01\",    \"LB\", \"01-1211\",    162, \"2013-01-08T12:13\",   \"PILOT01\",    \"LB\", \"09-1081\",     47, \"2014-02-01T10:55\",   \"PILOT01\",    \"LB\", \"09-1081\",    219, \"2014-05-10T11:15\",   \"PILOT01\",    \"LB\", \"09-1088\",    283, \"2014-09-27T12:13\",   \"PILOT01\",    \"LB\", \"09-1088\",    322, \"2014-10-09T13:25\" ) adsl <- tribble(   ~STUDYID,   ~USUBJID,              ~TRTEDTM,   \"PILOT01\", \"01-1130\", \"2014-08-16 23:59:59\",   \"PILOT01\", \"01-1133\", \"2013-04-28 23:59:59\",   \"PILOT01\", \"01-1211\", \"2013-01-12 23:59:59\",   \"PILOT01\", \"09-1081\", \"2014-04-27 23:59:59\",   \"PILOT01\", \"09-1088\", \"2014-10-09 23:59:59\" ) %>%   mutate(     TRTEDTM = as_datetime(TRTEDTM)   )  # derive last known alive datetime (LSTALVDTM) ae_start <- date_source(   dataset_name = \"ae\",   date = convert_dtc_to_dtm(AESTDTC, highest_imputation = \"M\"), ) ae_end <- date_source(   dataset_name = \"ae\",   date = convert_dtc_to_dtm(AEENDTC, highest_imputation = \"M\"), )  ae_ext <- ae %>%   derive_vars_dtm(     dtc = AESTDTC,     new_vars_prefix = \"AEST\",     highest_imputation = \"M\"   ) %>%   derive_vars_dtm(     dtc = AEENDTC,     new_vars_prefix = \"AEEN\",     highest_imputation = \"M\"   )  lb_date <- date_source(   dataset_name = \"lb\",   date = convert_dtc_to_dtm(LBDTC), )  lb_ext <- derive_vars_dtm(   lb,   dtc = LBDTC,   new_vars_prefix = \"LB\" )  adsl_date <- date_source(   dataset_name = \"adsl\",   date = TRTEDTM )  dm %>%   derive_var_extreme_dtm(     new_var = LSTALVDTM,     ae_start, ae_end, lb_date, adsl_date,     source_datasets = list(       adsl = adsl,       ae = ae_ext,       lb = lb_ext     ),     mode = \"last\"   ) %>%   select(USUBJID, LSTALVDTM) #> # A tibble: 5 × 2 #>   USUBJID LSTALVDTM           #>   <chr>   <dttm>              #> 1 01-1130 2014-08-16 23:59:59 #> 2 01-1133 2013-04-29 10:13:00 #> 3 01-1211 2013-01-14 00:00:00 #> 4 09-1081 2014-05-10 11:15:00 #> 5 09-1088 2014-10-09 23:59:59  # derive last alive datetime and traceability variables ae_start <- date_source(   dataset_name = \"ae\",   date = convert_dtc_to_dtm(AESTDTC, highest_imputation = \"M\"),   set_values_to = exprs(     LALVDOM = \"AE\",     LALVSEQ = AESEQ,     LALVVAR = \"AESTDTC\"   ) )  ae_end <- date_source(   dataset_name = \"ae\",   date = convert_dtc_to_dtm(AEENDTC, highest_imputation = \"M\"),   set_values_to = exprs(     LALVDOM = \"AE\",     LALVSEQ = AESEQ,     LALVVAR = \"AEENDTC\"   ) ) lb_date <- date_source(   dataset_name = \"lb\",   date = convert_dtc_to_dtm(LBDTC),   set_values_to = exprs(     LALVDOM = \"LB\",     LALVSEQ = LBSEQ,     LALVVAR = \"LBDTC\"   ) )  adsl_date <- date_source(   dataset_name = \"adsl\",   date = TRTEDTM,   set_values_to = exprs(     LALVDOM = \"ADSL\",     LALVSEQ = NA_integer_,     LALVVAR = \"TRTEDTM\"   ) )  dm %>%   derive_var_extreme_dtm(     new_var = LSTALVDTM,     ae_start, ae_end, lb_date, adsl_date,     source_datasets = list(       adsl = adsl,       ae = ae_ext,       lb = lb_ext     ),     mode = \"last\"   ) %>%   select(USUBJID, LSTALVDTM, LALVDOM, LALVSEQ, LALVVAR) #> # A tibble: 5 × 5 #>   USUBJID LSTALVDTM           LALVDOM LALVSEQ LALVVAR #>   <chr>   <dttm>              <chr>     <dbl> <chr>   #> 1 01-1130 2014-08-16 23:59:59 ADSL         NA TRTEDTM #> 2 01-1133 2013-04-29 10:13:00 LB          304 LBDTC   #> 3 01-1211 2013-01-14 00:00:00 AE            9 AEENDTC #> 4 09-1081 2014-05-10 11:15:00 LB          219 LBDTC   #> 5 09-1088 2014-10-09 23:59:59 ADSL         NA TRTEDTM"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_extreme_flag.html","id":null,"dir":"Reference","previous_headings":"","what":"Add a Variable Flagging the First or Last Observation Within Each By Group — derive_var_extreme_flag","title":"Add a Variable Flagging the First or Last Observation Within Each By Group — derive_var_extreme_flag","text":"Add variable flagging first last observation within group","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_extreme_flag.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add a Variable Flagging the First or Last Observation Within Each By Group — derive_var_extreme_flag","text":"","code":"derive_var_extreme_flag(   dataset,   by_vars,   order,   new_var,   mode,   true_value = \"Y\",   false_value = NA_character_,   flag_all = FALSE,   check_type = \"warning\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_extreme_flag.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add a Variable Flagging the First or Last Observation Within Each By Group — derive_var_extreme_flag","text":"dataset Input dataset variables specified by_vars argument expected dataset. by_vars Grouping variables Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) order Sort order first last observation determined respect specified order. handling NAs sorting variables see Sort Order. Permitted Values: list variables functions variables new_var Variable add specified variable added output dataset. set value set true_value first last observation (depending mode) group. Permitted Values: list name-value pairs mode Flag mode Determines first last observation flagged. Permitted Values: \"first\", \"last\" true_value True value value specified variable new_var, applicable first last observation (depending mode) group. Permitted Values: atomic scalar false_value False value value specified variable new_var, applicable first last observation (depending mode) group. Permitted Values: atomic scalar flag_all Flag setting logical value set TRUE, records flagged error warning issued first last record unique. check_type Check uniqueness? \"warning\" \"error\" specified, specified message issued observations input dataset unique respect variables order. Default: \"warning\" Permitted Values: \"none\", \"warning\", \"error\"","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_extreme_flag.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add a Variable Flagging the First or Last Observation Within Each By Group — derive_var_extreme_flag","text":"input dataset new flag variable added","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_extreme_flag.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Add a Variable Flagging the First or Last Observation Within Each By Group — derive_var_extreme_flag","text":"group (respect variables specified by_vars parameter), new_var set \"Y\" first last observation (respect order specified order parameter flag mode specified mode parameter). case user wants flag multiple records grouping, example records happen visit time, argument flag_all can set TRUE. Otherwise, new_var set NA. Thus, direction \"worst\" considered fixed parameters dataset depending order mode, .e. every parameter first last record flagged across whole dataset.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_extreme_flag.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add a Variable Flagging the First or Last Observation Within Each By Group — derive_var_extreme_flag","text":"","code":"library(tibble) library(dplyr, warn.conflicts = FALSE) example_vs <- tribble(   ~USUBJID, ~VSTESTCD,      ~VISIT, ~VISITNUM, ~VSTPTNUM, ~VSSTRESN,   \"1001\",     \"DIABP\", \"SCREENING\",         1,        10,        64,   \"1001\",     \"DIABP\", \"SCREENING\",         1,        11,        66,   \"1001\",     \"DIABP\",  \"BASELINE\",         2,       100,        68,   \"1001\",     \"DIABP\",  \"BASELINE\",         2,       101,        68,   \"1001\",     \"DIABP\",    \"WEEK 2\",         3,       200,        72,   \"1001\",     \"DIABP\",    \"WEEK 2\",         3,       201,        71,   \"1001\",     \"DIABP\",    \"WEEK 4\",         4,       300,        70,   \"1001\",     \"DIABP\",    \"WEEK 4\",         4,       301,        70 )  # Flag last value for each patient, test, and visit, baseline observations are ignored example_vs %>%   restrict_derivation(     derivation = derive_var_extreme_flag,     args = params(       by_vars = exprs(USUBJID, VSTESTCD, VISIT),       order = exprs(VSTPTNUM),       new_var = LASTFL,       mode = \"last\"     ),     filter = VISIT != \"BASELINE\"   ) %>%   arrange(USUBJID, VSTESTCD, VISITNUM, VSTPTNUM) %>%   select(USUBJID, VSTESTCD, VISIT, VSTPTNUM, VSSTRESN, LASTFL) #> # A tibble: 8 × 6 #>   USUBJID VSTESTCD VISIT     VSTPTNUM VSSTRESN LASTFL #>   <chr>   <chr>    <chr>        <dbl>    <dbl> <chr>  #> 1 1001    DIABP    SCREENING       10       64 NA     #> 2 1001    DIABP    SCREENING       11       66 Y      #> 3 1001    DIABP    BASELINE       100       68 NA     #> 4 1001    DIABP    BASELINE       101       68 NA     #> 5 1001    DIABP    WEEK 2         200       72 NA     #> 6 1001    DIABP    WEEK 2         201       71 Y      #> 7 1001    DIABP    WEEK 4         300       70 NA     #> 8 1001    DIABP    WEEK 4         301       70 Y       # Baseline (ABLFL) examples:  input <- tribble(   ~STUDYID, ~USUBJID,  ~PARAMCD,     ~AVISIT,                  ~ADT, ~AVAL,    ~DTYPE,   \"TEST01\",  \"PAT01\", \"PARAM01\",  \"BASELINE\", as.Date(\"2021-04-27\"),  15.0,        NA,   \"TEST01\",  \"PAT01\", \"PARAM01\",  \"BASELINE\", as.Date(\"2021-04-25\"),  14.0,        NA,   \"TEST01\",  \"PAT01\", \"PARAM01\",  \"BASELINE\", as.Date(\"2021-04-23\"),  15.0, \"AVERAGE\",   \"TEST01\",  \"PAT01\", \"PARAM01\",    \"WEEK 1\", as.Date(\"2021-04-27\"),  10.0, \"AVERAGE\",   \"TEST01\",  \"PAT01\", \"PARAM01\",    \"WEEK 2\", as.Date(\"2021-04-30\"),  12.0,        NA,   \"TEST01\",  \"PAT02\", \"PARAM01\", \"SCREENING\", as.Date(\"2021-04-27\"),  15.0, \"AVERAGE\",   \"TEST01\",  \"PAT02\", \"PARAM01\",  \"BASELINE\", as.Date(\"2021-04-25\"),  14.0, \"AVERAGE\",   \"TEST01\",  \"PAT02\", \"PARAM01\",  \"BASELINE\", as.Date(\"2021-04-23\"),  15.0, \"AVERAGE\",   \"TEST01\",  \"PAT02\", \"PARAM01\",    \"WEEK 1\", as.Date(\"2021-04-27\"),  10.0, \"AVERAGE\",   \"TEST01\",  \"PAT02\", \"PARAM01\",    \"WEEK 2\", as.Date(\"2021-04-30\"),  12.0, \"AVERAGE\",   \"TEST01\",  \"PAT01\", \"PARAM02\", \"SCREENING\", as.Date(\"2021-04-27\"),  15.0, \"AVERAGE\",   \"TEST01\",  \"PAT01\", \"PARAM02\", \"SCREENING\", as.Date(\"2021-04-25\"),  14.0, \"AVERAGE\",   \"TEST01\",  \"PAT01\", \"PARAM02\", \"SCREENING\", as.Date(\"2021-04-23\"),  15.0,        NA,   \"TEST01\",  \"PAT01\", \"PARAM02\",  \"BASELINE\", as.Date(\"2021-04-27\"),  10.0, \"AVERAGE\",   \"TEST01\",  \"PAT01\", \"PARAM02\",    \"WEEK 2\", as.Date(\"2021-04-30\"),  12.0,        NA,   \"TEST01\",  \"PAT02\", \"PARAM02\", \"SCREENING\", as.Date(\"2021-04-27\"),  15.0,        NA,   \"TEST01\",  \"PAT02\", \"PARAM02\",  \"BASELINE\", as.Date(\"2021-04-25\"),  14.0,        NA,   \"TEST01\",  \"PAT02\", \"PARAM02\",    \"WEEK 1\", as.Date(\"2021-04-23\"),  15.0,        NA,   \"TEST01\",  \"PAT02\", \"PARAM02\",    \"WEEK 1\", as.Date(\"2021-04-27\"),  10.0,        NA,   \"TEST01\",  \"PAT02\", \"PARAM02\",  \"BASELINE\", as.Date(\"2021-04-30\"),  12.0,        NA )  # Last observation restrict_derivation(   input,   derivation = derive_var_extreme_flag,   args = params(     by_vars = exprs(USUBJID, PARAMCD),     order = exprs(ADT),     new_var = ABLFL,     mode = \"last\"   ),   filter = AVISIT == \"BASELINE\" ) #> # A tibble: 20 × 8 #>    STUDYID USUBJID PARAMCD AVISIT    ADT         AVAL DTYPE   ABLFL #>    <chr>   <chr>   <chr>   <chr>     <date>     <dbl> <chr>   <chr> #>  1 TEST01  PAT01   PARAM01 BASELINE  2021-04-23    15 AVERAGE NA    #>  2 TEST01  PAT01   PARAM01 BASELINE  2021-04-25    14 NA      NA    #>  3 TEST01  PAT01   PARAM01 BASELINE  2021-04-27    15 NA      Y     #>  4 TEST01  PAT01   PARAM02 BASELINE  2021-04-27    10 AVERAGE Y     #>  5 TEST01  PAT02   PARAM01 BASELINE  2021-04-23    15 AVERAGE NA    #>  6 TEST01  PAT02   PARAM01 BASELINE  2021-04-25    14 AVERAGE Y     #>  7 TEST01  PAT02   PARAM02 BASELINE  2021-04-25    14 NA      NA    #>  8 TEST01  PAT02   PARAM02 BASELINE  2021-04-30    12 NA      Y     #>  9 TEST01  PAT01   PARAM01 WEEK 1    2021-04-27    10 AVERAGE NA    #> 10 TEST01  PAT01   PARAM01 WEEK 2    2021-04-30    12 NA      NA    #> 11 TEST01  PAT02   PARAM01 SCREENING 2021-04-27    15 AVERAGE NA    #> 12 TEST01  PAT02   PARAM01 WEEK 1    2021-04-27    10 AVERAGE NA    #> 13 TEST01  PAT02   PARAM01 WEEK 2    2021-04-30    12 AVERAGE NA    #> 14 TEST01  PAT01   PARAM02 SCREENING 2021-04-27    15 AVERAGE NA    #> 15 TEST01  PAT01   PARAM02 SCREENING 2021-04-25    14 AVERAGE NA    #> 16 TEST01  PAT01   PARAM02 SCREENING 2021-04-23    15 NA      NA    #> 17 TEST01  PAT01   PARAM02 WEEK 2    2021-04-30    12 NA      NA    #> 18 TEST01  PAT02   PARAM02 SCREENING 2021-04-27    15 NA      NA    #> 19 TEST01  PAT02   PARAM02 WEEK 1    2021-04-23    15 NA      NA    #> 20 TEST01  PAT02   PARAM02 WEEK 1    2021-04-27    10 NA      NA     # Worst observation - Direction = High restrict_derivation(   input,   derivation = derive_var_extreme_flag,   args = params(     by_vars = exprs(USUBJID, PARAMCD),     order = exprs(AVAL, ADT),     new_var = ABLFL,     mode = \"last\"   ),   filter = AVISIT == \"BASELINE\" ) #> # A tibble: 20 × 8 #>    STUDYID USUBJID PARAMCD AVISIT    ADT         AVAL DTYPE   ABLFL #>    <chr>   <chr>   <chr>   <chr>     <date>     <dbl> <chr>   <chr> #>  1 TEST01  PAT01   PARAM01 BASELINE  2021-04-25    14 NA      NA    #>  2 TEST01  PAT01   PARAM01 BASELINE  2021-04-23    15 AVERAGE NA    #>  3 TEST01  PAT01   PARAM01 BASELINE  2021-04-27    15 NA      Y     #>  4 TEST01  PAT01   PARAM02 BASELINE  2021-04-27    10 AVERAGE Y     #>  5 TEST01  PAT02   PARAM01 BASELINE  2021-04-25    14 AVERAGE NA    #>  6 TEST01  PAT02   PARAM01 BASELINE  2021-04-23    15 AVERAGE Y     #>  7 TEST01  PAT02   PARAM02 BASELINE  2021-04-30    12 NA      NA    #>  8 TEST01  PAT02   PARAM02 BASELINE  2021-04-25    14 NA      Y     #>  9 TEST01  PAT01   PARAM01 WEEK 1    2021-04-27    10 AVERAGE NA    #> 10 TEST01  PAT01   PARAM01 WEEK 2    2021-04-30    12 NA      NA    #> 11 TEST01  PAT02   PARAM01 SCREENING 2021-04-27    15 AVERAGE NA    #> 12 TEST01  PAT02   PARAM01 WEEK 1    2021-04-27    10 AVERAGE NA    #> 13 TEST01  PAT02   PARAM01 WEEK 2    2021-04-30    12 AVERAGE NA    #> 14 TEST01  PAT01   PARAM02 SCREENING 2021-04-27    15 AVERAGE NA    #> 15 TEST01  PAT01   PARAM02 SCREENING 2021-04-25    14 AVERAGE NA    #> 16 TEST01  PAT01   PARAM02 SCREENING 2021-04-23    15 NA      NA    #> 17 TEST01  PAT01   PARAM02 WEEK 2    2021-04-30    12 NA      NA    #> 18 TEST01  PAT02   PARAM02 SCREENING 2021-04-27    15 NA      NA    #> 19 TEST01  PAT02   PARAM02 WEEK 1    2021-04-23    15 NA      NA    #> 20 TEST01  PAT02   PARAM02 WEEK 1    2021-04-27    10 NA      NA     # Worst observation - Direction = Lo restrict_derivation(   input,   derivation = derive_var_extreme_flag,   args = params(     by_vars = exprs(USUBJID, PARAMCD),     order = exprs(desc(AVAL), ADT),     new_var = ABLFL,     mode = \"last\"   ),   filter = AVISIT == \"BASELINE\" ) #> # A tibble: 20 × 8 #>    STUDYID USUBJID PARAMCD AVISIT    ADT         AVAL DTYPE   ABLFL #>    <chr>   <chr>   <chr>   <chr>     <date>     <dbl> <chr>   <chr> #>  1 TEST01  PAT01   PARAM01 BASELINE  2021-04-23    15 AVERAGE NA    #>  2 TEST01  PAT01   PARAM01 BASELINE  2021-04-27    15 NA      NA    #>  3 TEST01  PAT01   PARAM01 BASELINE  2021-04-25    14 NA      Y     #>  4 TEST01  PAT01   PARAM02 BASELINE  2021-04-27    10 AVERAGE Y     #>  5 TEST01  PAT02   PARAM01 BASELINE  2021-04-23    15 AVERAGE NA    #>  6 TEST01  PAT02   PARAM01 BASELINE  2021-04-25    14 AVERAGE Y     #>  7 TEST01  PAT02   PARAM02 BASELINE  2021-04-25    14 NA      NA    #>  8 TEST01  PAT02   PARAM02 BASELINE  2021-04-30    12 NA      Y     #>  9 TEST01  PAT01   PARAM01 WEEK 1    2021-04-27    10 AVERAGE NA    #> 10 TEST01  PAT01   PARAM01 WEEK 2    2021-04-30    12 NA      NA    #> 11 TEST01  PAT02   PARAM01 SCREENING 2021-04-27    15 AVERAGE NA    #> 12 TEST01  PAT02   PARAM01 WEEK 1    2021-04-27    10 AVERAGE NA    #> 13 TEST01  PAT02   PARAM01 WEEK 2    2021-04-30    12 AVERAGE NA    #> 14 TEST01  PAT01   PARAM02 SCREENING 2021-04-27    15 AVERAGE NA    #> 15 TEST01  PAT01   PARAM02 SCREENING 2021-04-25    14 AVERAGE NA    #> 16 TEST01  PAT01   PARAM02 SCREENING 2021-04-23    15 NA      NA    #> 17 TEST01  PAT01   PARAM02 WEEK 2    2021-04-30    12 NA      NA    #> 18 TEST01  PAT02   PARAM02 SCREENING 2021-04-27    15 NA      NA    #> 19 TEST01  PAT02   PARAM02 WEEK 1    2021-04-23    15 NA      NA    #> 20 TEST01  PAT02   PARAM02 WEEK 1    2021-04-27    10 NA      NA     # Average observation restrict_derivation(   input,   derivation = derive_var_extreme_flag,   args = params(     by_vars = exprs(USUBJID, PARAMCD),     order = exprs(ADT, desc(AVAL)),     new_var = ABLFL,     mode = \"last\"   ),   filter = AVISIT == \"BASELINE\" & DTYPE == \"AVERAGE\" ) #> # A tibble: 20 × 8 #>    STUDYID USUBJID PARAMCD AVISIT    ADT         AVAL DTYPE   ABLFL #>    <chr>   <chr>   <chr>   <chr>     <date>     <dbl> <chr>   <chr> #>  1 TEST01  PAT01   PARAM01 BASELINE  2021-04-23    15 AVERAGE Y     #>  2 TEST01  PAT01   PARAM02 BASELINE  2021-04-27    10 AVERAGE Y     #>  3 TEST01  PAT02   PARAM01 BASELINE  2021-04-23    15 AVERAGE NA    #>  4 TEST01  PAT02   PARAM01 BASELINE  2021-04-25    14 AVERAGE Y     #>  5 TEST01  PAT01   PARAM01 BASELINE  2021-04-27    15 NA      NA    #>  6 TEST01  PAT01   PARAM01 BASELINE  2021-04-25    14 NA      NA    #>  7 TEST01  PAT01   PARAM01 WEEK 1    2021-04-27    10 AVERAGE NA    #>  8 TEST01  PAT01   PARAM01 WEEK 2    2021-04-30    12 NA      NA    #>  9 TEST01  PAT02   PARAM01 SCREENING 2021-04-27    15 AVERAGE NA    #> 10 TEST01  PAT02   PARAM01 WEEK 1    2021-04-27    10 AVERAGE NA    #> 11 TEST01  PAT02   PARAM01 WEEK 2    2021-04-30    12 AVERAGE NA    #> 12 TEST01  PAT01   PARAM02 SCREENING 2021-04-27    15 AVERAGE NA    #> 13 TEST01  PAT01   PARAM02 SCREENING 2021-04-25    14 AVERAGE NA    #> 14 TEST01  PAT01   PARAM02 SCREENING 2021-04-23    15 NA      NA    #> 15 TEST01  PAT01   PARAM02 WEEK 2    2021-04-30    12 NA      NA    #> 16 TEST01  PAT02   PARAM02 SCREENING 2021-04-27    15 NA      NA    #> 17 TEST01  PAT02   PARAM02 BASELINE  2021-04-25    14 NA      NA    #> 18 TEST01  PAT02   PARAM02 WEEK 1    2021-04-23    15 NA      NA    #> 19 TEST01  PAT02   PARAM02 WEEK 1    2021-04-27    10 NA      NA    #> 20 TEST01  PAT02   PARAM02 BASELINE  2021-04-30    12 NA      NA     # OCCURDS Examples example_ae <- tribble(   ~USUBJID,         ~AEBODSYS,    ~AEDECOD,   ~AESEV, ~AESTDY, ~AESEQ,   \"1015\", \"GENERAL DISORDERS\",  \"ERYTHEMA\",   \"MILD\",       2,      1,   \"1015\", \"GENERAL DISORDERS\",  \"PRURITUS\",   \"MILD\",       2,      2,   \"1015\",      \"GI DISORDERS\", \"DIARRHOEA\",   \"MILD\",       8,      3,   \"1023\", \"CARDIAC DISORDERS\",  \"AV BLOCK\",   \"MILD\",      22,      4,   \"1023\",    \"SKIN DISORDERS\",  \"ERYTHEMA\",   \"MILD\",       3,      1,   \"1023\",    \"SKIN DISORDERS\",  \"ERYTHEMA\", \"SEVERE\",       5,      2,   \"1023\",    \"SKIN DISORDERS\",  \"ERYTHEMA\",   \"MILD\",       8,      3 )  # Most severe AE first occurrence per patient example_ae %>%   mutate(     TEMP_AESEVN =       as.integer(factor(AESEV, levels = c(\"SEVERE\", \"MODERATE\", \"MILD\")))   ) %>%   derive_var_extreme_flag(     new_var = AOCCIFL,     by_vars = exprs(USUBJID),     order = exprs(TEMP_AESEVN, AESTDY, AESEQ),     mode = \"first\"   ) %>%   arrange(USUBJID, AESTDY, AESEQ) %>%   select(USUBJID, AEDECOD, AESEV, AESTDY, AESEQ, AOCCIFL) #> # A tibble: 7 × 6 #>   USUBJID AEDECOD   AESEV  AESTDY AESEQ AOCCIFL #>   <chr>   <chr>     <chr>   <dbl> <dbl> <chr>   #> 1 1015    ERYTHEMA  MILD        2     1 Y       #> 2 1015    PRURITUS  MILD        2     2 NA      #> 3 1015    DIARRHOEA MILD        8     3 NA      #> 4 1023    ERYTHEMA  MILD        3     1 NA      #> 5 1023    ERYTHEMA  SEVERE      5     2 Y       #> 6 1023    ERYTHEMA  MILD        8     3 NA      #> 7 1023    AV BLOCK  MILD       22     4 NA       # Most severe AE first occurrence per patient (flag all cases) example_ae %>%   mutate(     TEMP_AESEVN =       as.integer(factor(AESEV, levels = c(\"SEVERE\", \"MODERATE\", \"MILD\")))   ) %>%   derive_var_extreme_flag(     new_var = AOCCIFL,     by_vars = exprs(USUBJID),     order = exprs(TEMP_AESEVN, AESTDY),     mode = \"first\",     flag_all = TRUE   ) %>%   arrange(USUBJID, AESTDY) %>%   select(USUBJID, AEDECOD, AESEV, AESTDY, AOCCIFL) #> # A tibble: 7 × 5 #>   USUBJID AEDECOD   AESEV  AESTDY AOCCIFL #>   <chr>   <chr>     <chr>   <dbl> <chr>   #> 1 1015    ERYTHEMA  MILD        2 Y       #> 2 1015    PRURITUS  MILD        2 Y       #> 3 1015    DIARRHOEA MILD        8 NA      #> 4 1023    ERYTHEMA  MILD        3 NA      #> 5 1023    ERYTHEMA  SEVERE      5 Y       #> 6 1023    ERYTHEMA  MILD        8 NA      #> 7 1023    AV BLOCK  MILD       22 NA       # Most severe AE first occurrence per patient per body system example_ae %>%   mutate(     TEMP_AESEVN =       as.integer(factor(AESEV, levels = c(\"SEVERE\", \"MODERATE\", \"MILD\")))   ) %>%   derive_var_extreme_flag(     new_var = AOCCSIFL,     by_vars = exprs(USUBJID, AEBODSYS),     order = exprs(TEMP_AESEVN, AESTDY, AESEQ),     mode = \"first\"   ) %>%   arrange(USUBJID, AESTDY, AESEQ) %>%   select(USUBJID, AEBODSYS, AESEV, AESTDY, AOCCSIFL) #> # A tibble: 7 × 5 #>   USUBJID AEBODSYS          AESEV  AESTDY AOCCSIFL #>   <chr>   <chr>             <chr>   <dbl> <chr>    #> 1 1015    GENERAL DISORDERS MILD        2 Y        #> 2 1015    GENERAL DISORDERS MILD        2 NA       #> 3 1015    GI DISORDERS      MILD        8 Y        #> 4 1023    SKIN DISORDERS    MILD        3 NA       #> 5 1023    SKIN DISORDERS    SEVERE      5 Y        #> 6 1023    SKIN DISORDERS    MILD        8 NA       #> 7 1023    CARDIAC DISORDERS MILD       22 Y"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_joined_exist_flag.html","id":null,"dir":"Reference","previous_headings":"","what":"Derives a Flag Based on an Existing Flag — derive_var_joined_exist_flag","title":"Derives a Flag Based on an Existing Flag — derive_var_joined_exist_flag","text":"Derive flag depends observations dataset. example, flagging events need confirmed second event.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_joined_exist_flag.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derives a Flag Based on an Existing Flag — derive_var_joined_exist_flag","text":"","code":"derive_var_joined_exist_flag(   dataset,   dataset_add,   by_vars,   order,   new_var,   tmp_obs_nr_var = NULL,   join_vars,   join_type,   first_cond_lower = NULL,   first_cond_upper = NULL,   filter_add = NULL,   filter_join,   true_value = \"Y\",   false_value = NA_character_,   check_type = \"warning\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_joined_exist_flag.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derives a Flag Based on an Existing Flag — derive_var_joined_exist_flag","text":"dataset Input dataset variables specified by_vars join_vars arguments expected dataset. dataset_add Additional dataset variables specified by_vars, join_vars, order expected. by_vars Grouping variables specified variables used joining input dataset (dataset) additional dataset (dataset_add). Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) order Order observations ordered specified order. handling NAs sorting variables see Sort Order. new_var New variable specified variable added input dataset. tmp_obs_nr_var Temporary observation number specified variable added input dataset (dataset) additional dataset (dataset_add). set observation number respect order. group (by_vars) observation number starts 1. variable can used conditions (filter_join, first_cond_upper, first_cond_lower). included output dataset. can also used flag consecutive observations last observation (see last example ). join_vars Variables keep joined dataset variables needed observations specified parameter. specified variables added joined dataset suffix \".join\". example flag observations AVALC == \"Y\" AVALC == \"Y\" least one subsequent visit join_vars = exprs(AVALC, AVISITN) filter_join = AVALC == \"Y\" & AVALC.join == \"Y\" & AVISITN < AVISITN.join specified. *.join variables included output dataset. join_type Observations keep joining argument determines joined observations kept respect original observation. example, join_type = \"\" specified observations original observations kept. example confirmed response BOR oncology setting confirmed deterioration questionnaires confirmatory assessment must assessment. Thus join_type = \"\" used. Whereas, sometimes might allow confirmatory observations occur prior observation. example, identify AEs occurring seven days COVID AE. Thus join_type = \"\" used. Permitted Values: \"\", \"\", \"\" first_cond_lower Condition selecting range data () argument specified, observations restricted first observation current observation specified condition fulfilled current observation. condition fulfilled observations, observations considered, .e., observation flagged. parameter specified filter_join contains summary functions apply observations certain observation current observation current observation. example see last example . first_cond_upper Condition selecting range data () argument specified, observations restricted first observation specified condition fulfilled. condition fulfilled observations, observations considered, .e., observation flagged. parameter specified filter_join contains summary functions apply observations confirmation assessment. example see third example . filter_add Filter additional dataset (dataset_add) observations dataset_add fulfilling specified condition joined input dataset. argument specified, observations joined. Variables created order new_vars arguments can used condition. condition can include summary functions like () (). additional dataset grouped variables (by_vars). Permitted Values: condition filter_join Condition selecting observations filter applied joined dataset flagging confirmed observations. condition can include summary functions like () (). joined dataset grouped original observations. .e., summary function applied observations confirmation observation. example, filter_join = AVALC == \"CR\" & (AVALC.join %% c(\"CR\", \"NE\")) & count_vals(var = AVALC.join, val = \"NE\") <= 1 selects observations response \"CR\" observations confirmation observation response \"CR\" \"NE\" one \"NE\". true_value Value new_var flagged observations false_value Value new_var observations flagged check_type Check uniqueness? \"warning\" \"error\" specified, specified message issued observations input dataset unique respect variables order. Permitted Values: \"none\", \"warning\", \"error\"","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_joined_exist_flag.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derives a Flag Based on an Existing Flag — derive_var_joined_exist_flag","text":"input dataset variable specified new_var added.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_joined_exist_flag.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Derives a Flag Based on an Existing Flag — derive_var_joined_exist_flag","text":"example usage might flagging patient received two required medications within certain timeframe . oncology setting, example, function used flag response value can confirmed assessment. commonly used endpoints best overall response. following steps performed produce output dataset.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_joined_exist_flag.html","id":"step-","dir":"Reference","previous_headings":"","what":"Step 1","title":"Derives a Flag Based on an Existing Flag — derive_var_joined_exist_flag","text":"variables specified order added additional dataset (dataset_add). variables specified join_vars added additional dataset (dataset_add). records additional dataset (dataset_add) restricted matching filter_add condition. input dataset (dataset) joined restricted additional dataset variables specified by_vars. additional dataset variables specified join_vars kept. suffix \".join\" added variables also exist input dataset. example, by_vars = USUBJID, join_vars = exprs(AVISITN, AVALC) input dataset additional dataset   joined dataset ","code":"# A tibble: 2 x 4 USUBJID AVISITN AVALC  AVAL <chr>     <dbl> <chr> <dbl> 1             1 Y         1 1             2 N         0 A tibble: 4 x 6 USUBJID AVISITN AVALC  AVAL AVISITN.join AVALC.join <chr>     <dbl> <chr> <dbl>        <dbl> <chr> 1             1 Y         1            1 Y 1             1 Y         1            2 N 1             2 N         0            1 Y 1             2 N         0            2 N"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_joined_exist_flag.html","id":"step--1","dir":"Reference","previous_headings":"","what":"Step 2","title":"Derives a Flag Based on an Existing Flag — derive_var_joined_exist_flag","text":"joined dataset restricted observations respect join_type order. dataset example previous step join_type = \"\" order = exprs(AVISITN) restricted ","code":"A tibble: 4 x 6 USUBJID AVISITN AVALC  AVAL AVISITN.join AVALC.join <chr>     <dbl> <chr> <dbl>        <dbl> <chr> 1             1 Y         1            2 N"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_joined_exist_flag.html","id":"step--2","dir":"Reference","previous_headings":"","what":"Step 3","title":"Derives a Flag Based on an Existing Flag — derive_var_joined_exist_flag","text":"first_cond_lower specified, observation input dataset joined dataset restricted observations first observation first_cond_lower fulfilled (observation fulfilling condition included) observation input dataset. observation input dataset condition fulfilled, observation removed. first_cond_upper specified, observation input dataset joined dataset restricted observations first observation first_cond_upper fulfilled (observation fulfilling condition included). observation input dataset condition fulfilled, observation removed. example see last example \"Examples\" section.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_joined_exist_flag.html","id":"step--3","dir":"Reference","previous_headings":"","what":"Step 4","title":"Derives a Flag Based on an Existing Flag — derive_var_joined_exist_flag","text":"joined dataset grouped observations input dataset restricted observations fulfilling condition specified filter_join.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_joined_exist_flag.html","id":"step--4","dir":"Reference","previous_headings":"","what":"Step 5","title":"Derives a Flag Based on an Existing Flag — derive_var_joined_exist_flag","text":"first observation group selected","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_joined_exist_flag.html","id":"step--5","dir":"Reference","previous_headings":"","what":"Step 6","title":"Derives a Flag Based on an Existing Flag — derive_var_joined_exist_flag","text":"variable specified new_var added input dataset. set true_value observations selected previous step. observations set false_value.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_joined_exist_flag.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derives a Flag Based on an Existing Flag — derive_var_joined_exist_flag","text":"","code":"library(tibble)  # flag observations with a duration longer than 30 and # at, after, or up to 7 days before a COVID AE (ACOVFL == \"Y\") adae <- tribble(   ~USUBJID, ~ADY, ~ACOVFL, ~ADURN,   \"1\",        10, \"N\",          1,   \"1\",        21, \"N\",         50,   \"1\",        23, \"Y\",         14,   \"1\",        32, \"N\",         31,   \"1\",        42, \"N\",         20,   \"2\",        11, \"Y\",         13,   \"2\",        23, \"N\",          2,   \"3\",        13, \"Y\",         12,   \"4\",        14, \"N\",         32,   \"4\",        21, \"N\",         41 )  derive_var_joined_exist_flag(   adae,   dataset_add = adae,   new_var = ALCOVFL,   by_vars = exprs(USUBJID),   join_vars = exprs(ACOVFL, ADY),   join_type = \"all\",   order = exprs(ADY),   filter_join = ADURN > 30 & ACOVFL.join == \"Y\" & ADY >= ADY.join - 7 ) #> # A tibble: 10 × 5 #>    USUBJID   ADY ACOVFL ADURN ALCOVFL #>    <chr>   <dbl> <chr>  <dbl> <chr>   #>  1 1          10 N          1 NA      #>  2 1          21 N         50 Y       #>  3 1          23 Y         14 NA      #>  4 1          32 N         31 Y       #>  5 1          42 N         20 NA      #>  6 2          11 Y         13 NA      #>  7 2          23 N          2 NA      #>  8 3          13 Y         12 NA      #>  9 4          14 N         32 NA      #> 10 4          21 N         41 NA       # flag observations with AVALC == \"Y\" and AVALC == \"Y\" at one subsequent visit data <- tribble(   ~USUBJID, ~AVISITN, ~AVALC,   \"1\",      1,        \"Y\",   \"1\",      2,        \"N\",   \"1\",      3,        \"Y\",   \"1\",      4,        \"N\",   \"2\",      1,        \"Y\",   \"2\",      2,        \"N\",   \"3\",      1,        \"Y\",   \"4\",      1,        \"N\",   \"4\",      2,        \"N\", )  derive_var_joined_exist_flag(   data,   dataset_add = data,   by_vars = exprs(USUBJID),   new_var = CONFFL,   join_vars = exprs(AVALC, AVISITN),   join_type = \"after\",   order = exprs(AVISITN),   filter_join = AVALC == \"Y\" & AVALC.join == \"Y\" & AVISITN < AVISITN.join ) #> # A tibble: 9 × 4 #>   USUBJID AVISITN AVALC CONFFL #>   <chr>     <dbl> <chr> <chr>  #> 1 1             1 Y     Y      #> 2 1             2 N     NA     #> 3 1             3 Y     NA     #> 4 1             4 N     NA     #> 5 2             1 Y     NA     #> 6 2             2 N     NA     #> 7 3             1 Y     NA     #> 8 4             1 N     NA     #> 9 4             2 N     NA      # select observations with AVALC == \"CR\", AVALC == \"CR\" at a subsequent visit, # only \"CR\" or \"NE\" in between, and at most one \"NE\" in between data <- tribble(   ~USUBJID, ~AVISITN, ~AVALC,   \"1\",      1,        \"PR\",   \"1\",      2,        \"CR\",   \"1\",      3,        \"NE\",   \"1\",      4,        \"CR\",   \"1\",      5,        \"NE\",   \"2\",      1,        \"CR\",   \"2\",      2,        \"PR\",   \"2\",      3,        \"CR\",   \"3\",      1,        \"CR\",   \"4\",      1,        \"CR\",   \"4\",      2,        \"NE\",   \"4\",      3,        \"NE\",   \"4\",      4,        \"CR\",   \"4\",      5,        \"PR\" )  derive_var_joined_exist_flag(   data,   dataset_add = data,   by_vars = exprs(USUBJID),   join_vars = exprs(AVALC),   join_type = \"after\",   order = exprs(AVISITN),   new_var = CONFFL,   first_cond_upper = AVALC.join == \"CR\",   filter_join = AVALC == \"CR\" & all(AVALC.join %in% c(\"CR\", \"NE\")) &     count_vals(var = AVALC.join, val = \"NE\") <= 1 ) #> # A tibble: 14 × 4 #>    USUBJID AVISITN AVALC CONFFL #>    <chr>     <dbl> <chr> <chr>  #>  1 1             1 PR    NA     #>  2 1             2 CR    Y      #>  3 1             3 NE    NA     #>  4 1             4 CR    NA     #>  5 1             5 NE    NA     #>  6 2             1 CR    NA     #>  7 2             2 PR    NA     #>  8 2             3 CR    NA     #>  9 3             1 CR    NA     #> 10 4             1 CR    NA     #> 11 4             2 NE    NA     #> 12 4             3 NE    NA     #> 13 4             4 CR    NA     #> 14 4             5 PR    NA      # flag observations with AVALC == \"PR\", AVALC == \"CR\" or AVALC == \"PR\" # at a subsequent visit at least 20 days later, only \"CR\", \"PR\", or \"NE\" # in between, at most one \"NE\" in between, and \"CR\" is not followed by \"PR\" data <- tribble(   ~USUBJID, ~ADY, ~AVALC,   \"1\",         6, \"PR\",   \"1\",        12, \"CR\",   \"1\",        24, \"NE\",   \"1\",        32, \"CR\",   \"1\",        48, \"PR\",   \"2\",         3, \"PR\",   \"2\",        21, \"CR\",   \"2\",        33, \"PR\",   \"3\",        11, \"PR\",   \"4\",         7, \"PR\",   \"4\",        12, \"NE\",   \"4\",        24, \"NE\",   \"4\",        32, \"PR\",   \"4\",        55, \"PR\" )  derive_var_joined_exist_flag(   data,   dataset_add = data,   by_vars = exprs(USUBJID),   join_vars = exprs(AVALC, ADY),   join_type = \"after\",   order = exprs(ADY),   new_var = CONFFL,   first_cond_upper = AVALC.join %in% c(\"CR\", \"PR\") & ADY.join - ADY >= 20,   filter_join = AVALC == \"PR\" &     all(AVALC.join %in% c(\"CR\", \"PR\", \"NE\")) &     count_vals(var = AVALC.join, val = \"NE\") <= 1 &     (       min_cond(var = ADY.join, cond = AVALC.join == \"CR\") >         max_cond(var = ADY.join, cond = AVALC.join == \"PR\") |         count_vals(var = AVALC.join, val = \"CR\") == 0     ) ) #> # A tibble: 14 × 4 #>    USUBJID   ADY AVALC CONFFL #>    <chr>   <dbl> <chr> <chr>  #>  1 1           6 PR    NA     #>  2 1          12 CR    NA     #>  3 1          24 NE    NA     #>  4 1          32 CR    NA     #>  5 1          48 PR    NA     #>  6 2           3 PR    NA     #>  7 2          21 CR    NA     #>  8 2          33 PR    NA     #>  9 3          11 PR    NA     #> 10 4           7 PR    NA     #> 11 4          12 NE    NA     #> 12 4          24 NE    NA     #> 13 4          32 PR    Y      #> 14 4          55 PR    NA      # flag observations with CRIT1FL == \"Y\" at two consecutive visits or at the last visit data <- tribble(   ~USUBJID, ~AVISITN, ~CRIT1FL,   \"1\",      1,        \"Y\",   \"1\",      2,        \"N\",   \"1\",      3,        \"Y\",   \"1\",      5,        \"N\",   \"2\",      1,        \"Y\",   \"2\",      3,        \"Y\",   \"2\",      5,        \"N\",   \"3\",      1,        \"Y\",   \"4\",      1,        \"Y\",   \"4\",      2,        \"N\", )  derive_var_joined_exist_flag(   data,   dataset_add = data,   by_vars = exprs(USUBJID),   new_var = CONFFL,   tmp_obs_nr_var = tmp_obs_nr,   join_vars = exprs(CRIT1FL),   join_type = \"all\",   order = exprs(AVISITN),   filter_join = CRIT1FL == \"Y\" & CRIT1FL.join == \"Y\" &     (tmp_obs_nr + 1 == tmp_obs_nr.join | tmp_obs_nr == max(tmp_obs_nr.join)) ) #> # A tibble: 10 × 4 #>    USUBJID AVISITN CRIT1FL CONFFL #>    <chr>     <dbl> <chr>   <chr>  #>  1 1             1 Y       NA     #>  2 1             2 N       NA     #>  3 1             3 Y       NA     #>  4 1             5 N       NA     #>  5 2             1 Y       Y      #>  6 2             3 Y       NA     #>  7 2             5 N       NA     #>  8 3             1 Y       Y      #>  9 4             1 Y       NA     #> 10 4             2 N       NA      # first_cond_lower and first_cond_upper argument myd <- tribble(   ~subj, ~day, ~val,   \"1\",      1, \"++\",   \"1\",      2, \"-\",   \"1\",      3, \"0\",   \"1\",      4, \"+\",   \"1\",      5, \"++\",   \"1\",      6, \"-\",   \"2\",      1, \"-\",   \"2\",      2, \"++\",   \"2\",      3, \"+\",   \"2\",      4, \"0\",   \"2\",      5, \"-\",   \"2\",      6, \"++\" )  # flag \"0\" where all results from the first \"++\" before the \"0\" up to the \"0\" # (excluding the \"0\") are \"+\" or \"++\" derive_var_joined_exist_flag(   myd,   dataset_add = myd,   by_vars = exprs(subj),   order = exprs(day),   new_var = flag,   join_vars = exprs(val),   join_type = \"before\",   first_cond_lower = val.join == \"++\",   filter_join = val == \"0\" & all(val.join %in% c(\"+\", \"++\")) ) #> # A tibble: 12 × 4 #>    subj    day val   flag  #>    <chr> <dbl> <chr> <chr> #>  1 1         1 ++    NA    #>  2 1         2 -     NA    #>  3 1         3 0     NA    #>  4 1         4 +     NA    #>  5 1         5 ++    NA    #>  6 1         6 -     NA    #>  7 2         1 -     NA    #>  8 2         2 ++    NA    #>  9 2         3 +     NA    #> 10 2         4 0     Y     #> 11 2         5 -     NA    #> 12 2         6 ++    NA     # flag \"0\" where all results from the \"0\" (excluding the \"0\") up to the first # \"++\" after the \"0\" are \"+\" or \"++\" derive_var_joined_exist_flag(   myd,   dataset_add = myd,   by_vars = exprs(subj),   order = exprs(day),   new_var = flag,   join_vars = exprs(val),   join_type = \"after\",   first_cond_upper = val.join == \"++\",   filter_join = val == \"0\" & all(val.join %in% c(\"+\", \"++\")) ) #> # A tibble: 12 × 4 #>    subj    day val   flag  #>    <chr> <dbl> <chr> <chr> #>  1 1         1 ++    NA    #>  2 1         2 -     NA    #>  3 1         3 0     Y     #>  4 1         4 +     NA    #>  5 1         5 ++    NA    #>  6 1         6 -     NA    #>  7 2         1 -     NA    #>  8 2         2 ++    NA    #>  9 2         3 +     NA    #> 10 2         4 0     NA    #> 11 2         5 -     NA    #> 12 2         6 ++    NA     # flag each dose which is lower than the previous dose per subject ex <- tribble(   ~USUBJID, ~EXSTDTM,          ~EXDOSE,   \"1\",      \"2024-01-01T08:00\",      2,   \"1\",      \"2024-01-02T08:00\",      4,   \"2\",      \"2024-01-01T08:30\",      1,   \"2\",      \"2024-01-02T08:30\",      4,   \"2\",      \"2024-01-03T08:30\",      3,   \"2\",      \"2024-01-04T08:30\",      2,   \"2\",      \"2024-01-05T08:30\",      2 )  derive_var_joined_exist_flag(   ex,   dataset_add = ex,   by_vars = exprs(USUBJID),   order = exprs(EXSTDTM),   new_var = DOSREDFL,   tmp_obs_nr_var = tmp_dose_nr,   join_vars = exprs(EXDOSE),   join_type = \"before\",   filter_join = (     tmp_dose_nr == tmp_dose_nr.join + 1 # Look only at adjacent doses     & EXDOSE > 0 & EXDOSE.join > 0 # Both doses are valid     & EXDOSE < EXDOSE.join # Dose is lower than previous   ) ) #> # A tibble: 7 × 4 #>   USUBJID EXSTDTM          EXDOSE DOSREDFL #>   <chr>   <chr>             <dbl> <chr>    #> 1 1       2024-01-01T08:00      2 NA       #> 2 1       2024-01-02T08:00      4 NA       #> 3 2       2024-01-01T08:30      1 NA       #> 4 2       2024-01-02T08:30      4 NA       #> 5 2       2024-01-03T08:30      3 Y        #> 6 2       2024-01-04T08:30      2 Y        #> 7 2       2024-01-05T08:30      2 NA        # derive definitive deterioration flag as any deterioration (CHGCAT1 = \"Worsened\") # by parameter that is not followed by a non-deterioration adqs <- tribble(   ~USUBJID, ~PARAMCD, ~ADY, ~CHGCAT1,   \"1\",      \"QS1\",      10, \"Improved\",   \"1\",      \"QS1\",      21, \"Improved\",   \"1\",      \"QS1\",      23, \"Improved\",   \"1\",      \"QS2\",      32, \"Worsened\",   \"1\",      \"QS2\",      42, \"Improved\",   \"2\",      \"QS1\",      11, \"Worsened\",   \"2\",      \"QS1\",      24, \"Worsened\" )  derive_var_joined_exist_flag(   adqs,   dataset_add = adqs,   new_var = DDETERFL,   by_vars = exprs(USUBJID, PARAMCD),   join_vars = exprs(CHGCAT1),   join_type = \"all\",   order = exprs(ADY),   filter_join = all(CHGCAT1.join == \"Worsened\" | ADY > ADY.join) ) #> # A tibble: 7 × 5 #>   USUBJID PARAMCD   ADY CHGCAT1  DDETERFL #>   <chr>   <chr>   <dbl> <chr>    <chr>    #> 1 1       QS1        10 Improved NA       #> 2 1       QS1        21 Improved NA       #> 3 1       QS1        23 Improved NA       #> 4 1       QS2        32 Worsened NA       #> 5 1       QS2        42 Improved NA       #> 6 2       QS1        11 Worsened Y        #> 7 2       QS1        24 Worsened Y"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_merged_ef_msrc.html","id":null,"dir":"Reference","previous_headings":"","what":"Merge an Existence Flag From Multiple Sources — derive_var_merged_ef_msrc","title":"Merge an Existence Flag From Multiple Sources — derive_var_merged_ef_msrc","text":"Adds flag variable input dataset indicates exists least one observation one source datasets fulfilling certain condition. example, dose adjustment flag added ADEX dose adjustment information collected different datasets, e.g., EX, EC, FA.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_merged_ef_msrc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Merge an Existence Flag From Multiple Sources — derive_var_merged_ef_msrc","text":"","code":"derive_var_merged_ef_msrc(   dataset,   by_vars,   flag_events,   source_datasets,   new_var,   true_value = \"Y\",   false_value = NA_character_,   missing_value = NA_character_ )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_merged_ef_msrc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Merge an Existence Flag From Multiple Sources — derive_var_merged_ef_msrc","text":"dataset Input dataset variables specified by_vars argument expected dataset. by_vars Grouping variables Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) flag_events Flag events list flag_event() objects expected. event condition (condition field) evaluated source dataset referenced dataset_name field. evaluates TRUE least , new variable set true_value. source_datasets Source datasets named list datasets expected. dataset_name field flag_event() refers dataset provided list. new_var New variable specified variable added input dataset. true_value True value new variable (new_var) set specified value groups least one source object (sources) condition evaluate TRUE. values true_value, false_value, missing_value must type. false_value False value new variable (new_var) set specified value groups occur least one source (sources) condition never evaluates TRUE. values true_value, false_value, missing_value must type. missing_value Values used missing information new variable set specified value groups without observations sources (sources). values true_value, false_value, missing_value must type.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_merged_ef_msrc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Merge an Existence Flag From Multiple Sources — derive_var_merged_ef_msrc","text":"output dataset contains observations variables input dataset additionally variable specified new_var.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_merged_ef_msrc.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Merge an Existence Flag From Multiple Sources — derive_var_merged_ef_msrc","text":"flag_event() object specified flag_events: condition (condition) evaluated dataset referenced dataset_name. by_vars field specified dataset grouped specified variables evaluating condition. named elements used by_vars like by_vars = exprs(USUBJID, EXLNKID = ECLNKID), variables renamed evaluation. by_vars element specified, observations grouped variables specified by_vars argument. new variable (new_var) added input dataset set true value (true_value) group least one condition evaluates TRUE one sources. set false value (false_value) group least one observation exists observations condition evaluates FALSE NA. Otherwise, set missing value (missing_value).","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_merged_ef_msrc.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Merge an Existence Flag From Multiple Sources — derive_var_merged_ef_msrc","text":"","code":"library(dplyr)  # Derive a flag indicating anti-cancer treatment based on CM and PR adsl <- tribble(   ~USUBJID,   \"1\",   \"2\",   \"3\",   \"4\" )  cm <- tribble(   ~USUBJID, ~CMCAT,        ~CMSEQ,   \"1\",      \"ANTI-CANCER\",      1,   \"1\",      \"GENERAL\",          2,   \"2\",      \"GENERAL\",          1,   \"3\",      \"ANTI-CANCER\",      1 )  # Assuming all records in PR indicate cancer treatment pr <- tibble::tribble(   ~USUBJID, ~PRSEQ,   \"2\",      1,   \"3\",      1 )  derive_var_merged_ef_msrc(   adsl,   by_vars = exprs(USUBJID),   flag_events = list(     flag_event(       dataset_name = \"cm\",       condition = CMCAT == \"ANTI-CANCER\"     ),     flag_event(       dataset_name = \"pr\"     )   ),   source_datasets = list(cm = cm, pr = pr),   new_var = CANCTRFL ) #> # A tibble: 4 × 2 #>   USUBJID CANCTRFL #>   <chr>   <chr>    #> 1 1       Y        #> 2 2       Y        #> 3 3       Y        #> 4 4       NA        # Using different by variables depending on the source # Add a dose adjustment flag to ADEX based on ADEX, EC, and FA adex <- tribble(   ~USUBJID, ~EXLNKID, ~EXADJ,   \"1\",      \"1\",      \"AE\",   \"1\",      \"2\",      NA_character_,   \"1\",      \"3\",      NA_character_,   \"2\",      \"1\",      NA_character_,   \"3\",      \"1\",      NA_character_ )  ec <- tribble(   ~USUBJID, ~ECLNKID, ~ECADJ,   \"1\",      \"3\",      \"AE\",   \"3\",      \"1\",      NA_character_ )  fa <- tribble(   ~USUBJID, ~FALNKID, ~FATESTCD, ~FAOBJ,            ~FASTRESC,   \"3\",      \"1\",      \"OCCUR\",   \"DOSE ADJUSTMENT\", \"Y\" )  derive_var_merged_ef_msrc(   adex,   by_vars = exprs(USUBJID, EXLNKID),   flag_events = list(     flag_event(       dataset_name = \"ex\",       condition = !is.na(EXADJ)     ),     flag_event(       dataset_name = \"ec\",       condition = !is.na(ECADJ),       by_vars = exprs(USUBJID, EXLNKID = ECLNKID)     ),     flag_event(       dataset_name = \"fa\",       condition = FATESTCD == \"OCCUR\" & FAOBJ == \"DOSE ADJUSTMENT\" & FASTRESC == \"Y\",       by_vars = exprs(USUBJID, EXLNKID = FALNKID)     )   ),   source_datasets = list(ex = adex, ec = ec, fa = fa),   new_var = DOSADJFL ) #> # A tibble: 5 × 4 #>   USUBJID EXLNKID EXADJ DOSADJFL #>   <chr>   <chr>   <chr> <chr>    #> 1 1       1       AE    Y        #> 2 1       2       NA    NA       #> 3 1       3       NA    Y        #> 4 2       1       NA    NA       #> 5 3       1       NA    Y"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_merged_exist_flag.html","id":null,"dir":"Reference","previous_headings":"","what":"Merge an Existence Flag — derive_var_merged_exist_flag","title":"Merge an Existence Flag — derive_var_merged_exist_flag","text":"Adds flag variable input dataset indicates exists least one observation another dataset fulfilling certain condition. Note: wrapper function generic derive_vars_merged().","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_merged_exist_flag.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Merge an Existence Flag — derive_var_merged_exist_flag","text":"","code":"derive_var_merged_exist_flag(   dataset,   dataset_add,   by_vars,   new_var,   condition,   true_value = \"Y\",   false_value = NA_character_,   missing_value = NA_character_,   filter_add = NULL )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_merged_exist_flag.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Merge an Existence Flag — derive_var_merged_exist_flag","text":"dataset Input dataset variables specified by_vars argument expected dataset. dataset_add Additional dataset variables specified by_vars argument expected. by_vars Grouping variables Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) new_var New variable specified variable added input dataset. condition Condition condition evaluated additional dataset (dataset_add). groups evaluates TRUE least new variable set true value (true_value). groups evaluates FALSE NA observations new variable set false value (false_value). new variable set missing value (missing_value) groups present additional dataset. true_value True value false_value False value missing_value Values used missing information new variable set specified value groups without observations additional dataset. Permitted Value: character scalar filter_add Filter additional data observations fulfilling specified condition taken account flagging. argument specified, observations considered. Permitted Values: condition","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_merged_exist_flag.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Merge an Existence Flag — derive_var_merged_exist_flag","text":"output dataset contains observations variables input dataset additionally variable specified new_var derived additional dataset (dataset_add).","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_merged_exist_flag.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Merge an Existence Flag — derive_var_merged_exist_flag","text":"additional dataset restricted observations matching filter_add condition. new variable added input dataset set true value (true_value) group least one observation exists (restricted) additional dataset condition evaluates TRUE. set false value (false_value) group least one observation exists observations condition evaluates FALSE NA. Otherwise, set missing value (missing_value).","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_merged_exist_flag.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Merge an Existence Flag — derive_var_merged_exist_flag","text":"","code":"library(dplyr, warn.conflicts = FALSE)  dm <- tribble(   ~STUDYID,  ~DOMAIN,  ~USUBJID, ~AGE,   ~AGEU,   \"PILOT01\",    \"DM\", \"01-1028\",   71, \"YEARS\",   \"PILOT01\",    \"DM\", \"04-1127\",   84, \"YEARS\",   \"PILOT01\",    \"DM\", \"06-1049\",   60, \"YEARS\" )  ae <- tribble(   ~STUDYID,  ~DOMAIN,  ~USUBJID,    ~AETERM,     ~AEREL,   \"PILOT01\",    \"AE\", \"01-1028\", \"ERYTHEMA\", \"POSSIBLE\",   \"PILOT01\",    \"AE\", \"01-1028\", \"PRURITUS\", \"PROBABLE\",   \"PILOT01\",    \"AE\", \"06-1049\",  \"SYNCOPE\", \"POSSIBLE\",   \"PILOT01\",    \"AE\", \"06-1049\",  \"SYNCOPE\", \"PROBABLE\" )   derive_var_merged_exist_flag(   dm,   dataset_add = ae,   by_vars = exprs(STUDYID, USUBJID),   new_var = AERELFL,   condition = AEREL == \"PROBABLE\" ) %>%   select(STUDYID, USUBJID, AGE, AGEU, AERELFL) #> # A tibble: 3 × 5 #>   STUDYID USUBJID   AGE AGEU  AERELFL #>   <chr>   <chr>   <dbl> <chr> <chr>   #> 1 PILOT01 01-1028    71 YEARS Y       #> 2 PILOT01 04-1127    84 YEARS NA      #> 3 PILOT01 06-1049    60 YEARS Y        vs <- tribble(   ~STUDYID,  ~DOMAIN,  ~USUBJID,      ~VISIT, ~VSTESTCD, ~VSSTRESN, ~VSBLFL,   \"PILOT01\",    \"VS\", \"01-1028\", \"SCREENING\",  \"HEIGHT\",     177.8,      NA,   \"PILOT01\",    \"VS\", \"01-1028\", \"SCREENING\",  \"WEIGHT\",     98.88,      NA,   \"PILOT01\",    \"VS\", \"01-1028\",  \"BASELINE\",  \"WEIGHT\",     99.34,     \"Y\",   \"PILOT01\",    \"VS\", \"01-1028\",    \"WEEK 4\",  \"WEIGHT\",     98.88,      NA,   \"PILOT01\",    \"VS\", \"04-1127\", \"SCREENING\",  \"HEIGHT\",     165.1,      NA,   \"PILOT01\",    \"VS\", \"04-1127\", \"SCREENING\",  \"WEIGHT\",     42.87,      NA,   \"PILOT01\",    \"VS\", \"04-1127\",  \"BASELINE\",  \"WEIGHT\",     41.05,     \"Y\",   \"PILOT01\",    \"VS\", \"04-1127\",    \"WEEK 4\",  \"WEIGHT\",     41.73,      NA,   \"PILOT01\",    \"VS\", \"06-1049\", \"SCREENING\",  \"HEIGHT\",    167.64,      NA,   \"PILOT01\",    \"VS\", \"06-1049\", \"SCREENING\",  \"WEIGHT\",     57.61,      NA,   \"PILOT01\",    \"VS\", \"06-1049\",  \"BASELINE\",  \"WEIGHT\",     57.83,     \"Y\",   \"PILOT01\",    \"VS\", \"06-1049\",    \"WEEK 4\",  \"WEIGHT\",     58.97,      NA ) derive_var_merged_exist_flag(   dm,   dataset_add = vs,   by_vars = exprs(STUDYID, USUBJID),   filter_add = VSTESTCD == \"WEIGHT\" & VSBLFL == \"Y\",   new_var = WTBLHIFL,   condition = VSSTRESN > 90,   false_value = \"N\",   missing_value = \"M\" ) %>%   select(STUDYID, USUBJID, AGE, AGEU, WTBLHIFL) #> # A tibble: 3 × 5 #>   STUDYID USUBJID   AGE AGEU  WTBLHIFL #>   <chr>   <chr>   <dbl> <chr> <chr>    #> 1 PILOT01 01-1028    71 YEARS Y        #> 2 PILOT01 04-1127    84 YEARS N        #> 3 PILOT01 06-1049    60 YEARS N"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_merged_summary.html","id":null,"dir":"Reference","previous_headings":"","what":"Merge Summary Variables — derive_var_merged_summary","title":"Merge Summary Variables — derive_var_merged_summary","text":"Merge summary variable dataset input dataset.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_merged_summary.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Merge Summary Variables — derive_var_merged_summary","text":"","code":"derive_var_merged_summary(   dataset,   dataset_add,   by_vars,   new_vars = NULL,   filter_add = NULL,   missing_values = NULL )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_merged_summary.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Merge Summary Variables — derive_var_merged_summary","text":"dataset Input dataset variables specified by_vars argument expected dataset. dataset_add Additional dataset variables specified by_vars variables used left hand sides new_vars arguments expected. by_vars Grouping variables expressions left hand sides new_vars evaluated specified variables. resulting values merged input dataset (dataset) specified variables. Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) new_vars New variables add specified variables added input dataset. named list expressions expected: LHS refer variable. RHS refers values set variable. can string, symbol, numeric value, expression NA. summary functions used, values summarized variables specified by_vars. example:   filter_add Filter additional dataset (dataset_add) observations fulfilling specified condition taken account summarizing. argument specified, observations considered. Permitted Values: condition missing_values Values non-matching observations observations input dataset (dataset) matching observation additional dataset (dataset_add) values specified variables set specified value. variables specified new_vars can specified missing_values. Permitted Values: named list expressions, e.g., exprs(BASEC = \"MISSING\", BASE = -1)","code":"new_vars = exprs(     DOSESUM = sum(AVAL),     DOSEMEAN = mean(AVAL)   )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_merged_summary.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Merge Summary Variables — derive_var_merged_summary","text":"output dataset contains observations variables input dataset additionally variables specified new_vars.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_merged_summary.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Merge Summary Variables — derive_var_merged_summary","text":"records additional dataset (dataset_add) restricted matching filter_add condition. new variables (new_vars) created group (by_vars) additional dataset (dataset_add) calling summarize(). .e., observations group summarized single observation. new variables merged input dataset. observations without matching observation additional dataset new variables set NA. Observations additional dataset matching observation input dataset ignored.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_merged_summary.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Merge Summary Variables — derive_var_merged_summary","text":"","code":"library(tibble)  # Add a variable for the mean of AVAL within each visit adbds <- tribble(   ~USUBJID,  ~AVISIT,  ~ASEQ, ~AVAL,   \"1\",      \"WEEK 1\",      1,    10,   \"1\",      \"WEEK 1\",      2,    NA,   \"1\",      \"WEEK 2\",      3,    NA,   \"1\",      \"WEEK 3\",      4,    42,   \"1\",      \"WEEK 4\",      5,    12,   \"1\",      \"WEEK 4\",      6,    12,   \"1\",      \"WEEK 4\",      7,    15,   \"2\",      \"WEEK 1\",      1,    21,   \"2\",      \"WEEK 4\",      2,    22 )  derive_var_merged_summary(   adbds,   dataset_add = adbds,   by_vars = exprs(USUBJID, AVISIT),   new_vars = exprs(     MEANVIS = mean(AVAL, na.rm = TRUE),     MAXVIS = max(AVAL, na.rm = TRUE)   ) ) #> Warning: There was 1 warning in `summarise()`. #> ℹ In argument: `MAXVIS = max(AVAL, na.rm = TRUE)`. #> ℹ In group 2: `USUBJID = \"1\"` and `AVISIT = \"WEEK 2\"`. #> Caused by warning in `max()`: #> ! no non-missing arguments to max; returning -Inf #> # A tibble: 9 × 6 #>   USUBJID AVISIT  ASEQ  AVAL MEANVIS MAXVIS #>   <chr>   <chr>  <dbl> <dbl>   <dbl>  <dbl> #> 1 1       WEEK 1     1    10      10     10 #> 2 1       WEEK 1     2    NA      10     10 #> 3 1       WEEK 2     3    NA     NaN   -Inf #> 4 1       WEEK 3     4    42      42     42 #> 5 1       WEEK 4     5    12      13     15 #> 6 1       WEEK 4     6    12      13     15 #> 7 1       WEEK 4     7    15      13     15 #> 8 2       WEEK 1     1    21      21     21 #> 9 2       WEEK 4     2    22      22     22  # Add a variable listing the lesion ids at baseline adsl <- tribble(   ~USUBJID,   \"1\",   \"2\",   \"3\" )  adtr <- tribble(   ~USUBJID,     ~AVISIT, ~LESIONID,   \"1\",       \"BASELINE\",  \"INV-T1\",   \"1\",       \"BASELINE\",  \"INV-T2\",   \"1\",       \"BASELINE\",  \"INV-T3\",   \"1\",       \"BASELINE\",  \"INV-T4\",   \"1\",         \"WEEK 1\",  \"INV-T1\",   \"1\",         \"WEEK 1\",  \"INV-T2\",   \"1\",         \"WEEK 1\",  \"INV-T4\",   \"2\",       \"BASELINE\",  \"INV-T1\",   \"2\",       \"BASELINE\",  \"INV-T2\",   \"2\",       \"BASELINE\",  \"INV-T3\",   \"2\",         \"WEEK 1\",  \"INV-T1\",   \"2\",         \"WEEK 1\",  \"INV-N1\" )  derive_var_merged_summary(   adsl,   dataset_add = adtr,   by_vars = exprs(USUBJID),   filter_add = AVISIT == \"BASELINE\",   new_vars = exprs(LESIONSBL = paste(LESIONID, collapse = \", \")) ) #> # A tibble: 3 × 2 #>   USUBJID LESIONSBL                      #>   <chr>   <chr>                          #> 1 1       INV-T1, INV-T2, INV-T3, INV-T4 #> 2 2       INV-T1, INV-T2, INV-T3         #> 3 3       NA"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_obs_number.html","id":null,"dir":"Reference","previous_headings":"","what":"Adds a Variable Numbering the Observations Within Each By Group — derive_var_obs_number","title":"Adds a Variable Numbering the Observations Within Each By Group — derive_var_obs_number","text":"Adds variable numbering observations within group","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_obs_number.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adds a Variable Numbering the Observations Within Each By Group — derive_var_obs_number","text":"","code":"derive_var_obs_number(   dataset,   by_vars = NULL,   order = NULL,   new_var = ASEQ,   check_type = \"none\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_obs_number.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adds a Variable Numbering the Observations Within Each By Group — derive_var_obs_number","text":"dataset Input dataset variables specified by_vars order arguments expected dataset. by_vars Grouping variables Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) order Sort order Within group observations ordered specified order. handling NAs sorting variables see Sort Order. Permitted Values: list variables functions variables new_var Name variable create new variable set observation number group. numbering starts 1. Default: ASEQ check_type Check uniqueness? \"warning\" \"error\" specified, specified message issued observations input dataset unique respect variables order. Default: \"none\" Permitted Values: \"none\", \"warning\", \"error\"","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_obs_number.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adds a Variable Numbering the Observations Within Each By Group — derive_var_obs_number","text":"dataset containing observations variables input dataset additionally variable specified new_var parameter.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_obs_number.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Adds a Variable Numbering the Observations Within Each By Group — derive_var_obs_number","text":"group (respect variables specified by_vars parameter) first last observation (respect order specified order parameter mode specified mode parameter) included output dataset.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_obs_number.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Adds a Variable Numbering the Observations Within Each By Group — derive_var_obs_number","text":"","code":"library(dplyr, warn.conflicts = FALSE) vs <- tribble(   ~STUDYID,  ~DOMAIN,      ~USUBJID, ~VSTESTCD, ~VISITNUM, ~VSTPTNUM,   \"PILOT01\",    \"VS\", \"01-703-1182\",   \"DIABP\",         3,       815,   \"PILOT01\",    \"VS\", \"01-703-1182\",   \"DIABP\",         3,       816,   \"PILOT01\",    \"VS\", \"01-703-1182\",   \"DIABP\",         4,       815,   \"PILOT01\",    \"VS\", \"01-703-1182\",   \"DIABP\",         4,       816,   \"PILOT01\",    \"VS\", \"01-703-1182\",   \"PULSE\",         3,       815,   \"PILOT01\",    \"VS\", \"01-703-1182\",   \"PULSE\",         3,       816,   \"PILOT01\",    \"VS\", \"01-703-1182\",   \"PULSE\",         4,       815,   \"PILOT01\",    \"VS\", \"01-703-1182\",   \"PULSE\",         4,       816,   \"PILOT01\",    \"VS\", \"01-703-1182\",   \"SYSBP\",         3,       815,   \"PILOT01\",    \"VS\", \"01-703-1182\",   \"SYSBP\",         3,       816,   \"PILOT01\",    \"VS\", \"01-703-1182\",   \"SYSBP\",         4,       815,   \"PILOT01\",    \"VS\", \"01-703-1182\",   \"SYSBP\",         4,       816,   \"PILOT01\",    \"VS\", \"01-716-1229\",   \"DIABP\",         3,       815,   \"PILOT01\",    \"VS\", \"01-716-1229\",   \"DIABP\",         3,       816,   \"PILOT01\",    \"VS\", \"01-716-1229\",   \"DIABP\",         4,       815,   \"PILOT01\",    \"VS\", \"01-716-1229\",   \"DIABP\",         4,       816,   \"PILOT01\",    \"VS\", \"01-716-1229\",   \"PULSE\",         3,       815,   \"PILOT01\",    \"VS\", \"01-716-1229\",   \"PULSE\",         3,       816,   \"PILOT01\",    \"VS\", \"01-716-1229\",   \"PULSE\",         4,       815,   \"PILOT01\",    \"VS\", \"01-716-1229\",   \"PULSE\",         4,       816,   \"PILOT01\",    \"VS\", \"01-716-1229\",   \"SYSBP\",         3,       815,   \"PILOT01\",    \"VS\", \"01-716-1229\",   \"SYSBP\",         3,       816,   \"PILOT01\",    \"VS\", \"01-716-1229\",   \"SYSBP\",         4,       815,   \"PILOT01\",    \"VS\", \"01-716-1229\",   \"SYSBP\",         4,       816 ) vs %>%   derive_var_obs_number(     by_vars = exprs(USUBJID, VSTESTCD),     order = exprs(VISITNUM, desc(VSTPTNUM))   ) #> # A tibble: 24 × 7 #>    STUDYID DOMAIN USUBJID     VSTESTCD VISITNUM VSTPTNUM  ASEQ #>    <chr>   <chr>  <chr>       <chr>       <dbl>    <dbl> <int> #>  1 PILOT01 VS     01-703-1182 DIABP           3      816     1 #>  2 PILOT01 VS     01-703-1182 DIABP           3      815     2 #>  3 PILOT01 VS     01-703-1182 DIABP           4      816     3 #>  4 PILOT01 VS     01-703-1182 DIABP           4      815     4 #>  5 PILOT01 VS     01-703-1182 PULSE           3      816     1 #>  6 PILOT01 VS     01-703-1182 PULSE           3      815     2 #>  7 PILOT01 VS     01-703-1182 PULSE           4      816     3 #>  8 PILOT01 VS     01-703-1182 PULSE           4      815     4 #>  9 PILOT01 VS     01-703-1182 SYSBP           3      816     1 #> 10 PILOT01 VS     01-703-1182 SYSBP           3      815     2 #> # ℹ 14 more rows"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_ontrtfl.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive On-Treatment Flag Variable — derive_var_ontrtfl","title":"Derive On-Treatment Flag Variable — derive_var_ontrtfl","text":"Derive -treatment flag (ONTRTFL) ADaM dataset single assessment date (e.g ADT) event start end dates (e.g. ASTDT/AENDT).","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_ontrtfl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive On-Treatment Flag Variable — derive_var_ontrtfl","text":"","code":"derive_var_ontrtfl(   dataset,   new_var = ONTRTFL,   start_date,   end_date = NULL,   ref_start_date,   ref_end_date = NULL,   ref_end_window = 0,   ignore_time_for_ref_end_date = TRUE,   filter_pre_timepoint = NULL,   span_period = FALSE )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_ontrtfl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive On-Treatment Flag Variable — derive_var_ontrtfl","text":"dataset Input dataset Required columns start_date, end_date, ref_start_date ref_end_date. new_var -treatment flag variable name created. Default ONTRTFL. start_date start date (e.g. AESDT) assessment date (e.g. ADT) Required; date date-time object column expected. Refer derive_vars_dt() impute derive date date character vector date object. end_date end date assessment/event (e.g. AENDT) date date-time object column expected. Refer derive_vars_dt() impute derive date date character vector date object. Optional; Default null. used date value missing observation, assumed medication ongoing ONTRTFL set \"Y\". ref_start_date lower bound -treatment period Required; date date-time object column expected. Refer derive_vars_dt() impute derive date date character vector date object. ref_end_date upper bound -treatment period date date-time object column expected. Refer derive_vars_dt() impute derive date date character vector date object. Optional; can null everything ref_start_date considered -treatment. Default NULL. ref_end_window window add upper bound ref_end_date measured days (e.g. 7 7 days added upper bound) Optional; default 0. ignore_time_for_ref_end_date argument set TRUE, time part ignored checking event occurred ref_end_window days reference end date. Permitted Values: TRUE, FALSE filter_pre_timepoint expression filter observations -treatment date = ref_start_date. example, observations VSTPT = PRE considered -treatment date = ref_start_date, filter_pre_timepoint used denote -treatment flag set null. Optional; default NULL. span_period logical scalar. TRUE, events started prior ref_start_dateand ongoing end ref_start_date flagged \"Y\". Optional; default FALSE.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_ontrtfl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive On-Treatment Flag Variable — derive_var_ontrtfl","text":"input dataset additional column named ONTRTFL value \"Y\" NA","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_ontrtfl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Derive On-Treatment Flag Variable — derive_var_ontrtfl","text":"-Treatment calculated determining whether assessment date start/stop dates fall 2 dates. following logic used assign -treatment = \"Y\": start_date missing ref_start_dateis non-missing timepoint filter provided (filter_pre_timepoint) start_date ref_start_date non-missing start_date = ref_start_date timepoint provided (filter_pre_timepoint) start_date ref_start_date non-missing start_date = ref_start_date filter provided filter_pre_timepoint true. ref_end_date provided ref_start_date < start_date ref_end_date provided ref_start_date < start_date <= ref_end_date + ref_end_window. end_date provided end_date < ref_start_date ONTRTFL set NULL.applicable cases start_date missing ONTRTFL assigned \"Y\" . span_period TRUE, allows user assign ONTRTFL \"Y\" cases record started prior ref_start_date ongoing ended ref_start_date. date imputations needed done prior calling function.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_ontrtfl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive On-Treatment Flag Variable — derive_var_ontrtfl","text":"","code":"library(tibble) library(dplyr, warn.conflicts = FALSE) library(lubridate, warn.conflicts = FALSE)  advs <- tribble(   ~USUBJID, ~ADT,              ~TRTSDT,           ~TRTEDT,   \"P01\",    ymd(\"2020-02-24\"), ymd(\"2020-01-01\"), ymd(\"2020-03-01\"),   \"P02\",    ymd(\"2020-01-01\"), ymd(\"2020-01-01\"), ymd(\"2020-03-01\"),   \"P03\",    ymd(\"2019-12-31\"), ymd(\"2020-01-01\"), ymd(\"2020-03-01\") ) derive_var_ontrtfl(   advs,   start_date = ADT,   ref_start_date = TRTSDT,   ref_end_date = TRTEDT ) #> # A tibble: 3 × 5 #>   USUBJID ADT        TRTSDT     TRTEDT     ONTRTFL #>   <chr>   <date>     <date>     <date>     <chr>   #> 1 P01     2020-02-24 2020-01-01 2020-03-01 Y       #> 2 P02     2020-01-01 2020-01-01 2020-03-01 Y       #> 3 P03     2019-12-31 2020-01-01 2020-03-01 NA       advs <- tribble(   ~USUBJID, ~ADT,              ~TRTSDT,           ~TRTEDT,   \"P01\",    ymd(\"2020-07-01\"), ymd(\"2020-01-01\"), ymd(\"2020-03-01\"),   \"P02\",    ymd(\"2020-04-30\"), ymd(\"2020-01-01\"), ymd(\"2020-03-01\"),   \"P03\",    ymd(\"2020-03-15\"), ymd(\"2020-01-01\"), ymd(\"2020-03-01\") ) derive_var_ontrtfl(   advs,   start_date = ADT,   ref_start_date = TRTSDT,   ref_end_date = TRTEDT,   ref_end_window = 60 ) #> # A tibble: 3 × 5 #>   USUBJID ADT        TRTSDT     TRTEDT     ONTRTFL #>   <chr>   <date>     <date>     <date>     <chr>   #> 1 P01     2020-07-01 2020-01-01 2020-03-01 NA      #> 2 P02     2020-04-30 2020-01-01 2020-03-01 Y       #> 3 P03     2020-03-15 2020-01-01 2020-03-01 Y        advs <- tribble(   ~USUBJID, ~ADTM,                      ~TRTSDTM,                   ~TRTEDTM,   \"P01\",    ymd_hm(\"2020-01-02T12:00\"), ymd_hm(\"2020-01-01T12:00\"), ymd_hm(\"2020-03-01T12:00\"),   \"P02\",    ymd(\"2020-01-01\"),          ymd_hm(\"2020-01-01T12:00\"), ymd_hm(\"2020-03-01T12:00\"),   \"P03\",    ymd(\"2019-12-31\"),          ymd_hm(\"2020-01-01T12:00\"), ymd_hm(\"2020-03-01T12:00\"), ) %>%   mutate(TPT = c(NA, \"PRE\", NA)) derive_var_ontrtfl(   advs,   start_date = ADTM,   ref_start_date = TRTSDTM,   ref_end_date = TRTEDTM,   filter_pre_timepoint = TPT == \"PRE\" ) #> # A tibble: 3 × 6 #>   USUBJID ADTM                TRTSDTM             TRTEDTM             TPT   #>   <chr>   <dttm>              <dttm>              <dttm>              <chr> #> 1 P01     2020-01-02 12:00:00 2020-01-01 12:00:00 2020-03-01 12:00:00 NA    #> 2 P02     2020-01-01 00:00:00 2020-01-01 12:00:00 2020-03-01 12:00:00 PRE   #> 3 P03     2019-12-31 00:00:00 2020-01-01 12:00:00 2020-03-01 12:00:00 NA    #> # ℹ 1 more variable: ONTRTFL <chr>  advs <- tribble(   ~USUBJID, ~ASTDT,            ~TRTSDT,           ~TRTEDT,           ~AENDT,   \"P01\",    ymd(\"2020-03-15\"), ymd(\"2020-01-01\"), ymd(\"2020-03-01\"), ymd(\"2020-12-01\"),   \"P02\",    ymd(\"2019-04-30\"), ymd(\"2020-01-01\"), ymd(\"2020-03-01\"), ymd(\"2020-03-15\"),   \"P03\",    ymd(\"2019-04-30\"), ymd(\"2020-01-01\"), ymd(\"2020-03-01\"), NA, ) derive_var_ontrtfl(   advs,   start_date = ASTDT,   end_date = AENDT,   ref_start_date = TRTSDT,   ref_end_date = TRTEDT,   ref_end_window = 60,   span_period = TRUE ) #> # A tibble: 3 × 6 #>   USUBJID ASTDT      TRTSDT     TRTEDT     AENDT      ONTRTFL #>   <chr>   <date>     <date>     <date>     <date>     <chr>   #> 1 P01     2020-03-15 2020-01-01 2020-03-01 2020-12-01 Y       #> 2 P02     2019-04-30 2020-01-01 2020-03-01 2020-03-15 Y       #> 3 P03     2019-04-30 2020-01-01 2020-03-01 NA         Y        advs <- tribble(   ~USUBJID, ~ASTDT,            ~AP01SDT,          ~AP01EDT,          ~AENDT,   \"P01\",    ymd(\"2020-03-15\"), ymd(\"2020-01-01\"), ymd(\"2020-03-01\"), ymd(\"2020-12-01\"),   \"P02\",    ymd(\"2019-04-30\"), ymd(\"2020-01-01\"), ymd(\"2020-03-01\"), ymd(\"2020-03-15\"),   \"P03\",    ymd(\"2019-04-30\"), ymd(\"2020-01-01\"), ymd(\"2020-03-01\"), NA, ) derive_var_ontrtfl(   advs,   new_var = ONTR01FL,   start_date = ASTDT,   end_date = AENDT,   ref_start_date = AP01SDT,   ref_end_date = AP01EDT,   span_period = TRUE ) #> # A tibble: 3 × 6 #>   USUBJID ASTDT      AP01SDT    AP01EDT    AENDT      ONTR01FL #>   <chr>   <date>     <date>     <date>     <date>     <chr>    #> 1 P01     2020-03-15 2020-01-01 2020-03-01 2020-12-01 NA       #> 2 P02     2019-04-30 2020-01-01 2020-03-01 2020-03-15 Y        #> 3 P03     2019-04-30 2020-01-01 2020-03-01 NA         Y"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_pchg.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive Percent Change from Baseline — derive_var_pchg","title":"Derive Percent Change from Baseline — derive_var_pchg","text":"Derive percent change baseline (PCHG) BDS dataset","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_pchg.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive Percent Change from Baseline — derive_var_pchg","text":"","code":"derive_var_pchg(dataset)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_pchg.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive Percent Change from Baseline — derive_var_pchg","text":"dataset Input dataset AVAL BASE expected.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_pchg.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive Percent Change from Baseline — derive_var_pchg","text":"input dataset additional column named PCHG","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_pchg.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Derive Percent Change from Baseline — derive_var_pchg","text":"Percent change baseline calculated dividing change baseline absolute value baseline value multiplying result 100.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_pchg.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive Percent Change from Baseline — derive_var_pchg","text":"","code":"library(tibble)  advs <- tribble(   ~USUBJID, ~PARAMCD, ~AVAL, ~ABLFL, ~BASE,   \"P01\",    \"WEIGHT\", 80,    \"Y\",    80,   \"P01\",    \"WEIGHT\", 80.8,  NA,     80,   \"P01\",    \"WEIGHT\", 81.4,  NA,     80,   \"P02\",    \"WEIGHT\", 75.3,  \"Y\",    75.3,   \"P02\",    \"WEIGHT\", 76,    NA,     75.3 ) derive_var_pchg(advs) #> # A tibble: 5 × 6 #>   USUBJID PARAMCD  AVAL ABLFL  BASE  PCHG #>   <chr>   <chr>   <dbl> <chr> <dbl> <dbl> #> 1 P01     WEIGHT   80   Y      80   0     #> 2 P01     WEIGHT   80.8 NA     80   1.00  #> 3 P01     WEIGHT   81.4 NA     80   1.75  #> 4 P02     WEIGHT   75.3 Y      75.3 0     #> 5 P02     WEIGHT   76   NA     75.3 0.930"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_relative_flag.html","id":null,"dir":"Reference","previous_headings":"","what":"Flag Observations Before or After a Condition is Fulfilled — derive_var_relative_flag","title":"Flag Observations Before or After a Condition is Fulfilled — derive_var_relative_flag","text":"Flag observations observation specified condition fulfilled group. example, function called flag subject observations first disease progression flag AEs specific AE.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_relative_flag.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Flag Observations Before or After a Condition is Fulfilled — derive_var_relative_flag","text":"","code":"derive_var_relative_flag(   dataset,   by_vars,   order,   new_var,   condition,   mode,   selection,   inclusive,   flag_no_ref_groups = TRUE,   check_type = \"warning\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_relative_flag.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Flag Observations Before or After a Condition is Fulfilled — derive_var_relative_flag","text":"dataset Input dataset variables specified by_vars order arguments expected dataset. by_vars Grouping variables Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) order Sort order Within group observations ordered specified order. handling NAs sorting variables see Sort Order. Permitted Values: list expressions created exprs(), e.g., exprs(ADT, desc(AVAL)) new_var New variable variable added input dataset set \"Y\" observations condition fulfilled. observations set NA. condition Condition Reference Observation specified condition determines reference observation. output dataset observations (selection argument) reference observation flagged. mode Selection mode (first last) \"first\" specified, group observations (selection argument) observation condition (condition argument) fulfilled first time flagged output dataset. \"last\" specified, group observations (selection argument) observation condition (condition argument) fulfilled last time flagged output dataset. Permitted Values: \"first\", \"last\" selection Flag observations reference observation? Permitted Values: \"\", \"\" inclusive Flag reference observation? Permitted Values: TRUE, FALSE flag_no_ref_groups groups without reference observation flagged? Permitted Values: TRUE, FALSE check_type Check uniqueness? \"warning\" \"error\" specified, specified message issued observations input dataset unique respect variables order. Permitted Values: \"none\", \"warning\", \"error\"","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_relative_flag.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Flag Observations Before or After a Condition is Fulfilled — derive_var_relative_flag","text":"input dataset new variable (new_var) added","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_relative_flag.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Flag Observations Before or After a Condition is Fulfilled — derive_var_relative_flag","text":"group (by_vars argument) observations (selection argument) observations condition (condition argument) fulfilled first last time (order argument mode argument) flagged output dataset.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_relative_flag.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Flag Observations Before or After a Condition is Fulfilled — derive_var_relative_flag","text":"","code":"library(tibble) library(dplyr, warn.conflicts = FALSE)  # Flag all AEs after the first COVID AE adae <- tribble(   ~USUBJID, ~ASTDY, ~ACOVFL, ~AESEQ,   \"1\",           2, NA,           1,   \"1\",           5, \"Y\",          2,   \"1\",           5, NA,           3,   \"1\",          17, NA,           4,   \"1\",          27, \"Y\",          5,   \"1\",          32, NA,           6,   \"2\",           8, NA,           1,   \"2\",          11, NA,           2, )  derive_var_relative_flag(   adae,   by_vars = exprs(USUBJID),   order = exprs(ASTDY, AESEQ),   new_var = PSTCOVFL,   condition = ACOVFL == \"Y\",   mode = \"first\",   selection = \"after\",   inclusive = FALSE,   flag_no_ref_groups = FALSE ) #> # A tibble: 8 × 5 #>   USUBJID ASTDY ACOVFL AESEQ PSTCOVFL #>   <chr>   <dbl> <chr>  <dbl> <chr>    #> 1 1           2 NA         1 NA       #> 2 1           5 Y          2 NA       #> 3 1           5 NA         3 Y        #> 4 1          17 NA         4 Y        #> 5 1          27 Y          5 Y        #> 6 1          32 NA         6 Y        #> 7 2           8 NA         1 NA       #> 8 2          11 NA         2 NA        response <- tribble(   ~USUBJID, ~AVISITN, ~AVALC,   \"1\",      0,        \"PR\",   \"1\",      1,        \"CR\",   \"1\",      2,        \"CR\",   \"1\",      3,        \"SD\",   \"1\",      4,        \"NE\",   \"2\",      0,        \"SD\",   \"2\",      1,        \"PD\",   \"2\",      2,        \"PD\",   \"3\",      0,        \"SD\",   \"4\",      0,        \"SD\",   \"4\",      1,        \"PR\",   \"4\",      2,        \"PD\",   \"4\",      3,        \"SD\",   \"4\",      4,        \"PR\" )  # Flag observations up to first PD for each patient response %>%   derive_var_relative_flag(     by_vars = exprs(USUBJID),     order = exprs(AVISITN),     new_var = ANL02FL,     condition = AVALC == \"PD\",     mode = \"first\",     selection = \"before\",     inclusive = TRUE   ) #> # A tibble: 14 × 4 #>    USUBJID AVISITN AVALC ANL02FL #>    <chr>     <dbl> <chr> <chr>   #>  1 1             0 PR    Y       #>  2 1             1 CR    Y       #>  3 1             2 CR    Y       #>  4 1             3 SD    Y       #>  5 1             4 NE    Y       #>  6 2             0 SD    Y       #>  7 2             1 PD    Y       #>  8 2             2 PD    NA      #>  9 3             0 SD    Y       #> 10 4             0 SD    Y       #> 11 4             1 PR    Y       #> 12 4             2 PD    Y       #> 13 4             3 SD    NA      #> 14 4             4 PR    NA       # Flag observations up to first PD excluding baseline (AVISITN = 0) for each patient response %>%   restrict_derivation(     derivation = derive_var_relative_flag,     args = params(       by_vars = exprs(USUBJID),       order = exprs(AVISITN),       new_var = ANL02FL,       condition = AVALC == \"PD\",       mode = \"first\",       selection = \"before\",       inclusive = TRUE     ),     filter = AVISITN > 0   ) %>%   arrange(USUBJID, AVISITN) #> # A tibble: 14 × 4 #>    USUBJID AVISITN AVALC ANL02FL #>    <chr>     <dbl> <chr> <chr>   #>  1 1             0 PR    NA      #>  2 1             1 CR    Y       #>  3 1             2 CR    Y       #>  4 1             3 SD    Y       #>  5 1             4 NE    Y       #>  6 2             0 SD    NA      #>  7 2             1 PD    Y       #>  8 2             2 PD    NA      #>  9 3             0 SD    NA      #> 10 4             0 SD    NA      #> 11 4             1 PR    Y       #> 12 4             2 PD    Y       #> 13 4             3 SD    NA      #> 14 4             4 PR    NA"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_shift.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive Shift — derive_var_shift","title":"Derive Shift — derive_var_shift","text":"Derives character shift variable containing concatenated shift values based user-defined pairing, e.g., shift baseline analysis value, shift baseline grade analysis grade, ...","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_shift.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive Shift — derive_var_shift","text":"","code":"derive_var_shift(   dataset,   new_var,   from_var,   to_var,   missing_value = \"NULL\",   sep_val = \" to \" )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_shift.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive Shift — derive_var_shift","text":"dataset Input dataset variables specified from_var to_var arguments expected dataset. new_var Name character shift variable create. from_var Variable containing value shift . to_var Variable containing value shift . missing_value Character string replace missing values from_var to_var. Default: \"NULL\" sep_val Character string concatenate values from_var to_var. Default: \" \"","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_shift.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive Shift — derive_var_shift","text":"input dataset character shift variable added","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_shift.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Derive Shift — derive_var_shift","text":"new_var derived concatenating values from_var values to_var (e.g. \"NORMAL HIGH\"). from_var to_var missing value, missing value replaced missing_value (e.g. \"NORMAL NULL\").","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_shift.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive Shift — derive_var_shift","text":"","code":"library(tibble)  data <- tribble(   ~USUBJID, ~PARAMCD, ~AVAL, ~ABLFL, ~BNRIND,  ~ANRIND,   \"P01\",    \"ALB\",       33, \"Y\",    \"LOW\",    \"LOW\",   \"P01\",    \"ALB\",       38, NA,     \"LOW\",    \"NORMAL\",   \"P01\",    \"ALB\",       NA, NA,     \"LOW\",    NA,   \"P02\",    \"ALB\",       37, \"Y\",    \"NORMAL\", \"NORMAL\",   \"P02\",    \"ALB\",       49, NA,     \"NORMAL\", \"HIGH\",   \"P02\",    \"SODIUM\",   147, \"Y\",    \"HIGH\",   \"HIGH\" )  data %>%   convert_blanks_to_na() %>%   derive_var_shift(     new_var = SHIFT1,     from_var = BNRIND,     to_var = ANRIND   ) #> # A tibble: 6 × 7 #>   USUBJID PARAMCD  AVAL ABLFL BNRIND ANRIND SHIFT1           #>   <chr>   <chr>   <dbl> <chr> <chr>  <chr>  <chr>            #> 1 P01     ALB        33 Y     LOW    LOW    LOW to LOW       #> 2 P01     ALB        38 NA    LOW    NORMAL LOW to NORMAL    #> 3 P01     ALB        NA NA    LOW    NA     LOW to NULL      #> 4 P02     ALB        37 Y     NORMAL NORMAL NORMAL to NORMAL #> 5 P02     ALB        49 NA    NORMAL HIGH   NORMAL to HIGH   #> 6 P02     SODIUM    147 Y     HIGH   HIGH   HIGH to HIGH      # or only populate post-baseline records data %>%   convert_blanks_to_na() %>%   restrict_derivation(     derivation = derive_var_shift,     args = params(       new_var = SHIFT1,       from_var = BNRIND,       to_var = ANRIND     ),     filter = is.na(ABLFL)   ) #> # A tibble: 6 × 7 #>   USUBJID PARAMCD  AVAL ABLFL BNRIND ANRIND SHIFT1         #>   <chr>   <chr>   <dbl> <chr> <chr>  <chr>  <chr>          #> 1 P01     ALB        38 NA    LOW    NORMAL LOW to NORMAL  #> 2 P01     ALB        NA NA    LOW    NA     LOW to NULL    #> 3 P02     ALB        49 NA    NORMAL HIGH   NORMAL to HIGH #> 4 P01     ALB        33 Y     LOW    LOW    NA             #> 5 P02     ALB        37 Y     NORMAL NORMAL NA             #> 6 P02     SODIUM    147 Y     HIGH   HIGH   NA"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_trtdurd.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive Total Treatment Duration (Days) — derive_var_trtdurd","title":"Derive Total Treatment Duration (Days) — derive_var_trtdurd","text":"Derives total treatment duration (days) (TRTDURD). Note: wrapper function generic derive_vars_duration().","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_trtdurd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive Total Treatment Duration (Days) — derive_var_trtdurd","text":"","code":"derive_var_trtdurd(dataset, start_date = TRTSDT, end_date = TRTEDT)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_trtdurd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive Total Treatment Duration (Days) — derive_var_trtdurd","text":"dataset Input dataset variables specified start_date end_date arguments expected dataset. start_date start date date date-time object expected. Refer derive_vars_dt() impute derive date date character vector date object. Default: TRTSDT end_date end date date date-time object expected. Refer derive_vars_dt() impute derive date date character vector date object. Default: TRTEDT","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_trtdurd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive Total Treatment Duration (Days) — derive_var_trtdurd","text":"input dataset TRTDURD added","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_trtdurd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Derive Total Treatment Duration (Days) — derive_var_trtdurd","text":"total treatment duration derived number days start end date plus one.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_trtdurd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive Total Treatment Duration (Days) — derive_var_trtdurd","text":"","code":"library(tibble) library(lubridate)  data <- tribble(   ~TRTSDT, ~TRTEDT,   ymd(\"2020-01-01\"), ymd(\"2020-02-24\") )  derive_var_trtdurd(data) #> # A tibble: 1 × 3 #>   TRTSDT     TRTEDT     TRTDURD #>   <date>     <date>       <dbl> #> 1 2020-01-01 2020-02-24      55"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_trtemfl.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive Treatment-emergent Flag — derive_var_trtemfl","title":"Derive Treatment-emergent Flag — derive_var_trtemfl","text":"Derive treatment emergent analysis flag (e.g., TRTEMFL).","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_trtemfl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive Treatment-emergent Flag — derive_var_trtemfl","text":"","code":"derive_var_trtemfl(   dataset,   new_var = TRTEMFL,   start_date = ASTDTM,   end_date = AENDTM,   trt_start_date = TRTSDTM,   trt_end_date = NULL,   end_window = NULL,   ignore_time_for_trt_end = TRUE,   initial_intensity = NULL,   intensity = NULL,   group_var = NULL,   subject_keys = get_admiral_option(\"subject_keys\") )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_trtemfl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive Treatment-emergent Flag — derive_var_trtemfl","text":"dataset Input dataset variables specified start_date, end_date, trt_start_date, trt_end_date, initial_intensity, intensity expected. new_var New variable start_date Event start date Permitted Values: symbol referring date datetime variable input dataset end_date Event end date Permitted Values: symbol referring date datetime variable input dataset trt_start_date Treatment start date Permitted Values: symbol referring date datetime variable input dataset trt_end_date Treatment end date Permitted Values: symbol referring date datetime variable input dataset NULL end_window argument specified ('days'), events starting specified number days end treatment, flagged. Permitted Values: non-negative integer NULL ignore_time_for_trt_end argument set TRUE, time part ignored checking event occurred end_window days end treatment. Permitted Values: TRUE, FALSE initial_intensity Initial severity/intensity toxicity initial_intensity ignored group_var specified. argument specified group_var NULL, events start treatment start end treatment start (ongoing) worsened (.e., intensity greater initial intensity), flagged. values specified variable must comparable usual comparison operators. .e., intensity greater initial intensity initial_intensity < intensity must evaluate TRUE. Permitted Values: symbol referring variable input dataset NULL intensity Severity/intensity toxicity argument specified, events start treatment start end treatment start (ongoing) worsened (.e., intensity greater initial intensity), flagged. values specified variable must comparable usual comparison operators. .e., intensity greater initial intensity initial_intensity < intensity must evaluate TRUE. Permitted Values: symbol referring variable input dataset NULL group_var Grouping variable argument specified, assumes AEs recorded one episode AE multiple lines using grouping variable. Events starting treatment treatment worsening afterward flagged. AE record group flagged, subsequent records treatment window flagged regardless severity. Permitted Values: symbol referring variable input dataset NULL subject_keys Variables uniquely identify subject. list symbols created using exprs() expected. argument used group_var specified.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_trtemfl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive Treatment-emergent Flag — derive_var_trtemfl","text":"input dataset variable specified new_var added","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_trtemfl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Derive Treatment-emergent Flag — derive_var_trtemfl","text":"derivation new variable following cases considered order. first case applies, defines value variable. treated: trt_start_date NA, set NA_character_. event treatment: end_date trt_start_date (end_date NA), set NA_character_. event date: start_date NA, set \"Y\" cases usually considered conservative assume event treatment-emergent. event started treatment: end_window specified: start_date trt_start_date, set \"Y\", end_window specified: start_date trt_start_date start_date trt_end_date + end_window days, set \"Y\", event started treatment (possibly) worsened treatment: initial_intensity, intensity specified group_var specified: initial_intensity < intensity start_date trt_start_date end_date trt_start_date end_date NA, set \"Y\"; group_var specified: previous intensity < intensity start_date trt_start_date end_date trt_start_date end_date NA, set \"Y\"; Otherwise set NA_character_.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_trtemfl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive Treatment-emergent Flag — derive_var_trtemfl","text":"","code":"library(tibble) library(dplyr, warn.conflicts = FALSE) library(lubridate)  adae <- tribble(   ~USUBJID, ~ASTDTM,            ~AENDTM,            ~AEITOXGR, ~AETOXGR,   # before treatment   \"1\",      \"2021-12-13T20:15\", \"2021-12-15T12:45\", \"1\",       \"1\",   \"1\",      \"2021-12-14T20:15\", \"2021-12-14T22:00\", \"1\",       \"3\",   # starting before treatment and ending during treatment   \"1\",      \"2021-12-30T20:00\", \"2022-01-14T11:00\", \"1\",       \"3\",   \"1\",      \"2021-12-31T20:15\", \"2022-01-01T01:23\", \"1\",       \"1\",   # starting during treatment   \"1\",      \"2022-01-01T12:00\", \"2022-01-02T23:25\", \"3\",       \"4\",   # after treatment   \"1\",      \"2022-05-10T11:00\", \"2022-05-10T13:05\", \"2\",       \"2\",   \"1\",      \"2022-05-11T11:00\", \"2022-05-11T13:05\", \"2\",       \"2\",   # missing dates   \"1\",      \"\",                 \"\",                 \"3\",       \"4\",   \"1\",      \"2021-12-30T09:00\", \"\",                 \"3\",       \"4\",   \"1\",      \"2021-12-30T11:00\", \"\",                 \"3\",       \"3\",   \"1\",      \"\",                 \"2022-01-04T09:00\", \"3\",       \"4\",   \"1\",      \"\",                 \"2021-12-24T19:00\", \"3\",       \"4\",   \"1\",      \"\",                 \"2022-06-04T09:00\", \"3\",       \"4\",   # without treatment   \"2\",      \"\",                 \"2021-12-03T12:00\", \"1\",       \"2\",   \"2\",      \"2021-12-01T12:00\", \"2021-12-03T12:00\", \"1\",       \"2\",   \"2\",      \"2021-12-06T18:00\", \"\",                 \"1\",       \"2\" ) %>%   mutate(     ASTDTM = ymd_hm(ASTDTM),     AENDTM = ymd_hm(AENDTM),     TRTSDTM = if_else(USUBJID == \"1\", ymd_hm(\"2022-01-01T01:01\"), ymd_hms(\"\")),     TRTEDTM = if_else(USUBJID == \"1\", ymd_hm(\"2022-04-30T23:59\"), ymd_hms(\"\"))   )  # derive TRTEMFL without considering treatment end and worsening derive_var_trtemfl(adae) %>% select(ASTDTM, AENDTM, TRTSDTM, TRTEMFL) #> # A tibble: 16 × 4 #>    ASTDTM              AENDTM              TRTSDTM             TRTEMFL #>    <dttm>              <dttm>              <dttm>              <chr>   #>  1 2021-12-13 20:15:00 2021-12-15 12:45:00 2022-01-01 01:01:00 NA      #>  2 2021-12-14 20:15:00 2021-12-14 22:00:00 2022-01-01 01:01:00 NA      #>  3 2021-12-30 20:00:00 2022-01-14 11:00:00 2022-01-01 01:01:00 NA      #>  4 2021-12-31 20:15:00 2022-01-01 01:23:00 2022-01-01 01:01:00 NA      #>  5 2022-01-01 12:00:00 2022-01-02 23:25:00 2022-01-01 01:01:00 Y       #>  6 2022-05-10 11:00:00 2022-05-10 13:05:00 2022-01-01 01:01:00 Y       #>  7 2022-05-11 11:00:00 2022-05-11 13:05:00 2022-01-01 01:01:00 Y       #>  8 NA                  NA                  2022-01-01 01:01:00 Y       #>  9 2021-12-30 09:00:00 NA                  2022-01-01 01:01:00 NA      #> 10 2021-12-30 11:00:00 NA                  2022-01-01 01:01:00 NA      #> 11 NA                  2022-01-04 09:00:00 2022-01-01 01:01:00 Y       #> 12 NA                  2021-12-24 19:00:00 2022-01-01 01:01:00 NA      #> 13 NA                  2022-06-04 09:00:00 2022-01-01 01:01:00 Y       #> 14 NA                  2021-12-03 12:00:00 NA                  NA      #> 15 2021-12-01 12:00:00 2021-12-03 12:00:00 NA                  NA      #> 16 2021-12-06 18:00:00 NA                  NA                  NA       # derive TRTEM2FL taking treatment end and worsening into account derive_var_trtemfl(   adae,   new_var = TRTEM2FL,   trt_end_date = TRTEDTM,   end_window = 10,   initial_intensity = AEITOXGR,   intensity = AETOXGR ) %>% select(ASTDTM, AENDTM, AEITOXGR, AETOXGR, TRTEM2FL) #> # A tibble: 16 × 5 #>    ASTDTM              AENDTM              AEITOXGR AETOXGR TRTEM2FL #>    <dttm>              <dttm>              <chr>    <chr>   <chr>    #>  1 2021-12-13 20:15:00 2021-12-15 12:45:00 1        1       NA       #>  2 2021-12-14 20:15:00 2021-12-14 22:00:00 1        3       NA       #>  3 2021-12-30 20:00:00 2022-01-14 11:00:00 1        3       Y        #>  4 2021-12-31 20:15:00 2022-01-01 01:23:00 1        1       NA       #>  5 2022-01-01 12:00:00 2022-01-02 23:25:00 3        4       Y        #>  6 2022-05-10 11:00:00 2022-05-10 13:05:00 2        2       Y        #>  7 2022-05-11 11:00:00 2022-05-11 13:05:00 2        2       NA       #>  8 NA                  NA                  3        4       Y        #>  9 2021-12-30 09:00:00 NA                  3        4       Y        #> 10 2021-12-30 11:00:00 NA                  3        3       NA       #> 11 NA                  2022-01-04 09:00:00 3        4       Y        #> 12 NA                  2021-12-24 19:00:00 3        4       NA       #> 13 NA                  2022-06-04 09:00:00 3        4       Y        #> 14 NA                  2021-12-03 12:00:00 1        2       NA       #> 15 2021-12-01 12:00:00 2021-12-03 12:00:00 1        2       NA       #> 16 2021-12-06 18:00:00 NA                  1        2       NA        adae2 <- tribble(   ~USUBJID, ~ASTDTM, ~AENDTM, ~AEITOXGR, ~AETOXGR, ~AEGRPID,   # before treatment   \"1\", \"2021-12-13T20:15\", \"2021-12-15T12:45\", \"1\", \"1\", \"1\",   \"1\", \"2021-12-14T20:15\", \"2021-12-14T22:00\", \"1\", \"3\", \"1\",   # starting before treatment and ending during treatment   \"1\", \"2021-12-30T20:15\", \"2022-01-14T01:23\", \"3\", \"3\", \"2\",   \"1\", \"2022-01-05T20:00\", \"2022-06-01T11:00\", \"3\", \"1\", \"2\",   \"1\", \"2022-01-10T20:15\", \"2022-01-11T01:23\", \"3\", \"2\", \"2\",   \"1\", \"2022-01-13T20:15\", \"2022-03-01T01:23\", \"3\", \"1\", \"2\",   # starting during treatment   \"1\", \"2022-01-01T12:00\", \"2022-01-02T23:25\", \"4\", \"4\", \"3\",    # after treatment   \"1\", \"2022-05-10T11:00\", \"2022-05-10T13:05\", \"2\", \"2\", \"4\",   \"1\", \"2022-05-10T12:00\", \"2022-05-10T13:05\", \"2\", \"2\", \"4\",   \"1\", \"2022-05-11T11:00\", \"2022-05-11T13:05\", \"2\", \"2\", \"4\",   # missing dates   \"1\", \"\", \"\", \"3\", \"4\", \"5\",   \"1\", \"2021-12-30T09:00\", \"\", \"3\", \"4\", \"5\",   \"1\", \"2021-12-30T11:00\", \"\", \"3\", \"3\", \"5\",   \"1\", \"\", \"2022-01-04T09:00\", \"3\", \"4\", \"5\",   \"1\", \"\", \"2021-12-24T19:00\", \"3\", \"4\", \"5\",   \"1\", \"\", \"2022-06-04T09:00\", \"3\", \"4\", \"5\",   # without treatment   \"2\", \"\", \"2021-12-03T12:00\", \"1\", \"2\", \"1\",   \"2\", \"2021-12-01T12:00\", \"2021-12-03T12:00\", \"1\", \"2\", \"2\",   \"2\", \"2021-12-06T18:00\", \"\", \"1\", \"2\", \"3\" ) %>%   mutate(     STUDYID = \"ABC12345\",     ASTDTM = ymd_hm(ASTDTM),     AENDTM = ymd_hm(AENDTM),     TRTSDTM = if_else(USUBJID == \"1\", ymd_hm(\"2022-01-01T01:01\"), ymd_hms(\"\")),     TRTEDTM = if_else(USUBJID == \"1\", ymd_hm(\"2022-04-30T23:59\"), ymd_hms(\"\"))   ) # derive TRTEMFL taking treatment end and worsening into account within a grouping variable derive_var_trtemfl(   adae2,   new_var = TRTEMFL,   trt_end_date = TRTEDTM,   end_window = 10,   intensity = AETOXGR,   group_var = AEGRPID ) %>% select(ASTDTM, AENDTM, AEITOXGR, AETOXGR, AEGRPID, TRTEMFL) #> # A tibble: 19 × 6 #>    ASTDTM              AENDTM              AEITOXGR AETOXGR AEGRPID TRTEMFL #>    <dttm>              <dttm>              <chr>    <chr>   <chr>   <chr>   #>  1 2021-12-13 20:15:00 2021-12-15 12:45:00 1        1       1       NA      #>  2 2021-12-14 20:15:00 2021-12-14 22:00:00 1        3       1       NA      #>  3 2021-12-30 20:15:00 2022-01-14 01:23:00 3        3       2       NA      #>  4 2022-01-05 20:00:00 2022-06-01 11:00:00 3        1       2       NA      #>  5 2022-01-10 20:15:00 2022-01-11 01:23:00 3        2       2       Y       #>  6 2022-01-13 20:15:00 2022-03-01 01:23:00 3        1       2       Y       #>  7 2022-01-01 12:00:00 2022-01-02 23:25:00 4        4       3       Y       #>  8 2022-05-10 11:00:00 2022-05-10 13:05:00 2        2       4       Y       #>  9 2022-05-10 12:00:00 2022-05-10 13:05:00 2        2       4       Y       #> 10 2022-05-11 11:00:00 2022-05-11 13:05:00 2        2       4       NA      #> 11 2021-12-30 09:00:00 NA                  3        4       5       NA      #> 12 2021-12-30 11:00:00 NA                  3        3       5       NA      #> 13 NA                  NA                  3        4       5       Y       #> 14 NA                  2022-01-04 09:00:00 3        4       5       Y       #> 15 NA                  2021-12-24 19:00:00 3        4       5       NA      #> 16 NA                  2022-06-04 09:00:00 3        4       5       Y       #> 17 NA                  2021-12-03 12:00:00 1        2       1       NA      #> 18 2021-12-01 12:00:00 2021-12-03 12:00:00 1        2       2       NA      #> 19 2021-12-06 18:00:00 NA                  1        2       3       NA"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_aage.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive Analysis Age — derive_vars_aage","title":"Derive Analysis Age — derive_vars_aage","text":"Derives analysis age (AAGE) analysis age unit (AAGEU). Note: wrapper function generic derive_vars_duration().","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_aage.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive Analysis Age — derive_vars_aage","text":"","code":"derive_vars_aage(   dataset,   start_date = BRTHDT,   end_date = RANDDT,   age_unit = \"YEARS\",   type = \"interval\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_aage.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive Analysis Age — derive_vars_aage","text":"dataset Input dataset variables specified start_date end_date arguments expected dataset. start_date start date date date-time object expected. Refer derive_vars_dt() impute derive date date character vector date object. Default: BRTHDT end_date end date date date-time object expected. Refer derive_vars_dt() impute derive date date character vector date object. Default: RANDDT age_unit Age unit age derived specified unit Permitted Values (case-insensitive): years: \"year\", \"years\", \"yr\", \"yrs\", \"y\" months: \"month\", \"months\", \"mo\", \"mos\" weeks: \"week\", \"weeks\", \"wk\", \"wks\", \"w\" days: \"day\", \"days\", \"d\" hours: \"hour\", \"hours\", \"hr\", \"hrs\", \"h\" minutes: \"minute\", \"minutes\", \"min\", \"mins\" seconds: \"second\", \"seconds\", \"sec\", \"secs\", \"s\" type lubridate duration type. See details. Default: \"duration\" Permitted Values: \"duration\", \"interval\"","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_aage.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive Analysis Age — derive_vars_aage","text":"input dataset AAGE AAGEU added","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_aage.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Derive Analysis Age — derive_vars_aage","text":"duration derived time start end date specified output unit. end date start date, duration negative. start end date variable must present specified input dataset. lubridate package calculates two types spans two dates: duration interval. calculations largely , unit time period month year result can slightly different. difference arises ambiguity length \"1 month\" \"1 year\". Months may 31, 30, 28, 29 days, years 365 days 366 leap years. Durations intervals help solve ambiguity measures. interval 2000-02-01 2000-03-01 1 (.e. one month). duration two dates 0.95, accounts fact year 2000 leap year, February 29 days, average month length 30.4375, .e. 29 / 30.4375 = 0.95. additional details, review lubridate time span reference page.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_aage.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive Analysis Age — derive_vars_aage","text":"","code":"library(tibble) library(lubridate)  data <- tribble(   ~BRTHDT, ~RANDDT,   ymd(\"1984-09-06\"), ymd(\"2020-02-24\") )  derive_vars_aage(data) #> # A tibble: 1 × 4 #>   BRTHDT     RANDDT      AAGE AAGEU #>   <date>     <date>     <dbl> <chr> #> 1 1984-09-06 2020-02-24    35 YEARS"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_atc.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive ATC Class Variables — derive_vars_atc","title":"Derive ATC Class Variables — derive_vars_atc","text":"Add Anatomical Therapeutic Chemical class variables FACM ADCM. Note: wrapper function generic derive_vars_transposed().","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_atc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive ATC Class Variables — derive_vars_atc","text":"","code":"derive_vars_atc(   dataset,   dataset_facm,   by_vars = exprs(!!!get_admiral_option(\"subject_keys\"), CMREFID = FAREFID),   id_vars = NULL,   value_var = FASTRESC )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_atc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive ATC Class Variables — derive_vars_atc","text":"dataset Input dataset variables specified by_vars argument expected dataset. dataset_facm FACM dataset variables specified by_vars, id_vars, value_var arguments FATESTCD required. variables by_vars, id_vars, FATESTCD must unique key. by_vars Grouping variables Keys used merge dataset_facm dataset. id_vars ID variables Variables (excluding by_vars) uniquely identify observation dataset_merge. Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) value_var variable dataset_facm containing values transposed variables","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_atc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive ATC Class Variables — derive_vars_atc","text":"input dataset ATC variables added","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_atc.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive ATC Class Variables — derive_vars_atc","text":"","code":"library(tibble)  cm <- tribble(   ~STUDYID,  ~USUBJID,       ~CMGRPID, ~CMREFID,  ~CMDECOD,   \"STUDY01\", \"BP40257-1001\", \"14\",     \"1192056\", \"PARACETAMOL\",   \"STUDY01\", \"BP40257-1001\", \"18\",     \"2007001\", \"SOLUMEDROL\",   \"STUDY01\", \"BP40257-1002\", \"19\",     \"2791596\", \"SPIRONOLACTONE\" ) facm <- tribble(   ~STUDYID,  ~USUBJID,       ~FAGRPID, ~FAREFID,  ~FATESTCD,  ~FASTRESC,   \"STUDY01\", \"BP40257-1001\", \"1\",      \"1192056\", \"CMATC1CD\", \"N\",   \"STUDY01\", \"BP40257-1001\", \"1\",      \"1192056\", \"CMATC2CD\", \"N02\",   \"STUDY01\", \"BP40257-1001\", \"1\",      \"1192056\", \"CMATC3CD\", \"N02B\",   \"STUDY01\", \"BP40257-1001\", \"1\",      \"1192056\", \"CMATC4CD\", \"N02BE\",   \"STUDY01\", \"BP40257-1001\", \"1\",      \"2007001\", \"CMATC1CD\", \"D\",   \"STUDY01\", \"BP40257-1001\", \"1\",      \"2007001\", \"CMATC2CD\", \"D10\",   \"STUDY01\", \"BP40257-1001\", \"1\",      \"2007001\", \"CMATC3CD\", \"D10A\",   \"STUDY01\", \"BP40257-1001\", \"1\",      \"2007001\", \"CMATC4CD\", \"D10AA\",   \"STUDY01\", \"BP40257-1001\", \"2\",      \"2007001\", \"CMATC1CD\", \"D\",   \"STUDY01\", \"BP40257-1001\", \"2\",      \"2007001\", \"CMATC2CD\", \"D07\",   \"STUDY01\", \"BP40257-1001\", \"2\",      \"2007001\", \"CMATC3CD\", \"D07A\",   \"STUDY01\", \"BP40257-1001\", \"2\",      \"2007001\", \"CMATC4CD\", \"D07AA\",   \"STUDY01\", \"BP40257-1001\", \"3\",      \"2007001\", \"CMATC1CD\", \"H\",   \"STUDY01\", \"BP40257-1001\", \"3\",      \"2007001\", \"CMATC2CD\", \"H02\",   \"STUDY01\", \"BP40257-1001\", \"3\",      \"2007001\", \"CMATC3CD\", \"H02A\",   \"STUDY01\", \"BP40257-1001\", \"3\",      \"2007001\", \"CMATC4CD\", \"H02AB\",   \"STUDY01\", \"BP40257-1002\", \"1\",      \"2791596\", \"CMATC1CD\", \"C\",   \"STUDY01\", \"BP40257-1002\", \"1\",      \"2791596\", \"CMATC2CD\", \"C03\",   \"STUDY01\", \"BP40257-1002\", \"1\",      \"2791596\", \"CMATC3CD\", \"C03D\",   \"STUDY01\", \"BP40257-1002\", \"1\",      \"2791596\", \"CMATC4CD\", \"C03DA\" )  derive_vars_atc(cm, facm, id_vars = exprs(FAGRPID)) #> # A tibble: 5 × 9 #>   STUDYID USUBJID      CMGRPID CMREFID CMDECOD       ATC1CD ATC2CD ATC3CD ATC4CD #>   <chr>   <chr>        <chr>   <chr>   <chr>         <chr>  <chr>  <chr>  <chr>  #> 1 STUDY01 BP40257-1001 14      1192056 PARACETAMOL   N      N02    N02B   N02BE  #> 2 STUDY01 BP40257-1001 18      2007001 SOLUMEDROL    D      D10    D10A   D10AA  #> 3 STUDY01 BP40257-1001 18      2007001 SOLUMEDROL    D      D07    D07A   D07AA  #> 4 STUDY01 BP40257-1001 18      2007001 SOLUMEDROL    H      H02    H02A   H02AB  #> 5 STUDY01 BP40257-1002 19      2791596 SPIRONOLACTO… C      C03    C03D   C03DA"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_cat.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive Categorization Variables Like AVALCATy and AVALCAyN — derive_vars_cat","title":"Derive Categorization Variables Like AVALCATy and AVALCAyN — derive_vars_cat","text":"Derive Categorization Variables Like AVALCATy AVALCAyN","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_cat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive Categorization Variables Like AVALCATy and AVALCAyN — derive_vars_cat","text":"","code":"derive_vars_cat(dataset, definition, by_vars = NULL)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_cat.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive Categorization Variables Like AVALCATy and AVALCAyN — derive_vars_cat","text":"dataset Input dataset variables specified by_vars definition arguments expected dataset. definition List expressions created exprs(). Must rectangular format specified using syntax creating tibble using tribble() function. definition object converted tibble using tribble() inside function. Must contain: column condition converted logical expression used dataset input. least one additional column new column name category value(s) used logical expression. column specified by_vars (by_vars specified) e.g. by_vars specified:   e.g. by_vars specified exprs(VSTEST):   by_vars list expressions one element. NULL default. Allows specifying groups, e.g. exprs(PARAMCD). Variable must present dataset definition. conditions definition applied records match by_vars. categorization variables set NA records matching groups definition.","code":"exprs(~condition,   ~AVALCAT1, ~AVALCA1N,       AVAL >= 140, \">=140 cm\",         1,       AVAL < 140,   \"<140 cm\",         2) exprs(~VSTEST,   ~condition,  ~AVALCAT1, ~AVALCA1N,       \"Height\", AVAL >= 140, \">=140 cm\",         1,       \"Height\",  AVAL < 140,  \"<140 cm\",         2)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_cat.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive Categorization Variables Like AVALCATy and AVALCAyN — derive_vars_cat","text":"input dataset new variables defined definition added","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_cat.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Derive Categorization Variables Like AVALCATy and AVALCAyN — derive_vars_cat","text":"conditions overlapping, row order definitions must carefully considered. first match determine category. .e. AVAL = 155 definition :   AVALCAT1 \"<=170 cm\", first match AVAL. specify:   AVAL <= 160 lead AVALCAT1 == \"<=160 cm\", AVAL -160 170 lead AVALCAT1 == \"<=170 cm\", AVAL <= 170 lead AVALCAT1 == \">170 cm\". However, suggest explicit defining condition, avoid overlap. case, middle condition : AVAL <= 170 & AVAL > 160","code":"definition <- exprs(   ~VSTEST,   ~condition,  ~AVALCAT1, ~AVALCA1N,   \"Height\",  AVAL > 170,  \">170 cm\",         1,   \"Height\", AVAL <= 170, \"<=170 cm\",         2,   \"Height\", AVAL <= 160, \"<=160 cm\",         3 ) definition <- exprs(   ~VSTEST,   ~condition,  ~AVALCAT1, ~AVALCA1N,   \"Height\", AVAL <= 160, \"<=160 cm\",         3,   \"Height\", AVAL <= 170, \"<=170 cm\",         2,   \"Height\",  AVAL > 170,  \">170 cm\",         1 )"},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_cat.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive Categorization Variables Like AVALCATy and AVALCAyN — derive_vars_cat","text":"","code":"library(dplyr) library(tibble)  advs <- tibble::tribble(   ~USUBJID,       ~VSTEST,  ~AVAL,   \"01-701-1015\", \"Height\", 147.32,   \"01-701-1015\", \"Weight\",  53.98,   \"01-701-1023\", \"Height\", 162.56,   \"01-701-1023\", \"Weight\",     NA,   \"01-701-1028\", \"Height\",     NA,   \"01-701-1028\", \"Weight\",     NA,   \"01-701-1033\", \"Height\", 175.26,   \"01-701-1033\", \"Weight\",  88.45 )  definition <- exprs(   ~condition,                        ~AVALCAT1, ~AVALCA1N,  ~NEWCOL,   VSTEST == \"Height\" & AVAL > 160,   \">160 cm\",         1, \"extra1\",   VSTEST == \"Height\" & AVAL <= 160, \"<=160 cm\",         2, \"extra2\" ) derive_vars_cat(   dataset = advs,   definition = definition ) #> # A tibble: 8 × 6 #>   USUBJID     VSTEST  AVAL AVALCAT1 AVALCA1N NEWCOL #>   <chr>       <chr>  <dbl> <chr>       <dbl> <chr>  #> 1 01-701-1015 Height 147.  <=160 cm        2 extra2 #> 2 01-701-1015 Weight  54.0 NA             NA NA     #> 3 01-701-1023 Height 163.  >160 cm         1 extra1 #> 4 01-701-1023 Weight  NA   NA             NA NA     #> 5 01-701-1028 Height  NA   NA             NA NA     #> 6 01-701-1028 Weight  NA   NA             NA NA     #> 7 01-701-1033 Height 175.  >160 cm         1 extra1 #> 8 01-701-1033 Weight  88.4 NA             NA NA      # Using by_vars: definition2 <- exprs(   ~VSTEST,   ~condition,  ~AVALCAT1, ~AVALCA1N,   \"Height\",  AVAL > 160,  \">160 cm\",         1,   \"Height\", AVAL <= 160, \"<=160 cm\",         2,   \"Weight\",   AVAL > 70,   \">70 kg\",         1,   \"Weight\",  AVAL <= 70,  \"<=70 kg\",         2 )  derive_vars_cat(   dataset = advs,   definition = definition2,   by_vars = exprs(VSTEST) ) #> # A tibble: 8 × 5 #>   USUBJID     VSTEST  AVAL AVALCAT1 AVALCA1N #>   <chr>       <chr>  <dbl> <chr>       <dbl> #> 1 01-701-1015 Height 147.  <=160 cm        2 #> 2 01-701-1015 Weight  54.0 <=70 kg         2 #> 3 01-701-1023 Height 163.  >160 cm         1 #> 4 01-701-1023 Weight  NA   NA             NA #> 5 01-701-1028 Height  NA   NA             NA #> 6 01-701-1028 Weight  NA   NA             NA #> 7 01-701-1033 Height 175.  >160 cm         1 #> 8 01-701-1033 Weight  88.4 >70 kg          1  # With three conditions: definition3 <- exprs(   ~VSTEST,                ~condition,  ~AVALCAT1, ~AVALCA1N,   \"Height\",               AVAL > 170,  \">170 cm\",         1,   \"Height\", AVAL <= 170 & AVAL > 160, \"<=170 cm\",         2,   \"Height\",              AVAL <= 160, \"<=160 cm\",         3 )  derive_vars_cat(   dataset = advs,   definition = definition3,   by_vars = exprs(VSTEST) ) #> # A tibble: 8 × 5 #>   USUBJID     VSTEST  AVAL AVALCAT1 AVALCA1N #>   <chr>       <chr>  <dbl> <chr>       <dbl> #> 1 01-701-1015 Height 147.  <=160 cm        3 #> 2 01-701-1015 Weight  54.0 NA             NA #> 3 01-701-1023 Height 163.  <=170 cm        2 #> 4 01-701-1023 Weight  NA   NA             NA #> 5 01-701-1028 Height  NA   NA             NA #> 6 01-701-1028 Weight  NA   NA             NA #> 7 01-701-1033 Height 175.  >170 cm         1 #> 8 01-701-1033 Weight  88.4 NA             NA  # Let's derive both the MCRITyML and the MCRITyMN variables adlb <- tibble::tribble(   ~USUBJID,     ~PARAM, ~AVAL, ~AVALU,  ~ANRHI,   \"01-701-1015\", \"ALT\",   150,  \"U/L\",      40,   \"01-701-1023\", \"ALT\",    70,  \"U/L\",      40,   \"01-701-1036\", \"ALT\",   130,  \"U/L\",      40,   \"01-701-1048\", \"ALT\",    30,  \"U/L\",      40,   \"01-701-1015\", \"AST\",    50,  \"U/L\",      35 )  definition_mcrit <- exprs(   ~PARAM,                      ~condition,    ~MCRIT1ML, ~MCRIT1MN,   \"ALT\",                    AVAL <= ANRHI,    \"<=ANRHI\",         1,   \"ALT\", ANRHI < AVAL & AVAL <= 3 * ANRHI, \">1-3*ANRHI\",         2,   \"ALT\",                 3 * ANRHI < AVAL,   \">3*ANRHI\",         3 )  adlb %>%   derive_vars_cat(     definition = definition_mcrit,     by_vars = exprs(PARAM)   ) #> # A tibble: 5 × 7 #>   USUBJID     PARAM  AVAL AVALU ANRHI MCRIT1ML   MCRIT1MN #>   <chr>       <chr> <dbl> <chr> <dbl> <chr>         <dbl> #> 1 01-701-1015 ALT     150 U/L      40 >3*ANRHI          3 #> 2 01-701-1023 ALT      70 U/L      40 >1-3*ANRHI        2 #> 3 01-701-1036 ALT     130 U/L      40 >3*ANRHI          3 #> 4 01-701-1048 ALT      30 U/L      40 <=ANRHI           1 #> 5 01-701-1015 AST      50 U/L      35 NA               NA"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_computed.html","id":null,"dir":"Reference","previous_headings":"","what":"Adds Variable(s) Computed from the Analysis Value of one or more Parameters — derive_vars_computed","title":"Adds Variable(s) Computed from the Analysis Value of one or more Parameters — derive_vars_computed","text":"Adds Variable(s) computed analysis value one parameters. expected value new variable defined expression using analysis values parameters, addition/sum, subtraction/difference, multiplication/product, division/ratio, exponentiation/logarithm, formula.  example Body Mass Index Baseline (BMIBL) ADSL can derived HEIGHT WEIGHT parameters ADVS.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_computed.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adds Variable(s) Computed from the Analysis Value of one or more Parameters — derive_vars_computed","text":"","code":"derive_vars_computed(   dataset,   dataset_add,   by_vars,   parameters,   new_vars,   filter_add = NULL,   constant_by_vars = NULL,   constant_parameters = NULL )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_computed.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adds Variable(s) Computed from the Analysis Value of one or more Parameters — derive_vars_computed","text":"dataset variables specified by_vars parameter expected. dataset_add Additional dataset variables specified by_vars parameter expected. variable specified by_vars PARAMCD must unique key additional dataset restricting filter condition (filter_add parameter) parameters specified parameters. by_vars Grouping variables Grouping variables uniquely identifying set records new_vars calculated. Permitted Values: list variables created exprs() parameters Required parameter codes expected parameter codes (PARAMCD) required derive new variable specified parameter constant_parameters parameter. observations considered parameter code, e.g., SDTM dataset used, temporary parameter codes can derived specifying list expressions. name element defines temporary parameter code expression defines condition selecting records. example, parameters = exprs(HGHT = VSTESTCD == \"HEIGHT\") selects observations VSTESTCD == \"HEIGHT\" input data (dataset dataset_add), sets PARAMCD = \"HGHT\" observations, adds observations consider. Unnamed elements list expressions considered parameter codes. example, parameters = exprs(WEIGHT, HGHT = VSTESTCD == \"HEIGHT\") uses parameter code \"WEIGHT\" creates temporary parameter code \"HGHT\". Permitted Values: character vector PARAMCD values list expressions new_vars Name newly created variables specified variables set specified values. values variables parameters specified parameters can accessed using <variable name>.<parameter code>. example   defines value new variable. Variable names expression must contain one dot. Permitted Values: List variable-value pairs filter_add Filter condition additional dataset specified condition applied additional dataset deriving new variable, .e., observations fulfilling condition taken account. Permitted Values: condition constant_by_vars variables constant parameters constant parameters (parameters measured ) merged parameters using specified variables. (Refer Example) Permitted Values: list variables constant_parameters Required constant parameter codes expected parameter codes (PARAMCD) required derive new variable measured specified . example BMI derived height measured weight measured visit. Height specified constant_parameters parameter. (Refer Example) observations considered parameter code, e.g., SDTM dataset used, temporary parameter codes can derived specifying list expressions. name element defines temporary parameter code expression defines condition selecting records. example constant_parameters = exprs(HGHT = VSTESTCD == \"HEIGHT\") selects observations VSTESTCD == \"HEIGHT\" input data (dataset dataset_add), sets PARAMCD = \"HGHT\" observations, adds observations consider. Unnamed elements list expressions considered parameter codes. example, constant_parameters = exprs(WEIGHT, HGHT = VSTESTCD == \"HEIGHT\") uses parameter code \"WEIGHT\" creates temporary parameter code \"HGHT\". Permitted Values: character vector PARAMCD values list expressions","code":"exprs(   BMIBL = (AVAL.WEIGHT / (AVAL.HEIGHT/100)^2) )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_computed.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adds Variable(s) Computed from the Analysis Value of one or more Parameters — derive_vars_computed","text":"input dataset new variables added.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_computed.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Adds Variable(s) Computed from the Analysis Value of one or more Parameters — derive_vars_computed","text":"group (respect variables specified by_vars argument), values new variables (new_vars) computed based parameters additional dataset (dataset_add) new variables merged input dataset (dataset).","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_computed.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Adds Variable(s) Computed from the Analysis Value of one or more Parameters — derive_vars_computed","text":"","code":"library(tibble) library(dplyr)  # Example 1: Derive BMIBL adsl <- tribble(   ~STUDYID,   ~USUBJID, ~AGE,   ~AGEU,   \"PILOT01\", \"01-1302\",   61,   \"YEARS\",   \"PILOT01\", \"17-1344\",   64,   \"YEARS\" )  advs <- tribble(   ~STUDYID,  ~USUBJID,  ~PARAMCD, ~PARAM,        ~VISIT,      ~AVAL, ~AVALU, ~ABLFL,   \"PILOT01\", \"01-1302\", \"HEIGHT\", \"Height (cm)\", \"SCREENING\", 177.8, \"cm\",   \"Y\",   \"PILOT01\", \"01-1302\", \"WEIGHT\", \"Weight (kg)\", \"SCREENING\", 81.19, \"kg\",   NA,   \"PILOT01\", \"01-1302\", \"WEIGHT\", \"Weight (kg)\", \"BASELINE\",   82.1, \"kg\",   \"Y\",   \"PILOT01\", \"01-1302\", \"WEIGHT\", \"Weight (kg)\", \"WEEK 2\",    81.19, \"kg\",   NA,   \"PILOT01\", \"01-1302\", \"WEIGHT\", \"Weight (kg)\", \"WEEK 4\",    82.56, \"kg\",   NA,   \"PILOT01\", \"01-1302\", \"WEIGHT\", \"Weight (kg)\", \"WEEK 6\",    80.74, \"kg\",   NA,   \"PILOT01\", \"17-1344\", \"HEIGHT\", \"Height (cm)\", \"SCREENING\", 163.5, \"cm\",   \"Y\",   \"PILOT01\", \"17-1344\", \"WEIGHT\", \"Weight (kg)\", \"SCREENING\", 58.06, \"kg\",   NA,   \"PILOT01\", \"17-1344\", \"WEIGHT\", \"Weight (kg)\", \"BASELINE\",  58.06, \"kg\",   \"Y\",   \"PILOT01\", \"17-1344\", \"WEIGHT\", \"Weight (kg)\", \"WEEK 2\",    58.97, \"kg\",   NA,   \"PILOT01\", \"17-1344\", \"WEIGHT\", \"Weight (kg)\", \"WEEK 4\",    57.97, \"kg\",   NA,   \"PILOT01\", \"17-1344\", \"WEIGHT\", \"Weight (kg)\", \"WEEK 6\",    58.97, \"kg\",   NA )  derive_vars_computed(   dataset = adsl,   dataset_add = advs,   by_vars = exprs(STUDYID, USUBJID),   parameters = c(\"WEIGHT\", \"HEIGHT\"),   new_vars = exprs(BMIBL = compute_bmi(height = AVAL.HEIGHT, weight = AVAL.WEIGHT)),   filter_add = ABLFL == \"Y\" ) #> # A tibble: 2 × 5 #>   STUDYID USUBJID   AGE AGEU  BMIBL #>   <chr>   <chr>   <dbl> <chr> <dbl> #> 1 PILOT01 01-1302    61 YEARS  26.0 #> 2 PILOT01 17-1344    64 YEARS  21.7"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_crit_flag.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive Criterion Flag Variables CRITy, CRITyFL, and CRITyFN — derive_vars_crit_flag","title":"Derive Criterion Flag Variables CRITy, CRITyFL, and CRITyFN — derive_vars_crit_flag","text":"function derives ADaM compliant criterion flags, e.g., facilitate subgroup analyses. criterion flag derived function, derivation ADaM compliant. helps ensure condition criterion depends variables row, CRITyFL populated valid values, .e, either \"Y\" NA \"Y\", \"N\", NA, CRITy variable populated correctly, .e., set constant value within parameter CRITyFL populated \"Y\", \"N\", NA set constant value within parameter criterion condition fulfilled NA otherwise CRITyFL populated \"Y\",  NA","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_crit_flag.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive Criterion Flag Variables CRITy, CRITyFL, and CRITyFN — derive_vars_crit_flag","text":"","code":"derive_vars_crit_flag(   dataset,   crit_nr = 1,   condition,   description,   values_yn = FALSE,   create_numeric_flag = FALSE )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_crit_flag.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive Criterion Flag Variables CRITy, CRITyFL, and CRITyFN — derive_vars_crit_flag","text":"dataset Input dataset crit_nr criterion number, .e., y CRITy Permitted Values: positive integer condition Condition flagging records See description values_yn argument details CRITyFL variable populated. Permitted Values: unquoted expression evaluates logical (dataset) description description criterion CRITy variable set specified value. expression can specified set value depending parameter. Please note value must constant within parameter. Permitted Values: unquoted expression evaluates character (dataset) values_yn \"Y\" \"N\" used CRITyFL? set TRUE, CRITyFL variable set \"Y\" condition (condition) evaluates TRUE, set \"N\" condition evaluate FALSE, NA evaluates NA. Otherwise, CRITyFL variable set \"Y\" condition (condition) evaluates TRUE, NA otherwise. Permitted Values: TRUE, FALSE create_numeric_flag Create numeric flag? set TRUE, CRITyFN variable created. set 1 CRITyFL == \"Y\", set 0 CRITyFL == \"N\", NA otherwise. Permitted Values: TRUE, FALSE","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_crit_flag.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive Criterion Flag Variables CRITy, CRITyFL, and CRITyFN — derive_vars_crit_flag","text":"input dataset variables CRITy, CRITyFL, optionally CRITyFN added.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_crit_flag.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive Criterion Flag Variables CRITy, CRITyFL, and CRITyFN — derive_vars_crit_flag","text":"","code":"library(tibble) adbds <- tribble(   ~PARAMCD, ~AVAL,   \"AST\",    42,   \"AST\",    52,   \"AST\",    NA_real_,   \"ALT\",    33,   \"ALT\",    51 )  # Create a criterion flag with values \"Y\" and NA derive_vars_crit_flag(   adbds,   condition = AVAL > 50,   description = \"Absolute value > 50\" ) #> # A tibble: 5 × 4 #>   PARAMCD  AVAL CRIT1FL CRIT1               #>   <chr>   <dbl> <chr>   <chr>               #> 1 AST        42 NA      NA                  #> 2 AST        52 Y       Absolute value > 50 #> 3 AST        NA NA      NA                  #> 4 ALT        33 NA      NA                  #> 5 ALT        51 Y       Absolute value > 50  # Create criterion flag with values \"Y\", \"N\", and NA and parameter dependent # criterion description derive_vars_crit_flag(   adbds,   crit_nr = 2,   condition = AVAL > 50,   description = paste(PARAMCD, \"> 50\"),   values_yn = TRUE,   create_numeric_flag = TRUE ) #> # A tibble: 5 × 5 #>   PARAMCD  AVAL CRIT2FL CRIT2    CRIT2FN #>   <chr>   <dbl> <chr>   <chr>      <int> #> 1 AST        42 N       AST > 50       0 #> 2 AST        52 Y       AST > 50       1 #> 3 AST        NA NA      AST > 50      NA #> 4 ALT        33 N       ALT > 50       0 #> 5 ALT        51 Y       ALT > 50       1"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_dt.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive/Impute a Date from a Date Character Vector — derive_vars_dt","title":"Derive/Impute a Date from a Date Character Vector — derive_vars_dt","text":"Derive date ('--DT') date character vector ('--DTC'). date can imputed (see date_imputation argument) date imputation flag ('--DTF') can added.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_dt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive/Impute a Date from a Date Character Vector — derive_vars_dt","text":"","code":"derive_vars_dt(   dataset,   new_vars_prefix,   dtc,   highest_imputation = \"n\",   date_imputation = \"first\",   flag_imputation = \"auto\",   min_dates = NULL,   max_dates = NULL,   preserve = FALSE )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_dt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive/Impute a Date from a Date Character Vector — derive_vars_dt","text":"dataset Input dataset variables specified dtc argument expected dataset. new_vars_prefix Prefix used output variable(s). character scalar expected. date variable \"DT\" appended specified prefix date imputation flag \"DTF\". .e., new_vars_prefix = \"AST\" variables ASTDT ASTDTF created. dtc '--DTC' date impute character date expected format like yyyy-mm-dd yyyy-mm-ddThh:mm:ss. Trailing components can omitted - valid \"missing\" value component. highest_imputation Highest imputation level highest_imputation argument controls components DTC value imputed missing. components specified level imputed. component higher level highest imputation level missing, NA_character_ returned. example, highest_imputation = \"D\" \"2020\" results NA_character_ month missing. \"n\" specified imputation performed, .e., component missing, NA_character_ returned. \"Y\" specified, date_imputation \"first\" \"last\" min_dates max_dates specified respectively. Otherwise, NA_character_ returned year component missing. Permitted Values: \"Y\" (year, highest level), \"M\" (month), \"D\" (day), \"n\" (none, lowest level) date_imputation value impute day/month datepart missing. character value expected, either format month day specified \"mm-dd\": e.g. \"06-15\" 15th June (year can specified; imputing year \"first\" \"last\" together min_dates max_dates argument can used (see examples).), keyword: \"first\", \"mid\", \"last\" impute first/mid/last day/month. \"mid\" specified, missing components imputed middle possible range: month day missing, imputed \"06-30\" (middle year). day missing, imputed \"15\" (middle month). argument ignored highest_imputation less \"D\". flag_imputation Whether date imputation flag must also derived. \"auto\" specified highest_imputation argument \"n\", date imputation flag derived. \"date\" specified, date imputation flag derived. \"none\" specified, date imputation flag derived. Permitted Values: \"auto\", \"date\" \"none\" min_dates Minimum dates list dates expected. ensured imputed date specified dates, e.g., imputed adverse event start date first treatment date. dates range possible dates dtc value considered. possible dates defined missing parts dtc date (see example ). ensures non-missing parts dtc date changed. date date-time object expected. example   returns \"2020-11-11T11:11:11\" possible dates \"2020-11\" range \"2020-11-01T00:00:00\" \"2020-11-30T23:59:59\". Therefore \"2020-12-06T12:12:12\" ignored. Returning \"2020-12-06T12:12:12\" changed month although missing (dtc date). max_dates Maximum dates list dates expected. ensured imputed date specified dates, e.g., imputed date data cut date. dates range possible dates considered. date date-time object expected. preserve Preserve day month missing day present example \"2019---07\" return \"2019-06-07 preserve = TRUE (date_imputation = \"MID\"). Permitted Values: TRUE, FALSE","code":"impute_dtc_dtm(   \"2020-11\",   min_dates = list(    ymd_hms(\"2020-12-06T12:12:12\"),    ymd_hms(\"2020-11-11T11:11:11\")   ),   highest_imputation = \"M\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_dt.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive/Impute a Date from a Date Character Vector — derive_vars_dt","text":"input dataset date '--DT' (date imputation flag '--DTF' requested) added.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_dt.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Derive/Impute a Date from a Date Character Vector — derive_vars_dt","text":"{admiral} allow users pick single part date/time impute, enable impute highest level, .e. choose say impute months, days. presence '--DTF' variable checked already exists input dataset, warning issued '--DTF' overwritten.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_dt.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive/Impute a Date from a Date Character Vector — derive_vars_dt","text":"","code":"library(tibble) library(lubridate)  mhdt <- tribble(   ~MHSTDTC,   \"2019-07-18T15:25:40\",   \"2019-07-18T15:25\",   \"2019-07-18\",   \"2019-02\",   \"2019\",   \"2019---07\",   \"\" )  # Create ASTDT and ASTDTF # No imputation for partial date derive_vars_dt(   mhdt,   new_vars_prefix = \"AST\",   dtc = MHSTDTC ) #> # A tibble: 7 × 2 #>   MHSTDTC               ASTDT      #>   <chr>                 <date>     #> 1 \"2019-07-18T15:25:40\" 2019-07-18 #> 2 \"2019-07-18T15:25\"    2019-07-18 #> 3 \"2019-07-18\"          2019-07-18 #> 4 \"2019-02\"             NA         #> 5 \"2019\"                NA         #> 6 \"2019---07\"           NA         #> 7 \"\"                    NA          # Create ASTDT and ASTDTF # Impute partial dates to first day/month derive_vars_dt(   mhdt,   new_vars_prefix = \"AST\",   dtc = MHSTDTC,   highest_imputation = \"M\" ) #> # A tibble: 7 × 3 #>   MHSTDTC               ASTDT      ASTDTF #>   <chr>                 <date>     <chr>  #> 1 \"2019-07-18T15:25:40\" 2019-07-18 NA     #> 2 \"2019-07-18T15:25\"    2019-07-18 NA     #> 3 \"2019-07-18\"          2019-07-18 NA     #> 4 \"2019-02\"             2019-02-01 D      #> 5 \"2019\"                2019-01-01 M      #> 6 \"2019---07\"           2019-01-01 M      #> 7 \"\"                    NA         NA      # Impute partial dates to 6th of April derive_vars_dt(   mhdt,   new_vars_prefix = \"AST\",   dtc = MHSTDTC,   highest_imputation = \"M\",   date_imputation = \"04-06\" ) #> # A tibble: 7 × 3 #>   MHSTDTC               ASTDT      ASTDTF #>   <chr>                 <date>     <chr>  #> 1 \"2019-07-18T15:25:40\" 2019-07-18 NA     #> 2 \"2019-07-18T15:25\"    2019-07-18 NA     #> 3 \"2019-07-18\"          2019-07-18 NA     #> 4 \"2019-02\"             2019-02-06 D      #> 5 \"2019\"                2019-04-06 M      #> 6 \"2019---07\"           2019-04-06 M      #> 7 \"\"                    NA         NA      # Create AENDT and AENDTF # Impute partial dates to last day/month derive_vars_dt(   mhdt,   new_vars_prefix = \"AEN\",   dtc = MHSTDTC,   highest_imputation = \"M\",   date_imputation = \"last\" ) #> # A tibble: 7 × 3 #>   MHSTDTC               AENDT      AENDTF #>   <chr>                 <date>     <chr>  #> 1 \"2019-07-18T15:25:40\" 2019-07-18 NA     #> 2 \"2019-07-18T15:25\"    2019-07-18 NA     #> 3 \"2019-07-18\"          2019-07-18 NA     #> 4 \"2019-02\"             2019-02-28 D      #> 5 \"2019\"                2019-12-31 M      #> 6 \"2019---07\"           2019-12-31 M      #> 7 \"\"                    NA         NA      # Create BIRTHDT # Impute partial dates to 15th of June. No Date Imputation Flag derive_vars_dt(   mhdt,   new_vars_prefix = \"BIRTH\",   dtc = MHSTDTC,   highest_imputation = \"M\",   date_imputation = \"mid\",   flag_imputation = \"none\" ) #> # A tibble: 7 × 2 #>   MHSTDTC               BIRTHDT    #>   <chr>                 <date>     #> 1 \"2019-07-18T15:25:40\" 2019-07-18 #> 2 \"2019-07-18T15:25\"    2019-07-18 #> 3 \"2019-07-18\"          2019-07-18 #> 4 \"2019-02\"             2019-02-15 #> 5 \"2019\"                2019-06-30 #> 6 \"2019---07\"           2019-06-30 #> 7 \"\"                    NA          # Impute AE start date to the first date and ensure that the imputed date # is not before the treatment start date adae <- tribble(   ~AESTDTC, ~TRTSDTM,   \"2020-12\", ymd_hms(\"2020-12-06T12:12:12\"),   \"2020-11\", ymd_hms(\"2020-12-06T12:12:12\") )  derive_vars_dt(   adae,   dtc = AESTDTC,   new_vars_prefix = \"AST\",   highest_imputation = \"M\",   min_dates = exprs(TRTSDTM) ) #> # A tibble: 2 × 4 #>   AESTDTC TRTSDTM             ASTDT      ASTDTF #>   <chr>   <dttm>              <date>     <chr>  #> 1 2020-12 2020-12-06 12:12:12 2020-12-06 D      #> 2 2020-11 2020-12-06 12:12:12 2020-11-01 D       # A user imputing dates as middle month/day, i.e. date_imputation = \"mid\" can # use preserve argument to \"preserve\" partial dates.  For example, \"2019---07\", # will be displayed as \"2019-06-07\" rather than 2019-06-15 with preserve = TRUE  derive_vars_dt(   mhdt,   new_vars_prefix = \"AST\",   dtc = MHSTDTC,   highest_imputation = \"M\",   date_imputation = \"mid\",   preserve = TRUE ) #> # A tibble: 7 × 3 #>   MHSTDTC               ASTDT      ASTDTF #>   <chr>                 <date>     <chr>  #> 1 \"2019-07-18T15:25:40\" 2019-07-18 NA     #> 2 \"2019-07-18T15:25\"    2019-07-18 NA     #> 3 \"2019-07-18\"          2019-07-18 NA     #> 4 \"2019-02\"             2019-02-15 D      #> 5 \"2019\"                2019-06-30 M      #> 6 \"2019---07\"           2019-06-07 M      #> 7 \"\"                    NA         NA"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_dtm.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive/Impute a Datetime from a Date Character Vector — derive_vars_dtm","title":"Derive/Impute a Datetime from a Date Character Vector — derive_vars_dtm","text":"Derive datetime object ('--DTM') date character vector ('--DTC'). date time can imputed (see date_imputation/time_imputation arguments) date/time imputation flag ('--DTF', '--TMF') can added.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_dtm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive/Impute a Datetime from a Date Character Vector — derive_vars_dtm","text":"","code":"derive_vars_dtm(   dataset,   new_vars_prefix,   dtc,   highest_imputation = \"h\",   date_imputation = \"first\",   time_imputation = \"first\",   flag_imputation = \"auto\",   min_dates = NULL,   max_dates = NULL,   preserve = FALSE,   ignore_seconds_flag = FALSE )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_dtm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive/Impute a Datetime from a Date Character Vector — derive_vars_dtm","text":"dataset Input dataset variables specified dtc argument expected dataset. new_vars_prefix Prefix used output variable(s). character scalar expected. date variable \"DT\" appended specified prefix, date imputation flag \"DTF\", time imputation flag \"TMF\". .e., new_vars_prefix = \"AST\" variables ASTDT, ASTDTF, ASTTMF created. dtc '--DTC' date impute character date expected format like yyyy-mm-dd yyyy-mm-ddThh:mm:ss. Trailing components can omitted - valid \"missing\" value component. highest_imputation Highest imputation level highest_imputation argument controls components DTC value imputed missing. components specified level imputed. component higher level highest imputation level missing, NA_character_ returned. example, highest_imputation = \"D\" \"2020\" results NA_character_ month missing. \"n\" specified, imputation performed, .e., component missing, NA_character_ returned. \"Y\" specified, date_imputation \"first\" \"last\" min_dates max_dates specified respectively. Otherwise, NA_character_ returned year component missing. Permitted Values: \"Y\" (year, highest level), \"M\" (month), \"D\" (day), \"h\" (hour), \"m\" (minute), \"s\" (second), \"n\" (none, lowest level) date_imputation value impute day/month datepart missing. character value expected, either format month day specified \"mm-dd\": e.g. \"06-15\" 15th June (year can specified; imputing year \"first\" \"last\" together min_dates max_dates argument can used (see examples).), keyword: \"first\", \"mid\", \"last\" impute first/mid/last day/month. \"mid\" specified, missing components imputed middle possible range: month day missing, imputed \"06-30\" (middle year). day missing, imputed \"15\" (middle month). argument ignored highest_imputation less \"D\". time_imputation value impute time timepart missing. character value expected, either format hour, min sec specified \"hh:mm:ss\": e.g. \"00:00:00\" start day, keyword: \"first\",\"last\" impute start/end day. argument ignored highest_imputation = \"n\". flag_imputation Whether date/time imputation flag(s) must also derived. \"\" \"date\" specified, date imputation flag derived. \"auto\" specified highest_imputation argument greater \"h\", date imputation flag derived. \"\" \"time\" specified, time imputation flag derived. \"auto\" specified highest_imputation argument \"n\", time imputation flag derived. \"none\" specified, date time imputation flag derived. Permitted Values: \"auto\", \"date\", \"time\", \"\", \"none\" min_dates Minimum dates list dates expected. ensured imputed date specified dates, e.g., imputed adverse event start date first treatment date. dates range possible dates dtc value considered. possible dates defined missing parts dtc date (see example ). ensures non-missing parts dtc date changed. date date-time object expected. example   returns \"2020-11-11T11:11:11\" possible dates \"2020-11\" range \"2020-11-01T00:00:00\" \"2020-11-30T23:59:59\". Therefore \"2020-12-06T12:12:12\" ignored. Returning \"2020-12-06T12:12:12\" changed month although missing (dtc date). date variables (datetime) list time imputed \"00:00:00\". Specifying date variables makes sense date imputed. time imputed, date variables affect result. max_dates Maximum dates list dates expected. ensured imputed date specified dates, e.g., imputed date data cut date. dates range possible dates considered. date date-time object expected. date variables (datetime) list time imputed \"23:59:59\". Specifying date variables makes sense date imputed. time imputed, date variables affect result. preserve Preserve lower level date/time part higher order part missing, e.g. preserve day month missing preserve minute hour missing. example \"2019---07\" return \"2019-06-07 preserve = TRUE (date_imputation = \"mid\"). Permitted Values: TRUE, FALSE ignore_seconds_flag ADaM IG states given SDTM ('--DTC') variable, hours minutes ever collected, seconds imputed ('--DTM') 00, necessary set ('--TMF') 'S'. user can set TRUE 'S' Flag dropped ('--TMF'). Permitted Values: logical value","code":"impute_dtc_dtm(   \"2020-11\",   min_dates = list(    ymd_hms(\"2020-12-06T12:12:12\"),    ymd_hms(\"2020-11-11T11:11:11\")   ),   highest_imputation = \"M\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_dtm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive/Impute a Datetime from a Date Character Vector — derive_vars_dtm","text":"input dataset datetime '--DTM' (date/time imputation flag '--DTF', '--TMF') added.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_dtm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Derive/Impute a Datetime from a Date Character Vector — derive_vars_dtm","text":"{admiral} allow users pick single part date/time impute, enable impute highest level, .e. choose say impute months, days. presence '--DTF' variable checked variable derived already exists input dataset. However, '--TMF' already exists input dataset, warning issued '--TMF' overwritten.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_dtm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive/Impute a Datetime from a Date Character Vector — derive_vars_dtm","text":"","code":"library(tibble) library(lubridate)  mhdt <- tribble(   ~MHSTDTC,   \"2019-07-18T15:25:40\",   \"2019-07-18T15:25\",   \"2019-07-18\",   \"2019-02\",   \"2019\",   \"2019---07\",   \"\" )  derive_vars_dtm(   mhdt,   new_vars_prefix = \"AST\",   dtc = MHSTDTC,   highest_imputation = \"M\" ) #> # A tibble: 7 × 4 #>   MHSTDTC               ASTDTM              ASTDTF ASTTMF #>   <chr>                 <dttm>              <chr>  <chr>  #> 1 \"2019-07-18T15:25:40\" 2019-07-18 15:25:40 NA     NA     #> 2 \"2019-07-18T15:25\"    2019-07-18 15:25:00 NA     S      #> 3 \"2019-07-18\"          2019-07-18 00:00:00 NA     H      #> 4 \"2019-02\"             2019-02-01 00:00:00 D      H      #> 5 \"2019\"                2019-01-01 00:00:00 M      H      #> 6 \"2019---07\"           2019-01-01 00:00:00 M      H      #> 7 \"\"                    NA                  NA     NA      # Impute AE end date to the last date and ensure that the imputed date is not # after the death or data cut off date adae <- tribble(   ~AEENDTC, ~DTHDT, ~DCUTDT,   \"2020-12\", ymd(\"2020-12-06\"), ymd(\"2020-12-24\"),   \"2020-11\", ymd(\"2020-12-06\"), ymd(\"2020-12-24\") )  derive_vars_dtm(   adae,   dtc = AEENDTC,   new_vars_prefix = \"AEN\",   highest_imputation = \"M\",   date_imputation = \"last\",   time_imputation = \"last\",   max_dates = exprs(DTHDT, DCUTDT) ) #> # A tibble: 2 × 6 #>   AEENDTC DTHDT      DCUTDT     AENDTM              AENDTF AENTMF #>   <chr>   <date>     <date>     <dttm>              <chr>  <chr>  #> 1 2020-12 2020-12-06 2020-12-24 2020-12-06 23:59:59 D      H      #> 2 2020-11 2020-12-06 2020-12-24 2020-11-30 23:59:59 D      H       # Seconds has been removed from the input dataset.  Function now uses # ignore_seconds_flag to remove the 'S' from the --TMF variable. mhdt <- tribble(   ~MHSTDTC,   \"2019-07-18T15:25\",   \"2019-07-18T15:25\",   \"2019-07-18\",   \"2019-02\",   \"2019\",   \"2019---07\",   \"\" )  derive_vars_dtm(   mhdt,   new_vars_prefix = \"AST\",   dtc = MHSTDTC,   highest_imputation = \"M\",   ignore_seconds_flag = TRUE ) #> # A tibble: 7 × 4 #>   MHSTDTC            ASTDTM              ASTDTF ASTTMF #>   <chr>              <dttm>              <chr>  <chr>  #> 1 \"2019-07-18T15:25\" 2019-07-18 15:25:00 NA     NA     #> 2 \"2019-07-18T15:25\" 2019-07-18 15:25:00 NA     NA     #> 3 \"2019-07-18\"       2019-07-18 00:00:00 NA     H      #> 4 \"2019-02\"          2019-02-01 00:00:00 D      H      #> 5 \"2019\"             2019-01-01 00:00:00 M      H      #> 6 \"2019---07\"        2019-01-01 00:00:00 M      H      #> 7 \"\"                 NA                  NA     NA      # A user imputing dates as middle month/day, i.e. date_imputation = \"MID\" can # use preserve argument to \"preserve\" partial dates.  For example, \"2019---07\", # will be displayed as \"2019-06-07\" rather than 2019-06-15 with preserve = TRUE  derive_vars_dtm(   mhdt,   new_vars_prefix = \"AST\",   dtc = MHSTDTC,   highest_imputation = \"M\",   date_imputation = \"mid\",   preserve = TRUE ) #> # A tibble: 7 × 4 #>   MHSTDTC            ASTDTM              ASTDTF ASTTMF #>   <chr>              <dttm>              <chr>  <chr>  #> 1 \"2019-07-18T15:25\" 2019-07-18 15:25:00 NA     S      #> 2 \"2019-07-18T15:25\" 2019-07-18 15:25:00 NA     S      #> 3 \"2019-07-18\"       2019-07-18 00:00:00 NA     H      #> 4 \"2019-02\"          2019-02-15 00:00:00 D      H      #> 5 \"2019\"             2019-06-30 00:00:00 M      H      #> 6 \"2019---07\"        2019-06-07 00:00:00 M      H      #> 7 \"\"                 NA                  NA     NA"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_dtm_to_dt.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive Date Variables from Datetime Variables — derive_vars_dtm_to_dt","title":"Derive Date Variables from Datetime Variables — derive_vars_dtm_to_dt","text":"function creates date(s) output datetime variable(s)","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_dtm_to_dt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive Date Variables from Datetime Variables — derive_vars_dtm_to_dt","text":"","code":"derive_vars_dtm_to_dt(dataset, source_vars)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_dtm_to_dt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive Date Variables from Datetime Variables — derive_vars_dtm_to_dt","text":"dataset Input dataset variables specified source_vars argument expected dataset. source_vars list datetime variables created using exprs() dates extracted","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_dtm_to_dt.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive Date Variables from Datetime Variables — derive_vars_dtm_to_dt","text":"data frame containing input dataset corresponding date (--DT) variable(s) datetime variables (--DTM) specified source_vars.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_dtm_to_dt.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive Date Variables from Datetime Variables — derive_vars_dtm_to_dt","text":"","code":"library(tibble) library(dplyr, warn.conflicts = FALSE) library(lubridate)  adcm <- tribble(   ~USUBJID, ~TRTSDTM,              ~ASTDTM,               ~AENDTM,   \"PAT01\",  \"2012-02-25 23:00:00\", \"2012-02-28 19:00:00\", \"2012-02-25 23:00:00\",   \"PAT01\",  NA,                    \"2012-02-28 19:00:00\", NA,   \"PAT01\",  \"2017-02-25 23:00:00\", \"2013-02-25 19:00:00\", \"2014-02-25 19:00:00\",   \"PAT01\",  \"2017-02-25 16:00:00\", \"2017-02-25 14:00:00\", \"2017-03-25 23:00:00\",   \"PAT01\",  \"2017-02-25 16:00:00\", \"2017-02-25 14:00:00\", \"2017-04-29 14:00:00\", ) %>%   mutate(     TRTSDTM = as_datetime(TRTSDTM),     ASTDTM = as_datetime(ASTDTM),     AENDTM = as_datetime(AENDTM)   )  adcm %>%   derive_vars_dtm_to_dt(exprs(TRTSDTM, ASTDTM, AENDTM)) %>%   select(USUBJID, starts_with(\"TRT\"), starts_with(\"AST\"), starts_with(\"AEN\")) #> # A tibble: 5 × 7 #>   USUBJID TRTSDTM             TRTSDT     ASTDTM              ASTDT      #>   <chr>   <dttm>              <date>     <dttm>              <date>     #> 1 PAT01   2012-02-25 23:00:00 2012-02-25 2012-02-28 19:00:00 2012-02-28 #> 2 PAT01   NA                  NA         2012-02-28 19:00:00 2012-02-28 #> 3 PAT01   2017-02-25 23:00:00 2017-02-25 2013-02-25 19:00:00 2013-02-25 #> 4 PAT01   2017-02-25 16:00:00 2017-02-25 2017-02-25 14:00:00 2017-02-25 #> 5 PAT01   2017-02-25 16:00:00 2017-02-25 2017-02-25 14:00:00 2017-02-25 #> # ℹ 2 more variables: AENDTM <dttm>, AENDT <date>"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_dtm_to_tm.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive Time Variables from Datetime Variables — derive_vars_dtm_to_tm","title":"Derive Time Variables from Datetime Variables — derive_vars_dtm_to_tm","text":"function creates time variable(s) output datetime variable(s)","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_dtm_to_tm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive Time Variables from Datetime Variables — derive_vars_dtm_to_tm","text":"","code":"derive_vars_dtm_to_tm(dataset, source_vars)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_dtm_to_tm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive Time Variables from Datetime Variables — derive_vars_dtm_to_tm","text":"dataset Input dataset variables specified source_vars argument expected dataset. source_vars list datetime variables created using exprs() time extracted","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_dtm_to_tm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive Time Variables from Datetime Variables — derive_vars_dtm_to_tm","text":"data frame containing input dataset corresponding time (--TM) variable(s) datetime variables (--DTM) specified source_vars correct name.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_dtm_to_tm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Derive Time Variables from Datetime Variables — derive_vars_dtm_to_tm","text":"names newly added variables automatically set replacing --DTM suffix source_vars --TM. --TM variables created using {hms} package.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_dtm_to_tm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive Time Variables from Datetime Variables — derive_vars_dtm_to_tm","text":"","code":"library(tibble) library(dplyr, warn.conflicts = FALSE) library(lubridate)  adcm <- tribble(   ~USUBJID, ~TRTSDTM, ~ASTDTM, ~AENDTM,   \"PAT01\", \"2012-02-25 23:41:10\", \"2012-02-28 19:03:00\", \"2013-02-25 23:32:16\",   \"PAT01\", \"\", \"2012-02-28 19:00:00\", \"\",   \"PAT01\", \"2017-02-25 23:00:02\", \"2013-02-25 19:00:15\", \"2014-02-25 19:00:56\",   \"PAT01\", \"2017-02-25 16:00:00\", \"2017-02-25 14:25:00\", \"2017-03-25 23:00:00\",   \"PAT01\", \"2017-02-25 16:05:17\", \"2017-02-25 14:20:00\", \"2018-04-29 14:06:45\", ) %>%   mutate(     TRTSDTM = as_datetime(TRTSDTM),     ASTDTM = as_datetime(ASTDTM),     AENDTM = as_datetime(AENDTM)   )  adcm %>%   derive_vars_dtm_to_tm(exprs(TRTSDTM)) %>%   select(USUBJID, starts_with(\"TRT\"), everything()) #> # A tibble: 5 × 5 #>   USUBJID TRTSDTM             TRTSTM   ASTDTM              AENDTM              #>   <chr>   <dttm>              <time>   <dttm>              <dttm>              #> 1 PAT01   2012-02-25 23:41:10 23:41:10 2012-02-28 19:03:00 2013-02-25 23:32:16 #> 2 PAT01   NA                        NA 2012-02-28 19:00:00 NA                  #> 3 PAT01   2017-02-25 23:00:02 23:00:02 2013-02-25 19:00:15 2014-02-25 19:00:56 #> 4 PAT01   2017-02-25 16:00:00 16:00:00 2017-02-25 14:25:00 2017-03-25 23:00:00 #> 5 PAT01   2017-02-25 16:05:17 16:05:17 2017-02-25 14:20:00 2018-04-29 14:06:45  adcm %>%   derive_vars_dtm_to_tm(exprs(TRTSDTM, ASTDTM, AENDTM)) %>%   select(USUBJID, starts_with(\"TRT\"), starts_with(\"AS\"), starts_with(\"AE\")) #> # A tibble: 5 × 7 #>   USUBJID TRTSDTM             TRTSTM   ASTDTM              ASTTM    #>   <chr>   <dttm>              <time>   <dttm>              <time>   #> 1 PAT01   2012-02-25 23:41:10 23:41:10 2012-02-28 19:03:00 19:03:00 #> 2 PAT01   NA                        NA 2012-02-28 19:00:00 19:00:00 #> 3 PAT01   2017-02-25 23:00:02 23:00:02 2013-02-25 19:00:15 19:00:15 #> 4 PAT01   2017-02-25 16:00:00 16:00:00 2017-02-25 14:25:00 14:25:00 #> 5 PAT01   2017-02-25 16:05:17 16:05:17 2017-02-25 14:20:00 14:20:00 #> # ℹ 2 more variables: AENDTM <dttm>, AENTM <time>"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_duration.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive Duration — derive_vars_duration","title":"Derive Duration — derive_vars_duration","text":"Derives duration two dates, specified variables present input dataset e.g., duration adverse events, relative day, age, ...","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_duration.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive Duration — derive_vars_duration","text":"","code":"derive_vars_duration(   dataset,   new_var,   new_var_unit = NULL,   start_date,   end_date,   in_unit = \"days\",   out_unit = \"DAYS\",   floor_in = TRUE,   add_one = TRUE,   trunc_out = FALSE,   type = \"duration\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_duration.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive Duration — derive_vars_duration","text":"dataset Input dataset variables specified start_date end_date arguments expected dataset. new_var Name variable create new_var_unit Name unit variable parameter specified, variable unit created. start_date start date date date-time object expected. Refer derive_vars_dt() impute derive date date character vector date object. Refer convert_dtc_to_dt() obtain vector imputed dates. end_date end date date date-time object expected. Refer derive_vars_dt() impute derive date date character vector date object. Refer convert_dtc_to_dt() obtain vector imputed dates. in_unit Input unit See floor_in add_one parameter details. Permitted Values (case-insensitive): years: \"year\", \"years\", \"yr\", \"yrs\", \"y\" months: \"month\", \"months\", \"mo\", \"mos\" days: \"day\", \"days\", \"d\" hours: \"hour\", \"hours\", \"hr\", \"hrs\", \"h\" minutes: \"minute\", \"minutes\", \"min\", \"mins\" seconds: \"second\", \"seconds\", \"sec\", \"secs\", \"s\" out_unit Output unit duration derived specified unit Permitted Values (case-insensitive): years: \"year\", \"years\", \"yr\", \"yrs\", \"y\" months: \"month\", \"months\", \"mo\", \"mos\" weeks: \"week\", \"weeks\", \"wk\", \"wks\", \"w\" days: \"day\", \"days\", \"d\" hours: \"hour\", \"hours\", \"hr\", \"hrs\", \"h\" minutes: \"minute\", \"minutes\", \"min\", \"mins\" seconds: \"second\", \"seconds\", \"sec\", \"secs\", \"s\" floor_in Round input dates? input dates round respect input unit, e.g., input unit 'days', time input dates ignored. Default: TRUE Permitted Values: TRUE, FALSE add_one Add one input unit? duration non-negative, one input unit added. .e., duration can zero. Default: TRUE Permitted Values: TRUE, FALSE trunc_out Return integer part fractional part duration (output unit) removed, .e., integer part returned. Default: FALSE Permitted Values: TRUE, FALSE type lubridate duration type. See details. Default: \"duration\" Permitted Values: \"duration\", \"interval\"","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_duration.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive Duration — derive_vars_duration","text":"input dataset duration unit variable added","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_duration.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Derive Duration — derive_vars_duration","text":"duration derived time start end date specified output unit. end date start date, duration negative. start end date variable must present specified input dataset. lubridate package calculates two types spans two dates: duration interval. calculations largely , unit time period month year result can slightly different. difference arises ambiguity length \"1 month\" \"1 year\". Months may 31, 30, 28, 29 days, years 365 days 366 leap years. Durations intervals help solve ambiguity measures. interval 2000-02-01 2000-03-01 1 (.e. one month). duration two dates 0.95, accounts fact year 2000 leap year, February 29 days, average month length 30.4375, .e. 29 / 30.4375 = 0.95. additional details, review lubridate time span reference page.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_duration.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive Duration — derive_vars_duration","text":"","code":"library(lubridate) library(tibble)  # Derive age in years data <- tribble(   ~USUBJID, ~BRTHDT, ~RANDDT,   \"P01\", ymd(\"1984-09-06\"), ymd(\"2020-02-24\"),   \"P02\", ymd(\"1985-01-01\"), NA,   \"P03\", NA, ymd(\"2021-03-10\"),   \"P04\", NA, NA )  derive_vars_duration(data,   new_var = AAGE,   new_var_unit = AAGEU,   start_date = BRTHDT,   end_date = RANDDT,   out_unit = \"years\",   add_one = FALSE,   trunc_out = TRUE ) #> # A tibble: 4 × 5 #>   USUBJID BRTHDT     RANDDT      AAGE AAGEU #>   <chr>   <date>     <date>     <dbl> <chr> #> 1 P01     1984-09-06 2020-02-24    35 years #> 2 P02     1985-01-01 NA            NA NA    #> 3 P03     NA         2021-03-10    NA NA    #> 4 P04     NA         NA            NA NA     # Derive adverse event duration in days data <- tribble(   ~USUBJID, ~ASTDT, ~AENDT,   \"P01\", ymd(\"2021-03-05\"), ymd(\"2021-03-02\"),   \"P02\", ymd(\"2019-09-18\"), ymd(\"2019-09-18\"),   \"P03\", ymd(\"1985-01-01\"), NA,   \"P04\", NA, NA )  derive_vars_duration(data,   new_var = ADURN,   new_var_unit = ADURU,   start_date = ASTDT,   end_date = AENDT,   out_unit = \"days\" ) #> # A tibble: 4 × 5 #>   USUBJID ASTDT      AENDT      ADURN ADURU #>   <chr>   <date>     <date>     <dbl> <chr> #> 1 P01     2021-03-05 2021-03-02    -3 days  #> 2 P02     2019-09-18 2019-09-18     1 days  #> 3 P03     1985-01-01 NA            NA NA    #> 4 P04     NA         NA            NA NA     # Derive adverse event duration in minutes data <- tribble(   ~USUBJID, ~ADTM, ~TRTSDTM,   \"P01\", ymd_hms(\"2019-08-09T04:30:56\"), ymd_hms(\"2019-08-09T05:00:00\"),   \"P02\", ymd_hms(\"2019-11-11T10:30:00\"), ymd_hms(\"2019-11-11T11:30:00\"),   \"P03\", ymd_hms(\"2019-11-11T00:00:00\"), ymd_hms(\"2019-11-11T04:00:00\"),   \"P04\", NA, ymd_hms(\"2019-11-11T12:34:56\"), )  derive_vars_duration(data,   new_var = ADURN,   new_var_unit = ADURU,   start_date = ADTM,   end_date = TRTSDTM,   in_unit = \"minutes\",   out_unit = \"minutes\",   add_one = FALSE ) #> # A tibble: 4 × 5 #>   USUBJID ADTM                TRTSDTM             ADURN ADURU   #>   <chr>   <dttm>              <dttm>              <dbl> <chr>   #> 1 P01     2019-08-09 04:30:56 2019-08-09 05:00:00    30 minutes #> 2 P02     2019-11-11 10:30:00 2019-11-11 11:30:00    60 minutes #> 3 P03     2019-11-11 00:00:00 2019-11-11 04:00:00   240 minutes #> 4 P04     NA                  2019-11-11 12:34:56    NA NA       # Derive adverse event start time since last dose in hours data <- tribble(   ~USUBJID, ~ASTDTM, ~LDOSEDTM,   \"P01\", ymd_hms(\"2019-08-09T04:30:56\"), ymd_hms(\"2019-08-08T10:05:00\"),   \"P02\", ymd_hms(\"2019-11-11T23:59:59\"), ymd_hms(\"2019-10-11T11:37:00\"),   \"P03\", ymd_hms(\"2019-11-11T00:00:00\"), ymd_hms(\"2019-11-10T23:59:59\"),   \"P04\", ymd_hms(\"2019-11-11T12:34:56\"), NA,   \"P05\", NA, ymd_hms(\"2019-09-28T12:34:56\") ) derive_vars_duration(   data,   new_var = LDRELTM,   new_var_unit = LDRELTMU,   start_date = LDOSEDTM,   end_date = ASTDTM,   in_unit = \"hours\",   out_unit = \"hours\",   add_one = FALSE ) #> # A tibble: 5 × 5 #>   USUBJID ASTDTM              LDOSEDTM            LDRELTM LDRELTMU #>   <chr>   <dttm>              <dttm>                <dbl> <chr>    #> 1 P01     2019-08-09 04:30:56 2019-08-08 10:05:00      18 hours    #> 2 P02     2019-11-11 23:59:59 2019-10-11 11:37:00     756 hours    #> 3 P03     2019-11-11 00:00:00 2019-11-10 23:59:59       1 hours    #> 4 P04     2019-11-11 12:34:56 NA                       NA NA       #> 5 P05     NA                  2019-09-28 12:34:56      NA NA"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_dy.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive Relative Day Variables — derive_vars_dy","title":"Derive Relative Day Variables — derive_vars_dy","text":"Adds relative day variables (--DY) dataset, e.g., ASTDY AENDY.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_dy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive Relative Day Variables — derive_vars_dy","text":"","code":"derive_vars_dy(dataset, reference_date, source_vars)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_dy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive Relative Day Variables — derive_vars_dy","text":"dataset Input dataset variables specified reference_date source_vars arguments expected dataset. reference_date date date-time column, e.g., date first treatment date-time last exposure treatment. Refer derive_vars_dt() impute derive date date character vector date object. source_vars list datetime date variables created using exprs() dates extracted. can either list date(time) variables named --DY variables corresponding –DT(M) variables e.g. exprs(TRTSDTM, ASTDTM, AENDT) exprs(TRTSDT, ASTDTM, AENDT, DEATHDY = DTHDT). source variable end –DT(M), name resulting --DY variable must provided.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_dy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive Relative Day Variables — derive_vars_dy","text":"input dataset --DY corresponding --DTM --DT source variable(s) added","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_dy.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Derive Relative Day Variables — derive_vars_dy","text":"relative day derived number days reference date end date. nonnegative, one added. .e., relative day reference date 1. Unless name explicitly specified, name resulting relative day variable generated source variable name replacing DT (DTM appropriate) DY.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_dy.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive Relative Day Variables — derive_vars_dy","text":"","code":"library(tibble) library(lubridate) library(dplyr, warn.conflicts = FALSE)  datain <- tribble(   ~TRTSDTM, ~ASTDTM, ~AENDT,   \"2014-01-17T23:59:59\", \"2014-01-18T13:09:O9\", \"2014-01-20\" ) %>%   mutate(     TRTSDTM = as_datetime(TRTSDTM),     ASTDTM = as_datetime(ASTDTM),     AENDT = ymd(AENDT)   )  derive_vars_dy(   datain,   reference_date = TRTSDTM,   source_vars = exprs(TRTSDTM, ASTDTM, AENDT) ) #> # A tibble: 1 × 6 #>   TRTSDTM             ASTDTM              AENDT      TRTSDY ASTDY AENDY #>   <dttm>              <dttm>              <date>      <dbl> <dbl> <dbl> #> 1 2014-01-17 23:59:59 2014-01-18 13:09:09 2014-01-20      1     2     4  # specifying name of new variables datain <- tribble(   ~TRTSDT, ~DTHDT,   \"2014-01-17\", \"2014-02-01\" ) %>%   mutate(     TRTSDT = ymd(TRTSDT),     DTHDT = ymd(DTHDT)   )  derive_vars_dy(   datain,   reference_date = TRTSDT,   source_vars = exprs(TRTSDT, DEATHDY = DTHDT) ) #> # A tibble: 1 × 4 #>   TRTSDT     DTHDT      TRTSDY DEATHDY #>   <date>     <date>      <dbl>   <dbl> #> 1 2014-01-17 2014-02-01      1      16"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_extreme_event.html","id":null,"dir":"Reference","previous_headings":"","what":"Add the Worst or Best Observation for Each By Group as New Variables — derive_vars_extreme_event","title":"Add the Worst or Best Observation for Each By Group as New Variables — derive_vars_extreme_event","text":"Add first available record events group new variables, variables selected observation kept. can used selecting extreme observation series user-defined events.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_extreme_event.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add the Worst or Best Observation for Each By Group as New Variables — derive_vars_extreme_event","text":"","code":"derive_vars_extreme_event(   dataset,   by_vars,   events,   tmp_event_nr_var = NULL,   order,   mode,   source_datasets = NULL,   check_type = \"warning\",   new_vars )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_extreme_event.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add the Worst or Best Observation for Each By Group as New Variables — derive_vars_extreme_event","text":"dataset Input dataset variables specified by_vars order arguments expected dataset. by_vars Grouping variables Default: NULL Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) events Conditions new values defining events list event() event_joined() objects expected. observations listed events considered deriving extreme event. multiple records meet filter condition, take first record sorted order. data grouped by_vars, .e., summary functions like () () can used condition. event_joined() events observations selected calling filter_joined(). condition field passed filter_join argument. tmp_event_nr_var Temporary event number variable specified variable added source datasets set number event selecting records event. can used order determine record used records one event selected. variable included output dataset. order Sort order particular event events one observation, within event group, records ordered specified order. handling NAs sorting variables see Sort Order. Permitted Values: list expressions created exprs(), e.g., exprs(ADT, desc(AVAL)) mode Selection mode (first last) particular event events one observation, \"first\"/\"last\" used select first/last record type event sorting order. Permitted Values: \"first\", \"last\" source_datasets Source datasets named list datasets expected. dataset_name field event() event_joined() refers dataset provided list. check_type Check uniqueness? \"warning\" \"error\" specified, specified message issued observations input dataset unique respect variables order. Default: \"warning\" Permitted Values: \"none\", \"warning\", \"error\" new_vars Variables add specified variables events added output dataset. Variables can renamed naming element, .e., new_vars = exprs(<new name> = <old name>).","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_extreme_event.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add the Worst or Best Observation for Each By Group as New Variables — derive_vars_extreme_event","text":"input dataset best worst observation group added new variables.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_extreme_event.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Add the Worst or Best Observation for Each By Group as New Variables — derive_vars_extreme_event","text":"event select observations consider: event class event, observations source dataset restricted condition first last (mode) observation per group (by_vars) selected. event class event_joined, filter_joined() called select observations. variables specified set_values_to field event added selected observations. variable specified tmp_event_nr_var added set number event. selected observations bound together. group (respect variables specified by_vars parameter) first last observation (respect order specified order parameter mode specified mode parameter) selected. variables specified new_vars parameter added selected observations. variables added input dataset.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_extreme_event.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add the Worst or Best Observation for Each By Group as New Variables — derive_vars_extreme_event","text":"","code":"library(tibble) library(dplyr) library(lubridate)  adsl <- tribble(   ~STUDYID, ~USUBJID, ~TRTEDT, ~DTHDT,   \"PILOT01\", \"01-1130\", ymd(\"2014-08-16\"), ymd(\"2014-09-13\"),   \"PILOT01\", \"01-1133\", ymd(\"2013-04-28\"), ymd(\"\"),   \"PILOT01\", \"01-1211\", ymd(\"2013-01-12\"), ymd(\"\"),   \"PILOT01\", \"09-1081\", ymd(\"2014-04-27\"), ymd(\"\"),   \"PILOT01\", \"09-1088\", ymd(\"2014-10-09\"), ymd(\"2014-11-01\"), )  lb <- tribble(   ~STUDYID,  ~DOMAIN,  ~USUBJID, ~LBSEQ,             ~LBDTC,   \"PILOT01\",    \"LB\", \"01-1130\",    219, \"2014-06-07T13:20\",   \"PILOT01\",    \"LB\", \"01-1130\",    322, \"2014-08-16T13:10\",   \"PILOT01\",    \"LB\", \"01-1133\",    268, \"2013-04-18T15:30\",   \"PILOT01\",    \"LB\", \"01-1133\",    304, \"2013-05-01T10:13\",   \"PILOT01\",    \"LB\", \"01-1211\",      8, \"2012-10-30T14:26\",   \"PILOT01\",    \"LB\", \"01-1211\",    162, \"2013-01-08T12:13\",   \"PILOT01\",    \"LB\", \"09-1081\",     47, \"2014-02-01T10:55\",   \"PILOT01\",    \"LB\", \"09-1081\",    219, \"2014-05-10T11:15\",   \"PILOT01\",    \"LB\", \"09-1088\",    283, \"2014-09-27T12:13\",   \"PILOT01\",    \"LB\", \"09-1088\",    322, \"2014-10-09T13:25\" ) %>%   mutate(     ADT = convert_dtc_to_dt(LBDTC)   )  derive_vars_extreme_event(   adsl,   by_vars = exprs(STUDYID, USUBJID),   events = list(     event(       dataset_name = \"adsl\",       condition = !is.na(DTHDT),       set_values_to = exprs(LSTALVDT = DTHDT, DTHFL = \"Y\")     ),     event(       dataset_name = \"lb\",       condition = !is.na(ADT),       order = exprs(ADT),       mode = \"last\",       set_values_to = exprs(LSTALVDT = ADT, DTHFL = \"N\")     ),     event(       dataset_name = \"adsl\",       condition = !is.na(TRTEDT),       order = exprs(TRTEDT),       mode = \"last\",       set_values_to = exprs(LSTALVDT = TRTEDT, DTHFL = \"N\")     )   ),   source_datasets = list(adsl = adsl, lb = lb),   tmp_event_nr_var = event_nr,   order = exprs(LSTALVDT, event_nr),   mode = \"last\",   new_vars = exprs(LSTALVDT, DTHFL) ) #> # A tibble: 5 × 6 #>   STUDYID USUBJID TRTEDT     DTHDT      LSTALVDT   DTHFL #>   <chr>   <chr>   <date>     <date>     <date>     <chr> #> 1 PILOT01 01-1130 2014-08-16 2014-09-13 2014-09-13 Y     #> 2 PILOT01 01-1133 2013-04-28 NA         2013-05-01 N     #> 3 PILOT01 01-1211 2013-01-12 NA         2013-01-12 N     #> 4 PILOT01 09-1081 2014-04-27 NA         2014-05-10 N     #> 5 PILOT01 09-1088 2014-10-09 2014-11-01 2014-11-01 Y      # Derive DTHCAUS from AE and DS domain data adsl <- tribble(   ~STUDYID,  ~USUBJID,   \"STUDY01\", \"PAT01\",   \"STUDY01\", \"PAT02\",   \"STUDY01\", \"PAT03\" ) ae <- tribble(   ~STUDYID, ~USUBJID, ~AESEQ, ~AEDECOD, ~AEOUT, ~AEDTHDTC,   \"STUDY01\", \"PAT01\", 12, \"SUDDEN DEATH\", \"FATAL\", \"2021-04-04\",   \"STUDY01\", \"PAT01\", 13, \"CARDIAC ARREST\", \"FATAL\", \"2021-04-03\", )  ds <- tribble(   ~STUDYID, ~USUBJID, ~DSSEQ, ~DSDECOD, ~DSTERM, ~DSSTDTC,   \"STUDY01\", \"PAT02\", 1, \"INFORMED CONSENT OBTAINED\", \"INFORMED CONSENT OBTAINED\", \"2021-04-03\",   \"STUDY01\", \"PAT02\", 2, \"RANDOMIZATION\", \"RANDOMIZATION\", \"2021-04-11\",   \"STUDY01\", \"PAT02\", 3, \"DEATH\", \"DEATH DUE TO PROGRESSION OF DISEASE\", \"2022-02-01\",   \"STUDY01\", \"PAT03\", 1, \"DEATH\", \"POST STUDY REPORTING OF DEATH\", \"2022-03-03\" )  derive_vars_extreme_event(   adsl,   by_vars = exprs(STUDYID, USUBJID),   events = list(     event(       dataset_name = \"ae\",       condition = AEOUT == \"FATAL\",       set_values_to = exprs(DTHCAUS = AEDECOD, DTHDT = convert_dtc_to_dt(AEDTHDTC)),       order = exprs(DTHDT)     ),     event(       dataset_name = \"ds\",       condition = DSDECOD == \"DEATH\" & grepl(\"DEATH DUE TO\", DSTERM),       set_values_to = exprs(DTHCAUS = DSTERM, DTHDT = convert_dtc_to_dt(DSSTDTC)),       order = exprs(DTHDT)     )   ),   source_datasets = list(ae = ae, ds = ds),   tmp_event_nr_var = event_nr,   order = exprs(DTHDT, event_nr),   mode = \"first\",   new_vars = exprs(DTHCAUS, DTHDT) ) #> # A tibble: 3 × 4 #>   STUDYID USUBJID DTHCAUS                             DTHDT      #>   <chr>   <chr>   <chr>                               <date>     #> 1 STUDY01 PAT01   CARDIAC ARREST                      2021-04-03 #> 2 STUDY01 PAT02   DEATH DUE TO PROGRESSION OF DISEASE 2022-02-01 #> 3 STUDY01 PAT03   NA                                  NA"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_joined.html","id":null,"dir":"Reference","previous_headings":"","what":"Add Variables from an Additional Dataset Based on Conditions from Both Datasets — derive_vars_joined","title":"Add Variables from an Additional Dataset Based on Conditions from Both Datasets — derive_vars_joined","text":"function adds variables additional dataset input dataset. selection observations additional dataset can depend variables datasets. example, add lowest value (nadir) current observation.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_joined.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add Variables from an Additional Dataset Based on Conditions from Both Datasets — derive_vars_joined","text":"","code":"derive_vars_joined(   dataset,   dataset_add,   by_vars = NULL,   order = NULL,   new_vars = NULL,   tmp_obs_nr_var = NULL,   join_vars = NULL,   join_type,   filter_add = NULL,   first_cond_lower = NULL,   first_cond_upper = NULL,   filter_join = NULL,   mode = NULL,   exist_flag = NULL,   true_value = \"Y\",   false_value = NA_character_,   missing_values = NULL,   check_type = \"warning\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_joined.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add Variables from an Additional Dataset Based on Conditions from Both Datasets — derive_vars_joined","text":"dataset Input dataset variables specified by_vars argument expected dataset. dataset_add Additional dataset variables specified by_vars, new_vars, join_vars, order argument expected. by_vars Grouping variables two datasets joined specified variables. Variables can renamed naming element, .e. by_vars = exprs(<name input dataset> = <name additional dataset>), similar dplyr joins. Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) order Sort order argument set non-null value, observation input dataset first last observation joined dataset selected respect specified order. specified variables expected additional dataset (dataset_add). variable available dataset dataset_add, one dataset_add used sorting. expression named, e.g., exprs(EXSTDT = convert_dtc_to_dt(EXSTDTC), EXSEQ), corresponding variable (EXSTDT) added additional dataset can used filter conditions (filter_add, filter_join) join_vars new_vars. variable included output dataset. handling NAs sorting variables see Sort Order. Permitted Values: list expressions created exprs(), e.g., exprs(ADT, desc(AVAL)) NULL new_vars Variables add specified variables additional dataset added output dataset. Variables can renamed naming element, .e., new_vars = exprs(<new name> = <old name>). example new_vars = exprs(var1, var2) adds variables var1 var2 dataset_add input dataset. new_vars = exprs(var1, new_var2 = old_var2) takes var1 old_var2 dataset_add adds input dataset renaming old_var2 new_var2. Values added variables can modified specifying expression. example, new_vars = LASTRSP = exprs(str_to_upper(AVALC)) adds variable LASTRSP dataset sets upper case value AVALC. argument specified set NULL, variables additional dataset (dataset_add) added. Permitted Values: list variables named expressions created exprs() tmp_obs_nr_var Temporary observation number specified variable added input dataset (dataset) additional dataset (dataset_add). set observation number respect order. group (by_vars) observation number starts 1. variable can used conditions (filter_join, first_cond_upper, first_cond_lower). can also used select consecutive observations last observation. variable included output dataset. include specify new_vars. join_vars Variables use additional dataset extra variables required additional dataset filter_join specified argument. Variables specified new_vars need repeated join_vars. specified variable exists input dataset additional dataset, suffix \".join\" added variable additional dataset. expression named, e.g., exprs(EXTDT = convert_dtc_to_dt(EXSTDTC)), corresponding variable added additional dataset can used filter conditions (filter_add, filter_join) new_vars. variable included output dataset. variables included output dataset. Permitted Values: list variables named expressions created exprs() join_type Observations keep joining argument determines joined observations kept respect original observation. example, join_type = \"\" specified observations original observations kept. example confirmed response BOR oncology setting confirmed deterioration questionnaires confirmatory assessment must assessment. Thus join_type = \"\" used. Whereas, sometimes might allow confirmatory observations occur prior observation. example, identify AEs occurring seven days COVID AE. Thus join_type = \"\" used. Permitted Values: \"\", \"\", \"\" filter_add Filter additional dataset (dataset_add) observations dataset_add fulfilling specified condition joined input dataset. argument specified, observations joined. Variables created order new_vars arguments can used condition. condition can include summary functions like () (). additional dataset grouped variables (by_vars). Permitted Values: condition first_cond_lower Condition selecting range data () argument specified, observations restricted first observation current observation specified condition fulfilled current observation. condition fulfilled observations, observations considered. argument specified filter_join contains summary functions apply observations certain observation current observation current observation. example see last example . first_cond_upper Condition selecting range data () argument specified, observations restricted first observation specified condition fulfilled. condition fulfilled observations, observations considered. argument specified filter_join contains summary functions apply observations confirmation assessment. example see last example . filter_join Filter joined dataset specified condition applied joined dataset. Therefore variables datasets dataset dataset_add can used. Variables created order new_vars arguments can used condition. condition can include summary functions like () (). joined dataset grouped original observations. Permitted Values: condition mode Selection mode Determines first last observation selected. order argument specified, mode must non-null. order argument specified, mode argument ignored. Permitted Values: \"first\", \"last\", NULL exist_flag Exist flag argument specified (e.g., exist_flag = FLAG), specified variable (e.g., FLAG) added input dataset. variable value provided true_value selected records dataset_add merged input dataset, value provided false_value otherwise. Permitted Values: Variable name true_value True value value specified variable exist_flag, applicable first last observation (depending mode) group. Permitted Values: atomic scalar false_value False value value specified variable exist_flag, applicable first last observation (depending mode) group. Permitted Values: atomic scalar missing_values Values non-matching observations observations input dataset (dataset) matching observation additional dataset (dataset_add) values specified variables set specified value. variables specified new_vars can specified missing_values. Permitted Values: named list expressions, e.g., exprs(BASEC = \"MISSING\", BASE = -1) check_type Check uniqueness? \"warning\" \"error\" specified, specified message issued observations (restricted) joined dataset unique respect variables order. argument ignored order specified. case error issued independent check_type restricted joined dataset contains one observation observations input dataset. Permitted Values: \"none\", \"warning\", \"error\"","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_joined.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add Variables from an Additional Dataset Based on Conditions from Both Datasets — derive_vars_joined","text":"output dataset contains observations variables input dataset additionally variables specified new_vars additional dataset (dataset_add).","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_joined.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Add Variables from an Additional Dataset Based on Conditions from Both Datasets — derive_vars_joined","text":"variables specified order added additional dataset (dataset_add). variables specified join_vars added additional dataset (dataset_add). records additional dataset (dataset_add) restricted matching filter_add condition. input dataset (restricted) additional dataset left joined grouping variables (by_vars). grouping variables specified, full join performed. first_cond_lower specified, observation input dataset joined dataset restricted observations first observation first_cond_lower fulfilled (observation fulfilling condition included) observation input dataset. observation input dataset condition fulfilled, observation removed. first_cond_upper specified, observation input dataset joined dataset restricted observations first observation first_cond_upper fulfilled (observation fulfilling condition included). observation input dataset condition fulfilled, observation removed. example see last example \"Examples\" section. joined dataset restricted filter_join condition. order specified, observation input dataset first last observation (depending mode) selected. variables specified new_vars created (requested) merged input dataset. .e., output dataset contains observations input dataset. observations without matching observation joined dataset new variables set specified missing_values (NA variables missing_values). Observations additional dataset matching observation input dataset ignored.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_joined.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add Variables from an Additional Dataset Based on Conditions from Both Datasets — derive_vars_joined","text":"","code":"library(tibble) library(lubridate) library(dplyr, warn.conflicts = FALSE) library(tidyr)  # Add AVISIT (based on time windows), AWLO, and AWHI adbds <- tribble(   ~USUBJID, ~ADY,   \"1\",       -33,   \"1\",        -2,   \"1\",         3,   \"1\",        24,   \"2\",        NA, )  windows <- tribble(   ~AVISIT,    ~AWLO, ~AWHI,   \"BASELINE\",   -30,     1,   \"WEEK 1\",       2,     7,   \"WEEK 2\",       8,    15,   \"WEEK 3\",      16,    22,   \"WEEK 4\",      23,    30 )  derive_vars_joined(   adbds,   dataset_add = windows,   join_type = \"all\",   filter_join = AWLO <= ADY & ADY <= AWHI ) #> # A tibble: 5 × 5 #>   USUBJID   ADY AVISIT    AWLO  AWHI #>   <chr>   <dbl> <chr>    <dbl> <dbl> #> 1 1         -33 NA          NA    NA #> 2 1          -2 BASELINE   -30     1 #> 3 1           3 WEEK 1       2     7 #> 4 1          24 WEEK 4      23    30 #> 5 2          NA NA          NA    NA  # derive the nadir after baseline and before the current observation adbds <- tribble(   ~USUBJID, ~ADY, ~AVAL,   \"1\",        -7,    10,   \"1\",         1,    12,   \"1\",         8,    11,   \"1\",        15,     9,   \"1\",        20,    14,   \"1\",        24,    12,   \"2\",        13,     8 )  derive_vars_joined(   adbds,   dataset_add = adbds,   by_vars = exprs(USUBJID),   order = exprs(AVAL),   new_vars = exprs(NADIR = AVAL),   join_vars = exprs(ADY),   join_type = \"all\",   filter_add = ADY > 0,   filter_join = ADY.join < ADY,   mode = \"first\",   check_type = \"none\" ) #> # A tibble: 7 × 4 #>   USUBJID   ADY  AVAL NADIR #>   <chr>   <dbl> <dbl> <dbl> #> 1 1          -7    10    NA #> 2 1           1    12    NA #> 3 1           8    11    12 #> 4 1          15     9    11 #> 5 1          20    14     9 #> 6 1          24    12     9 #> 7 2          13     8    NA  # add highest hemoglobin value within two weeks before AE, # take earliest if more than one adae <- tribble(   ~USUBJID, ~ASTDY,   \"1\",           3,   \"1\",          22,   \"2\",           2 )  adlb <- tribble(   ~USUBJID, ~PARAMCD, ~ADY, ~AVAL,   \"1\",      \"HGB\",       1,   8.5,   \"1\",      \"HGB\",       3,   7.9,   \"1\",      \"HGB\",       5,   8.9,   \"1\",      \"HGB\",       8,   8.0,   \"1\",      \"HGB\",       9,   8.0,   \"1\",      \"HGB\",      16,   7.4,   \"1\",      \"HGB\",      24,   8.1,   \"1\",      \"ALB\",       1,    42, )  derive_vars_joined(   adae,   dataset_add = adlb,   by_vars = exprs(USUBJID),   order = exprs(AVAL, desc(ADY)),   new_vars = exprs(HGB_MAX = AVAL, HGB_DY = ADY),   join_type = \"all\",   filter_add = PARAMCD == \"HGB\",   filter_join = ASTDY - 14 <= ADY & ADY <= ASTDY,   mode = \"last\" ) #> # A tibble: 3 × 4 #>   USUBJID ASTDY HGB_MAX HGB_DY #>   <chr>   <dbl>   <dbl>  <dbl> #> 1 1           3     8.5      1 #> 2 1          22     8        8 #> 3 2           2    NA       NA  # Add APERIOD, APERIODC based on ADSL adsl <- tribble(   ~USUBJID, ~AP01SDT,     ~AP01EDT,     ~AP02SDT,     ~AP02EDT,   \"1\",      \"2021-01-04\", \"2021-02-06\", \"2021-02-07\", \"2021-03-07\",   \"2\",      \"2021-02-02\", \"2021-03-02\", \"2021-03-03\", \"2021-04-01\" ) %>%   mutate(across(ends_with(\"DT\"), ymd)) %>%   mutate(STUDYID = \"xyz\")  period_ref <- create_period_dataset(   adsl,   new_vars = exprs(APERSDT = APxxSDT, APEREDT = APxxEDT) )  period_ref #> # A tibble: 4 × 5 #>   STUDYID USUBJID APERIOD APERSDT    APEREDT    #>   <chr>   <chr>     <int> <date>     <date>     #> 1 xyz     1             1 2021-01-04 2021-02-06 #> 2 xyz     1             2 2021-02-07 2021-03-07 #> 3 xyz     2             1 2021-02-02 2021-03-02 #> 4 xyz     2             2 2021-03-03 2021-04-01  adae <- tribble(   ~USUBJID, ~ASTDT,   \"1\",      \"2021-01-01\",   \"1\",      \"2021-01-05\",   \"1\",      \"2021-02-05\",   \"1\",      \"2021-03-05\",   \"1\",      \"2021-04-05\",   \"2\",      \"2021-02-15\", ) %>%   mutate(     ASTDT = ymd(ASTDT),     STUDYID = \"xyz\"   )  derive_vars_joined(   adae,   dataset_add = period_ref,   by_vars = exprs(STUDYID, USUBJID),   join_vars = exprs(APERSDT, APEREDT),   join_type = \"all\",   filter_join = APERSDT <= ASTDT & ASTDT <= APEREDT ) #> # A tibble: 6 × 6 #>   USUBJID ASTDT      STUDYID APERIOD APERSDT    APEREDT    #>   <chr>   <date>     <chr>     <int> <date>     <date>     #> 1 1       2021-01-01 xyz          NA NA         NA         #> 2 1       2021-01-05 xyz           1 2021-01-04 2021-02-06 #> 3 1       2021-02-05 xyz           1 2021-01-04 2021-02-06 #> 4 1       2021-03-05 xyz           2 2021-02-07 2021-03-07 #> 5 1       2021-04-05 xyz          NA NA         NA         #> 6 2       2021-02-15 xyz           1 2021-02-02 2021-03-02  # Add day since last dose (LDRELD) adae <- tribble(   ~USUBJID, ~ASTDT,       ~AESEQ,   \"1\",      \"2020-02-02\",      1,   \"1\",      \"2020-02-04\",      2 ) %>%   mutate(ASTDT = ymd(ASTDT))  ex <- tribble(   ~USUBJID, ~EXSDTC,   \"1\",      \"2020-01-10\",   \"1\",      \"2020-01\",   \"1\",      \"2020-01-20\",   \"1\",      \"2020-02-03\" )  ## Please note that EXSDT is created via the order argument and then used ## for new_vars, filter_add, and filter_join derive_vars_joined(   adae,   dataset_add = ex,   by_vars = exprs(USUBJID),   order = exprs(EXSDT = convert_dtc_to_dt(EXSDTC)),   join_type = \"all\",   new_vars = exprs(LDRELD = compute_duration(     start_date = EXSDT, end_date = ASTDT   )),   filter_add = !is.na(EXSDT),   filter_join = EXSDT <= ASTDT,   mode = \"last\" ) #> # A tibble: 2 × 4 #>   USUBJID ASTDT      AESEQ LDRELD #>   <chr>   <date>     <dbl>  <dbl> #> 1 1       2020-02-02     1     14 #> 2 1       2020-02-04     2      2  # first_cond_lower and first_cond_upper argument myd <- tribble(   ~subj, ~day, ~val,   \"1\",      1, \"++\",   \"1\",      2, \"-\",   \"1\",      3, \"0\",   \"1\",      4, \"+\",   \"1\",      5, \"++\",   \"1\",      6, \"-\",   \"2\",      1, \"-\",   \"2\",      2, \"++\",   \"2\",      3, \"+\",   \"2\",      4, \"0\",   \"2\",      5, \"-\",   \"2\",      6, \"++\" )  # derive last \"++\" day before \"0\" where all results in between are \"+\" or \"++\" derive_vars_joined(   myd,   dataset_add = myd,   by_vars = exprs(subj),   order = exprs(day),   mode = \"first\",   new_vars = exprs(prev_plus_day = day),   join_vars = exprs(val),   join_type = \"before\",   first_cond_lower = val.join == \"++\",   filter_join = val == \"0\" & all(val.join %in% c(\"+\", \"++\")) ) #> # A tibble: 12 × 4 #>    subj    day val   prev_plus_day #>    <chr> <dbl> <chr>         <dbl> #>  1 1         1 ++               NA #>  2 1         2 -                NA #>  3 1         3 0                NA #>  4 1         4 +                NA #>  5 1         5 ++               NA #>  6 1         6 -                NA #>  7 2         1 -                NA #>  8 2         2 ++               NA #>  9 2         3 +                NA #> 10 2         4 0                 2 #> 11 2         5 -                NA #> 12 2         6 ++               NA  # derive first \"++\" day after \"0\" where all results in between are \"+\" or \"++\" derive_vars_joined(   myd,   dataset_add = myd,   by_vars = exprs(subj),   order = exprs(day),   mode = \"last\",   new_vars = exprs(next_plus_day = day),   join_vars = exprs(val),   join_type = \"after\",   first_cond_upper = val.join == \"++\",   filter_join = val == \"0\" & all(val.join %in% c(\"+\", \"++\")) ) #> # A tibble: 12 × 4 #>    subj    day val   next_plus_day #>    <chr> <dbl> <chr>         <dbl> #>  1 1         1 ++               NA #>  2 1         2 -                NA #>  3 1         3 0                 5 #>  4 1         4 +                NA #>  5 1         5 ++               NA #>  6 1         6 -                NA #>  7 2         1 -                NA #>  8 2         2 ++               NA #>  9 2         3 +                NA #> 10 2         4 0                NA #> 11 2         5 -                NA #> 12 2         6 ++               NA"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_merged.html","id":null,"dir":"Reference","previous_headings":"","what":"Add New Variable(s) to the Input Dataset Based on Variables from Another Dataset — derive_vars_merged","title":"Add New Variable(s) to the Input Dataset Based on Variables from Another Dataset — derive_vars_merged","text":"Add new variable(s) input dataset based variables another dataset. observations merge can selected condition (filter_add argument) /selecting first last observation group (order mode argument).","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_merged.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add New Variable(s) to the Input Dataset Based on Variables from Another Dataset — derive_vars_merged","text":"","code":"derive_vars_merged(   dataset,   dataset_add,   by_vars,   order = NULL,   new_vars = NULL,   filter_add = NULL,   mode = NULL,   exist_flag = NULL,   true_value = \"Y\",   false_value = NA_character_,   missing_values = NULL,   check_type = \"warning\",   duplicate_msg = NULL,   relationship = NULL )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_merged.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add New Variable(s) to the Input Dataset Based on Variables from Another Dataset — derive_vars_merged","text":"dataset Input dataset variables specified by_vars argument expected dataset. dataset_add Additional dataset variables specified by_vars, new_vars, order argument expected. by_vars Grouping variables input dataset selected observations additional dataset merged specified variables. Variables can renamed naming element, .e. by_vars = exprs(<name input dataset> = <name additional dataset>), similar dplyr joins. Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) order Sort order argument set non-null value, group first last observation additional dataset selected respect specified order. Variables defined new_vars argument can used sort order. handling NAs sorting variables see Sort Order. Permitted Values: list expressions created exprs(), e.g., exprs(ADT, desc(AVAL)) NULL new_vars Variables add specified variables additional dataset added output dataset. Variables can renamed naming element, .e., new_vars = exprs(<new name> = <old name>). example new_vars = exprs(var1, var2) adds variables var1 var2 dataset_add input dataset. new_vars = exprs(var1, new_var2 = old_var2) takes var1 old_var2 dataset_add adds input dataset renaming old_var2 new_var2. Values added variables can modified specifying expression. example, new_vars = LASTRSP = exprs(str_to_upper(AVALC)) adds variable LASTRSP dataset sets upper case value AVALC. argument specified set NULL, variables additional dataset (dataset_add) added. Permitted Values: list variables named expressions created exprs() filter_add Filter additional dataset (dataset_add) observations fulfilling specified condition taken account merging. argument specified, observations considered. Variables defined new_vars argument can used filter condition. Permitted Values: condition mode Selection mode Determines first last observation selected. order argument specified, mode must non-null. order argument specified, mode argument ignored. Permitted Values: \"first\", \"last\", NULL exist_flag Exist flag argument specified (e.g., exist_flag = FLAG), specified variable (e.g., FLAG) added input dataset. variable value provided true_value selected records dataset_add merged input dataset, value provided false_value otherwise. Permitted Values: Variable name true_value True value value specified variable exist_flag, applicable first last observation (depending mode) group. Permitted Values: atomic scalar false_value False value value specified variable exist_flag, applicable first last observation (depending mode) group. Permitted Values: atomic scalar missing_values Values non-matching observations observations input dataset (dataset) matching observation additional dataset (dataset_add) values specified variables set specified value. variables specified new_vars can specified missing_values. Permitted Values: named list expressions, e.g., exprs(BASEC = \"MISSING\", BASE = -1) check_type Check uniqueness? \"warning\" \"error\" specified, specified message issued observations (restricted) additional dataset unique respect variables order. order argument specified, check_type argument ignored: observations (restricted) additional dataset unique respect variables, error issued. Permitted Values: \"none\", \"warning\", \"error\" duplicate_msg Message unique check uniqueness check fails, specified message displayed. Default:   relationship Expected merge-relationship by_vars variable(s) dataset (input dataset) dataset_add (additional dataset) containing additional new_vars. argument passed dplyr::left_join() function. See https://dplyr.tidyverse.org/reference/mutate-joins.html#arguments details. Permitted Values: \"one--one\", \"many--one\", NULL.","code":"paste(   \"Dataset {.arg dataset_add} contains duplicate records with respect to\",   \"{.var {vars2chr(by_vars)}}.\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_merged.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add New Variable(s) to the Input Dataset Based on Variables from Another Dataset — derive_vars_merged","text":"output dataset contains observations variables input dataset additionally variables specified new_vars additional dataset (dataset_add).","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_merged.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Add New Variable(s) to the Input Dataset Based on Variables from Another Dataset — derive_vars_merged","text":"new variables (new_vars) added additional dataset (dataset_add). records additional dataset (dataset_add) restricted matching filter_add condition. order specified, group first last observation (depending mode) selected. variables specified new_vars merged input dataset using left_join(). .e., output dataset contains observations input dataset. observations without matching observation additional dataset new variables set specified missing_values (NA variables missing_values). Observations additional dataset matching observation input dataset ignored.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_merged.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add New Variable(s) to the Input Dataset Based on Variables from Another Dataset — derive_vars_merged","text":"","code":"library(dplyr, warn.conflicts = FALSE) vs <- tribble(   ~STUDYID,  ~DOMAIN,  ~USUBJID, ~VSTESTCD,      ~VISIT, ~VSSTRESN, ~VSSTRESU,       ~VSDTC,   \"PILOT01\",    \"VS\", \"01-1302\",  \"HEIGHT\", \"SCREENING\",     177.8,      \"cm\", \"2013-08-20\",   \"PILOT01\",    \"VS\", \"01-1302\",  \"WEIGHT\", \"SCREENING\",     81.19,      \"kg\", \"2013-08-20\",   \"PILOT01\",    \"VS\", \"01-1302\",  \"WEIGHT\",  \"BASELINE\",      82.1,      \"kg\", \"2013-08-29\",   \"PILOT01\",    \"VS\", \"01-1302\",  \"WEIGHT\",    \"WEEK 2\",     81.19,      \"kg\", \"2013-09-15\",   \"PILOT01\",    \"VS\", \"01-1302\",  \"WEIGHT\",    \"WEEK 4\",     82.56,      \"kg\", \"2013-09-24\",   \"PILOT01\",    \"VS\", \"01-1302\",  \"WEIGHT\",    \"WEEK 6\",     80.74,      \"kg\", \"2013-10-08\",   \"PILOT01\",    \"VS\", \"01-1302\",  \"WEIGHT\",    \"WEEK 8\",      82.1,      \"kg\", \"2013-10-22\",   \"PILOT01\",    \"VS\", \"01-1302\",  \"WEIGHT\",   \"WEEK 12\",      82.1,      \"kg\", \"2013-11-05\",   \"PILOT01\",    \"VS\", \"17-1344\",  \"HEIGHT\", \"SCREENING\",     163.5,      \"cm\", \"2014-01-01\",   \"PILOT01\",    \"VS\", \"17-1344\",  \"WEIGHT\", \"SCREENING\",     58.06,      \"kg\", \"2014-01-01\",   \"PILOT01\",    \"VS\", \"17-1344\",  \"WEIGHT\",  \"BASELINE\",     58.06,      \"kg\", \"2014-01-11\",   \"PILOT01\",    \"VS\", \"17-1344\",  \"WEIGHT\",    \"WEEK 2\",     58.97,      \"kg\", \"2014-01-24\",   \"PILOT01\",    \"VS\", \"17-1344\",  \"WEIGHT\",    \"WEEK 4\",     57.97,      \"kg\", \"2014-02-07\",   \"PILOT01\",    \"VS\", \"17-1344\",  \"WEIGHT\",    \"WEEK 6\",     58.97,      \"kg\", \"2014-02-19\",   \"PILOT01\",    \"VS\", \"17-1344\",  \"WEIGHT\",    \"WEEK 8\",     57.79,      \"kg\", \"2014-03-14\" )  dm <- tribble(   ~STUDYID,  ~DOMAIN,  ~USUBJID, ~AGE,   ~AGEU,   \"PILOT01\",    \"DM\", \"01-1302\",   61, \"YEARS\",   \"PILOT01\",    \"DM\", \"17-1344\",   64, \"YEARS\" )   # Merging all dm variables to vs derive_vars_merged(   vs,   dataset_add = select(dm, -DOMAIN),   by_vars = exprs(STUDYID, USUBJID) ) %>%   select(STUDYID, USUBJID, VSTESTCD, VISIT, VSSTRESN, AGE, AGEU) #> # A tibble: 15 × 7 #>    STUDYID USUBJID VSTESTCD VISIT     VSSTRESN   AGE AGEU  #>    <chr>   <chr>   <chr>    <chr>        <dbl> <dbl> <chr> #>  1 PILOT01 01-1302 HEIGHT   SCREENING    178.     61 YEARS #>  2 PILOT01 01-1302 WEIGHT   SCREENING     81.2    61 YEARS #>  3 PILOT01 01-1302 WEIGHT   BASELINE      82.1    61 YEARS #>  4 PILOT01 01-1302 WEIGHT   WEEK 2        81.2    61 YEARS #>  5 PILOT01 01-1302 WEIGHT   WEEK 4        82.6    61 YEARS #>  6 PILOT01 01-1302 WEIGHT   WEEK 6        80.7    61 YEARS #>  7 PILOT01 01-1302 WEIGHT   WEEK 8        82.1    61 YEARS #>  8 PILOT01 01-1302 WEIGHT   WEEK 12       82.1    61 YEARS #>  9 PILOT01 17-1344 HEIGHT   SCREENING    164.     64 YEARS #> 10 PILOT01 17-1344 WEIGHT   SCREENING     58.1    64 YEARS #> 11 PILOT01 17-1344 WEIGHT   BASELINE      58.1    64 YEARS #> 12 PILOT01 17-1344 WEIGHT   WEEK 2        59.0    64 YEARS #> 13 PILOT01 17-1344 WEIGHT   WEEK 4        58.0    64 YEARS #> 14 PILOT01 17-1344 WEIGHT   WEEK 6        59.0    64 YEARS #> 15 PILOT01 17-1344 WEIGHT   WEEK 8        57.8    64 YEARS   # Merge last weight to adsl adsl <- tribble(   ~STUDYID,   ~USUBJID, ~AGE,   ~AGEU,   \"PILOT01\", \"01-1302\",   61, \"YEARS\",   \"PILOT01\", \"17-1344\",   64, \"YEARS\" )   derive_vars_merged(   adsl,   dataset_add = vs,   by_vars = exprs(STUDYID, USUBJID),   order = exprs(convert_dtc_to_dtm(VSDTC)),   mode = \"last\",   new_vars = exprs(LASTWGT = VSSTRESN, LASTWGTU = VSSTRESU),   filter_add = VSTESTCD == \"WEIGHT\",   exist_flag = vsdatafl ) %>%   select(STUDYID, USUBJID, AGE, AGEU, LASTWGT, LASTWGTU, vsdatafl) #> # A tibble: 2 × 7 #>   STUDYID USUBJID   AGE AGEU  LASTWGT LASTWGTU vsdatafl #>   <chr>   <chr>   <dbl> <chr>   <dbl> <chr>    <chr>    #> 1 PILOT01 01-1302    61 YEARS    82.1 kg       Y        #> 2 PILOT01 17-1344    64 YEARS    57.8 kg       Y          # Derive treatment start datetime (TRTSDTM) ex <- tribble(   ~STUDYID,  ~DOMAIN,  ~USUBJID, ~EXSTDY, ~EXENDY,     ~EXSTDTC,     ~EXENDTC,   \"PILOT01\",    \"EX\", \"01-1302\",       1,      18, \"2013-08-29\", \"2013-09-15\",   \"PILOT01\",    \"EX\", \"01-1302\",      19,      69, \"2013-09-16\", \"2013-11-05\",   \"PILOT01\",    \"EX\", \"17-1344\",       1,      14, \"2014-01-11\", \"2014-01-24\",   \"PILOT01\",    \"EX\", \"17-1344\",      15,      63, \"2014-01-25\", \"2014-03-14\" ) ## Impute exposure start date to first date/time ex_ext <- derive_vars_dtm(   ex,   dtc = EXSTDTC,   new_vars_prefix = \"EXST\",   highest_imputation = \"M\", ) ## Add first exposure datetime and imputation flags to adsl derive_vars_merged(   select(dm, STUDYID, USUBJID),   dataset_add = ex_ext,   by_vars = exprs(STUDYID, USUBJID),   new_vars = exprs(TRTSDTM = EXSTDTM, TRTSDTF = EXSTDTF, TRTSTMF = EXSTTMF),   order = exprs(EXSTDTM),   mode = \"first\" ) #> # A tibble: 2 × 5 #>   STUDYID USUBJID TRTSDTM             TRTSDTF TRTSTMF #>   <chr>   <chr>   <dttm>              <chr>   <chr>   #> 1 PILOT01 01-1302 2013-08-29 00:00:00 NA      H       #> 2 PILOT01 17-1344 2014-01-11 00:00:00 NA      H        # Derive treatment end datetime (TRTEDTM) ## Impute exposure end datetime to last time, no date imputation ex_ext <- derive_vars_dtm(   ex,   dtc = EXENDTC,   new_vars_prefix = \"EXEN\",   time_imputation = \"last\", ) ## Add last exposure datetime and imputation flag to adsl derive_vars_merged(   select(adsl, STUDYID, USUBJID),   dataset_add = ex_ext,   filter_add = !is.na(EXENDTM),   by_vars = exprs(STUDYID, USUBJID),   new_vars = exprs(TRTEDTM = EXENDTM, TRTETMF = EXENTMF),   order = exprs(EXENDTM),   mode = \"last\" ) #> # A tibble: 2 × 4 #>   STUDYID USUBJID TRTEDTM             TRTETMF #>   <chr>   <chr>   <dttm>              <chr>   #> 1 PILOT01 01-1302 2013-11-05 23:59:59 H       #> 2 PILOT01 17-1344 2014-03-14 23:59:59 H       # Modify merged values and set value for non matching observations adsl <- tribble(   ~USUBJID, ~SEX, ~COUNTRY,   \"ST42-1\", \"F\",  \"AUT\",   \"ST42-2\", \"M\",  \"MWI\",   \"ST42-3\", \"M\",  \"NOR\",   \"ST42-4\", \"F\",  \"UGA\" )  advs <- tribble(   ~USUBJID, ~PARAMCD, ~AVISIT,    ~AVISITN, ~AVAL,   \"ST42-1\", \"WEIGHT\", \"BASELINE\",        0,    66,   \"ST42-1\", \"WEIGHT\", \"WEEK 2\",          1,    68,   \"ST42-2\", \"WEIGHT\", \"BASELINE\",        0,    88,   \"ST42-3\", \"WEIGHT\", \"WEEK 2\",          1,    55,   \"ST42-3\", \"WEIGHT\", \"WEEK 4\",          2,    50 )  derive_vars_merged(   adsl,   dataset_add = advs,   by_vars = exprs(USUBJID),   new_vars = exprs(     LSTVSCAT = if_else(AVISIT == \"BASELINE\", \"BASELINE\", \"POST-BASELINE\")   ),   order = exprs(AVISITN),   mode = \"last\",   missing_values = exprs(LSTVSCAT = \"MISSING\") ) #> # A tibble: 4 × 4 #>   USUBJID SEX   COUNTRY LSTVSCAT      #>   <chr>   <chr> <chr>   <chr>         #> 1 ST42-1  F     AUT     POST-BASELINE #> 2 ST42-2  M     MWI     BASELINE      #> 3 ST42-3  M     NOR     POST-BASELINE #> 4 ST42-4  F     UGA     MISSING"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_merged_lookup.html","id":null,"dir":"Reference","previous_headings":"","what":"Merge Lookup Table with Source Dataset — derive_vars_merged_lookup","title":"Merge Lookup Table with Source Dataset — derive_vars_merged_lookup","text":"Merge user-defined lookup table input dataset. Optionally print list records input dataset corresponding mapping lookup table.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_merged_lookup.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Merge Lookup Table with Source Dataset — derive_vars_merged_lookup","text":"","code":"derive_vars_merged_lookup(   dataset,   dataset_add,   by_vars,   order = NULL,   new_vars = NULL,   mode = NULL,   filter_add = NULL,   check_type = \"warning\",   duplicate_msg = NULL,   print_not_mapped = TRUE )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_merged_lookup.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Merge Lookup Table with Source Dataset — derive_vars_merged_lookup","text":"dataset Input dataset variables specified by_vars argument expected dataset. dataset_add Lookup table variables specified by_vars argument expected. by_vars Grouping variables input dataset selected observations additional dataset merged specified variables. Variables can renamed naming element, .e. by_vars = exprs(<name input dataset> = <name additional dataset>), similar dplyr joins. Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) order Sort order argument set non-null value, group first last observation additional dataset selected respect specified order. Variables defined new_vars argument can used sort order. handling NAs sorting variables see Sort Order. Permitted Values: list expressions created exprs(), e.g., exprs(ADT, desc(AVAL)) NULL new_vars Variables add specified variables additional dataset added output dataset. Variables can renamed naming element, .e., new_vars = exprs(<new name> = <old name>). example new_vars = exprs(var1, var2) adds variables var1 var2 dataset_add input dataset. new_vars = exprs(var1, new_var2 = old_var2) takes var1 old_var2 dataset_add adds input dataset renaming old_var2 new_var2. Values added variables can modified specifying expression. example, new_vars = LASTRSP = exprs(str_to_upper(AVALC)) adds variable LASTRSP dataset sets upper case value AVALC. argument specified set NULL, variables additional dataset (dataset_add) added. Permitted Values: list variables named expressions created exprs() mode Selection mode Determines first last observation selected. order argument specified, mode must non-null. order argument specified, mode argument ignored. Permitted Values: \"first\", \"last\", NULL filter_add Filter additional dataset (dataset_add) observations fulfilling specified condition taken account merging. argument specified, observations considered. Variables defined new_vars argument can used filter condition. Permitted Values: condition check_type Check uniqueness? \"warning\" \"error\" specified, specified message issued observations (restricted) additional dataset unique respect variables order. order argument specified, check_type argument ignored: observations (restricted) additional dataset unique respect variables, error issued. Permitted Values: \"none\", \"warning\", \"error\" duplicate_msg Message unique check uniqueness check fails, specified message displayed. Default:   print_not_mapped Print list unique by_vars values corresponding records lookup table? Default: TRUE Permitted Values: TRUE, FALSE","code":"paste(   \"Dataset {.arg dataset_add} contains duplicate records with respect to\",   \"{.var {vars2chr(by_vars)}}.\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_merged_lookup.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Merge Lookup Table with Source Dataset — derive_vars_merged_lookup","text":"output dataset contains observations variables input dataset, add variables specified new_vars lookup table specified dataset_add. Optionally prints list unique by_vars values corresponding records lookup table (specifying print_not_mapped = TRUE).","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_merged_lookup.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Merge Lookup Table with Source Dataset — derive_vars_merged_lookup","text":"","code":"library(dplyr, warn.conflicts = FALSE) vs <- tribble(   ~STUDYID,  ~DOMAIN,  ~USUBJID,        ~VISIT, ~VSTESTCD,       ~VSTEST,   \"PILOT01\",    \"VS\", \"01-1028\",   \"SCREENING\",  \"HEIGHT\",      \"Height\",   \"PILOT01\",    \"VS\", \"01-1028\",   \"SCREENING\",    \"TEMP\", \"Temperature\",   \"PILOT01\",    \"VS\", \"01-1028\",    \"BASELINE\",    \"TEMP\", \"Temperature\",   \"PILOT01\",    \"VS\", \"01-1028\",      \"WEEK 4\",    \"TEMP\", \"Temperature\",   \"PILOT01\",    \"VS\", \"01-1028\", \"SCREENING 1\",  \"WEIGHT\",      \"Weight\",   \"PILOT01\",    \"VS\", \"01-1028\",    \"BASELINE\",  \"WEIGHT\",      \"Weight\",   \"PILOT01\",    \"VS\", \"01-1028\",      \"WEEK 4\",  \"WEIGHT\",      \"Weight\",   \"PILOT01\",    \"VS\", \"04-1325\",   \"SCREENING\",  \"HEIGHT\",      \"Height\",   \"PILOT01\",    \"VS\", \"04-1325\",   \"SCREENING\",    \"TEMP\", \"Temperature\",   \"PILOT01\",    \"VS\", \"04-1325\",    \"BASELINE\",    \"TEMP\", \"Temperature\",   \"PILOT01\",    \"VS\", \"04-1325\",      \"WEEK 4\",    \"TEMP\", \"Temperature\",   \"PILOT01\",    \"VS\", \"04-1325\", \"SCREENING 1\",  \"WEIGHT\",      \"Weight\",   \"PILOT01\",    \"VS\", \"04-1325\",    \"BASELINE\",  \"WEIGHT\",      \"Weight\",   \"PILOT01\",    \"VS\", \"04-1325\",      \"WEEK 4\",  \"WEIGHT\",      \"Weight\",   \"PILOT01\",    \"VS\", \"10-1027\",   \"SCREENING\",  \"HEIGHT\",      \"Height\",   \"PILOT01\",    \"VS\", \"10-1027\",   \"SCREENING\",    \"TEMP\", \"Temperature\",   \"PILOT01\",    \"VS\", \"10-1027\",    \"BASELINE\",    \"TEMP\", \"Temperature\",   \"PILOT01\",    \"VS\", \"10-1027\",      \"WEEK 4\",    \"TEMP\", \"Temperature\",   \"PILOT01\",    \"VS\", \"10-1027\", \"SCREENING 1\",  \"WEIGHT\",      \"Weight\",   \"PILOT01\",    \"VS\", \"10-1027\",    \"BASELINE\",  \"WEIGHT\",      \"Weight\",   \"PILOT01\",    \"VS\", \"10-1027\",      \"WEEK 4\",  \"WEIGHT\",      \"Weight\" )  param_lookup <- tribble(   ~VSTESTCD,                 ~VSTEST, ~PARAMCD,                       ~PARAM,   \"SYSBP\", \"Systolic Blood Pressure\",  \"SYSBP\", \"Syst Blood Pressure (mmHg)\",   \"WEIGHT\",                 \"Weight\", \"WEIGHT\",                \"Weight (kg)\",   \"HEIGHT\",                 \"Height\", \"HEIGHT\",                \"Height (cm)\",   \"TEMP\",              \"Temperature\",   \"TEMP\",            \"Temperature (C)\",   \"MAP\",    \"Mean Arterial Pressure\",    \"MAP\",   \"Mean Art Pressure (mmHg)\",   \"BMI\",           \"Body Mass Index\",    \"BMI\",    \"Body Mass Index(kg/m^2)\",   \"BSA\",         \"Body Surface Area\",    \"BSA\",     \"Body Surface Area(m^2)\" )  derive_vars_merged_lookup(   dataset = vs,   dataset_add = param_lookup,   by_vars = exprs(VSTESTCD),   new_vars = exprs(PARAMCD, PARAM),   print_not_mapped = TRUE ) #> All `VSTESTCD` are mapped. #> # A tibble: 21 × 8 #>    STUDYID DOMAIN USUBJID VISIT       VSTESTCD VSTEST      PARAMCD PARAM         #>    <chr>   <chr>  <chr>   <chr>       <chr>    <chr>       <chr>   <chr>         #>  1 PILOT01 VS     01-1028 SCREENING   HEIGHT   Height      HEIGHT  Height (cm)   #>  2 PILOT01 VS     01-1028 SCREENING   TEMP     Temperature TEMP    Temperature … #>  3 PILOT01 VS     01-1028 BASELINE    TEMP     Temperature TEMP    Temperature … #>  4 PILOT01 VS     01-1028 WEEK 4      TEMP     Temperature TEMP    Temperature … #>  5 PILOT01 VS     01-1028 SCREENING 1 WEIGHT   Weight      WEIGHT  Weight (kg)   #>  6 PILOT01 VS     01-1028 BASELINE    WEIGHT   Weight      WEIGHT  Weight (kg)   #>  7 PILOT01 VS     01-1028 WEEK 4      WEIGHT   Weight      WEIGHT  Weight (kg)   #>  8 PILOT01 VS     04-1325 SCREENING   HEIGHT   Height      HEIGHT  Height (cm)   #>  9 PILOT01 VS     04-1325 SCREENING   TEMP     Temperature TEMP    Temperature … #> 10 PILOT01 VS     04-1325 BASELINE    TEMP     Temperature TEMP    Temperature … #> # ℹ 11 more rows"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_period.html","id":null,"dir":"Reference","previous_headings":"","what":"Add Subperiod, Period, or Phase Variables to ADSL — derive_vars_period","title":"Add Subperiod, Period, or Phase Variables to ADSL — derive_vars_period","text":"function adds subperiod, period, phase variables like P01S1SDT, P01S2SDT, AP01SDTM, AP02SDTM, TRT01A, TRT02A, PH1SDT, PH2SDT, ... input dataset. values variables defined period reference dataset one observations per patient subperiod, period, phase.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_period.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add Subperiod, Period, or Phase Variables to ADSL — derive_vars_period","text":"","code":"derive_vars_period(   dataset,   dataset_ref,   new_vars,   subject_keys = get_admiral_option(\"subject_keys\") )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_period.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add Subperiod, Period, or Phase Variables to ADSL — derive_vars_period","text":"dataset Input dataset variables specified subject_keys argument expected dataset. dataset_ref Period reference dataset variables specified new_vars subject_keys expected. subperiod variables requested, APERIOD ASPER expected. period variables requested. APERIOD expected. phase variables requested, APHASEN expected. new_vars New variables named list variables like exprs(PHwSDT = PHSDT, PHwEDT = PHEDT, APHASEw = APHASE) expected. left hand side elements defines set variables (CDISC notation) added output dataset. right hand side defines source variable period reference dataset. lower case letter \"w\"  used refers phase variable, lower case letters \"xx\" used refers period variable, \"xx\" \"w\" used refers subperiod variable. one type must used, e.g., left hand side values must refer period variables. allowed mix example period subperiod variables. period subperiod variables required, separate calls must used. subject_keys Variables uniquely identify subject list expressions expressions symbols returned exprs() expected.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_period.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add Subperiod, Period, or Phase Variables to ADSL — derive_vars_period","text":"input dataset subperiod/period/phase variables added (see \"Details\" section)","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_period.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Add Subperiod, Period, or Phase Variables to ADSL — derive_vars_period","text":"subperiod/period/phase period reference dataset element new_vars variable (LHS value new_vars) added output dataset set value source variable (RHS value new_vars.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_period.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add Subperiod, Period, or Phase Variables to ADSL — derive_vars_period","text":"","code":"library(tibble) library(dplyr, warn.conflicts = FALSE) library(lubridate)  adsl <- tibble(STUDYID = \"xyz\", USUBJID = c(\"1\", \"2\"))  # Add period variables to ADSL period_ref <- tribble(   ~USUBJID, ~APERIOD, ~APERSDT,     ~APEREDT,   \"1\",             1, \"2021-01-04\", \"2021-02-06\",   \"1\",             2, \"2021-02-07\", \"2021-03-07\",   \"2\",             1, \"2021-02-02\", \"2021-03-02\",   \"2\",             2, \"2021-03-03\", \"2021-04-01\" ) %>%   mutate(     STUDYID = \"xyz\",     APERIOD = as.integer(APERIOD),     across(matches(\"APER[ES]DT\"), ymd)   )  derive_vars_period(   adsl,   dataset_ref = period_ref,   new_vars = exprs(APxxSDT = APERSDT, APxxEDT = APEREDT) ) %>%   select(STUDYID, USUBJID, AP01SDT, AP01EDT, AP02SDT, AP02EDT) #> # A tibble: 2 × 6 #>   STUDYID USUBJID AP01SDT    AP01EDT    AP02SDT    AP02EDT    #>   <chr>   <chr>   <date>     <date>     <date>     <date>     #> 1 xyz     1       2021-01-04 2021-02-06 2021-02-07 2021-03-07 #> 2 xyz     2       2021-02-02 2021-03-02 2021-03-03 2021-04-01  # Add phase variables to ADSL phase_ref <- tribble(   ~USUBJID, ~APHASEN, ~PHSDT,       ~PHEDT,       ~APHASE,   \"1\",             1, \"2021-01-04\", \"2021-02-06\", \"TREATMENT\",   \"1\",             2, \"2021-02-07\", \"2021-03-07\", \"FUP\",   \"2\",             1, \"2021-02-02\", \"2021-03-02\", \"TREATMENT\" ) %>%   mutate(     STUDYID = \"xyz\",     APHASEN = as.integer(APHASEN),     across(matches(\"PH[ES]DT\"), ymd)   )  derive_vars_period(   adsl,   dataset_ref = phase_ref,   new_vars = exprs(PHwSDT = PHSDT, PHwEDT = PHEDT, APHASEw = APHASE) ) %>%   select(STUDYID, USUBJID, PH1SDT, PH1EDT, PH2SDT, PH2EDT, APHASE1, APHASE2) #> # A tibble: 2 × 8 #>   STUDYID USUBJID PH1SDT     PH1EDT     PH2SDT     PH2EDT     APHASE1   APHASE2 #>   <chr>   <chr>   <date>     <date>     <date>     <date>     <chr>     <chr>   #> 1 xyz     1       2021-01-04 2021-02-06 2021-02-07 2021-03-07 TREATMENT FUP     #> 2 xyz     2       2021-02-02 2021-03-02 NA         NA         TREATMENT NA       # Add subperiod variables to ADSL subperiod_ref <- tribble(   ~USUBJID, ~APERIOD, ~ASPER, ~ASPRSDT,     ~ASPREDT,   \"1\",             1,      1, \"2021-01-04\", \"2021-01-19\",   \"1\",             1,      2, \"2021-01-20\", \"2021-02-06\",   \"1\",             2,      1, \"2021-02-07\", \"2021-03-07\",   \"2\",             1,      1, \"2021-02-02\", \"2021-03-02\",   \"2\",             2,      1, \"2021-03-03\", \"2021-04-01\" ) %>%   mutate(     STUDYID = \"xyz\",     APERIOD = as.integer(APERIOD),     ASPER = as.integer(ASPER),     across(matches(\"ASPR[ES]DT\"), ymd)   )  derive_vars_period(   adsl,   dataset_ref = subperiod_ref,   new_vars = exprs(PxxSwSDT = ASPRSDT, PxxSwEDT = ASPREDT) ) %>%   select(STUDYID, USUBJID, P01S1SDT, P01S1EDT, P01S2SDT, P01S2EDT, P02S1SDT, P02S1EDT) #> # A tibble: 2 × 8 #>   STUDYID USUBJID P01S1SDT   P01S1EDT   P01S2SDT   P01S2EDT   P02S1SDT   #>   <chr>   <chr>   <date>     <date>     <date>     <date>     <date>     #> 1 xyz     1       2021-01-04 2021-01-19 2021-01-20 2021-02-06 2021-02-07 #> 2 xyz     2       2021-02-02 2021-03-02 NA         NA         2021-03-03 #> # ℹ 1 more variable: P02S1EDT <date>"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_query.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive Query Variables — derive_vars_query","title":"Derive Query Variables — derive_vars_query","text":"Derive Query Variables","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_query.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive Query Variables — derive_vars_query","text":"","code":"derive_vars_query(dataset, dataset_queries)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_query.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive Query Variables — derive_vars_query","text":"dataset Input dataset dataset_queries dataset containing required columns PREFIX, GRPNAME, SRCVAR, TERMCHAR /TERMNUM, optional columns GRPID, SCOPE, SCOPEN. create_query_data() can used create dataset.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_query.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive Query Variables — derive_vars_query","text":"input dataset query variables derived.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_query.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Derive Query Variables — derive_vars_query","text":"function can used derive CDISC variables SMQzzNAM, SMQzzCD, SMQzzSC, SMQzzSCN, CQzzNAM ADAE ADMH, variables SDGzzNAM, SDGzzCD, SDGzzSC ADCM. example usage function can found OCCDS vignette. query dataset expected input function. See Queries Dataset Documentation vignette descriptions, call data(\"queries\") example query dataset. unique element PREFIX, corresponding \"NAM\" variable created. unique PREFIX, GRPID \"\" NA, corresponding \"CD\" variable created; similarly, SCOPE \"\" NA, corresponding \"SC\" variable created; SCOPEN \"\" NA, corresponding \"SCN\" variable created. record dataset, \"NAM\" variable takes value GRPNAME value TERMCHAR TERMNUM dataset_queries matches value respective SRCVAR dataset. Note TERMCHAR dataset_queries dataset may NA TERMNUM non-NA vice versa. matching case insensitive. \"CD\", \"SC\", \"SCN\" variables derived accordingly based GRPID, SCOPE, SCOPEN respectively, whenever missing.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_query.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive Query Variables — derive_vars_query","text":"","code":"library(tibble) data(\"queries\") adae <- tribble(   ~USUBJID, ~ASTDTM, ~AETERM, ~AESEQ, ~AEDECOD, ~AELLT, ~AELLTCD,   \"01\", \"2020-06-02 23:59:59\", \"ALANINE AMINOTRANSFERASE ABNORMAL\",   3, \"Alanine aminotransferase abnormal\", NA_character_, NA_integer_,   \"02\", \"2020-06-05 23:59:59\", \"BASEDOW'S DISEASE\",   5, \"Basedow's disease\", NA_character_, 1L,   \"03\", \"2020-06-07 23:59:59\", \"SOME TERM\",   2, \"Some query\", \"Some term\", NA_integer_,   \"05\", \"2020-06-09 23:59:59\", \"ALVEOLAR PROTEINOSIS\",   7, \"Alveolar proteinosis\", NA_character_, NA_integer_ ) derive_vars_query(adae, queries) #> # A tibble: 4 × 24 #>   USUBJID ASTDTM     AETERM AESEQ AEDECOD AELLT AELLTCD SMQ02NAM SMQ02CD SMQ02SC #>   <chr>   <chr>      <chr>  <dbl> <chr>   <chr>   <int> <chr>      <int> <chr>   #> 1 01      2020-06-0… ALANI…     3 Alanin… NA         NA NA            NA NA      #> 2 02      2020-06-0… BASED…     5 Basedo… NA          1 NA            NA NA      #> 3 03      2020-06-0… SOME …     2 Some q… Some…      NA NA            NA NA      #> 4 05      2020-06-0… ALVEO…     7 Alveol… NA         NA NA            NA NA      #> # ℹ 14 more variables: SMQ02SCN <int>, SMQ03NAM <chr>, SMQ03CD <int>, #> #   SMQ03SC <chr>, SMQ03SCN <int>, SMQ05NAM <chr>, SMQ05CD <int>, #> #   SMQ05SC <chr>, SMQ05SCN <int>, CQ01NAM <chr>, CQ04NAM <chr>, CQ04CD <int>, #> #   CQ06NAM <chr>, CQ06CD <int>"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_transposed.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive Variables by Transposing and Merging a Second Dataset — derive_vars_transposed","title":"Derive Variables by Transposing and Merging a Second Dataset — derive_vars_transposed","text":"Adds variables vertical dataset transposing wide one.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_transposed.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive Variables by Transposing and Merging a Second Dataset — derive_vars_transposed","text":"","code":"derive_vars_transposed(   dataset,   dataset_merge,   by_vars,   id_vars = NULL,   key_var,   value_var,   filter = NULL,   relationship = NULL )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_transposed.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive Variables by Transposing and Merging a Second Dataset — derive_vars_transposed","text":"dataset Input dataset variables specified by_vars argument expected dataset. dataset_merge Dataset transpose merge variables specified by_vars, id_vars, key_var value_var arguments expected. variables by_vars, id_vars, key_var unique key. by_vars Grouping variables Keys used merge dataset_merge dataset. id_vars ID variables Variables (excluding by_vars) uniquely identify observation dataset_merge. Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) key_var variable dataset_merge containing names transposed variables value_var variable dataset_merge containing values transposed variables filter Expression used restrict records dataset_merge prior transposing relationship Expected merge-relationship by_vars variable(s) dataset dataset_merge (transposition) argument passed dplyr::left_join() function. See https://dplyr.tidyverse.org/reference/mutate-joins.html#arguments details. Permitted Values: \"one--one\", \"one--many\", \"many--one\", \"many--many\", NULL","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_transposed.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive Variables by Transposing and Merging a Second Dataset — derive_vars_transposed","text":"input dataset transposed variables dataset_merge added","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_transposed.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Derive Variables by Transposing and Merging a Second Dataset — derive_vars_transposed","text":"filtering dataset_merge based upon condition provided filter, dataset transposed subsequently merged onto dataset using by_vars keys.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_transposed.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive Variables by Transposing and Merging a Second Dataset — derive_vars_transposed","text":"","code":"library(tibble) library(dplyr, warn.conflicts = FALSE)  cm <- tribble(   ~USUBJID,       ~CMGRPID, ~CMREFID,  ~CMDECOD,   \"BP40257-1001\", \"14\",     \"1192056\", \"PARACETAMOL\",   \"BP40257-1001\", \"18\",     \"2007001\", \"SOLUMEDROL\",   \"BP40257-1002\", \"19\",     \"2791596\", \"SPIRONOLACTONE\" ) facm <- tribble(   ~USUBJID,       ~FAGRPID, ~FAREFID,  ~FATESTCD,  ~FASTRESC,   \"BP40257-1001\", \"1\",      \"1192056\", \"CMATC1CD\", \"N\",   \"BP40257-1001\", \"1\",      \"1192056\", \"CMATC2CD\", \"N02\",   \"BP40257-1001\", \"1\",      \"1192056\", \"CMATC3CD\", \"N02B\",   \"BP40257-1001\", \"1\",      \"1192056\", \"CMATC4CD\", \"N02BE\",   \"BP40257-1001\", \"1\",      \"2007001\", \"CMATC1CD\", \"D\",   \"BP40257-1001\", \"1\",      \"2007001\", \"CMATC2CD\", \"D10\",   \"BP40257-1001\", \"1\",      \"2007001\", \"CMATC3CD\", \"D10A\",   \"BP40257-1001\", \"1\",      \"2007001\", \"CMATC4CD\", \"D10AA\",   \"BP40257-1001\", \"2\",      \"2007001\", \"CMATC1CD\", \"D\",   \"BP40257-1001\", \"2\",      \"2007001\", \"CMATC2CD\", \"D07\",   \"BP40257-1001\", \"2\",      \"2007001\", \"CMATC3CD\", \"D07A\",   \"BP40257-1001\", \"2\",      \"2007001\", \"CMATC4CD\", \"D07AA\",   \"BP40257-1001\", \"3\",      \"2007001\", \"CMATC1CD\", \"H\",   \"BP40257-1001\", \"3\",      \"2007001\", \"CMATC2CD\", \"H02\",   \"BP40257-1001\", \"3\",      \"2007001\", \"CMATC3CD\", \"H02A\",   \"BP40257-1001\", \"3\",      \"2007001\", \"CMATC4CD\", \"H02AB\",   \"BP40257-1002\", \"1\",      \"2791596\", \"CMATC1CD\", \"C\",   \"BP40257-1002\", \"1\",      \"2791596\", \"CMATC2CD\", \"C03\",   \"BP40257-1002\", \"1\",      \"2791596\", \"CMATC3CD\", \"C03D\",   \"BP40257-1002\", \"1\",      \"2791596\", \"CMATC4CD\", \"C03DA\" )  cm %>%   derive_vars_transposed(     facm,     by_vars = exprs(USUBJID, CMREFID = FAREFID),     id_vars = exprs(FAGRPID),     key_var = FATESTCD,     value_var = FASTRESC   ) %>%   select(USUBJID, CMDECOD, starts_with(\"CMATC\")) #> # A tibble: 5 × 6 #>   USUBJID      CMDECOD        CMATC1CD CMATC2CD CMATC3CD CMATC4CD #>   <chr>        <chr>          <chr>    <chr>    <chr>    <chr>    #> 1 BP40257-1001 PARACETAMOL    N        N02      N02B     N02BE    #> 2 BP40257-1001 SOLUMEDROL     D        D10      D10A     D10AA    #> 3 BP40257-1001 SOLUMEDROL     D        D07      D07A     D07AA    #> 4 BP40257-1001 SOLUMEDROL     H        H02      H02A     H02AB    #> 5 BP40257-1002 SPIRONOLACTONE C        C03      C03D     C03DA"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/dose_freq_lookup.html","id":null,"dir":"Reference","previous_headings":"","what":"Pre-Defined Dose Frequencies — dose_freq_lookup","title":"Pre-Defined Dose Frequencies — dose_freq_lookup","text":"pre-defined dose frequencies sourced CDISC. number rows generate using create_single_dose_dataset() arguments start_date end_date derived DOSE_COUNT, DOSE_WINDOW, CONVERSION_FACTOR appropriate functions lubridate.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/dose_freq_lookup.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pre-Defined Dose Frequencies — dose_freq_lookup","text":"","code":"dose_freq_lookup"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/dose_freq_lookup.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Pre-Defined Dose Frequencies — dose_freq_lookup","text":"object class tbl_df (inherits tbl, data.frame) 86 rows 5 columns.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/dose_freq_lookup.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Pre-Defined Dose Frequencies — dose_freq_lookup","text":"NCI_CODE CDISC_VALUE included CDISC source traceability. DOSE_COUNT represents number doses received one single unit DOSE_WINDOW. example, CDISC_VALUE==\"10 DAYS PER MONTH\", DOSE_WINDOW==\"MONTH\" DOSE_COUNT==10. Similarly, CDISC_VALUE==\"EVERY 2 WEEKS\", DOSE_WINDOW==\"WEEK\" DOSE_COUNT==0.5 (yield one dose every two weeks). CONVERSION_FACTOR used convert DOSE_WINDOW units \"WEEK\", \"MONTH\", \"YEAR\" unit \"DAY\". example, CDISC_VALUE==\"10 DAYS PER MONTH\", CONVERSION_FACTOR 0.0329. One day month assumed 1 / 30.4375 month (one day assumed 1/365.25 year). Given start_date end_date aggregate dataset, CONVERSION_FACTOR used calculate specific dates forstart_date end_date resulting single dose dataset doses occur. cases, doses assumed occur evenly spaced increments interval. see entire table console, run print(dose_freq_lookup).","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/dt_level.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a dt_level object — dt_level","title":"Create a dt_level object — dt_level","text":"Create dt_level object","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/dt_level.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a dt_level object — dt_level","text":"","code":"dt_level(level)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/dt_level.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a dt_level object — dt_level","text":"level Date level Permitted Values: \"Y\" (year, highest level), \"M\" (month), \"D\" (day), \"n\" (none, lowest level)","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/dt_level.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a dt_level object — dt_level","text":"dt_level object","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/dt_level.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create a dt_level object — dt_level","text":"dt_level object ordered factor, .e., two objects can compared.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/dthcaus_source.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a dthcaus_source Object — dthcaus_source","title":"Create a dthcaus_source Object — dthcaus_source","text":"derive_var_dthcaus() function dthcaus_source() superseded favor derive_vars_extreme_event().","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/dthcaus_source.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a dthcaus_source Object — dthcaus_source","text":"","code":"dthcaus_source(   dataset_name,   filter,   date,   order = NULL,   mode = \"first\",   dthcaus,   set_values_to = NULL )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/dthcaus_source.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a dthcaus_source Object — dthcaus_source","text":"dataset_name name dataset, .e. string, used search death cause. filter expression used filtering dataset. date date datetime variable expression used sorting dataset. order Sort order Additional variables/expressions used sorting dataset. dataset ordered date order. Can used avoid duplicate record warning. Permitted Values: list expressions created exprs(), e.g., exprs(ADT, desc(AVAL)) NULL mode One \"first\" \"last\". Either \"first\" \"last\" observation preserved dataset ordered date. dthcaus variable name, expression, string literal variable name specified, e.g., AEDECOD, variable source dataset used assign values DTHCAUS; expression, e.g., str_to_upper(AEDECOD), evaluated source dataset results assigned DTHCAUS; string literal, e.g. \"Adverse Event\", fixed value assigned DTHCAUS. set_values_to Variables set trace source dataset","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/dthcaus_source.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a dthcaus_source Object — dthcaus_source","text":"object class \"dthcaus_source\".","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/dthcaus_source.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a dthcaus_source Object — dthcaus_source","text":"","code":"# Deaths sourced from AE src_ae <- dthcaus_source(   dataset_name = \"ae\",   filter = AEOUT == \"FATAL\",   date = AEDTHDT,   mode = \"first\",   dthcaus = AEDECOD )  # Deaths sourced from DS src_ds <- dthcaus_source(   dataset_name = \"ds\",   filter = DSDECOD == \"DEATH\",   date = convert_dtc_to_dt(DSSTDTC),   mode = \"first\",   dthcaus = DSTERM )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/dtm_level.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a dtm_level object — dtm_level","title":"Create a dtm_level object — dtm_level","text":"Create dtm_level object","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/dtm_level.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a dtm_level object — dtm_level","text":"","code":"dtm_level(level)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/dtm_level.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a dtm_level object — dtm_level","text":"level Datetime level Permitted Values: \"Y\" (year, highest level), \"M\" (month), \"D\" (day), \"h\" (hour), \"m\" (minute), \"s\" (second, lowest level), \"n\" (none)","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/dtm_level.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a dtm_level object — dtm_level","text":"dtm_level object","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/dtm_level.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create a dtm_level object — dtm_level","text":"dtm_level object ordered factor, .e., two objects can compared.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/event.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a event Object — event","title":"Create a event Object — event","text":"event object used define events input derive_extreme_event() derive_vars_extreme_event() functions.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/event.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a event Object — event","text":"","code":"event(   dataset_name = NULL,   condition = NULL,   mode = NULL,   order = NULL,   set_values_to = NULL,   keep_source_vars = NULL,   description = NULL )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/event.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a event Object — event","text":"dataset_name Dataset name dataset used input event. name refers dataset specified source_datasets derive_extreme_event(). argument specified, input dataset (dataset) derive_extreme_event() used. Permitted Values: character scalar condition unquoted condition selecting observations, contribute extreme event. condition contains summary functions like (), evaluated group separately. Permitted Values: unquoted condition mode specified, first last observation respect order selected group. Permitted Values: \"first\", \"last\", NULL order specified variables expressions used select first last observation mode specified. handling NAs sorting variables see Sort Order. Permitted Values: list expressions created exprs(), e.g., exprs(ADT, desc(AVAL)) NULL set_values_to named list returned exprs() defining variables set event, e.g. exprs(PARAMCD = \"WSP\", PARAM  = \"Worst Sleeping Problems\"). values can symbol, character string, numeric value, NA expression. Permitted Values: named list expressions, e.g., created exprs() keep_source_vars Variables keep source dataset specified variables kept selected observations. variables specified by_vars (derive_extreme_event()) created set_values_to always kept. Permitted Values: list expressions element symbol tidyselect expression, e.g., exprs(VISIT, VISITNUM, starts_with(\"RS\")). description Description event description affect derivations event used. intended documentation . Permitted Values: character scalar","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/event.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a event Object — event","text":"object class event","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/event_joined.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a event_joined Object — event_joined","title":"Create a event_joined Object — event_joined","text":"event_joined object used define events input derive_extreme_event() derive_vars_extreme_event() functions. object used event depend single observation source dataset multiple observations. example, event needs confirmed second observation source dataset. events selected calling filter_joined(). See documentation details.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/event_joined.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a event_joined Object — event_joined","text":"","code":"event_joined(   dataset_name = NULL,   condition,   order = NULL,   join_vars,   join_type,   first_cond_lower = NULL,   first_cond_upper = NULL,   set_values_to = NULL,   keep_source_vars = NULL,   description = NULL )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/event_joined.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a event_joined Object — event_joined","text":"dataset_name Dataset name dataset used input event. name refers dataset specified source_datasets derive_extreme_event(). argument specified, input dataset (dataset) derive_extreme_event() used. Permitted Values: character scalar condition unquoted condition selecting observations, contribute extreme event. condition applied joined dataset selecting confirmed observations. condition can include summary functions like () (). joined dataset grouped original observations. .e., summary function applied observations confirmation observation. example oncology setting using function confirmed best overall response,  condition = AVALC == \"CR\" & (AVALC.join %% c(\"CR\", \"NE\")) & count_vals(var = AVALC.join, val = \"NE\") <= 1 selects observations response \"CR\" observations confirmation observation response \"CR\" \"NE\" one \"NE\". Permitted Values: unquoted condition order specified, specified variables expressions used select first observation. handling NAs sorting variables see Sort Order. Permitted Values: list expressions created exprs(), e.g., exprs(ADT, desc(AVAL)) NULL join_vars Variables keep joined dataset variables needed observations specified parameter. specified variables added joined dataset suffix \".join\". example select observations AVALC == \"Y\" AVALC == \"Y\" least one subsequent visit join_vars = exprs(AVALC, AVISITN) condition = AVALC == \"Y\" & AVALC.join == \"Y\" & AVISITN < AVISITN.join specified. *.join variables included output dataset. Permitted Values: named list expressions, e.g., created exprs() join_type Observations keep joining argument determines joined observations kept respect original observation. example, join_type = \"\" specified observations original observations kept. Permitted Values: \"\", \"\", \"\" first_cond_lower Condition selecting range data () argument specified, observations restricted first observation current observation specified condition fulfilled current observation. condition fulfilled observations, observations considered, .e., observation flagged. parameter specified condition contains summary functions apply observations certain observation current observation current observation. Permitted Values: unquoted condition first_cond_upper Condition selecting range data () argument specified, observations restricted first observation specified condition fulfilled. condition fulfilled observations, observations considered, .e., observation flagged. parameter specified condition contains summary functions apply observations confirmation assessment. Permitted Values: unquoted condition set_values_to named list returned exprs() defining variables set event, e.g. exprs(PARAMCD = \"WSP\", PARAM  = \"Worst Sleeping Problems\"). values can symbol, character string, numeric value, NA expression. Permitted Values: named list expressions, e.g., created exprs() keep_source_vars Variables keep source dataset specified variables kept selected observations. variables specified by_vars (derive_extreme_event()) created set_values_to always kept. Permitted Values: list expressions element symbol tidyselect expression, e.g., exprs(VISIT, VISITNUM, starts_with(\"RS\")). description Description event description affect derivations event used. intended documentation . Permitted Values: character scalar","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/event_joined.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a event_joined Object — event_joined","text":"object class event_joined","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/event_joined.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a event_joined Object — event_joined","text":"","code":"library(tibble) library(dplyr) library(lubridate) # Derive confirmed best overall response (using event_joined()) # CR - complete response, PR - partial response, SD - stable disease # NE - not evaluable, PD - progressive disease adsl <- tribble(   ~USUBJID, ~TRTSDTC,   \"1\",      \"2020-01-01\",   \"2\",      \"2019-12-12\",   \"3\",      \"2019-11-11\",   \"4\",      \"2019-12-30\",   \"5\",      \"2020-01-01\",   \"6\",      \"2020-02-02\",   \"7\",      \"2020-02-02\",   \"8\",      \"2020-02-01\" ) %>%   mutate(TRTSDT = ymd(TRTSDTC))  adrs <- tribble(   ~USUBJID, ~ADTC,        ~AVALC,   \"1\",      \"2020-01-01\", \"PR\",   \"1\",      \"2020-02-01\", \"CR\",   \"1\",      \"2020-02-16\", \"NE\",   \"1\",      \"2020-03-01\", \"CR\",   \"1\",      \"2020-04-01\", \"SD\",   \"2\",      \"2020-01-01\", \"SD\",   \"2\",      \"2020-02-01\", \"PR\",   \"2\",      \"2020-03-01\", \"SD\",   \"2\",      \"2020-03-13\", \"CR\",   \"4\",      \"2020-01-01\", \"PR\",   \"4\",      \"2020-03-01\", \"NE\",   \"4\",      \"2020-04-01\", \"NE\",   \"4\",      \"2020-05-01\", \"PR\",   \"5\",      \"2020-01-01\", \"PR\",   \"5\",      \"2020-01-10\", \"PR\",   \"5\",      \"2020-01-20\", \"PR\",   \"6\",      \"2020-02-06\", \"PR\",   \"6\",      \"2020-02-16\", \"CR\",   \"6\",      \"2020-03-30\", \"PR\",   \"7\",      \"2020-02-06\", \"PR\",   \"7\",      \"2020-02-16\", \"CR\",   \"7\",      \"2020-04-01\", \"NE\",   \"8\",      \"2020-02-16\", \"PD\" ) %>%   mutate(     ADT = ymd(ADTC),     PARAMCD = \"OVR\",     PARAM = \"Overall Response by Investigator\"   ) %>%   derive_vars_merged(     dataset_add = adsl,     by_vars = exprs(USUBJID),     new_vars = exprs(TRTSDT)   )  derive_extreme_event(   adrs,   by_vars = exprs(USUBJID),   order = exprs(ADT),   mode = \"first\",   source_datasets = list(adsl = adsl),   events = list(     event_joined(       description = paste(         \"CR needs to be confirmed by a second CR at least 28 days later\",         \"at most one NE is acceptable between the two assessments\"       ),       join_vars = exprs(AVALC, ADT),       join_type = \"after\",       first_cond_upper = AVALC.join == \"CR\" &         ADT.join >= ADT + 28,       condition = AVALC == \"CR\" &         all(AVALC.join %in% c(\"CR\", \"NE\")) &         count_vals(var = AVALC.join, val = \"NE\") <= 1,       set_values_to = exprs(         AVALC = \"CR\"       )     ),     event_joined(       description = paste(         \"PR needs to be confirmed by a second CR or PR at least 28 days later,\",         \"at most one NE is acceptable between the two assessments\"       ),       join_vars = exprs(AVALC, ADT),       join_type = \"after\",       first_cond_upper = AVALC.join %in% c(\"CR\", \"PR\") &         ADT.join >= ADT + 28,       condition = AVALC == \"PR\" &         all(AVALC.join %in% c(\"CR\", \"PR\", \"NE\")) &         count_vals(var = AVALC.join, val = \"NE\") <= 1,       set_values_to = exprs(         AVALC = \"PR\"       )     ),     event(       description = paste(         \"CR, PR, or SD are considered as SD if occurring at least 28\",         \"after treatment start\"       ),       condition = AVALC %in% c(\"CR\", \"PR\", \"SD\") & ADT >= TRTSDT + 28,       set_values_to = exprs(         AVALC = \"SD\"       )     ),     event(       condition = AVALC == \"PD\",       set_values_to = exprs(         AVALC = \"PD\"       )     ),     event(       condition = AVALC %in% c(\"CR\", \"PR\", \"SD\", \"NE\"),       set_values_to = exprs(         AVALC = \"NE\"       )     ),     event(       description = \"set response to MISSING for patients without records in ADRS\",       dataset_name = \"adsl\",       condition = TRUE,       set_values_to = exprs(         AVALC = \"MISSING\"       ),       keep_source_vars = exprs(TRTSDT)     )   ),   set_values_to = exprs(     PARAMCD = \"CBOR\",     PARAM = \"Best Confirmed Overall Response by Investigator\"   ) ) %>%   filter(PARAMCD == \"CBOR\") #> Warning: Check duplicates: the dataset which consists of all records selected for any of #> the events defined by `events` contains duplicate records with respect to #> `USUBJID` and `ADT` #> ℹ Run `admiral::get_duplicates_dataset()` to access the duplicate records #> # A tibble: 8 × 7 #>   USUBJID ADTC       AVALC   ADT        PARAMCD PARAM                 TRTSDT     #>   <chr>   <chr>      <chr>   <date>     <chr>   <chr>                 <date>     #> 1 1       2020-01-01 PR      2020-01-01 CBOR    Best Confirmed Overa… 2020-01-01 #> 2 2       2020-01-01 NE      2020-01-01 CBOR    Best Confirmed Overa… 2019-12-12 #> 3 3       NA         MISSING NA         CBOR    Best Confirmed Overa… 2019-11-11 #> 4 4       2020-01-01 NE      2020-01-01 CBOR    Best Confirmed Overa… 2019-12-30 #> 5 5       2020-01-01 NE      2020-01-01 CBOR    Best Confirmed Overa… 2020-01-01 #> 6 6       2020-02-06 PR      2020-02-06 CBOR    Best Confirmed Overa… 2020-02-02 #> 7 7       2020-02-06 NE      2020-02-06 CBOR    Best Confirmed Overa… 2020-02-02 #> 8 8       2020-02-16 PD      2020-02-16 CBOR    Best Confirmed Overa… 2020-02-01"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/event_source.html","id":null,"dir":"Reference","previous_headings":"","what":"Create an event_source Object — event_source","title":"Create an event_source Object — event_source","text":"event_source objects used define events input derive_param_tte() function. Note: wrapper function generic tte_source().","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/event_source.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create an event_source Object — event_source","text":"","code":"event_source(dataset_name, filter = NULL, date, set_values_to = NULL)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/event_source.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create an event_source Object — event_source","text":"dataset_name name source dataset name refers dataset provided source_datasets parameter derive_param_tte(). filter unquoted condition selecting observations dataset events possible censoring time points. date variable expression providing date event censoring. date, datetime can specified. unquoted symbol expression expected. Refer derive_vars_dt() convert_dtc_to_dt() impute derive date date character vector date object. set_values_to named list returned exprs() defining variables set event censoring, e.g. exprs(EVENTDESC = \"DEATH\", SRCDOM = \"ADSL\", SRCVAR = \"DTHDT\"). values must symbol, character string, numeric value, expression, NA.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/event_source.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create an event_source Object — event_source","text":"object class event_source, inheriting class tte_source","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/event_source.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create an event_source Object — event_source","text":"","code":"# Death event  event_source(   dataset_name = \"adsl\",   filter = DTHFL == \"Y\",   date = DTHDT,   set_values_to = exprs(     EVNTDESC = \"DEATH\",     SRCDOM = \"ADSL\",     SRCVAR = \"DTHDT\"   ) ) #> <event_source> object #> dataset_name: \"adsl\" #> filter: DTHFL == \"Y\" #> date: DTHDT #> censor: 0 #> set_values_to: #>   EVNTDESC: \"DEATH\" #>   SRCDOM: \"ADSL\" #>   SRCVAR: \"DTHDT\""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/ex_single.html","id":null,"dir":"Reference","previous_headings":"","what":"Single Dose Exposure Dataset — ex_single","title":"Single Dose Exposure Dataset — ex_single","text":"derived dataset single dose per date.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/ex_single.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Single Dose Exposure Dataset — ex_single","text":"","code":"ex_single"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/ex_single.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Single Dose Exposure Dataset — ex_single","text":"object class tbl_df (inherits tbl, data.frame) 22439 rows 17 columns.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/ex_single.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Single Dose Exposure Dataset — ex_single","text":"Derived ex dataset using {admiral} {dplyr} (https://github.com/pharmaverse/admiral/blob/main/inst/example_scripts/derive_single_dose.R)","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/example_qs.html","id":null,"dir":"Reference","previous_headings":"","what":"Example QS Dataset — example_qs","title":"Example QS Dataset — example_qs","text":"example QS dataset based examples CDISC ADaM Supplements Generalized Anxiety Disorder 7-Item Version 2 (GAD-7) Geriatric Depression Scale Short Form (GDS-SF).","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/example_qs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Example QS Dataset — example_qs","text":"","code":"example_qs"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/example_qs.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Example QS Dataset — example_qs","text":"object class tbl_df (inherits tbl, data.frame) 161 rows 11 columns.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/example_qs.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Example QS Dataset — example_qs","text":"Created (https://github.com/pharmaverse/admiral/blob/main/inst/example_scripts/example_qs.R)","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/extend_condition.html","id":null,"dir":"Reference","previous_headings":"","what":"Extend a condition string by adding a new condition based on a variable and its value — extend_condition","title":"Extend a condition string by adding a new condition based on a variable and its value — extend_condition","text":"internal helper function extends condition string appending new condition checks variable equals specific value.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/extend_condition.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extend a condition string by adding a new condition based on a variable and its value — extend_condition","text":"","code":"extend_condition(cond, var, is)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/extend_condition.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extend a condition string by adding a new condition based on a variable and its value — extend_condition","text":"cond character string representing existing condition. var character string representing name variable check. character string representing value variable equal .","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/extend_condition.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extend a condition string by adding a new condition based on a variable and its value — extend_condition","text":"character string representing extended condition.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/extend_condition.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extend a condition string by adding a new condition based on a variable and its value — extend_condition","text":"","code":"# Extend an existing condition to include a check for 'AGE == \"30\"' admiral:::extend_condition(\"SEX == 'M'\", \"AGE\", \"30\") #> [1] \"SEX == 'M' & AGE == '30'\""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/extract_duplicate_records.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract Duplicate Records — extract_duplicate_records","title":"Extract Duplicate Records — extract_duplicate_records","text":"Extract Duplicate Records","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/extract_duplicate_records.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract Duplicate Records — extract_duplicate_records","text":"","code":"extract_duplicate_records(dataset, by_vars)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/extract_duplicate_records.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract Duplicate Records — extract_duplicate_records","text":"dataset Input dataset variables specified by_vars argument expected dataset. by_vars Grouping variables Defines groups records look duplicates. Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT)","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/extract_duplicate_records.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract Duplicate Records — extract_duplicate_records","text":"data.frame duplicate records within dataset","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/extract_duplicate_records.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract Duplicate Records — extract_duplicate_records","text":"","code":"data(admiral_adsl)  # Duplicate the first record adsl <- rbind(admiral_adsl[1L, ], admiral_adsl)  extract_duplicate_records(adsl, exprs(USUBJID)) #> # A tibble: 2 × 54 #>   USUBJID     STUDYID  SUBJID RFSTDTC RFENDTC RFXSTDTC RFXENDTC RFICDTC RFPENDTC #>   <chr>       <chr>    <chr>  <chr>   <chr>   <chr>    <chr>    <chr>   <chr>    #> 1 01-701-1015 CDISCPI… 1015   2014-0… 2014-0… 2014-01… 2014-07… NA      2014-07… #> 2 01-701-1015 CDISCPI… 1015   2014-0… 2014-0… 2014-01… 2014-07… NA      2014-07… #> # ℹ 45 more variables: DTHDTC <chr>, DTHFL <chr>, SITEID <chr>, AGE <dbl>, #> #   AGEU <chr>, SEX <chr>, RACE <chr>, ETHNIC <chr>, ARMCD <chr>, ARM <chr>, #> #   ACTARMCD <chr>, ACTARM <chr>, COUNTRY <chr>, DMDTC <chr>, DMDY <dbl>, #> #   TRT01P <chr>, TRT01A <chr>, TRTSDTM <dttm>, TRTSTMF <chr>, TRTEDTM <dttm>, #> #   TRTETMF <chr>, TRTSDT <date>, TRTEDT <date>, TRTDURD <dbl>, SCRFDT <date>, #> #   EOSDT <date>, EOSSTT <chr>, FRVDT <date>, RANDDT <date>, DTHDT <date>, #> #   DTHDTF <chr>, DTHADY <dbl>, LDDTHELD <dbl>, DTHCAUS <chr>, DTHDOM <chr>, …"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/extract_unit.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract Unit From Parameter Description — extract_unit","title":"Extract Unit From Parameter Description — extract_unit","text":"Extract unit parameter description like \"Param (unit)\".","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/extract_unit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract Unit From Parameter Description — extract_unit","text":"","code":"extract_unit(x)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/extract_unit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract Unit From Parameter Description — extract_unit","text":"x parameter description","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/extract_unit.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract Unit From Parameter Description — extract_unit","text":"string","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/extract_unit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract Unit From Parameter Description — extract_unit","text":"","code":"extract_unit(\"Height (cm)\") #> [1] \"cm\"  extract_unit(\"Diastolic Blood Pressure (mmHg)\") #> [1] \"mmHg\""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_exist.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns records that fit into existing by groups in a filtered source dataset — filter_exist","title":"Returns records that fit into existing by groups in a filtered source dataset — filter_exist","text":"Returns records input dataset belong groups present source dataset, source dataset optionally filtered. example, used return ADSL records subjects experienced certain adverse event course study (per records ADAE).","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_exist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns records that fit into existing by groups in a filtered source dataset — filter_exist","text":"","code":"filter_exist(dataset, dataset_add, by_vars, filter_add = NULL)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_exist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns records that fit into existing by groups in a filtered source dataset — filter_exist","text":"dataset Input dataset variables specified by_vars argument expected dataset. dataset_add Source dataset source dataset, determines groups returned input dataset, based groups exist dataset subset filter_add. variables specified by_vars filter_add parameters expected dataset. by_vars Grouping variables Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) filter_add Filter source dataset filter condition used subset source dataset. Alternatively, filter condition supplied, subsetting source dataset performed. Default: NULL (.e. filtering performed)","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_exist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns records that fit into existing by groups in a filtered source dataset — filter_exist","text":"records input dataset contained within existing group filtered source dataset.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_exist.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Returns records that fit into existing by groups in a filtered source dataset — filter_exist","text":"Returns records dataset match existing group dataset_add, filtered according filter_add. groups exist datasets, empty dataset returned.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_exist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Returns records that fit into existing by groups in a filtered source dataset — filter_exist","text":"","code":"# Get demographic information about subjects who have suffered from moderate or # severe fatigue  library(tibble)  adsl <- tribble(   ~USUBJID,      ~AGE, ~SEX,   \"01-701-1015\", 63,   \"F\",   \"01-701-1034\", 77,   \"F\",   \"01-701-1115\", 84,   \"M\",   \"01-701-1146\", 75,   \"F\",   \"01-701-1444\", 63,   \"M\" )  adae <- tribble(   ~USUBJID,      ~AEDECOD,                    ~AESEV,     ~AESTDTC,   \"01-701-1015\", \"DIARRHOEA\",                 \"MODERATE\", \"2014-01-09\",   \"01-701-1034\", \"FATIGUE\",                   \"SEVERE\",   \"2014-11-02\",   \"01-701-1034\", \"APPLICATION SITE PRURITUS\", \"MODERATE\", \"2014-08-27\",   \"01-701-1115\", \"FATIGUE\",                   \"MILD\",     \"2013-01-14\",   \"01-701-1146\", \"FATIGUE\",                   \"MODERATE\", \"2013-06-03\" )  filter_exist(   dataset = adsl,   dataset_add = adae,   by_vars = exprs(USUBJID),   filter_add = AEDECOD == \"FATIGUE\" & AESEV %in% c(\"MODERATE\", \"SEVERE\") ) #> # A tibble: 2 × 3 #>   USUBJID       AGE SEX   #>   <chr>       <dbl> <chr> #> 1 01-701-1034    77 F     #> 2 01-701-1146    75 F"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_extreme.html","id":null,"dir":"Reference","previous_headings":"","what":"Filter the First or Last Observation for Each By Group — filter_extreme","title":"Filter the First or Last Observation for Each By Group — filter_extreme","text":"Filters first last observation group.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_extreme.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filter the First or Last Observation for Each By Group — filter_extreme","text":"","code":"filter_extreme(dataset, by_vars = NULL, order, mode, check_type = \"warning\")"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_extreme.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filter the First or Last Observation for Each By Group — filter_extreme","text":"dataset Input dataset variables specified by_vars order arguments expected dataset. by_vars Grouping variables Default: NULL Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) order Sort order Within group observations ordered specified order. Permitted Values: list expressions created exprs(), e.g., exprs(ADT, desc(AVAL)) mode Selection mode (first last) \"first\" specified, first observation group included output dataset. \"last\" specified, last observation group included output dataset. Permitted Values:  \"first\", \"last\" check_type Check uniqueness? \"warning\" \"error\" specified, specified message issued observations input dataset unique respect variables order. Default: \"warning\" Permitted Values: \"none\", \"warning\", \"error\"","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_extreme.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filter the First or Last Observation for Each By Group — filter_extreme","text":"dataset containing first last observation group","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_extreme.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Filter the First or Last Observation for Each By Group — filter_extreme","text":"group (respect variables specified by_vars parameter) first last observation (respect order specified order parameter mode specified mode parameter) included output dataset.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_extreme.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Filter the First or Last Observation for Each By Group — filter_extreme","text":"","code":"library(dplyr, warn.conflicts = FALSE)  ex <- tribble(   ~STUDYID,  ~DOMAIN,  ~USUBJID, ~EXSEQ, ~EXDOSE,    ~EXTRT,   \"PILOT01\",    \"EX\", \"01-1442\",      1,      54,    \"XANO\",   \"PILOT01\",    \"EX\", \"01-1442\",      2,      54,    \"XANO\",   \"PILOT01\",    \"EX\", \"01-1442\",      3,      54,    \"XANO\",   \"PILOT01\",    \"EX\", \"01-1444\",      1,      54,    \"XANO\",   \"PILOT01\",    \"EX\", \"01-1444\",      2,      81,    \"XANO\",   \"PILOT01\",    \"EX\", \"05-1382\",      1,      54,    \"XANO\",   \"PILOT01\",    \"EX\", \"08-1213\",      1,      54,    \"XANO\",   \"PILOT01\",    \"EX\", \"10-1053\",      1,      54,    \"XANO\",   \"PILOT01\",    \"EX\", \"10-1053\",      2,      54,    \"XANO\",   \"PILOT01\",    \"EX\", \"10-1183\",      1,       0, \"PLACEBO\",   \"PILOT01\",    \"EX\", \"10-1183\",      2,       0, \"PLACEBO\",   \"PILOT01\",    \"EX\", \"10-1183\",      3,       0, \"PLACEBO\",   \"PILOT01\",    \"EX\", \"11-1036\",      1,       0, \"PLACEBO\",   \"PILOT01\",    \"EX\", \"11-1036\",      2,       0, \"PLACEBO\",   \"PILOT01\",    \"EX\", \"11-1036\",      3,       0, \"PLACEBO\",   \"PILOT01\",    \"EX\", \"14-1425\",      1,      54,    \"XANO\",   \"PILOT01\",    \"EX\", \"15-1319\",      1,      54,    \"XANO\",   \"PILOT01\",    \"EX\", \"15-1319\",      2,      81,    \"XANO\",   \"PILOT01\",    \"EX\", \"16-1151\",      1,      54,    \"XANO\",   \"PILOT01\",    \"EX\", \"16-1151\",      2,      54,    \"XANO\" )   # Select first dose for each patient ex %>%   filter_extreme(     by_vars = exprs(USUBJID),     order = exprs(EXSEQ),     mode = \"first\"   ) %>%   select(USUBJID, EXSEQ) #> # A tibble: 10 × 2 #>    USUBJID EXSEQ #>    <chr>   <dbl> #>  1 01-1442     1 #>  2 01-1444     1 #>  3 05-1382     1 #>  4 08-1213     1 #>  5 10-1053     1 #>  6 10-1183     1 #>  7 11-1036     1 #>  8 14-1425     1 #>  9 15-1319     1 #> 10 16-1151     1  # Select highest dose for each patient on the active drug ex %>%   filter(EXTRT != \"PLACEBO\") %>%   filter_extreme(     by_vars = exprs(USUBJID),     order = exprs(EXDOSE),     mode = \"last\",     check_type = \"none\"   ) %>%   select(USUBJID, EXTRT, EXDOSE) #> # A tibble: 8 × 3 #>   USUBJID EXTRT EXDOSE #>   <chr>   <chr>  <dbl> #> 1 01-1442 XANO      54 #> 2 01-1444 XANO      81 #> 3 05-1382 XANO      54 #> 4 08-1213 XANO      54 #> 5 10-1053 XANO      54 #> 6 14-1425 XANO      54 #> 7 15-1319 XANO      81 #> 8 16-1151 XANO      54"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_joined.html","id":null,"dir":"Reference","previous_headings":"","what":"Filter Observations Taking Other Observations into Account — filter_joined","title":"Filter Observations Taking Other Observations into Account — filter_joined","text":"function filters observation using condition taking observations account. example, select observations AVALC == \"Y\" AVALC == \"Y\" least one subsequent observation. input dataset joined enable conditions taking variables current observation observations account. suffix \".join\" added variables subsequent observations. example usage might checking patient received two required medications within certain timeframe . oncology setting, example, use processing check response value can confirmed subsequent assessment. commonly used endpoints best overall response.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_joined.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filter Observations Taking Other Observations into Account — filter_joined","text":"","code":"filter_joined(   dataset,   dataset_add,   by_vars,   join_vars,   join_type,   first_cond_lower = NULL,   first_cond_upper = NULL,   order,   tmp_obs_nr_var = NULL,   filter_add = NULL,   filter_join,   check_type = \"warning\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_joined.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filter Observations Taking Other Observations into Account — filter_joined","text":"dataset Input dataset variables specified by_vars order arguments expected dataset. dataset_add Additional dataset variables specified by_vars, join_vars, order expected. by_vars variables specified variables used variables joining input dataset . Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) join_vars Variables keep joined dataset variables needed observations specified parameter. specified variables added joined dataset suffix \".join\". example select observations AVALC == \"Y\" AVALC == \"Y\" least one subsequent visit join_vars = exprs(AVALC, AVISITN) filter_join = AVALC == \"Y\" & AVALC.join == \"Y\" & AVISITN < AVISITN.join specified. *.join variables included output dataset. join_type Observations keep joining argument determines joined observations kept respect original observation. example, join_type = \"\" specified observations original observations kept. example confirmed response BOR oncology setting confirmed deterioration questionnaires confirmatory assessment must assessment. Thus join_type = \"\" used. Whereas, sometimes might allow confirmatory observations occur prior observation. example, identify AEs occurring seven days COVID AE. Thus join_type = \"\" used. Permitted Values: \"\", \"\", \"\" first_cond_lower Condition selecting range data () argument specified, observations restricted first observation current observation specified condition fulfilled current observation. condition fulfilled observations, observations considered, .e., observation flagged. parameter specified filter_join contains summary functions apply observations certain observation current observation current observation. example see last example . first_cond_upper Condition selecting range data () argument specified, observations restricted first observation specified condition fulfilled. condition fulfilled observations, observations considered, .e., observation flagged. parameter specified filter_join contains summary functions apply observations confirmation assessment. example see last example . order Order observations ordered specified order. handling NAs sorting variables see Sort Order. Permitted Values: list expressions created exprs(), e.g., exprs(ADT, desc(AVAL)) tmp_obs_nr_var Temporary observation number specified variable added input dataset (dataset) additional dataset (dataset_add). set observation number respect order. group (by_vars) observation number starts 1. variable can used conditions (filter_join, first_cond_upper, first_cond_lower). included output dataset. can also used select consecutive observations last observation (see example ). filter_add Filter additional dataset (dataset_add) observations dataset_add fulfilling specified condition joined input dataset. argument specified, observations joined. Variables created order argument can used condition. condition can include summary functions. additional dataset grouped variables (by_vars). filter_join Condition selecting observations filter applied joined dataset selecting confirmed observations. condition can include summary functions like () (). joined dataset grouped original observations. .e., summary function applied observations confirmation observation. example oncology setting using function confirmed best overall response,  filter_join = AVALC == \"CR\" & (AVALC.join %% c(\"CR\", \"NE\")) & count_vals(var = AVALC.join, val = \"NE\") <= 1 selects observations response \"CR\" observations confirmation observation response \"CR\" \"NE\" one \"NE\". check_type Check uniqueness? \"warning\" \"error\" specified, specified message issued observations input dataset unique respect variables order. Permitted Values: \"none\", \"warning\", \"error\"","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_joined.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filter Observations Taking Other Observations into Account — filter_joined","text":"subset observations input dataset. variables input dataset included output dataset.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_joined.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Filter Observations Taking Other Observations into Account — filter_joined","text":"following steps performed produce output dataset.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_joined.html","id":"step-","dir":"Reference","previous_headings":"","what":"Step 1","title":"Filter Observations Taking Other Observations into Account — filter_joined","text":"variables specified order added additional dataset (dataset_add). variables specified join_vars added additional dataset (dataset_add). records additional dataset (dataset_add) restricted matching filter_add condition.  input dataset (dataset) joined restricted additional dataset variables specified by_vars. additional dataset variables specified join_vars kept. suffix \".join\" added variables also present input dataset. example, by_vars = USUBJID, join_vars = exprs(AVISITN, AVALC) input dataset additional dataset   joined dataset ","code":"# A tibble: 2 x 4 USUBJID AVISITN AVALC  AVAL <chr>     <dbl> <chr> <dbl> 1             1 Y         1 1             2 N         0 A tibble: 4 x 6 USUBJID AVISITN AVALC  AVAL AVISITN.join AVALC.join <chr>     <dbl> <chr> <dbl>        <dbl> <chr> 1             1 Y         1            1 Y 1             1 Y         1            2 N 1             2 N         0            1 Y 1             2 N         0            2 N"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_joined.html","id":"step--1","dir":"Reference","previous_headings":"","what":"Step 2","title":"Filter Observations Taking Other Observations into Account — filter_joined","text":"joined dataset restricted observations respect join_type order. dataset example previous step join_type = \"\" order = exprs(AVISITN) restricted ","code":"A tibble: 4 x 6 USUBJID AVISITN AVALC  AVAL AVISITN.join AVALC.join <chr>     <dbl> <chr> <dbl>        <dbl> <chr> 1             1 Y         1            2 N"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_joined.html","id":"step--2","dir":"Reference","previous_headings":"","what":"Step 3","title":"Filter Observations Taking Other Observations into Account — filter_joined","text":"first_cond_lower specified, observation input dataset joined dataset restricted observations first observation first_cond_lower fulfilled (observation fulfilling condition included) observation input dataset. observation input dataset condition fulfilled, observation removed. first_cond_upper specified, observation input dataset joined dataset restricted observations first observation first_cond_upper fulfilled (observation fulfilling condition included). observation input dataset condition fulfilled, observation removed. example see last example \"Examples\" section.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_joined.html","id":"step--3","dir":"Reference","previous_headings":"","what":"Step 4","title":"Filter Observations Taking Other Observations into Account — filter_joined","text":"joined dataset grouped observations input dataset restricted observations fulfilling condition specified filter_join.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_joined.html","id":"step--4","dir":"Reference","previous_headings":"","what":"Step 5","title":"Filter Observations Taking Other Observations into Account — filter_joined","text":"first observation group selected *.join variables dropped.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_joined.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Filter Observations Taking Other Observations into Account — filter_joined","text":"","code":"library(tibble) library(admiral)  # filter observations with a duration longer than 30 and # on or after 7 days before a COVID AE (ACOVFL == \"Y\") adae <- tribble(   ~USUBJID, ~ADY, ~ACOVFL, ~ADURN,   \"1\",        10, \"N\",          1,   \"1\",        21, \"N\",         50,   \"1\",        23, \"Y\",         14,   \"1\",        32, \"N\",         31,   \"1\",        42, \"N\",         20,   \"2\",        11, \"Y\",         13,   \"2\",        23, \"N\",          2,   \"3\",        13, \"Y\",         12,   \"4\",        14, \"N\",         32,   \"4\",        21, \"N\",         41 )  filter_joined(   adae,   dataset_add = adae,   by_vars = exprs(USUBJID),   join_vars = exprs(ACOVFL, ADY),   join_type = \"all\",   order = exprs(ADY),   filter_join = ADURN > 30 & ACOVFL.join == \"Y\" & ADY >= ADY.join - 7 ) #> # A tibble: 2 × 4 #>   USUBJID   ADY ACOVFL ADURN #>   <chr>   <dbl> <chr>  <dbl> #> 1 1          21 N         50 #> 2 1          32 N         31  # filter observations with AVALC == \"Y\" and AVALC == \"Y\" at a subsequent visit data <- tribble(   ~USUBJID, ~AVISITN, ~AVALC,   \"1\",      1,        \"Y\",   \"1\",      2,        \"N\",   \"1\",      3,        \"Y\",   \"1\",      4,        \"N\",   \"2\",      1,        \"Y\",   \"2\",      2,        \"N\",   \"3\",      1,        \"Y\",   \"4\",      1,        \"N\",   \"4\",      2,        \"N\", )  filter_joined(   data,   dataset_add = data,   by_vars = exprs(USUBJID),   join_vars = exprs(AVALC, AVISITN),   join_type = \"after\",   order = exprs(AVISITN),   filter_join = AVALC == \"Y\" & AVALC.join == \"Y\" & AVISITN < AVISITN.join ) #> # A tibble: 1 × 3 #>   USUBJID AVISITN AVALC #>   <chr>     <dbl> <chr> #> 1 1             1 Y      # select observations with AVALC == \"CR\", AVALC == \"CR\" at a subsequent visit, # only \"CR\" or \"NE\" in between, and at most one \"NE\" in between data <- tribble(   ~USUBJID, ~AVISITN, ~AVALC,   \"1\",      1,        \"PR\",   \"1\",      2,        \"CR\",   \"1\",      3,        \"NE\",   \"1\",      4,        \"CR\",   \"1\",      5,        \"NE\",   \"2\",      1,        \"CR\",   \"2\",      2,        \"PR\",   \"2\",      3,        \"CR\",   \"3\",      1,        \"CR\",   \"4\",      1,        \"CR\",   \"4\",      2,        \"NE\",   \"4\",      3,        \"NE\",   \"4\",      4,        \"CR\",   \"4\",      5,        \"PR\" )  filter_joined(   data,   dataset_add = data,   by_vars = exprs(USUBJID),   join_vars = exprs(AVALC),   join_type = \"after\",   order = exprs(AVISITN),   first_cond_upper = AVALC.join == \"CR\",   filter_join = AVALC == \"CR\" & all(AVALC.join %in% c(\"CR\", \"NE\")) &     count_vals(var = AVALC.join, val = \"NE\") <= 1 ) #> # A tibble: 1 × 3 #>   USUBJID AVISITN AVALC #>   <chr>     <dbl> <chr> #> 1 1             2 CR     # select observations with AVALC == \"PR\", AVALC == \"CR\" or AVALC == \"PR\" # at a subsequent visit at least 20 days later, only \"CR\", \"PR\", or \"NE\" # in between, at most one \"NE\" in between, and \"CR\" is not followed by \"PR\" data <- tribble(   ~USUBJID, ~ADY, ~AVALC,   \"1\",         6, \"PR\",   \"1\",        12, \"CR\",   \"1\",        24, \"NE\",   \"1\",        32, \"CR\",   \"1\",        48, \"PR\",   \"2\",         3, \"PR\",   \"2\",        21, \"CR\",   \"2\",        33, \"PR\",   \"3\",        11, \"PR\",   \"4\",         7, \"PR\",   \"4\",        12, \"NE\",   \"4\",        24, \"NE\",   \"4\",        32, \"PR\",   \"4\",        55, \"PR\" )  filter_joined(   data,   dataset_add = data,   by_vars = exprs(USUBJID),   join_vars = exprs(AVALC, ADY),   join_type = \"after\",   order = exprs(ADY),   first_cond_upper = AVALC.join %in% c(\"CR\", \"PR\") & ADY.join - ADY >= 20,   filter_join = AVALC == \"PR\" &     all(AVALC.join %in% c(\"CR\", \"PR\", \"NE\")) &     count_vals(var = AVALC.join, val = \"NE\") <= 1 &     (       min_cond(var = ADY.join, cond = AVALC.join == \"CR\") >         max_cond(var = ADY.join, cond = AVALC.join == \"PR\") |         count_vals(var = AVALC.join, val = \"CR\") == 0     ) ) #> # A tibble: 1 × 3 #>   USUBJID   ADY AVALC #>   <chr>   <dbl> <chr> #> 1 4          32 PR     # select observations with CRIT1FL == \"Y\" at two consecutive visits or at the last visit data <- tribble(   ~USUBJID, ~AVISITN, ~CRIT1FL,   \"1\",      1,        \"Y\",   \"1\",      2,        \"N\",   \"1\",      3,        \"Y\",   \"1\",      5,        \"N\",   \"2\",      1,        \"Y\",   \"2\",      3,        \"Y\",   \"2\",      5,        \"N\",   \"3\",      1,        \"Y\",   \"4\",      1,        \"Y\",   \"4\",      2,        \"N\", )  filter_joined(   data,   dataset_add = data,   by_vars = exprs(USUBJID),   tmp_obs_nr_var = tmp_obs_nr,   join_vars = exprs(CRIT1FL),   join_type = \"all\",   order = exprs(AVISITN),   filter_join = CRIT1FL == \"Y\" & CRIT1FL.join == \"Y\" &     (tmp_obs_nr + 1 == tmp_obs_nr.join | tmp_obs_nr == max(tmp_obs_nr.join)) ) #> # A tibble: 2 × 3 #>   USUBJID AVISITN CRIT1FL #>   <chr>     <dbl> <chr>   #> 1 2             1 Y       #> 2 3             1 Y        # first_cond_lower and first_cond_upper argument myd <- tribble(   ~subj, ~day, ~val,   \"1\",      1, \"++\",   \"1\",      2, \"-\",   \"1\",      3, \"0\",   \"1\",      4, \"+\",   \"1\",      5, \"++\",   \"1\",      6, \"-\",   \"2\",      1, \"-\",   \"2\",      2, \"++\",   \"2\",      3, \"+\",   \"2\",      4, \"0\",   \"2\",      5, \"-\",   \"2\",      6, \"++\" )  # select \"0\" where all results from the first \"++\" before the \"0\" up to the \"0\" # (excluding the \"0\") are \"+\" or \"++\" filter_joined(   myd,   dataset_add = myd,   by_vars = exprs(subj),   order = exprs(day),   join_vars = exprs(val),   join_type = \"before\",   first_cond_lower = val.join == \"++\",   filter_join = val == \"0\" & all(val.join %in% c(\"+\", \"++\")) ) #> # A tibble: 1 × 3 #>   subj    day val   #>   <chr> <dbl> <chr> #> 1 2         4 0      # select \"0\" where all results from the \"0\" (excluding the \"0\") up to the first # \"++\" after the \"0\" are \"+\" or \"++\" filter_joined(   myd,   dataset_add = myd,   by_vars = exprs(subj),   order = exprs(day),   join_vars = exprs(val),   join_type = \"after\",   first_cond_upper = val.join == \"++\",   filter_join = val == \"0\" & all(val.join %in% c(\"+\", \"++\")) ) #> # A tibble: 1 × 3 #>   subj    day val   #>   <chr> <dbl> <chr> #> 1 1         3 0"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_not_exist.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns records that don't fit into existing by groups in a filtered source dataset — filter_not_exist","title":"Returns records that don't fit into existing by groups in a filtered source dataset — filter_not_exist","text":"Returns records input dataset belong groups present source dataset, source dataset optionally filtered. example, used return ADSL records subjects take certain concomitant medications course study (per records ADCM).","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_not_exist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns records that don't fit into existing by groups in a filtered source dataset — filter_not_exist","text":"","code":"filter_not_exist(dataset, dataset_add, by_vars, filter_add = NULL)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_not_exist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns records that don't fit into existing by groups in a filtered source dataset — filter_not_exist","text":"dataset Input dataset variables specified by_vars argument expected dataset. dataset_add Source dataset source dataset, determines groups returned input dataset, based groups exist dataset subset filter_add. variables specified by_vars filter_add parameters expected dataset. by_vars Grouping variables Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) filter_add Filter source dataset filter condition used subset source dataset. Alternatively, filter condition supplied, subsetting source dataset performed. Default: NULL (.e. filtering performed)","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_not_exist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns records that don't fit into existing by groups in a filtered source dataset — filter_not_exist","text":"records input dataset contained within existing group filtered source dataset.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_not_exist.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Returns records that don't fit into existing by groups in a filtered source dataset — filter_not_exist","text":"Returns records dataset match existing groups dataset_add, filtered according filter_add. groups exist dataset exist dataset_add, empty dataset returned.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_not_exist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Returns records that don't fit into existing by groups in a filtered source dataset — filter_not_exist","text":"","code":"# Get demographic information about subjects who didn't take vitamin supplements # during the study  library(tibble)  adsl <- tribble(   ~USUBJID,      ~AGE, ~SEX,   \"01-701-1015\", 63,   \"F\",   \"01-701-1023\", 64,   \"M\",   \"01-701-1034\", 77,   \"F\",   \"01-701-1118\", 52,   \"M\" )  adcm <- tribble(   ~USUBJID,      ~CMTRT,         ~CMSTDTC,   \"01-701-1015\", \"ASPIRIN\",      \"2013-05-14\",   \"01-701-1023\", \"MYLANTA\",      \"2014-01-04\",   \"01-701-1023\", \"CALCIUM\",      \"2014-02-25\",   \"01-701-1034\", \"VITAMIN C\",    \"2013-12-12\",   \"01-701-1034\", \"CALCIUM\",      \"2013-03-27\",   \"01-701-1118\", \"MULTIVITAMIN\", \"2013-02-21\" )  filter_not_exist(   dataset = adsl,   dataset_add = adcm,   by_vars = exprs(USUBJID),   filter_add = str_detect(CMTRT, \"VITAMIN\") ) #> # A tibble: 2 × 3 #>   USUBJID       AGE SEX   #>   <chr>       <dbl> <chr> #> 1 01-701-1015    63 F     #> 2 01-701-1023    64 M"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_relative.html","id":null,"dir":"Reference","previous_headings":"","what":"Filter the Observations Before or After a Condition is Fulfilled — filter_relative","title":"Filter the Observations Before or After a Condition is Fulfilled — filter_relative","text":"Filters observations observation specified condition fulfilled group. example, function called select subject observations first disease progression.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_relative.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filter the Observations Before or After a Condition is Fulfilled — filter_relative","text":"","code":"filter_relative(   dataset,   by_vars,   order,   condition,   mode,   selection,   inclusive,   keep_no_ref_groups = TRUE,   check_type = \"warning\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_relative.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filter the Observations Before or After a Condition is Fulfilled — filter_relative","text":"dataset Input dataset variables specified by_vars order arguments expected dataset. by_vars Grouping variables Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) order Sort order Within group observations ordered specified order. handling NAs sorting variables see Sort Order. Permitted Values: list expressions created exprs(), e.g., exprs(ADT, desc(AVAL)) condition Condition Reference Observation specified condition determines reference observation. output dataset contains observations (selection parameter) reference observation. mode Selection mode (first last) \"first\" specified, group observations (selection parameter) observation condition (condition parameter) fulfilled first time included output dataset. \"last\" specified, group observations (selection parameter) observation condition (condition parameter) fulfilled last time included output dataset. Permitted Values: \"first\", \"last\" selection Select observations reference observation? Permitted Values: \"\", \"\" inclusive Include reference observation? Permitted Values: TRUE, FALSE keep_no_ref_groups groups without reference observation kept? Permitted Values: TRUE, FALSE check_type Check uniqueness? \"warning\" \"error\" specified, specified message issued observations input dataset unique respect variables order. Permitted Values: \"none\", \"warning\", \"error\"","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_relative.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filter the Observations Before or After a Condition is Fulfilled — filter_relative","text":"dataset containing group observations observation condition fulfilled first last time","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_relative.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Filter the Observations Before or After a Condition is Fulfilled — filter_relative","text":"group ( by_vars parameter) observations (selection parameter) observations condition (condition parameter) fulfilled first last time (order parameter mode parameter) included output dataset.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_relative.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Filter the Observations Before or After a Condition is Fulfilled — filter_relative","text":"","code":"library(tibble)  response <- tribble(   ~USUBJID, ~AVISITN, ~AVALC,   \"1\",      1,        \"PR\",   \"1\",      2,        \"CR\",   \"1\",      3,        \"CR\",   \"1\",      4,        \"SD\",   \"1\",      5,        \"NE\",   \"2\",      1,        \"SD\",   \"2\",      2,        \"PD\",   \"2\",      3,        \"PD\",   \"3\",      1,        \"SD\",   \"4\",      1,        \"SD\",   \"4\",      2,        \"PR\",   \"4\",      3,        \"PD\",   \"4\",      4,        \"SD\",   \"4\",      5,        \"PR\" )  # Select observations up to first PD for each patient response %>%   filter_relative(     by_vars = exprs(USUBJID),     order = exprs(AVISITN),     condition = AVALC == \"PD\",     mode = \"first\",     selection = \"before\",     inclusive = TRUE   ) #> # A tibble: 11 × 3 #>    USUBJID AVISITN AVALC #>    <chr>     <dbl> <chr> #>  1 1             1 PR    #>  2 1             2 CR    #>  3 1             3 CR    #>  4 1             4 SD    #>  5 1             5 NE    #>  6 2             1 SD    #>  7 2             2 PD    #>  8 3             1 SD    #>  9 4             1 SD    #> 10 4             2 PR    #> 11 4             3 PD     # Select observations after last CR, PR, or SD for each patient response %>%   filter_relative(     by_vars = exprs(USUBJID),     order = exprs(AVISITN),     condition = AVALC %in% c(\"CR\", \"PR\", \"SD\"),     mode = \"last\",     selection = \"after\",     inclusive = FALSE   ) #> # A tibble: 3 × 3 #>   USUBJID AVISITN AVALC #>   <chr>     <dbl> <chr> #> 1 1             5 NE    #> 2 2             2 PD    #> 3 2             3 PD     # Select observations from first response to first PD response %>%   filter_relative(     by_vars = exprs(USUBJID),     order = exprs(AVISITN),     condition = AVALC %in% c(\"CR\", \"PR\"),     mode = \"first\",     selection = \"after\",     inclusive = TRUE,     keep_no_ref_groups = FALSE   ) %>%   filter_relative(     by_vars = exprs(USUBJID),     order = exprs(AVISITN),     condition = AVALC == \"PD\",     mode = \"first\",     selection = \"before\",     inclusive = TRUE   ) #> # A tibble: 7 × 3 #>   USUBJID AVISITN AVALC #>   <chr>     <dbl> <chr> #> 1 1             1 PR    #> 2 1             2 CR    #> 3 1             3 CR    #> 4 1             4 SD    #> 5 1             5 NE    #> 6 4             2 PR    #> 7 4             3 PD"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/flag_event.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a flag_event Object — flag_event","title":"Create a flag_event Object — flag_event","text":"flag_event object used define events input derive_var_merged_ef_msrc() function.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/flag_event.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a flag_event Object — flag_event","text":"","code":"flag_event(dataset_name, condition = NULL, by_vars = NULL)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/flag_event.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a flag_event Object — flag_event","text":"dataset_name Dataset name dataset used input event. name refers dataset specified source_datasets derive_var_merged_ef_msrc(). Permitted Values: character scalar condition Condition condition evaluated dataset referenced dataset_name. groups evaluates TRUE least new variable set true value (true_value). by_vars Grouping variables specified, dataset grouped specified variables condition evaluated. named elements used by_vars like by_vars = exprs(USUBJID, EXLNKID = ECLNKID), variables renamed evaluation. by_vars element specified, observations grouped variables specified by_vars argument derive_var_merged_ef_msrc().","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/format.basket_select.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns a Character Representation of a basket_select() Object — format.basket_select","title":"Returns a Character Representation of a basket_select() Object — format.basket_select","text":"function returns character representation basket_select() object. can used error messages example.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/format.basket_select.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns a Character Representation of a basket_select() Object — format.basket_select","text":"","code":"# S3 method for class 'basket_select' format(x, ...)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/format.basket_select.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns a Character Representation of a basket_select() Object — format.basket_select","text":"x basket_select() object ... used","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/format.basket_select.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns a Character Representation of a basket_select() Object — format.basket_select","text":"character representation basket_select() object","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/format.basket_select.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Returns a Character Representation of a basket_select() Object — format.basket_select","text":"","code":"format(basket_select(id = 42, scope = \"NARROW\", type = \"smq\")) #> [1] \"basket_select(name = NULL, id = 42, scope = \\\"NARROW\\\", type = \\\"smq\\\")\""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_admiral_option.html","id":null,"dir":"Reference","previous_headings":"","what":"Get the Value of an Admiral Option — get_admiral_option","title":"Get the Value of an Admiral Option — get_admiral_option","text":"Get Value Admiral Option Can Modified Advanced Users.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_admiral_option.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get the Value of an Admiral Option — get_admiral_option","text":"","code":"get_admiral_option(option)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_admiral_option.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get the Value of an Admiral Option — get_admiral_option","text":"option character scalar commonly used admiral function inputs. now, support available \"subject_keys\" \"signif_digits\". See set_admiral_options() description options.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_admiral_option.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get the Value of an Admiral Option — get_admiral_option","text":"value specified option.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_admiral_option.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get the Value of an Admiral Option — get_admiral_option","text":"function allows flexibility function inputs may need repeated multiple times script, subject_keys.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_admiral_option.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get the Value of an Admiral Option — get_admiral_option","text":"","code":"library(dplyr, warn.conflicts = FALSE) dm <- tribble(   ~STUDYID, ~DOMAIN,  ~USUBJID, ~AGE,   ~AGEU,   \"PILOT01\",   \"DM\", \"01-1302\",   61, \"YEARS\",   \"PILOT01\",   \"DM\", \"17-1344\",   64, \"YEARS\" )  vs <- tribble(   ~STUDYID,  ~DOMAIN,  ~USUBJID, ~VSTESTCD,     ~VISIT,     ~VSTPT, ~VSSTRESN,   \"PILOT01\",    \"VS\", \"01-1302\",   \"DIABP\", \"BASELINE\",    \"LYING\",        76,   \"PILOT01\",    \"VS\", \"01-1302\",   \"DIABP\", \"BASELINE\", \"STANDING\",        87,   \"PILOT01\",    \"VS\", \"01-1302\",   \"DIABP\",   \"WEEK 2\",    \"LYING\",        71,   \"PILOT01\",    \"VS\", \"01-1302\",   \"DIABP\",   \"WEEK 2\", \"STANDING\",        79,   \"PILOT01\",    \"VS\", \"17-1344\",   \"DIABP\", \"BASELINE\",    \"LYING\",        88,   \"PILOT01\",    \"VS\", \"17-1344\",   \"DIABP\", \"BASELINE\", \"STANDING\",        86,   \"PILOT01\",    \"VS\", \"17-1344\",   \"DIABP\",   \"WEEK 2\",    \"LYING\",        84,   \"PILOT01\",    \"VS\", \"17-1344\",   \"DIABP\",   \"WEEK 2\", \"STANDING\",        82 )  # Merging all dm variables to vs derive_vars_merged(   vs,   dataset_add = select(dm, -DOMAIN),   by_vars = get_admiral_option(\"subject_keys\") ) #> # A tibble: 8 × 9 #>   STUDYID DOMAIN USUBJID VSTESTCD VISIT    VSTPT    VSSTRESN   AGE AGEU  #>   <chr>   <chr>  <chr>   <chr>    <chr>    <chr>       <dbl> <dbl> <chr> #> 1 PILOT01 VS     01-1302 DIABP    BASELINE LYING          76    61 YEARS #> 2 PILOT01 VS     01-1302 DIABP    BASELINE STANDING       87    61 YEARS #> 3 PILOT01 VS     01-1302 DIABP    WEEK 2   LYING          71    61 YEARS #> 4 PILOT01 VS     01-1302 DIABP    WEEK 2   STANDING       79    61 YEARS #> 5 PILOT01 VS     17-1344 DIABP    BASELINE LYING          88    64 YEARS #> 6 PILOT01 VS     17-1344 DIABP    BASELINE STANDING       86    64 YEARS #> 7 PILOT01 VS     17-1344 DIABP    WEEK 2   LYING          84    64 YEARS #> 8 PILOT01 VS     17-1344 DIABP    WEEK 2   STANDING       82    64 YEARS"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_duplicates_dataset.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Duplicate Records that Led to a Prior Error — get_duplicates_dataset","title":"Get Duplicate Records that Led to a Prior Error — get_duplicates_dataset","text":"Get Duplicate Records Led Prior Error","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_duplicates_dataset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Duplicate Records that Led to a Prior Error — get_duplicates_dataset","text":"","code":"get_duplicates_dataset()"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_duplicates_dataset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Duplicate Records that Led to a Prior Error — get_duplicates_dataset","text":"data.frame NULL","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_duplicates_dataset.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get Duplicate Records that Led to a Prior Error — get_duplicates_dataset","text":"Many {admiral} function check input dataset contains one record per by_vars group throw error otherwise. get_duplicates_dataset() function allows one retrieve duplicate records lead error. Note function always returns dataset duplicates last error thrown current R session. Thus, restarting R sessions get_duplicates_dataset() return NULL second error thrown, dataset first error can longer accessed (unless saved variable).","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_duplicates_dataset.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get Duplicate Records that Led to a Prior Error — get_duplicates_dataset","text":"","code":"data(admiral_adsl)  # Duplicate the first record adsl <- rbind(admiral_adsl[1L, ], admiral_adsl)  signal_duplicate_records(adsl, exprs(USUBJID), cnd_type = \"warning\") #> Warning: Dataset contains duplicate records with respect to `USUBJID` #> ℹ Run `admiral::get_duplicates_dataset()` to access the duplicate records  get_duplicates_dataset() #> Duplicate records with respect to `USUBJID`. #> # A tibble: 2 × 54 #>   USUBJID     STUDYID  SUBJID RFSTDTC RFENDTC RFXSTDTC RFXENDTC RFICDTC RFPENDTC #> * <chr>       <chr>    <chr>  <chr>   <chr>   <chr>    <chr>    <chr>   <chr>    #> 1 01-701-1015 CDISCPI… 1015   2014-0… 2014-0… 2014-01… 2014-07… NA      2014-07… #> 2 01-701-1015 CDISCPI… 1015   2014-0… 2014-0… 2014-01… 2014-07… NA      2014-07… #> # ℹ 45 more variables: DTHDTC <chr>, DTHFL <chr>, SITEID <chr>, AGE <dbl>, #> #   AGEU <chr>, SEX <chr>, RACE <chr>, ETHNIC <chr>, ARMCD <chr>, ARM <chr>, #> #   ACTARMCD <chr>, ACTARM <chr>, COUNTRY <chr>, DMDTC <chr>, DMDY <dbl>, #> #   TRT01P <chr>, TRT01A <chr>, TRTSDTM <dttm>, TRTSTMF <chr>, TRTEDTM <dttm>, #> #   TRTETMF <chr>, TRTSDT <date>, TRTEDT <date>, TRTDURD <dbl>, SCRFDT <date>, #> #   EOSDT <date>, EOSSTT <chr>, FRVDT <date>, RANDDT <date>, DTHDT <date>, #> #   DTHDTF <chr>, DTHADY <dbl>, LDDTHELD <dbl>, DTHCAUS <chr>, DTHDOM <chr>, …"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_flagged_records.html","id":null,"dir":"Reference","previous_headings":"","what":"Create an Existence Flag — get_flagged_records","title":"Create an Existence Flag — get_flagged_records","text":"Create flag variable input dataset indicates exists least one observation input dataset fulfilling certain condition. Note: helper function derive_vars_merged_exist_flag() inputs result derive_vars_merged().","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_flagged_records.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create an Existence Flag — get_flagged_records","text":"","code":"get_flagged_records(dataset, new_var, condition, filter = NULL)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_flagged_records.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create an Existence Flag — get_flagged_records","text":"dataset Input dataset new_var New variable specified variable added input dataset. condition Condition condition evaluated dataset (dataset). rows evaluates TRUE new variable set 1 new column. Otherwise, set 0. filter Filter additional data observations fulfilling specified condition taken account flagging. argument specified, observations considered. Permitted Values: condition","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_flagged_records.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create an Existence Flag — get_flagged_records","text":"output dataset input dataset filtered filter condition variable specified new_var representing flag condition.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_flagged_records.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create an Existence Flag — get_flagged_records","text":"","code":"library(dplyr, warn.conflicts = FALSE)   ae <- tribble(   ~STUDYID,  ~DOMAIN,  ~USUBJID,    ~AETERM,     ~AEREL,   \"PILOT01\",    \"AE\", \"01-1028\", \"ERYTHEMA\", \"POSSIBLE\",   \"PILOT01\",    \"AE\", \"01-1028\", \"PRURITUS\", \"PROBABLE\",   \"PILOT01\",    \"AE\", \"06-1049\",  \"SYNCOPE\", \"POSSIBLE\",   \"PILOT01\",    \"AE\", \"06-1049\",  \"SYNCOPE\", \"PROBABLE\" )   get_flagged_records(   dataset = ae,   new_var = AERELFL,   condition = AEREL == \"PROBABLE\" ) %>%   select(STUDYID, USUBJID, AERELFL) #> # A tibble: 4 × 3 #>   STUDYID USUBJID AERELFL #>   <chr>   <chr>     <dbl> #> 1 PILOT01 01-1028       0 #> 2 PILOT01 01-1028       1 #> 3 PILOT01 06-1049       0 #> 4 PILOT01 06-1049       1  vs <- tribble(   ~STUDYID,  ~DOMAIN,  ~USUBJID,      ~VISIT, ~VSTESTCD, ~VSSTRESN, ~VSBLFL,   \"PILOT01\",    \"VS\", \"01-1028\", \"SCREENING\",  \"HEIGHT\",     177.8,      NA,   \"PILOT01\",    \"VS\", \"01-1028\", \"SCREENING\",  \"WEIGHT\",     98.88,      NA,   \"PILOT01\",    \"VS\", \"01-1028\",  \"BASELINE\",  \"WEIGHT\",     99.34,     \"Y\",   \"PILOT01\",    \"VS\", \"01-1028\",    \"WEEK 4\",  \"WEIGHT\",     98.88,      NA,   \"PILOT01\",    \"VS\", \"04-1127\", \"SCREENING\",  \"HEIGHT\",     165.1,      NA,   \"PILOT01\",    \"VS\", \"04-1127\", \"SCREENING\",  \"WEIGHT\",     42.87,      NA,   \"PILOT01\",    \"VS\", \"04-1127\",  \"BASELINE\",  \"WEIGHT\",     41.05,     \"Y\",   \"PILOT01\",    \"VS\", \"04-1127\",    \"WEEK 4\",  \"WEIGHT\",     41.73,      NA,   \"PILOT01\",    \"VS\", \"06-1049\", \"SCREENING\",  \"HEIGHT\",    167.64,      NA,   \"PILOT01\",    \"VS\", \"06-1049\", \"SCREENING\",  \"WEIGHT\",     57.61,      NA,   \"PILOT01\",    \"VS\", \"06-1049\",  \"BASELINE\",  \"WEIGHT\",     57.83,     \"Y\",   \"PILOT01\",    \"VS\", \"06-1049\",    \"WEEK 4\",  \"WEIGHT\",     58.97,      NA ) get_flagged_records(   dataset = vs,   new_var = WTBLHIFL,   condition = VSSTRESN > 90,   filter = VSTESTCD == \"WEIGHT\" & VSBLFL == \"Y\" ) %>%   select(STUDYID, USUBJID, WTBLHIFL) #> # A tibble: 3 × 3 #>   STUDYID USUBJID WTBLHIFL #>   <chr>   <chr>      <dbl> #> 1 PILOT01 01-1028        1 #> 2 PILOT01 04-1127        0 #> 3 PILOT01 06-1049        0"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_hori_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Creating Temporary Parameters and <variable>.<parameter> Variables — get_hori_data","title":"Creating Temporary Parameters and <variable>.<parameter> Variables — get_hori_data","text":"function creates temporary parameters variables form <variable>.<parameter>, e.g., AVAL.WEIGHT.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_hori_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creating Temporary Parameters and <variable>.<parameter> Variables — get_hori_data","text":"","code":"get_hori_data(dataset, by_vars, parameters, set_values_to, filter)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_hori_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creating Temporary Parameters and <variable>.<parameter> Variables — get_hori_data","text":"dataset Input dataset variables specified by_vars argument expected dataset. by_vars Grouping variables Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) parameters List parameter codes input dataset restricted specified parameter codes. expression specified, new parameter code added input dataset. name element defines parameter code expression observations select. Permitted Values: character vector PARAMCD values list expressions set_values_to variables form <variable>.<parameter> like AVAL.WEIGHT added input dataset. set value variable parameter. E.g., AVAL.WEIGHT set value AVAL PARAMCD == \"WEIGHT\". Permitted Values: list expressions filter Filter condition used restricting input dataset specified filter condition used warnings . applied input dataset. Permitted Values: unquoted expression","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_hori_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Creating Temporary Parameters and <variable>.<parameter> Variables — get_hori_data","text":"dataset one observation per group. contains variables specified by_vars variables form <variable>.<parameter> occurring set_values_to.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_imputation_target_date.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Date Imputation Targets — get_imputation_target_date","title":"Get Date Imputation Targets — get_imputation_target_date","text":"Get Date Imputation Targets","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_imputation_target_date.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Date Imputation Targets — get_imputation_target_date","text":"","code":"get_imputation_target_date(date_imputation, month)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_imputation_target_date.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Date Imputation Targets — get_imputation_target_date","text":"date_imputation value impute day/month datepart missing. character value expected, either format month day specified \"mm-dd\": e.g. \"06-15\" 15th June, keyword: \"first\", \"mid\", \"last\" impute first/mid/last day/month. month Month component partial date","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_imputation_target_date.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Date Imputation Targets — get_imputation_target_date","text":"list character vectors. elements list named \"year\", \"month\", \"day\".","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_imputation_target_date.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get Date Imputation Targets — get_imputation_target_date","text":"date_imputation = \"first\" \"0000\", \"01\", \"01\" returned. date_imputation = \"mid\" \"xxxx\", \"06\", \"30\" month NA \"15\" otherwise returned. date_imputation = \"last\" \"9999\", \"12\", \"31\" returned. date_imputation = \"<mm>-<dd>\" \"xxxx\", \"<mm>\", \"<dd>\" returned. \"xxxx\" indicates component undefined. undefined component occurs imputed DTC value, imputed DTC value set NA_character_ imputation functions.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_imputation_target_time.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Time Imputation Targets — get_imputation_target_time","title":"Get Time Imputation Targets — get_imputation_target_time","text":"Get Time Imputation Targets","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_imputation_target_time.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Time Imputation Targets — get_imputation_target_time","text":"","code":"get_imputation_target_time(time_imputation)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_imputation_target_time.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Time Imputation Targets — get_imputation_target_time","text":"time_imputation value impute time timepart missing. character value expected, either format hour, min sec specified \"hh:mm:ss\": e.g. \"00:00:00\" start day, keyword: \"first\",\"last\" impute start/end day.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_imputation_target_time.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Time Imputation Targets — get_imputation_target_time","text":"list character vectors. elements list named \"hour\", \"minute\", \"second\".","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_imputation_target_time.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get Time Imputation Targets — get_imputation_target_time","text":"time_imputation = \"first\" \"00\", \"00\", \"00\" returned. time_imputation = \"last\" \"23\", \"59\", \"59\" returned. time_imputation = \"<hh>:<mm>:<ss>\" \"<hh>\", \"<mm>\", \"<ss>\" returned.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_joined_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Join Data for ","title":"Join Data for ","text":"helper function joins data \"joined\" functions. .join variables included output dataset.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_joined_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Join Data for ","text":"","code":"get_joined_data(   dataset,   dataset_add,   by_vars = NULL,   join_vars = NULL,   join_type,   first_cond_lower = NULL,   first_cond_upper = NULL,   order = NULL,   tmp_obs_nr_var = NULL,   filter_add = NULL,   filter_join = NULL,   check_type = \"warning\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_joined_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Join Data for ","text":"dataset Input dataset variables specified by_vars argument expected dataset. dataset_add Additional dataset variables specified by_vars, new_vars, join_vars, order argument expected. by_vars Grouping variables two datasets joined specified variables. Variables can renamed naming element, .e. by_vars = exprs(<name input dataset> = <name additional dataset>), similar dplyr joins. Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) join_vars Variables use additional dataset extra variables required additional dataset filter_join specified argument. Variables specified new_vars need repeated join_vars. specified variable exists input dataset additional dataset, suffix \".join\" added variable additional dataset. expression named, e.g., exprs(EXTDT = convert_dtc_to_dt(EXSTDTC)), corresponding variable added additional dataset can used filter conditions (filter_add, filter_join) new_vars. variable included output dataset. variables included output dataset. Permitted Values: list variables named expressions created exprs() join_type Observations keep joining argument determines joined observations kept respect original observation. example, join_type = \"\" specified observations original observations kept. example confirmed response BOR oncology setting confirmed deterioration questionnaires confirmatory assessment must assessment. Thus join_type = \"\" used. Whereas, sometimes might allow confirmatory observations occur prior observation. example, identify AEs occurring seven days COVID AE. Thus join_type = \"\" used. Permitted Values: \"\", \"\", \"\" first_cond_lower Condition selecting range data () argument specified, observations restricted first observation current observation specified condition fulfilled current observation. condition fulfilled observations, observations considered, .e., observation flagged. argument specified filter_join contains summary functions apply observations certain observation current observation current observation. first_cond_upper Condition selecting range data () argument specified, observations restricted first observation specified condition fulfilled. condition fulfilled observations, observations considered, .e., observation flagged. argument specified filter_join contains summary functions apply observations confirmation assessment. order Sort order argument set non-null value, observation input dataset first last observation joined dataset selected respect specified order. specified variables expected additional dataset (dataset_add). variable available dataset dataset_add, one dataset_add used sorting. expression named, e.g., exprs(EXSTDT = convert_dtc_to_dt(EXSTDTC), EXSEQ), corresponding variable (EXSTDT) added additional dataset can used filter conditions (filter_add, filter_join) join_vars new_vars. variable included output dataset. handling NAs sorting variables see Sort Order. Permitted Values: list expressions created exprs(), e.g., exprs(ADT, desc(AVAL)) NULL tmp_obs_nr_var Temporary observation number specified variable added input dataset (dataset) additional dataset (dataset_add). set observation number respect order. group (by_vars) observation number starts 1. variable can used conditions (filter_join, first_cond_upper, first_cond_lower). can also used select consecutive observations last observation. filter_add Filter additional dataset (dataset_add) observations dataset_add fulfilling specified condition joined input dataset. argument specified, observations joined. Variables created order new_vars arguments can used condition. condition can include summary functions like () (). additional dataset grouped variables (by_vars). Permitted Values: condition filter_join Filter joined dataset specified condition applied joined dataset. Therefore variables datasets dataset dataset_add can used. Variables created order new_vars arguments can used condition. condition can include summary functions like () (). joined dataset grouped original observations. Permitted Values: condition check_type Check uniqueness? \"warning\" \"error\" specified, specified message issued observations (restricted) joined dataset unique respect variables order. argument ignored order specified. case error issued independent check_type restricted joined dataset contains one observation observations input dataset. Permitted Values: \"none\", \"warning\", \"error\"","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_joined_data.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Join Data for ","text":"variables specified order added additional dataset (dataset_add). variables specified join_vars added additional dataset (dataset_add). records additional dataset (dataset_add) restricted matching filter_add condition. input dataset (restricted) additional dataset left joined grouping variables (by_vars). grouping variables specified, full join performed. joined dataset restricted filter_join condition.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_joined_sub_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Join Data for ","title":"Join Data for ","text":"helper function joins data \"joined\" functions. .join variables included output dataset. called get_joined_data() process group separately. reduces memory consumption.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_joined_sub_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Join Data for ","text":"","code":"get_joined_sub_data(   dataset,   dataset_add,   by_vars,   tmp_obs_nr_var,   tmp_obs_nr_left,   join_type,   first_cond_upper,   first_cond_lower,   filter_join )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_joined_sub_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Join Data for ","text":"dataset Input dataset variables specified by_vars argument expected dataset. dataset_add Additional dataset variables specified by_vars, new_vars, join_vars, order argument expected. by_vars Grouping variables two datasets joined specified variables. Variables can renamed naming element, .e. by_vars = exprs(<name input dataset> = <name additional dataset>), similar dplyr joins. Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) tmp_obs_nr_var Temporary observation number specified variable added input dataset (dataset) additional dataset (dataset_add). set observation number respect order. group (by_vars) observation number starts 1. variable can used conditions (filter_join, first_cond_upper, first_cond_lower). can also used select consecutive observations last observation. join_type Observations keep joining argument determines joined observations kept respect original observation. example, join_type = \"\" specified observations original observations kept. example confirmed response BOR oncology setting confirmed deterioration questionnaires confirmatory assessment must assessment. Thus join_type = \"\" used. Whereas, sometimes might allow confirmatory observations occur prior observation. example, identify AEs occurring seven days COVID AE. Thus join_type = \"\" used. Permitted Values: \"\", \"\", \"\" first_cond_upper Condition selecting range data () argument specified, observations restricted first observation specified condition fulfilled. condition fulfilled observations, observations considered, .e., observation flagged. argument specified filter_join contains summary functions apply observations confirmation assessment. first_cond_lower Condition selecting range data () argument specified, observations restricted first observation current observation specified condition fulfilled current observation. condition fulfilled observations, observations considered, .e., observation flagged. argument specified filter_join contains summary functions apply observations certain observation current observation current observation. filter_join Filter joined dataset specified condition applied joined dataset. Therefore variables datasets dataset dataset_add can used. Variables created order new_vars arguments can used condition. condition can include summary functions like () (). joined dataset grouped original observations. Permitted Values: condition","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_joined_sub_data.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Join Data for ","text":"input dataset (dataset) additional dataset (dataset_add) left joined grouping variables (by_vars). grouping variables specified, full join performed. joined dataset restricted specified arguments join_type, first_cond_upper, first_cond_lower. See argument descriptions details. joined dataset restricted filter_join condition.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_many_to_one_dataset.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Many to One Values that Led to a Prior Error — get_many_to_one_dataset","title":"Get Many to One Values that Led to a Prior Error — get_many_to_one_dataset","text":"Get Many One Values Led Prior Error","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_many_to_one_dataset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Many to One Values that Led to a Prior Error — get_many_to_one_dataset","text":"","code":"get_many_to_one_dataset()"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_many_to_one_dataset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Many to One Values that Led to a Prior Error — get_many_to_one_dataset","text":"data.frame NULL","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_many_to_one_dataset.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get Many to One Values that Led to a Prior Error — get_many_to_one_dataset","text":"assert_one_to_one() detects issue, many one values stored dataset. dataset can retrieved get_many_to_one_dataset(). Note function always returns many one values last error thrown current R session. Thus, restarting R sessions get_many_to_one_dataset() return NULL second error thrown, dataset first error can longer accessed (unless saved variable).","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_many_to_one_dataset.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get Many to One Values that Led to a Prior Error — get_many_to_one_dataset","text":"","code":"library(admiraldev, warn.conflicts = FALSE) data(admiral_adsl)  try(   assert_one_to_one(admiral_adsl, exprs(SITEID), exprs(STUDYID)) ) #> Error in eval(expr, envir) :  #>   There is more than one value of \"SITEID\" for some values of \"STUDYID\" #> ℹ Call `admiral::get_many_to_one_dataset()` to get all many-to-one values.  get_many_to_one_dataset() #> # A tibble: 17 × 2 #> # Groups:   STUDYID [1] #>    SITEID STUDYID      #>    <chr>  <chr>        #>  1 701    CDISCPILOT01 #>  2 702    CDISCPILOT01 #>  3 703    CDISCPILOT01 #>  4 704    CDISCPILOT01 #>  5 705    CDISCPILOT01 #>  6 706    CDISCPILOT01 #>  7 707    CDISCPILOT01 #>  8 708    CDISCPILOT01 #>  9 709    CDISCPILOT01 #> 10 710    CDISCPILOT01 #> 11 711    CDISCPILOT01 #> 12 713    CDISCPILOT01 #> 13 714    CDISCPILOT01 #> 14 715    CDISCPILOT01 #> 15 716    CDISCPILOT01 #> 16 717    CDISCPILOT01 #> 17 718    CDISCPILOT01"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_not_mapped.html","id":null,"dir":"Reference","previous_headings":"","what":"Get list of records not mapped from the lookup table. — get_not_mapped","title":"Get list of records not mapped from the lookup table. — get_not_mapped","text":"Get list records mapped lookup table.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_not_mapped.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get list of records not mapped from the lookup table. — get_not_mapped","text":"","code":"get_not_mapped()"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_not_mapped.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get list of records not mapped from the lookup table. — get_not_mapped","text":"data.frame NULL","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_one_to_many_dataset.html","id":null,"dir":"Reference","previous_headings":"","what":"Get One to Many Values that Led to a Prior Error — get_one_to_many_dataset","title":"Get One to Many Values that Led to a Prior Error — get_one_to_many_dataset","text":"Get One Many Values Led Prior Error","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_one_to_many_dataset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get One to Many Values that Led to a Prior Error — get_one_to_many_dataset","text":"","code":"get_one_to_many_dataset()"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_one_to_many_dataset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get One to Many Values that Led to a Prior Error — get_one_to_many_dataset","text":"data.frame NULL","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_one_to_many_dataset.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get One to Many Values that Led to a Prior Error — get_one_to_many_dataset","text":"assert_one_to_one() detects issue, one many values stored dataset. dataset can retrieved get_one_to_many_dataset(). Note function always returns one many values last error thrown current R session. Thus, restarting R sessions get_one_to_many_dataset() return NULL second error thrown, dataset first error can longer accessed (unless saved variable).","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_one_to_many_dataset.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get One to Many Values that Led to a Prior Error — get_one_to_many_dataset","text":"","code":"library(admiraldev, warn.conflicts = FALSE) data(admiral_adsl)  try(   assert_one_to_one(admiral_adsl, exprs(STUDYID), exprs(SITEID)) ) #> Error in eval(expr, envir) :  #>   For some values of \"STUDYID\" there is more than one value of \"SITEID\" #> ℹ Call `admiral::get_one_to_many_dataset()` to get all one-to-many values.  get_one_to_many_dataset() #> # A tibble: 17 × 2 #> # Groups:   STUDYID [1] #>    STUDYID      SITEID #>    <chr>        <chr>  #>  1 CDISCPILOT01 701    #>  2 CDISCPILOT01 702    #>  3 CDISCPILOT01 703    #>  4 CDISCPILOT01 704    #>  5 CDISCPILOT01 705    #>  6 CDISCPILOT01 706    #>  7 CDISCPILOT01 707    #>  8 CDISCPILOT01 708    #>  9 CDISCPILOT01 709    #> 10 CDISCPILOT01 710    #> 11 CDISCPILOT01 711    #> 12 CDISCPILOT01 713    #> 13 CDISCPILOT01 714    #> 14 CDISCPILOT01 715    #> 15 CDISCPILOT01 716    #> 16 CDISCPILOT01 717    #> 17 CDISCPILOT01 718"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_partialdatetime.html","id":null,"dir":"Reference","previous_headings":"","what":"Parse DTC variable and Determine Components — get_partialdatetime","title":"Parse DTC variable and Determine Components — get_partialdatetime","text":"Parse DTC variable Determine Components","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_partialdatetime.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Parse DTC variable and Determine Components — get_partialdatetime","text":"","code":"get_partialdatetime(dtc)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_partialdatetime.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Parse DTC variable and Determine Components — get_partialdatetime","text":"dtc '--DTC' date parse character date expected format like yyyy-mm-dd yyyy-mm-ddThh:mm:ss. Trailing components can omitted - valid value component.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_partialdatetime.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Parse DTC variable and Determine Components — get_partialdatetime","text":"list character vectors. elements list named \"year\", \"month\", \"day\", \"hour\", \"minute\", \"second\". Missing components set NA_character_.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_partialdatetime.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Parse DTC variable and Determine Components — get_partialdatetime","text":"function can replaced parttime parser available.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_summary_records.html","id":null,"dir":"Reference","previous_headings":"","what":"Create Summary Records — get_summary_records","title":"Create Summary Records — get_summary_records","text":"Development get_summary_records() complete, new code recommend switching using dataset_add argument derive_summary_records(). uncommon analysis need whereby one needs derive analysis value (AVAL) multiple records. ADaM basic dataset structure variable DTYPE available indicate new derived records added dataset.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_summary_records.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create Summary Records — get_summary_records","text":"","code":"get_summary_records(dataset, by_vars, filter = NULL, set_values_to = NULL)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_summary_records.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create Summary Records — get_summary_records","text":"dataset Input dataset variables specified by_vars argument expected dataset. by_vars Grouping variables Variables consider generation groupwise summary records. Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) filter Filter condition logical expression apply summary calculation. default, filtering expressions computed within by_vars help aggregating, lagging, ranking function involved. example, filter_rows = (AVAL > mean(AVAL, na.rm = TRUE)) filter AVAL values greater mean AVAL by_vars. filter_rows = (dplyr::n() > 2) filter n count by_vars greater 2. set_values_to Variables set specified variables set specified values new observations. Set list variables specified value new records LHS refer variable. RHS refers values set variable. can string, symbol, numeric value, expression NA. summary functions used, values summarized variables specified by_vars. example:","code":"set_values_to = exprs(     AVAL = sum(AVAL),     PARAMCD = \"TDOSE\",     PARCAT1 = \"OVERALL\"   )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_summary_records.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create Summary Records — get_summary_records","text":"data frame derived records.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_summary_records.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create Summary Records — get_summary_records","text":"function creates derived observations append original dataset observations. like instead, see derive_summary_records() function.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_summary_records.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create Summary Records — get_summary_records","text":"","code":"library(tibble)  adeg <- tribble(   ~USUBJID,   ~EGSEQ, ~PARAM,             ~AVISIT,    ~EGDTC,             ~AVAL, ~TRTA,   \"XYZ-1001\", 1,      \"QTcF Int. (msec)\", \"Baseline\", \"2016-02-24T07:50\", 385,   NA_character_,   \"XYZ-1001\", 2,      \"QTcF Int. (msec)\", \"Baseline\", \"2016-02-24T07:52\", 399,   NA_character_,   \"XYZ-1001\", 3,      \"QTcF Int. (msec)\", \"Baseline\", \"2016-02-24T07:56\", 396,   NA_character_,   \"XYZ-1001\", 4,      \"QTcF Int. (msec)\", \"Visit 2\",  \"2016-03-08T09:45\", 384,   \"Placebo\",   \"XYZ-1001\", 5,      \"QTcF Int. (msec)\", \"Visit 2\",  \"2016-03-08T09:48\", 393,   \"Placebo\",   \"XYZ-1001\", 6,      \"QTcF Int. (msec)\", \"Visit 2\",  \"2016-03-08T09:51\", 388,   \"Placebo\",   \"XYZ-1001\", 7,      \"QTcF Int. (msec)\", \"Visit 3\",  \"2016-03-22T10:45\", 385,   \"Placebo\",   \"XYZ-1001\", 8,      \"QTcF Int. (msec)\", \"Visit 3\",  \"2016-03-22T10:48\", 394,   \"Placebo\",   \"XYZ-1001\", 9,      \"QTcF Int. (msec)\", \"Visit 3\",  \"2016-03-22T10:51\", 402,   \"Placebo\",   \"XYZ-1002\", 1,      \"QTcF Int. (msec)\", \"Baseline\", \"2016-02-22T07:58\", 399,   NA_character_,   \"XYZ-1002\", 2,      \"QTcF Int. (msec)\", \"Baseline\", \"2016-02-22T07:58\", 410,   NA_character_,   \"XYZ-1002\", 3,      \"QTcF Int. (msec)\", \"Baseline\", \"2016-02-22T08:01\", 392,   NA_character_,   \"XYZ-1002\", 4,      \"QTcF Int. (msec)\", \"Visit 2\",  \"2016-03-06T09:50\", 401,   \"Active 20mg\",   \"XYZ-1002\", 5,      \"QTcF Int. (msec)\", \"Visit 2\",  \"2016-03-06T09:53\", 407,   \"Active 20mg\",   \"XYZ-1002\", 6,      \"QTcF Int. (msec)\", \"Visit 2\",  \"2016-03-06T09:56\", 400,   \"Active 20mg\",   \"XYZ-1002\", 7,      \"QTcF Int. (msec)\", \"Visit 3\",  \"2016-03-24T10:50\", 412,   \"Active 20mg\",   \"XYZ-1002\", 8,      \"QTcF Int. (msec)\", \"Visit 3\",  \"2016-03-24T10:53\", 414,   \"Active 20mg\",   \"XYZ-1002\", 9,      \"QTcF Int. (msec)\", \"Visit 3\",  \"2016-03-24T10:56\", 402,   \"Active 20mg\" )  # Summarize the average of the triplicate ECG interval values (AVAL) get_summary_records(   adeg,   by_vars = exprs(USUBJID, PARAM, AVISIT),   set_values_to = exprs(     AVAL = mean(AVAL, na.rm = TRUE),     DTYPE = \"AVERAGE\"   ) ) #> # A tibble: 6 × 5 #>   USUBJID  PARAM            AVISIT    AVAL DTYPE   #>   <chr>    <chr>            <chr>    <dbl> <chr>   #> 1 XYZ-1001 QTcF Int. (msec) Baseline  393. AVERAGE #> 2 XYZ-1001 QTcF Int. (msec) Visit 2   388. AVERAGE #> 3 XYZ-1001 QTcF Int. (msec) Visit 3   394. AVERAGE #> 4 XYZ-1002 QTcF Int. (msec) Baseline  400. AVERAGE #> 5 XYZ-1002 QTcF Int. (msec) Visit 2   403. AVERAGE #> 6 XYZ-1002 QTcF Int. (msec) Visit 3   409. AVERAGE  # Derive more than one summary variable get_summary_records(   adeg,   by_vars = exprs(USUBJID, PARAM, AVISIT),   set_values_to = exprs(     AVAL = mean(AVAL),     ASTDTM = min(convert_dtc_to_dtm(EGDTC)),     AENDTM = max(convert_dtc_to_dtm(EGDTC)),     DTYPE = \"AVERAGE\"   ) ) #> # A tibble: 6 × 7 #>   USUBJID  PARAM      AVISIT  AVAL ASTDTM              AENDTM              DTYPE #>   <chr>    <chr>      <chr>  <dbl> <dttm>              <dttm>              <chr> #> 1 XYZ-1001 QTcF Int.… Basel…  393. 2016-02-24 07:50:00 2016-02-24 07:56:00 AVER… #> 2 XYZ-1001 QTcF Int.… Visit…  388. 2016-03-08 09:45:00 2016-03-08 09:51:00 AVER… #> 3 XYZ-1001 QTcF Int.… Visit…  394. 2016-03-22 10:45:00 2016-03-22 10:51:00 AVER… #> 4 XYZ-1002 QTcF Int.… Basel…  400. 2016-02-22 07:58:00 2016-02-22 08:01:00 AVER… #> 5 XYZ-1002 QTcF Int.… Visit…  403. 2016-03-06 09:50:00 2016-03-06 09:56:00 AVER… #> 6 XYZ-1002 QTcF Int.… Visit…  409. 2016-03-24 10:50:00 2016-03-24 10:56:00 AVER…  # Sample ADEG dataset with triplicate record for only AVISIT = 'Baseline' adeg <- tribble(   ~USUBJID,   ~EGSEQ, ~PARAM,             ~AVISIT,    ~EGDTC,             ~AVAL, ~TRTA,   \"XYZ-1001\", 1,      \"QTcF Int. (msec)\", \"Baseline\", \"2016-02-24T07:50\", 385,   NA_character_,   \"XYZ-1001\", 2,      \"QTcF Int. (msec)\", \"Baseline\", \"2016-02-24T07:52\", 399,   NA_character_,   \"XYZ-1001\", 3,      \"QTcF Int. (msec)\", \"Baseline\", \"2016-02-24T07:56\", 396,   NA_character_,   \"XYZ-1001\", 4,      \"QTcF Int. (msec)\", \"Visit 2\",  \"2016-03-08T09:48\", 393,   \"Placebo\",   \"XYZ-1001\", 5,      \"QTcF Int. (msec)\", \"Visit 2\",  \"2016-03-08T09:51\", 388,   \"Placebo\",   \"XYZ-1001\", 6,      \"QTcF Int. (msec)\", \"Visit 3\",  \"2016-03-22T10:48\", 394,   \"Placebo\",   \"XYZ-1001\", 7,      \"QTcF Int. (msec)\", \"Visit 3\",  \"2016-03-22T10:51\", 402,   \"Placebo\",   \"XYZ-1002\", 1,      \"QTcF Int. (msec)\", \"Baseline\", \"2016-02-22T07:58\", 399,   NA_character_,   \"XYZ-1002\", 2,      \"QTcF Int. (msec)\", \"Baseline\", \"2016-02-22T07:58\", 410,   NA_character_,   \"XYZ-1002\", 3,      \"QTcF Int. (msec)\", \"Baseline\", \"2016-02-22T08:01\", 392,   NA_character_,   \"XYZ-1002\", 4,      \"QTcF Int. (msec)\", \"Visit 2\",  \"2016-03-06T09:53\", 407,   \"Active 20mg\",   \"XYZ-1002\", 5,      \"QTcF Int. (msec)\", \"Visit 2\",  \"2016-03-06T09:56\", 400,   \"Active 20mg\",   \"XYZ-1002\", 6,      \"QTcF Int. (msec)\", \"Visit 3\",  \"2016-03-24T10:53\", 414,   \"Active 20mg\",   \"XYZ-1002\", 7,      \"QTcF Int. (msec)\", \"Visit 3\",  \"2016-03-24T10:56\", 402,   \"Active 20mg\" )  # Compute the average of AVAL only if there are more than 2 records within the # by group get_summary_records(   adeg,   by_vars = exprs(USUBJID, PARAM, AVISIT),   filter = n() > 2,   set_values_to = exprs(     AVAL = mean(AVAL, na.rm = TRUE),     DTYPE = \"AVERAGE\"   ) ) #> # A tibble: 2 × 5 #>   USUBJID  PARAM            AVISIT    AVAL DTYPE   #>   <chr>    <chr>            <chr>    <dbl> <chr>   #> 1 XYZ-1001 QTcF Int. (msec) Baseline  393. AVERAGE #> 2 XYZ-1002 QTcF Int. (msec) Baseline  400. AVERAGE"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_terms_from_db.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Terms from the Queries Database — get_terms_from_db","title":"Get Terms from the Queries Database — get_terms_from_db","text":"function checks requirements access database fulfilled (version access function available, see assert_db_requirements()), reads terms database, checks dataset terms expected format (see assert_terms()).","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_terms_from_db.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Terms from the Queries Database — get_terms_from_db","text":"","code":"get_terms_from_db(   version,   fun,   queries,   definition,   expect_grpname = FALSE,   expect_grpid = FALSE,   i,   temp_env )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_terms_from_db.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Terms from the Queries Database — get_terms_from_db","text":"version Version version must non null. Otherwise, error issued. value passed access function (fun). fun Access function access function must non null. Otherwise, error issued. function called retrieve terms. queries Queries List queries passed create_query_data(). used error messages. definition Definition query definition passed access function. defines terms returned. expect_grpname GRPNAME expected output dataset? expect_grpid GRPID expected output dataset? Index definition queries value used error messages. temp_env Temporary environment value passed access function.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_terms_from_db.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Terms from the Queries Database — get_terms_from_db","text":"Output dataset access function","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_vars_query.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Query Variables — get_vars_query","title":"Get Query Variables — get_vars_query","text":"Create table input dataset binds necessary rows derive_vars_query() call relevant SRCVAR, TERM_NAME_ID temporary index necessary Note: function first step performed derive_vars_query() requested users present independently .","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_vars_query.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Query Variables — get_vars_query","text":"","code":"get_vars_query(dataset, dataset_queries)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_vars_query.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Query Variables — get_vars_query","text":"dataset Input dataset dataset_queries dataset containing required columns PREFIX, GRPNAME, SRCVAR, TERMCHAR /TERMNUM, optional columns GRPID, SCOPE, SCOPEN. create_query_data() can used create dataset.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_vars_query.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Query Variables — get_vars_query","text":"processed query dataset SRCVAR TERM_NAME_ID can merged input dataset execute derivations outlined dataset_queries.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_vars_query.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get Query Variables — get_vars_query","text":"function can used derive CDISC variables SMQzzNAM, SMQzzCD, SMQzzSC, SMQzzSCN, CQzzNAM ADAE ADMH, variables SDGzzNAM, SDGzzCD, SDGzzSC ADCM. example usage function can found OCCDS vignette. query dataset expected input function. See Queries Dataset Documentation vignette descriptions, call data(\"queries\") example query dataset. unique element PREFIX, corresponding \"NAM\" variable created. unique PREFIX, GRPID \"\" NA, corresponding \"CD\" variable created; similarly, SCOPE \"\" NA, corresponding \"SC\" variable created; SCOPEN \"\" NA, corresponding \"SCN\" variable created. record dataset, \"NAM\" variable takes value GRPNAME value TERMCHAR TERMNUM dataset_queries matches value respective SRCVAR dataset. Note TERMCHAR dataset_queries dataset may NA TERMNUM non-NA vice versa. matching case insensitive. \"CD\", \"SC\", \"SCN\" variables derived accordingly based GRPID, SCOPE, SCOPEN respectively, whenever missing.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_vars_query.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get Query Variables — get_vars_query","text":"","code":"library(tibble) data(\"queries\") adae <- tribble(   ~USUBJID, ~ASTDTM, ~AETERM, ~AESEQ, ~AEDECOD, ~AELLT, ~AELLTCD,   \"01\", \"2020-06-02 23:59:59\", \"ALANINE AMINOTRANSFERASE ABNORMAL\",   3, \"Alanine aminotransferase abnormal\", NA_character_, NA_integer_,   \"02\", \"2020-06-05 23:59:59\", \"BASEDOW'S DISEASE\",   5, \"Basedow's disease\", NA_character_, 1L,   \"03\", \"2020-06-07 23:59:59\", \"SOME TERM\",   2, \"Some query\", \"Some term\", NA_integer_,   \"05\", \"2020-06-09 23:59:59\", \"ALVEOLAR PROTEINOSIS\",   7, \"Alveolar proteinosis\", NA_character_, NA_integer_ ) get_vars_query(adae, queries) #> # A tibble: 2 × 21 #>   USUBJID ASTDTM AETERM AESEQ SMQ02NAM SMQ02CD SMQ02SC SMQ02SCN SMQ03NAM SMQ03CD #>   <chr>   <chr>  <chr>  <dbl> <chr>      <int> <chr>      <int> <chr>      <int> #> 1 02      2020-… BASED…     5 NA            NA NA            NA NA            NA #> 2 05      2020-… ALVEO…     7 NA            NA NA            NA NA            NA #> # ℹ 11 more variables: SMQ03SC <chr>, SMQ03SCN <int>, SMQ05NAM <chr>, #> #   SMQ05CD <int>, SMQ05SC <chr>, SMQ05SCN <int>, CQ01NAM <chr>, CQ04NAM <chr>, #> #   CQ04CD <int>, CQ06NAM <chr>, CQ06CD <int>"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/impute_dtc_dt.html","id":null,"dir":"Reference","previous_headings":"","what":"Impute Partial Date Portion of a '--DTC' Variable — impute_dtc_dt","title":"Impute Partial Date Portion of a '--DTC' Variable — impute_dtc_dt","text":"Imputation partial date portion '--DTC' variable based user input.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/impute_dtc_dt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Impute Partial Date Portion of a '--DTC' Variable — impute_dtc_dt","text":"","code":"impute_dtc_dt(   dtc,   highest_imputation = \"n\",   date_imputation = \"first\",   min_dates = NULL,   max_dates = NULL,   preserve = FALSE )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/impute_dtc_dt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Impute Partial Date Portion of a '--DTC' Variable — impute_dtc_dt","text":"dtc '--DTC' date impute character date expected format like yyyy-mm-dd yyyy-mm-ddThh:mm:ss. Trailing components can omitted - valid \"missing\" value component. highest_imputation Highest imputation level highest_imputation argument controls components DTC value imputed missing. components specified level imputed. component higher level highest imputation level missing, NA_character_ returned. example, highest_imputation = \"D\" \"2020\" results NA_character_ month missing. \"n\" specified imputation performed, .e., component missing, NA_character_ returned. \"Y\" specified, date_imputation \"first\" \"last\" min_dates max_dates specified respectively. Otherwise, NA_character_ returned year component missing. Permitted Values: \"Y\" (year, highest level), \"M\" (month), \"D\" (day), \"n\" (none, lowest level) date_imputation value impute day/month datepart missing. character value expected, either format month day specified \"mm-dd\": e.g. \"06-15\" 15th June (year can specified; imputing year \"first\" \"last\" together min_dates max_dates argument can used (see examples).), keyword: \"first\", \"mid\", \"last\" impute first/mid/last day/month. \"mid\" specified, missing components imputed middle possible range: month day missing, imputed \"06-30\" (middle year). day missing, imputed \"15\" (middle month). argument ignored highest_imputation less \"D\". min_dates Minimum dates list dates expected. ensured imputed date specified dates, e.g., imputed adverse event start date first treatment date. dates range possible dates dtc value considered. possible dates defined missing parts dtc date (see example ). ensures non-missing parts dtc date changed. date date-time object expected. example   returns \"2020-11-11T11:11:11\" possible dates \"2020-11\" range \"2020-11-01T00:00:00\" \"2020-11-30T23:59:59\". Therefore \"2020-12-06T12:12:12\" ignored. Returning \"2020-12-06T12:12:12\" changed month although missing (dtc date). max_dates Maximum dates list dates expected. ensured imputed date specified dates, e.g., imputed date data cut date. dates range possible dates considered. date date-time object expected. preserve Preserve day month missing day present example \"2019---07\" return \"2019-06-07 preserve = TRUE (date_imputation = \"MID\"). Permitted Values: TRUE, FALSE","code":"impute_dtc_dtm(   \"2020-11\",   min_dates = list(    ymd_hms(\"2020-12-06T12:12:12\"),    ymd_hms(\"2020-11-11T11:11:11\")   ),   highest_imputation = \"M\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/impute_dtc_dt.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Impute Partial Date Portion of a '--DTC' Variable — impute_dtc_dt","text":"character vector","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/impute_dtc_dt.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Impute Partial Date Portion of a '--DTC' Variable — impute_dtc_dt","text":"Usually computation function can used %>%.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/impute_dtc_dt.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Impute Partial Date Portion of a '--DTC' Variable — impute_dtc_dt","text":"","code":"library(lubridate)  dates <- c(   \"2019-07-18T15:25:40\",   \"2019-07-18T15:25\",   \"2019-07-18T15\",   \"2019-07-18\",   \"2019-02\",   \"2019\",   \"2019\",   \"2019---07\",   \"\" )  # No date imputation (highest_imputation defaulted to \"n\") impute_dtc_dt(dtc = dates) #> [1] \"2019-07-18\" \"2019-07-18\" \"2019-07-18\" \"2019-07-18\" NA           #> [6] NA           NA           NA           NA            # Impute to first day/month if date is partial impute_dtc_dt(   dtc = dates,   highest_imputation = \"M\" ) #> [1] \"2019-07-18\" \"2019-07-18\" \"2019-07-18\" \"2019-07-18\" \"2019-02-01\" #> [6] \"2019-01-01\" \"2019-01-01\" \"2019-01-01\" NA           # Same as above impute_dtc_dt(   dtc = dates,   highest_imputation = \"M\",   date_imputation = \"01-01\" ) #> [1] \"2019-07-18\" \"2019-07-18\" \"2019-07-18\" \"2019-07-18\" \"2019-02-01\" #> [6] \"2019-01-01\" \"2019-01-01\" \"2019-01-01\" NA            # Impute to last day/month if date is partial impute_dtc_dt(   dtc = dates,   highest_imputation = \"M\",   date_imputation = \"last\", ) #> [1] \"2019-07-18\" \"2019-07-18\" \"2019-07-18\" \"2019-07-18\" \"2019-02-28\" #> [6] \"2019-12-31\" \"2019-12-31\" \"2019-12-31\" NA            # Impute to mid day/month if date is partial impute_dtc_dt(   dtc = dates,   highest_imputation = \"M\",   date_imputation = \"mid\" ) #> [1] \"2019-07-18\" \"2019-07-18\" \"2019-07-18\" \"2019-07-18\" \"2019-02-15\" #> [6] \"2019-06-30\" \"2019-06-30\" \"2019-06-30\" NA            # Impute a date and ensure that the imputed date is not before a list of # minimum dates impute_dtc_dt(   \"2020-12\",   min_dates = list(     ymd(\"2020-12-06\"),     ymd(\"2020-11-11\")   ),   highest_imputation = \"M\" ) #> [1] \"2020-12-06\"  # Impute completely missing dates (only possible if min_dates or max_dates is specified) impute_dtc_dt(   c(\"2020-12\", NA_character_),   min_dates = list(     ymd(\"2020-12-06\", \"2020-01-01\"),     ymd(\"2020-11-11\", NA)   ),   highest_imputation = \"Y\" ) #> [1] \"2020-12-06\" \"2020-01-01\""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/impute_dtc_dtm.html","id":null,"dir":"Reference","previous_headings":"","what":"Impute Partial Date(-time) Portion of a '--DTC' Variable — impute_dtc_dtm","title":"Impute Partial Date(-time) Portion of a '--DTC' Variable — impute_dtc_dtm","text":"Imputation partial date/time portion '--DTC' variable. based user input.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/impute_dtc_dtm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Impute Partial Date(-time) Portion of a '--DTC' Variable — impute_dtc_dtm","text":"","code":"impute_dtc_dtm(   dtc,   highest_imputation = \"h\",   date_imputation = \"first\",   time_imputation = \"first\",   min_dates = NULL,   max_dates = NULL,   preserve = FALSE )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/impute_dtc_dtm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Impute Partial Date(-time) Portion of a '--DTC' Variable — impute_dtc_dtm","text":"dtc '--DTC' date impute character date expected format like yyyy-mm-dd yyyy-mm-ddThh:mm:ss. Trailing components can omitted - valid \"missing\" value component. highest_imputation Highest imputation level highest_imputation argument controls components DTC value imputed missing. components specified level imputed. component higher level highest imputation level missing, NA_character_ returned. example, highest_imputation = \"D\" \"2020\" results NA_character_ month missing. \"n\" specified, imputation performed, .e., component missing, NA_character_ returned. \"Y\" specified, date_imputation \"first\" \"last\" min_dates max_dates specified respectively. Otherwise, NA_character_ returned year component missing. Permitted Values: \"Y\" (year, highest level), \"M\" (month), \"D\" (day), \"h\" (hour), \"m\" (minute), \"s\" (second), \"n\" (none, lowest level) date_imputation value impute day/month datepart missing. character value expected, either format month day specified \"mm-dd\": e.g. \"06-15\" 15th June (year can specified; imputing year \"first\" \"last\" together min_dates max_dates argument can used (see examples).), keyword: \"first\", \"mid\", \"last\" impute first/mid/last day/month. \"mid\" specified, missing components imputed middle possible range: month day missing, imputed \"06-30\" (middle year). day missing, imputed \"15\" (middle month). argument ignored highest_imputation less \"D\". time_imputation value impute time timepart missing. character value expected, either format hour, min sec specified \"hh:mm:ss\": e.g. \"00:00:00\" start day, keyword: \"first\",\"last\" impute start/end day. argument ignored highest_imputation = \"n\". min_dates Minimum dates list dates expected. ensured imputed date specified dates, e.g., imputed adverse event start date first treatment date. dates range possible dates dtc value considered. possible dates defined missing parts dtc date (see example ). ensures non-missing parts dtc date changed. date date-time object expected. example   returns \"2020-11-11T11:11:11\" possible dates \"2020-11\" range \"2020-11-01T00:00:00\" \"2020-11-30T23:59:59\". Therefore \"2020-12-06T12:12:12\" ignored. Returning \"2020-12-06T12:12:12\" changed month although missing (dtc date). date variables (datetime) list time imputed \"00:00:00\". Specifying date variables makes sense date imputed. time imputed, date variables affect result. max_dates Maximum dates list dates expected. ensured imputed date specified dates, e.g., imputed date data cut date. dates range possible dates considered. date date-time object expected. date variables (datetime) list time imputed \"23:59:59\". Specifying date variables makes sense date imputed. time imputed, date variables affect result. preserve Preserve lower level date/time part higher order part missing, e.g. preserve day month missing preserve minute hour missing. example \"2019---07\" return \"2019-06-07 preserve = TRUE (date_imputation = \"mid\"). Permitted Values: TRUE, FALSE","code":"impute_dtc_dtm(   \"2020-11\",   min_dates = list(    ymd_hms(\"2020-12-06T12:12:12\"),    ymd_hms(\"2020-11-11T11:11:11\")   ),   highest_imputation = \"M\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/impute_dtc_dtm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Impute Partial Date(-time) Portion of a '--DTC' Variable — impute_dtc_dtm","text":"character vector","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/impute_dtc_dtm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Impute Partial Date(-time) Portion of a '--DTC' Variable — impute_dtc_dtm","text":"Usually computation function can used %>%.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/impute_dtc_dtm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Impute Partial Date(-time) Portion of a '--DTC' Variable — impute_dtc_dtm","text":"","code":"library(lubridate)  dates <- c(   \"2019-07-18T15:25:40\",   \"2019-07-18T15:25\",   \"2019-07-18T15\",   \"2019-07-18\",   \"2019-02\",   \"2019\",   \"2019\",   \"2019---07\",   \"\" )  # No date imputation (highest_imputation defaulted to \"h\") # Missing time part imputed with 00:00:00 portion by default impute_dtc_dtm(dtc = dates) #> [1] \"2019-07-18T15:25:40\" \"2019-07-18T15:25:00\" \"2019-07-18T15:00:00\" #> [4] \"2019-07-18T00:00:00\" NA                    NA                    #> [7] NA                    NA                    NA                     # No date imputation (highest_imputation defaulted to \"h\") # Missing time part imputed with 23:59:59 portion impute_dtc_dtm(   dtc = dates,   time_imputation = \"23:59:59\" ) #> [1] \"2019-07-18T15:25:40\" \"2019-07-18T15:25:59\" \"2019-07-18T15:59:59\" #> [4] \"2019-07-18T23:59:59\" NA                    NA                    #> [7] NA                    NA                    NA                     # Same as above impute_dtc_dtm(   dtc = dates,   time_imputation = \"last\" ) #> [1] \"2019-07-18T15:25:40\" \"2019-07-18T15:25:59\" \"2019-07-18T15:59:59\" #> [4] \"2019-07-18T23:59:59\" NA                    NA                    #> [7] NA                    NA                    NA                     # Impute to first day/month if date is partial # Missing time part imputed with 00:00:00 portion by default impute_dtc_dtm(   dtc = dates,   highest_imputation = \"M\" ) #> [1] \"2019-07-18T15:25:40\" \"2019-07-18T15:25:00\" \"2019-07-18T15:00:00\" #> [4] \"2019-07-18T00:00:00\" \"2019-02-01T00:00:00\" \"2019-01-01T00:00:00\" #> [7] \"2019-01-01T00:00:00\" \"2019-01-01T00:00:00\" NA                    # same as above impute_dtc_dtm(   dtc = dates,   highest_imputation = \"M\",   date_imputation = \"01-01\" ) #> [1] \"2019-07-18T15:25:40\" \"2019-07-18T15:25:00\" \"2019-07-18T15:00:00\" #> [4] \"2019-07-18T00:00:00\" \"2019-02-01T00:00:00\" \"2019-01-01T00:00:00\" #> [7] \"2019-01-01T00:00:00\" \"2019-01-01T00:00:00\" NA                     # Impute to last day/month if date is partial # Missing time part imputed with 23:59:59 portion impute_dtc_dtm(   dtc = dates,   date_imputation = \"last\",   time_imputation = \"last\" ) #> [1] \"2019-07-18T15:25:40\" \"2019-07-18T15:25:59\" \"2019-07-18T15:59:59\" #> [4] \"2019-07-18T23:59:59\" NA                    NA                    #> [7] NA                    NA                    NA                     # Impute to mid day/month if date is partial # Missing time part imputed with 00:00:00 portion by default impute_dtc_dtm(   dtc = dates,   highest_imputation = \"M\",   date_imputation = \"mid\" ) #> [1] \"2019-07-18T15:25:40\" \"2019-07-18T15:25:00\" \"2019-07-18T15:00:00\" #> [4] \"2019-07-18T00:00:00\" \"2019-02-15T00:00:00\" \"2019-06-30T00:00:00\" #> [7] \"2019-06-30T00:00:00\" \"2019-06-30T00:00:00\" NA                     # Impute a date and ensure that the imputed date is not before a list of # minimum dates impute_dtc_dtm(   \"2020-12\",   min_dates = list(     ymd_hms(\"2020-12-06T12:12:12\"),     ymd_hms(\"2020-11-11T11:11:11\")   ),   highest_imputation = \"M\" ) #> [1] \"2020-12-06T12:12:12\"  # Impute completely missing dates (only possible if min_dates or max_dates is specified) impute_dtc_dtm(   c(\"2020-12\", NA_character_),   min_dates = list(     ymd_hms(\"2020-12-06T12:12:12\", \"2020-01-01T01:01:01\"),     ymd_hms(\"2020-11-11T11:11:11\", NA)   ),   highest_imputation = \"Y\" ) #> [1] \"2020-12-06T12:12:12\" \"2020-01-01T01:01:01\""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/list_all_templates.html","id":null,"dir":"Reference","previous_headings":"","what":"List All Available ADaM Templates — list_all_templates","title":"List All Available ADaM Templates — list_all_templates","text":"List Available ADaM Templates","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/list_all_templates.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"List All Available ADaM Templates — list_all_templates","text":"","code":"list_all_templates(package = \"admiral\")"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/list_all_templates.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"List All Available ADaM Templates — list_all_templates","text":"package R package look templates. default \"admiral\".","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/list_all_templates.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"List All Available ADaM Templates — list_all_templates","text":"character vector available templates","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/list_all_templates.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"List All Available ADaM Templates — list_all_templates","text":"","code":"list_all_templates() #> Existing ADaM templates in package 'admiral': #> • ADAE #> • ADCM #> • ADEG #> • ADEX #> • ADLB #> • ADLBHY #> • ADMH #> • ADPC #> • ADPP #> • ADPPK #> • ADSL #> • ADVS"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/list_tte_source_objects.html","id":null,"dir":"Reference","previous_headings":"","what":"List all tte_source Objects Available in a Package — list_tte_source_objects","title":"List all tte_source Objects Available in a Package — list_tte_source_objects","text":"List tte_source Objects Available Package","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/list_tte_source_objects.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"List all tte_source Objects Available in a Package — list_tte_source_objects","text":"","code":"list_tte_source_objects(package = \"admiral\")"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/list_tte_source_objects.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"List all tte_source Objects Available in a Package — list_tte_source_objects","text":"package name package search tte_source objects","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/list_tte_source_objects.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"List all tte_source Objects Available in a Package — list_tte_source_objects","text":"data.frame row corresponds one tte_source object NULL package contain tte_source objects","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/list_tte_source_objects.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"List all tte_source Objects Available in a Package — list_tte_source_objects","text":"","code":"list_tte_source_objects() #>              object dataset_name                                        filter #> 1      ae_gr3_event         adae                TRTEMFL == \"Y\" & ATOXGR == \"3\" #> 2       ae_wd_event         adae    TRTEMFL == \"Y\" & AEACN == \"DRUG WITHDRAWN\" #> 3     ae_gr35_event         adae TRTEMFL == \"Y\" & ATOXGR %in% c(\"3\", \"4\", \"5\") #> 4  lastalive_censor         adsl                                          NULL #> 5      ae_gr1_event         adae                TRTEMFL == \"Y\" & ATOXGR == \"1\" #> 6      ae_ser_event         adae                 TRTEMFL == \"Y\" & AESER == \"Y\" #> 7      ae_gr2_event         adae                TRTEMFL == \"Y\" & ATOXGR == \"2\" #> 8          ae_event         adae                                TRTEMFL == \"Y\" #> 9      ae_gr4_event         adae                TRTEMFL == \"Y\" & ATOXGR == \"4\" #> 10     ae_gr5_event         adae                TRTEMFL == \"Y\" & ATOXGR == \"5\" #> 11     ae_sev_event         adae            TRTEMFL == \"Y\" & AESEV == \"SEVERE\" #> 12      death_event         adsl                                  DTHFL == \"Y\" #>        date censor #> 1     ASTDT      0 #> 2     ASTDT      0 #> 3     ASTDT      0 #> 4  LSTALVDT      1 #> 5     ASTDT      0 #> 6     ASTDT      0 #> 7     ASTDT      0 #> 8     ASTDT      0 #> 9     ASTDT      0 #> 10    ASTDT      0 #> 11    ASTDT      0 #> 12    DTHDT      0 #>                                                                                                 set_values_to #> 1                     EVNTDESC: \"GRADE 3 ADVERSE EVENT\"<br>SRCDOM: \"ADAE\"<br>SRCVAR: \"ASTDT\"<br>SRCSEQ: AESEQ #> 2  EVNTDESC: \"ADVERSE EVENT LEADING TO DRUG WITHDRAWAL\"<br>SRCDOM: \"ADAE\"<br>SRCVAR: \"ASTDT\"<br>SRCSEQ: AESEQ #> 3                   EVNTDESC: \"GRADE 3-5 ADVERSE EVENT\"<br>SRCDOM: \"ADAE\"<br>SRCVAR: \"ASTDT\"<br>SRCSEQ: AESEQ #> 4                                                   EVNTDESC: \"ALIVE\"<br>SRCDOM: \"ADSL\"<br>SRCVAR: \"LSTALVDT\" #> 5                     EVNTDESC: \"GRADE 1 ADVERSE EVENT\"<br>SRCDOM: \"ADAE\"<br>SRCVAR: \"ASTDT\"<br>SRCSEQ: AESEQ #> 6                     EVNTDESC: \"SERIOUS ADVERSE EVENT\"<br>SRCDOM: \"ADAE\"<br>SRCVAR: \"ASTDT\"<br>SRCSEQ: AESEQ #> 7                     EVNTDESC: \"GRADE 2 ADVERSE EVENT\"<br>SRCDOM: \"ADAE\"<br>SRCVAR: \"ASTDT\"<br>SRCSEQ: AESEQ #> 8                             EVNTDESC: \"ADVERSE EVENT\"<br>SRCDOM: \"ADAE\"<br>SRCVAR: \"ASTDT\"<br>SRCSEQ: AESEQ #> 9                     EVNTDESC: \"GRADE 4 ADVERSE EVENT\"<br>SRCDOM: \"ADAE\"<br>SRCVAR: \"ASTDT\"<br>SRCSEQ: AESEQ #> 10                    EVNTDESC: \"GRADE 5 ADVERSE EVENT\"<br>SRCDOM: \"ADAE\"<br>SRCVAR: \"ASTDT\"<br>SRCSEQ: AESEQ #> 11                     EVNTDESC: \"SEVERE ADVERSE EVENT\"<br>SRCDOM: \"ADAE\"<br>SRCVAR: \"ASTDT\"<br>SRCSEQ: AESEQ #> 12                                                     EVNTDESC: \"DEATH\"<br>SRCDOM: \"ADSL\"<br>SRCVAR: \"DTHDT\""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/max_cond.html","id":null,"dir":"Reference","previous_headings":"","what":"Maximum Value on a Subset — max_cond","title":"Maximum Value on a Subset — max_cond","text":"function derives maximum value vector/column subset entries/observations.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/max_cond.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Maximum Value on a Subset — max_cond","text":"","code":"max_cond(var, cond)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/max_cond.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Maximum Value on a Subset — max_cond","text":"var vector cond condition","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/max_cond.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Maximum Value on a Subset — max_cond","text":"","code":"library(tibble) library(dplyr, warn.conflicts = FALSE) library(admiral) data <- tribble(   ~USUBJID, ~AVISITN, ~AVALC,   \"1\",      1,        \"PR\",   \"1\",      2,        \"CR\",   \"1\",      3,        \"NE\",   \"1\",      4,        \"CR\",   \"1\",      5,        \"NE\",   \"2\",      1,        \"CR\",   \"2\",      2,        \"PR\",   \"2\",      3,        \"CR\", )  # In oncology setting, when needing to check the first time a patient had # a Complete Response (CR) to compare to see if any Partial Response (PR) # occurred after this add variable indicating if PR occurred after CR group_by(data, USUBJID) %>% mutate(   first_cr_vis = min_cond(var = AVISITN, cond = AVALC == \"CR\"),   last_pr_vis = max_cond(var = AVISITN, cond = AVALC == \"PR\"),   pr_after_cr = last_pr_vis > first_cr_vis ) #> # A tibble: 8 × 6 #> # Groups:   USUBJID [2] #>   USUBJID AVISITN AVALC first_cr_vis last_pr_vis pr_after_cr #>   <chr>     <dbl> <chr>        <dbl>       <dbl> <lgl>       #> 1 1             1 PR               2           1 FALSE       #> 2 1             2 CR               2           1 FALSE       #> 3 1             3 NE               2           1 FALSE       #> 4 1             4 CR               2           1 FALSE       #> 5 1             5 NE               2           1 FALSE       #> 6 2             1 CR               1           2 TRUE        #> 7 2             2 PR               1           2 TRUE        #> 8 2             3 CR               1           2 TRUE"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/min_cond.html","id":null,"dir":"Reference","previous_headings":"","what":"Minimum Value on a Subset — min_cond","title":"Minimum Value on a Subset — min_cond","text":"function derives minimum value vector/column subset entries/observations.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/min_cond.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Minimum Value on a Subset — min_cond","text":"","code":"min_cond(var, cond)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/min_cond.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Minimum Value on a Subset — min_cond","text":"var vector cond condition","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/min_cond.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Minimum Value on a Subset — min_cond","text":"","code":"library(tibble) library(dplyr, warn.conflicts = FALSE) library(admiral) data <- tribble(   ~USUBJID, ~AVISITN, ~AVALC,   \"1\",      1,        \"PR\",   \"1\",      2,        \"CR\",   \"1\",      3,        \"NE\",   \"1\",      4,        \"CR\",   \"1\",      5,        \"NE\",   \"2\",      1,        \"CR\",   \"2\",      2,        \"PR\",   \"2\",      3,        \"CR\", )  # In oncology setting, when needing to check the first time a patient had # a Complete Response (CR) to compare to see if any Partial Response (PR) # occurred after this add variable indicating if PR occurred after CR group_by(data, USUBJID) %>% mutate(   first_cr_vis = min_cond(var = AVISITN, cond = AVALC == \"CR\"),   last_pr_vis = max_cond(var = AVISITN, cond = AVALC == \"PR\"),   pr_after_cr = last_pr_vis > first_cr_vis ) #> # A tibble: 8 × 6 #> # Groups:   USUBJID [2] #>   USUBJID AVISITN AVALC first_cr_vis last_pr_vis pr_after_cr #>   <chr>     <dbl> <chr>        <dbl>       <dbl> <lgl>       #> 1 1             1 PR               2           1 FALSE       #> 2 1             2 CR               2           1 FALSE       #> 3 1             3 NE               2           1 FALSE       #> 4 1             4 CR               2           1 FALSE       #> 5 1             5 NE               2           1 FALSE       #> 6 2             1 CR               1           2 TRUE        #> 7 2             2 PR               1           2 TRUE        #> 8 2             3 CR               1           2 TRUE"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/negate_vars.html","id":null,"dir":"Reference","previous_headings":"","what":"Negate List of Variables — negate_vars","title":"Negate List of Variables — negate_vars","text":"function adds minus sign prefix variable.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/negate_vars.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Negate List of Variables — negate_vars","text":"","code":"negate_vars(vars = NULL)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/negate_vars.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Negate List of Variables — negate_vars","text":"vars List variables created exprs()","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/negate_vars.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Negate List of Variables — negate_vars","text":"list expressions","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/negate_vars.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Negate List of Variables — negate_vars","text":"useful list variables removed dataset, e.g., select(!!!negate_vars(by_vars)) removes variables.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/negate_vars.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Negate List of Variables — negate_vars","text":"","code":"negate_vars(exprs(USUBJID, STUDYID)) #> [[1]] #> -USUBJID #>  #> [[2]] #> -STUDYID #>"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/params.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a Set of Parameters — params","title":"Create a Set of Parameters — params","text":"Create set variable parameters/function arguments used call_derivation().","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a Set of Parameters — params","text":"","code":"params(...)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a Set of Parameters — params","text":"... One named arguments","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/params.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a Set of Parameters — params","text":"object class params","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/params.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a Set of Parameters — params","text":"","code":"library(dplyr, warn.conflicts = FALSE)  adsl <- tribble(   ~STUDYID,   ~USUBJID,      ~TRTSDT,      ~TRTEDT,   \"PILOT01\", \"01-1307\",           NA,           NA,   \"PILOT01\", \"05-1377\", \"2014-01-04\", \"2014-01-25\",   \"PILOT01\", \"06-1384\", \"2012-09-15\", \"2012-09-24\",   \"PILOT01\", \"15-1085\", \"2013-02-16\", \"2013-08-18\",   \"PILOT01\", \"16-1298\", \"2013-04-08\", \"2013-06-28\" ) %>%   mutate(     across(TRTSDT:TRTEDT, as.Date)   )  ae <- tribble(   ~STUDYID,  ~DOMAIN,  ~USUBJID,     ~AESTDTC,     ~AEENDTC,   \"PILOT01\",    \"AE\", \"06-1384\", \"2012-09-15\", \"2012-09-29\",   \"PILOT01\",    \"AE\", \"06-1384\", \"2012-09-15\", \"2012-09-29\",   \"PILOT01\",    \"AE\", \"06-1384\", \"2012-09-23\", \"2012-09-29\",   \"PILOT01\",    \"AE\", \"06-1384\", \"2012-09-23\", \"2012-09-29\",   \"PILOT01\",    \"AE\", \"06-1384\", \"2012-09-15\", \"2012-09-29\",   \"PILOT01\",    \"AE\", \"06-1384\", \"2012-09-15\", \"2012-09-29\",   \"PILOT01\",    \"AE\", \"06-1384\", \"2012-09-15\", \"2012-09-29\",   \"PILOT01\",    \"AE\", \"06-1384\", \"2012-09-15\", \"2012-09-29\",   \"PILOT01\",    \"AE\", \"06-1384\", \"2012-09-23\", \"2012-09-29\",   \"PILOT01\",    \"AE\", \"06-1384\", \"2012-09-23\", \"2012-09-29\",   \"PILOT01\",    \"AE\", \"16-1298\", \"2013-06-08\", \"2013-07-06\",   \"PILOT01\",    \"AE\", \"16-1298\", \"2013-06-08\", \"2013-07-06\",   \"PILOT01\",    \"AE\", \"16-1298\", \"2013-04-22\", \"2013-07-06\",   \"PILOT01\",    \"AE\", \"16-1298\", \"2013-04-22\", \"2013-07-06\",   \"PILOT01\",    \"AE\", \"16-1298\", \"2013-04-22\", \"2013-07-06\",   \"PILOT01\",    \"AE\", \"16-1298\", \"2013-04-22\", \"2013-07-06\" )  adae <- ae %>%   select(USUBJID, AESTDTC, AEENDTC) %>%   derive_vars_merged(     dataset_add = adsl,     new_vars = exprs(TRTSDT, TRTEDT),     by_vars = exprs(USUBJID)   )  ## In order to derive both `ASTDT` and `AENDT` in `ADAE`, one can use `derive_vars_dt()` adae %>%   derive_vars_dt(     new_vars_prefix = \"AST\",     dtc = AESTDTC,     date_imputation = \"first\",     min_dates = exprs(TRTSDT),     max_dates = exprs(TRTEDT)   ) %>%   derive_vars_dt(     new_vars_prefix = \"AEN\",     dtc = AEENDTC,     date_imputation = \"last\",     min_dates = exprs(TRTSDT),     max_dates = exprs(TRTEDT)   ) #> # A tibble: 16 × 7 #>    USUBJID AESTDTC    AEENDTC    TRTSDT     TRTEDT     ASTDT      AENDT      #>    <chr>   <chr>      <chr>      <date>     <date>     <date>     <date>     #>  1 06-1384 2012-09-15 2012-09-29 2012-09-15 2012-09-24 2012-09-15 2012-09-29 #>  2 06-1384 2012-09-15 2012-09-29 2012-09-15 2012-09-24 2012-09-15 2012-09-29 #>  3 06-1384 2012-09-23 2012-09-29 2012-09-15 2012-09-24 2012-09-23 2012-09-29 #>  4 06-1384 2012-09-23 2012-09-29 2012-09-15 2012-09-24 2012-09-23 2012-09-29 #>  5 06-1384 2012-09-15 2012-09-29 2012-09-15 2012-09-24 2012-09-15 2012-09-29 #>  6 06-1384 2012-09-15 2012-09-29 2012-09-15 2012-09-24 2012-09-15 2012-09-29 #>  7 06-1384 2012-09-15 2012-09-29 2012-09-15 2012-09-24 2012-09-15 2012-09-29 #>  8 06-1384 2012-09-15 2012-09-29 2012-09-15 2012-09-24 2012-09-15 2012-09-29 #>  9 06-1384 2012-09-23 2012-09-29 2012-09-15 2012-09-24 2012-09-23 2012-09-29 #> 10 06-1384 2012-09-23 2012-09-29 2012-09-15 2012-09-24 2012-09-23 2012-09-29 #> 11 16-1298 2013-06-08 2013-07-06 2013-04-08 2013-06-28 2013-06-08 2013-07-06 #> 12 16-1298 2013-06-08 2013-07-06 2013-04-08 2013-06-28 2013-06-08 2013-07-06 #> 13 16-1298 2013-04-22 2013-07-06 2013-04-08 2013-06-28 2013-04-22 2013-07-06 #> 14 16-1298 2013-04-22 2013-07-06 2013-04-08 2013-06-28 2013-04-22 2013-07-06 #> 15 16-1298 2013-04-22 2013-07-06 2013-04-08 2013-06-28 2013-04-22 2013-07-06 #> 16 16-1298 2013-04-22 2013-07-06 2013-04-08 2013-06-28 2013-04-22 2013-07-06   ## While `derive_vars_dt()` can only add one variable at a time, using `call_derivation()` ## one can add multiple variables in one go. ## The function arguments which are different from a variable to another (e.g. `new_vars_prefix`, ## `dtc`, and `date_imputation`) are specified as a list of `params()` in the `variable_params` ## argument of `call_derivation()`. All other arguments which are common to all variables ## (e.g. `min_dates` and `max_dates`) are specified outside of `variable_params` (i.e. in `...`). call_derivation(   dataset = adae,   derivation = derive_vars_dt,   variable_params = list(     params(dtc = AESTDTC, date_imputation = \"first\", new_vars_prefix = \"AST\"),     params(dtc = AEENDTC, date_imputation = \"last\", new_vars_prefix = \"AEN\")   ),   min_dates = exprs(TRTSDT),   max_dates = exprs(TRTEDT) ) #> # A tibble: 16 × 7 #>    USUBJID AESTDTC    AEENDTC    TRTSDT     TRTEDT     ASTDT      AENDT      #>    <chr>   <chr>      <chr>      <date>     <date>     <date>     <date>     #>  1 06-1384 2012-09-15 2012-09-29 2012-09-15 2012-09-24 2012-09-15 2012-09-29 #>  2 06-1384 2012-09-15 2012-09-29 2012-09-15 2012-09-24 2012-09-15 2012-09-29 #>  3 06-1384 2012-09-23 2012-09-29 2012-09-15 2012-09-24 2012-09-23 2012-09-29 #>  4 06-1384 2012-09-23 2012-09-29 2012-09-15 2012-09-24 2012-09-23 2012-09-29 #>  5 06-1384 2012-09-15 2012-09-29 2012-09-15 2012-09-24 2012-09-15 2012-09-29 #>  6 06-1384 2012-09-15 2012-09-29 2012-09-15 2012-09-24 2012-09-15 2012-09-29 #>  7 06-1384 2012-09-15 2012-09-29 2012-09-15 2012-09-24 2012-09-15 2012-09-29 #>  8 06-1384 2012-09-15 2012-09-29 2012-09-15 2012-09-24 2012-09-15 2012-09-29 #>  9 06-1384 2012-09-23 2012-09-29 2012-09-15 2012-09-24 2012-09-23 2012-09-29 #> 10 06-1384 2012-09-23 2012-09-29 2012-09-15 2012-09-24 2012-09-23 2012-09-29 #> 11 16-1298 2013-06-08 2013-07-06 2013-04-08 2013-06-28 2013-06-08 2013-07-06 #> 12 16-1298 2013-06-08 2013-07-06 2013-04-08 2013-06-28 2013-06-08 2013-07-06 #> 13 16-1298 2013-04-22 2013-07-06 2013-04-08 2013-06-28 2013-04-22 2013-07-06 #> 14 16-1298 2013-04-22 2013-07-06 2013-04-08 2013-06-28 2013-04-22 2013-07-06 #> 15 16-1298 2013-04-22 2013-07-06 2013-04-08 2013-06-28 2013-04-22 2013-07-06 #> 16 16-1298 2013-04-22 2013-07-06 2013-04-08 2013-06-28 2013-04-22 2013-07-06  ## The above call using `call_derivation()` is equivalent to the call using `derive_vars_dt()` ## to derive variables `ASTDT` and `AENDT` separately at the beginning."},{"path":"https://pharmaverse.github.io/admiral/dev/reference/pipe.html","id":null,"dir":"Reference","previous_headings":"","what":"Pipe operator — %>%","title":"Pipe operator — %>%","text":"See magrittr::%>% details.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/pipe.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pipe operator — %>%","text":"","code":"lhs %>% rhs"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/pipe.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pipe operator — %>%","text":"lhs value magrittr placeholder. rhs function call using magrittr semantics.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/print.adam_templates.html","id":null,"dir":"Reference","previous_headings":"","what":"Print adam_templates Objects — print.adam_templates","title":"Print adam_templates Objects — print.adam_templates","text":"Print adam_templates Objects","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/print.adam_templates.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print adam_templates Objects — print.adam_templates","text":"","code":"# S3 method for class 'adam_templates' print(x, ...)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/print.adam_templates.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print adam_templates Objects — print.adam_templates","text":"x adam_templates object ... used","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/print.adam_templates.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print adam_templates Objects — print.adam_templates","text":"return value, called side effects","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/print.adam_templates.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Print adam_templates Objects — print.adam_templates","text":"","code":"templates <- list_all_templates() print(templates) #> Existing ADaM templates in package 'admiral': #> • ADAE #> • ADCM #> • ADEG #> • ADEX #> • ADLB #> • ADLBHY #> • ADMH #> • ADPC #> • ADPP #> • ADPPK #> • ADSL #> • ADVS"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/print.duplicates.html","id":null,"dir":"Reference","previous_headings":"","what":"Print duplicates Objects — print.duplicates","title":"Print duplicates Objects — print.duplicates","text":"Print duplicates Objects","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/print.duplicates.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print duplicates Objects — print.duplicates","text":"","code":"# S3 method for class 'duplicates' print(x, ...)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/print.duplicates.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print duplicates Objects — print.duplicates","text":"x duplicates object ... used","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/print.duplicates.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print duplicates Objects — print.duplicates","text":"return value, called side effects","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/print.source.html","id":null,"dir":"Reference","previous_headings":"","what":"Print source Objects — print.source","title":"Print source Objects — print.source","text":"Print source Objects","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/print.source.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print source Objects — print.source","text":"","code":"# S3 method for class 'source' print(x, ...)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/print.source.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print source Objects — print.source","text":"x source object ... indent = <numeric value> specified output indented specified number characters.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/print.source.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print source Objects — print.source","text":"return value, called side effects","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/print.source.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Print source Objects — print.source","text":"","code":"print(death_event) #> <event_source> object #> dataset_name: \"adsl\" #> filter: DTHFL == \"Y\" #> date: DTHDT #> censor: 0 #> set_values_to: #>   EVNTDESC: \"DEATH\" #>   SRCDOM: \"ADSL\" #>   SRCVAR: \"DTHDT\""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/print_named_list.html","id":null,"dir":"Reference","previous_headings":"","what":"Print Named List — print_named_list","title":"Print Named List — print_named_list","text":"Print Named List","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/print_named_list.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print Named List — print_named_list","text":"","code":"print_named_list(list, indent = 0)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/print_named_list.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print Named List — print_named_list","text":"list named list indent Indent output indented specified number characters.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/print_named_list.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print Named List — print_named_list","text":"return value, called side effects","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/print_named_list.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Print Named List — print_named_list","text":"","code":"print_named_list(death_event) #> dataset_name: \"adsl\" #> filter: DTHFL == \"Y\" #> date: DTHDT #> censor: 0 #> set_values_to: #>   EVNTDESC: \"DEATH\" #>   SRCDOM: \"ADSL\" #>   SRCVAR: \"DTHDT\""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/queries.html","id":null,"dir":"Reference","previous_headings":"","what":"Queries Dataset — queries","title":"Queries Dataset — queries","text":"Queries Dataset","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/queries.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Queries Dataset — queries","text":"","code":"queries"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/queries.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Queries Dataset — queries","text":"object class tbl_df (inherits tbl, data.frame) 15 rows 8 columns.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/queries.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Queries Dataset — queries","text":"example standard query dataset used deriving Standardized MedDRA Query variables ADAE","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/queries_mh.html","id":null,"dir":"Reference","previous_headings":"","what":"Queries MH Dataset — queries_mh","title":"Queries MH Dataset — queries_mh","text":"Queries MH Dataset","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/queries_mh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Queries MH Dataset — queries_mh","text":"","code":"queries_mh"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/queries_mh.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Queries MH Dataset — queries_mh","text":"object class tbl_df (inherits tbl, data.frame) 14 rows 8 columns.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/queries_mh.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Queries MH Dataset — queries_mh","text":"example standard query MH dataset used deriving Standardized MedDRA Query variables ADMH","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/query.html","id":null,"dir":"Reference","previous_headings":"","what":"Create an query object — query","title":"Create an query object — query","text":"query object defines query, e.g., Standard MedDRA Query (SMQ), Standardized Drug Grouping (SDG), customized query (CQ). used input create_query_data().","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/query.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create an query object — query","text":"","code":"query(prefix, name = auto, id = NULL, add_scope_num = FALSE, definition = NULL)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/query.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create an query object — query","text":"prefix value used populate PREFIX output dataset create_query_data(), e.g., \"SMQ03\" name value used populate GRPNAME output dataset create_query_data(). auto keyword specified, variable set name query SMQ/SDG database. Permitted Values: character scalar auto keyword. auto keyword permitted queries defined basket_select() object. id value used populate GRPID output dataset create_query_data(). auto keyword specified, variable set id query SMQ/SDG database. Permitted Values: integer scalar auto keyword. auto keyword permitted queries defined basket_select() object. add_scope_num Determines  SCOPEN output dataset create_query_data() populated parameter set TRUE, definition must basket_select() object. Default: FALSE Permitted Values: TRUE, FALSE definition Definition terms belonging query three different ways define terms: basket_select() object specified select query SMQ database. data frame columns SRCVAR TERMCHAR TERMNUM can specified define terms customized query. SRCVAR set name variable used select terms, e.g., \"AEDECOD\" \"AELLTCD\". SRCVAR need constant within query. example query can based AEDECOD AELLT. SRCVAR refers character variable, TERMCHAR set value variable. refers numeric variable, TERMNUM set value variable. character variables numeric variables used, TERMNUM TERMCHAR respectively can omitted. list data frames basket_select() objects can specified define customized query based custom terms SMQs. data frames must structure described previous item. Permitted Values: basket_select() object, data frame, list data frames basket_select() objects.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/query.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create an query object — query","text":"object class query.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/query.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create an query object — query","text":"","code":"# create a query for an SMQ library(tibble) library(dplyr, warn.conflicts = FALSE)  # create a query for a SMQ query(   prefix = \"SMQ02\",   id = auto,   definition = basket_select(     name = \"Pregnancy and neonatal topics (SMQ)\",     scope = \"NARROW\",     type = \"smq\"   ) ) #> <query> object #> prefix: \"SMQ02\" #> name: auto #> id: auto #> add_scope_num: FALSE #> definition: #>   <basket_select> object #>   name: \"Pregnancy and neonatal topics (SMQ)\" #>   id: NULL #>   scope: \"NARROW\" #>   type: \"smq\"  # create a query for an SDG query(   prefix = \"SDG01\",   id = auto,   definition = basket_select(     name = \"5-aminosalicylates for ulcerative colitis\",     scope = NA_character_,     type = \"sdg\"   ) ) #> <query> object #> prefix: \"SDG01\" #> name: auto #> id: auto #> add_scope_num: FALSE #> definition: #>   <basket_select> object #>   name: \"5-aminosalicylates for ulcerative colitis\" #>   id: NULL #>   scope: \"NA\" #>   type: \"sdg\"  # creating a query for a customized query cqterms <- tribble(   ~TERMCHAR, ~TERMNUM,   \"APPLICATION SITE ERYTHEMA\", 10003041L,   \"APPLICATION SITE PRURITUS\", 10003053L ) %>%   mutate(SRCVAR = \"AEDECOD\")  query(   prefix = \"CQ01\",   name = \"Application Site Issues\",   definition = cqterms ) #> <query> object #> prefix: \"CQ01\" #> name: \"Application Site Issues\" #> add_scope_num: FALSE #> definition: #> # A tibble: 2 × 3 #>   TERMCHAR                   TERMNUM SRCVAR  #>   <chr>                        <int> <chr>   #> 1 APPLICATION SITE ERYTHEMA 10003041 AEDECOD #> 2 APPLICATION SITE PRURITUS 10003053 AEDECOD  # creating a customized query based on SMQs and additional terms query(   prefix = \"CQ03\",   name = \"Special issues of interest\",   definition = list(     cqterms,     basket_select(       name = \"Pregnancy and neonatal topics (SMQ)\",       scope = \"NARROW\",       type = \"smq\"     ),     basket_select(       id = 8050L,       scope = \"BROAD\",       type = \"smq\"     )   ) ) #> <query> object #> prefix: \"CQ03\" #> name: \"Special issues of interest\" #> add_scope_num: FALSE #> definition: #>   c(\"APPLICATION SITE ERYTHEMA\", \"APPLICATION SITE PRURITUS\") #> c(10003041, 10003053) #> c(\"AEDECOD\", \"AEDECOD\") #>   Pregnancy and neonatal topics (SMQ) #> NULL #> NARROW #> smq #>   NULL #> 8050 #> BROAD #> smq"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/records_source.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a records_source Object — records_source","title":"Create a records_source Object — records_source","text":"records_source object used find extreme records interest.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/records_source.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a records_source Object — records_source","text":"","code":"records_source(dataset_name, filter = NULL, new_vars)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/records_source.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a records_source Object — records_source","text":"dataset_name name source dataset name refers dataset provided source_datasets argument derive_param_extreme_record(). filter unquoted condition selecting observations dataset. new_vars Variables add specified variables source datasets added output dataset. Variables can renamed naming element, .e., new_vars = exprs(<new name> = <old name>). example new_vars = exprs(var1, var2) adds variables var1 var2 input dataset. new_vars = exprs(var1, new_var2 = old_var2) takes var1 old_var2 source dataset adds input dataset renaming old_var2 new_var2. Expressions can used create new variables (see example new_vars argument derive_vars_merged()). Permitted Values: list expressions created exprs(), e.g., exprs(ADT, desc(AVAL))","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/records_source.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a records_source Object — records_source","text":"object class records_source","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/reexport-desc.html","id":null,"dir":"Reference","previous_headings":"","what":"dplyr desc — desc","title":"dplyr desc — desc","text":"See dplyr::desc details.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/reexport-exprs.html","id":null,"dir":"Reference","previous_headings":"","what":"rlang exprs — exprs","title":"rlang exprs — exprs","text":"See rlang::exprs details.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/restrict_derivation.html","id":null,"dir":"Reference","previous_headings":"","what":"Execute a Derivation on a Subset of the Input Dataset — restrict_derivation","title":"Execute a Derivation on a Subset of the Input Dataset — restrict_derivation","text":"Execute derivation subset input dataset.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/restrict_derivation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Execute a Derivation on a Subset of the Input Dataset — restrict_derivation","text":"","code":"restrict_derivation(dataset, derivation, args = NULL, filter)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/restrict_derivation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Execute a Derivation on a Subset of the Input Dataset — restrict_derivation","text":"dataset Input dataset derivation Derivation function performs specific derivation expected. derivation adds variables observations dataset. first argument derivation must expect dataset derivation must return dataset. function must provide dataset argument arguments specified params() objects passed arg argument. args Arguments derivation params() object expected. filter Filter condition","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/restrict_derivation.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Execute a Derivation on a Subset of the Input Dataset — restrict_derivation","text":"","code":"library(tibble)  adlb <- tribble(   ~USUBJID, ~AVISITN, ~AVAL, ~ABLFL,   \"1\",            -1,   113, NA_character_,   \"1\",             0,   113, \"Y\",   \"1\",             3,   117, NA_character_,   \"2\",             0,    95, \"Y\",   \"3\",             0,   111, \"Y\",   \"3\",             1,   101, NA_character_,   \"3\",             2,   123, NA_character_ )  # Derive BASE for post-baseline records only (derive_var_base() can not be used in this case # as it requires the baseline observation to be in the input dataset) restrict_derivation(   adlb,   derivation = derive_vars_merged,   args = params(     by_vars = exprs(USUBJID),     dataset_add = adlb,     filter_add = ABLFL == \"Y\",     new_vars = exprs(BASE = AVAL)   ),   filter = AVISITN > 0 ) #> # A tibble: 7 × 5 #>   USUBJID AVISITN  AVAL ABLFL  BASE #>   <chr>     <dbl> <dbl> <chr> <dbl> #> 1 1             3   117 NA      113 #> 2 3             1   101 NA      111 #> 3 3             2   123 NA      111 #> 4 1            -1   113 NA       NA #> 5 1             0   113 Y        NA #> 6 2             0    95 Y        NA #> 7 3             0   111 Y        NA  # Derive BASE for baseline and post-baseline records only restrict_derivation(   adlb,   derivation = derive_var_base,   args = params(     by_vars = exprs(USUBJID)   ),   filter = AVISITN >= 0 ) %>%   # Derive CHG for post-baseline records only   restrict_derivation(     derivation = derive_var_chg,     filter = AVISITN > 0   ) #> # A tibble: 7 × 6 #>   USUBJID AVISITN  AVAL ABLFL  BASE   CHG #>   <chr>     <dbl> <dbl> <chr> <dbl> <dbl> #> 1 1             3   117 NA      113     4 #> 2 3             1   101 NA      111   -10 #> 3 3             2   123 NA      111    12 #> 4 1             0   113 Y       113    NA #> 5 2             0    95 Y        95    NA #> 6 3             0   111 Y       111    NA #> 7 1            -1   113 NA       NA    NA"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/restrict_imputed_dtc_dt.html","id":null,"dir":"Reference","previous_headings":"","what":"Restrict Imputed DTC date to Minimum/Maximum Dates — restrict_imputed_dtc_dt","title":"Restrict Imputed DTC date to Minimum/Maximum Dates — restrict_imputed_dtc_dt","text":"Restrict Imputed DTC date Minimum/Maximum Dates","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/restrict_imputed_dtc_dt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Restrict Imputed DTC date to Minimum/Maximum Dates — restrict_imputed_dtc_dt","text":"","code":"restrict_imputed_dtc_dt(dtc, imputed_dtc, min_dates, max_dates)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/restrict_imputed_dtc_dt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Restrict Imputed DTC date to Minimum/Maximum Dates — restrict_imputed_dtc_dt","text":"dtc '--DTC' date impute character date expected format like yyyy-mm-dd yyyy-mm-ddThh:mm:ss. Trailing components can omitted - valid \"missing\" value component. imputed_dtc imputed DTC date min_dates Minimum dates list dates expected. ensured imputed date specified dates, e.g., imputed adverse event start date first treatment date. dates range possible dates dtc value considered. possible dates defined missing parts dtc date (see example ). ensures non-missing parts dtc date changed. date date-time object expected. example   returns \"2020-11-11T11:11:11\" possible dates \"2020-11\" range \"2020-11-01T00:00:00\" \"2020-11-30T23:59:59\". Therefore \"2020-12-06T12:12:12\" ignored. Returning \"2020-12-06T12:12:12\" changed month although missing (dtc date). max_dates Maximum dates list dates expected. ensured imputed date specified dates, e.g., imputed date data cut date. dates range possible dates considered. date date-time object expected.","code":"impute_dtc_dtm(   \"2020-11\",   min_dates = list(    ymd_hms(\"2020-12-06T12:12:12\"),    ymd_hms(\"2020-11-11T11:11:11\")   ),   highest_imputation = \"M\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/restrict_imputed_dtc_dt.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Restrict Imputed DTC date to Minimum/Maximum Dates — restrict_imputed_dtc_dt","text":"last minimum dates (min_dates) range partial DTC date (dtc) first maximum dates (max_dates) range partial DTC date (dtc) imputed_dtc partial DTC date (dtc) range minimum maximum dates.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/restrict_imputed_dtc_dtm.html","id":null,"dir":"Reference","previous_headings":"","what":"Restrict Imputed DTC date to Minimum/Maximum Dates — restrict_imputed_dtc_dtm","title":"Restrict Imputed DTC date to Minimum/Maximum Dates — restrict_imputed_dtc_dtm","text":"Restrict Imputed DTC date Minimum/Maximum Dates","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/restrict_imputed_dtc_dtm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Restrict Imputed DTC date to Minimum/Maximum Dates — restrict_imputed_dtc_dtm","text":"","code":"restrict_imputed_dtc_dtm(dtc, imputed_dtc, min_dates, max_dates)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/restrict_imputed_dtc_dtm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Restrict Imputed DTC date to Minimum/Maximum Dates — restrict_imputed_dtc_dtm","text":"dtc '--DTC' date impute character date expected format like yyyy-mm-dd yyyy-mm-ddThh:mm:ss. Trailing components can omitted - valid \"missing\" value component. imputed_dtc imputed DTC date min_dates Minimum dates list dates expected. ensured imputed date specified dates, e.g., imputed adverse event start date first treatment date. dates range possible dates dtc value considered. possible dates defined missing parts dtc date (see example ). ensures non-missing parts dtc date changed. date date-time object expected. example   returns \"2020-11-11T11:11:11\" possible dates \"2020-11\" range \"2020-11-01T00:00:00\" \"2020-11-30T23:59:59\". Therefore \"2020-12-06T12:12:12\" ignored. Returning \"2020-12-06T12:12:12\" changed month although missing (dtc date). date variables (datetime) list time imputed \"00:00:00\". Specifying date variables makes sense date imputed. time imputed, date variables affect result. max_dates Maximum dates list dates expected. ensured imputed date specified dates, e.g., imputed date data cut date. dates range possible dates considered. date date-time object expected. date variables (datetime) list time imputed \"23:59:59\". Specifying date variables makes sense date imputed. time imputed, date variables affect result.","code":"impute_dtc_dtm(   \"2020-11\",   min_dates = list(    ymd_hms(\"2020-12-06T12:12:12\"),    ymd_hms(\"2020-11-11T11:11:11\")   ),   highest_imputation = \"M\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/restrict_imputed_dtc_dtm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Restrict Imputed DTC date to Minimum/Maximum Dates — restrict_imputed_dtc_dtm","text":"last minimum dates (min_dates) range partial DTC date (dtc) first maximum dates (max_dates) range partial DTC date (dtc) imputed_dtc partial DTC date (dtc) range minimum maximum dates.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/set_admiral_options.html","id":null,"dir":"Reference","previous_headings":"","what":"Set the Value of Admiral Options — set_admiral_options","title":"Set the Value of Admiral Options — set_admiral_options","text":"Set Values Admiral Options Can Modified Advanced Users.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/set_admiral_options.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set the Value of Admiral Options — set_admiral_options","text":"","code":"set_admiral_options(subject_keys, signif_digits)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/set_admiral_options.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set the Value of Admiral Options — set_admiral_options","text":"subject_keys Variables uniquely identify subject, defaults exprs(STUDYID, USUBJID). option used default value subject_keys argument admiral functions. signif_digits Holds number significant digits comparing numeric variables, defaults 15. option used default value  signif_dig argument admiral functions derive_var_atoxgr_dir() derive_var_anrind().","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/set_admiral_options.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set the Value of Admiral Options — set_admiral_options","text":"return value, called side effects.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/set_admiral_options.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Set the Value of Admiral Options — set_admiral_options","text":"Modify admiral option, e.g subject_keys, automatically affects downstream function inputs get_admiral_option() called derive_param_exist_flag().","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/set_admiral_options.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Set the Value of Admiral Options — set_admiral_options","text":"","code":"library(lubridate) library(dplyr, warn.conflicts = FALSE) library(tibble) set_admiral_options(subject_keys = exprs(STUDYID, USUBJID2))  # Derive a new parameter for measurable disease at baseline adsl <- tribble(   ~USUBJID2,   \"1\",   \"2\",   \"3\" ) %>%   mutate(STUDYID = \"XX1234\")  tu <- tribble(   ~USUBJID2,      ~VISIT,    ~TUSTRESC,   \"1\",       \"SCREENING\",     \"TARGET\",   \"1\",          \"WEEK 1\",     \"TARGET\",   \"1\",          \"WEEK 5\",     \"TARGET\",   \"1\",          \"WEEK 9\", \"NON-TARGET\",   \"2\",       \"SCREENING\", \"NON-TARGET\",   \"2\",       \"SCREENING\", \"NON-TARGET\" ) %>%   mutate(     STUDYID = \"XX1234\",     TUTESTCD = \"TUMIDENT\"   )  derive_param_exist_flag(   dataset_ref = adsl,   dataset_add = tu,   filter_add = TUTESTCD == \"TUMIDENT\" & VISIT == \"SCREENING\",   condition = TUSTRESC == \"TARGET\",   false_value = \"N\",   missing_value = \"N\",   set_values_to = exprs(     PARAMCD = \"MDIS\",     PARAM = \"Measurable Disease at Baseline\"   ) ) #> # A tibble: 3 × 5 #>   USUBJID2 STUDYID AVALC PARAMCD PARAM                          #>   <chr>    <chr>   <chr> <chr>   <chr>                          #> 1 1        XX1234  Y     MDIS    Measurable Disease at Baseline #> 2 2        XX1234  N     MDIS    Measurable Disease at Baseline #> 3 3        XX1234  N     MDIS    Measurable Disease at Baseline  set_admiral_options(signif_digits = 14)  # Derive ANRIND for ADVS advs <- tribble(   ~PARAMCD, ~AVAL, ~ANRLO, ~ANRHI,   \"DIABP\",     59,     60,     80,   \"SYSBP\",    120,     90,    130,   \"RESP\",      21,      8,     20, )  derive_var_anrind(advs) #> # A tibble: 3 × 5 #>   PARAMCD  AVAL ANRLO ANRHI ANRIND #>   <chr>   <dbl> <dbl> <dbl> <chr>  #> 1 DIABP      59    60    80 LOW    #> 2 SYSBP     120    90   130 NORMAL #> 3 RESP       21     8    20 HIGH"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/signal_duplicate_records.html","id":null,"dir":"Reference","previous_headings":"","what":"Signal Duplicate Records — signal_duplicate_records","title":"Signal Duplicate Records — signal_duplicate_records","text":"Signal Duplicate Records","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/signal_duplicate_records.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Signal Duplicate Records — signal_duplicate_records","text":"","code":"signal_duplicate_records(   dataset,   by_vars,   msg = paste(\"Dataset contains duplicate records\", \"with respect to\",     \"{.var {replace_values_by_names(by_vars)}}\"),   cnd_type = \"error\",   class = NULL )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/signal_duplicate_records.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Signal Duplicate Records — signal_duplicate_records","text":"dataset Input dataset variables specified by_vars argument expected dataset. by_vars Grouping variables Defines groups records look duplicates. Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) msg condition message cnd_type Type condition signal detecting duplicate records. One \"message\", \"warning\" \"error\". Default \"error\". class Class condition specified classes added classes condition. c(\"duplicate_records\", \"assert-admiral\") always added.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/signal_duplicate_records.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Signal Duplicate Records — signal_duplicate_records","text":"return value, called side effects","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/signal_duplicate_records.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Signal Duplicate Records — signal_duplicate_records","text":"","code":"data(admiral_adsl)  # Duplicate the first record adsl <- rbind(admiral_adsl[1L, ], admiral_adsl)  signal_duplicate_records(adsl, exprs(USUBJID), cnd_type = \"message\") #> Dataset contains duplicate records with respect to `USUBJID` #> ℹ Run `admiral::get_duplicates_dataset()` to access the duplicate records"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/slice_derivation.html","id":null,"dir":"Reference","previous_headings":"","what":"Execute a Derivation with Different Arguments for Subsets of the Input Dataset — slice_derivation","title":"Execute a Derivation with Different Arguments for Subsets of the Input Dataset — slice_derivation","text":"input dataset split slices (subsets) slice derivation called separately. arguments derivation may vary depending slice.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/slice_derivation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Execute a Derivation with Different Arguments for Subsets of the Input Dataset — slice_derivation","text":"","code":"slice_derivation(dataset, derivation, args = NULL, ...)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/slice_derivation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Execute a Derivation with Different Arguments for Subsets of the Input Dataset — slice_derivation","text":"dataset Input dataset derivation Derivation function performs specific derivation expected. derivation adds variables observations dataset. first argument derivation must expect dataset derivation must return dataset. function must provide dataset argument arguments specified params() objects passed arg argument. Please note possible specify {dplyr} functions like mutate() summarize(). args Arguments derivation params() object expected. ... derivation_slice() object expected slice defines subset input dataset parameters derivation. derivation called subset parameters specified args parameter args field derivation_slice() object. parameter specified , value derivation_slice() overwrites one args.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/slice_derivation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Execute a Derivation with Different Arguments for Subsets of the Input Dataset — slice_derivation","text":"input dataset variables derived derivation added","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/slice_derivation.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Execute a Derivation with Different Arguments for Subsets of the Input Dataset — slice_derivation","text":"slice derivation called subset defined filter field derivation_slice() object parameters specified args parameter args field derivation_slice() object. parameter specified , value derivation_slice() overwrites one args. Observations match one slice considered first matching slice. Observations match slices included output dataset derivation called .","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/slice_derivation.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Execute a Derivation with Different Arguments for Subsets of the Input Dataset — slice_derivation","text":"","code":"library(tibble) library(stringr) advs <- tribble(   ~USUBJID, ~VSDTC,       ~VSTPT,   \"1\",      \"2020-04-16\", NA_character_,   \"1\",      \"2020-04-16\", \"BEFORE TREATMENT\" )  # For the second slice filter is set to TRUE. Thus derive_vars_dtm is called # with time_imputation = \"last\" for all observations which do not match for the # first slice. slice_derivation(   advs,   derivation = derive_vars_dtm,   args = params(     dtc = VSDTC,     new_vars_prefix = \"A\"   ),   derivation_slice(     filter = str_detect(VSTPT, \"PRE|BEFORE\"),     args = params(time_imputation = \"first\")   ),   derivation_slice(     filter = TRUE,     args = params(time_imputation = \"last\")   ) ) #> # A tibble: 2 × 5 #>   USUBJID VSDTC      VSTPT            ADTM                ATMF  #>   <chr>   <chr>      <chr>            <dttm>              <chr> #> 1 1       2020-04-16 NA               2020-04-16 23:59:59 H     #> 2 1       2020-04-16 BEFORE TREATMENT 2020-04-16 00:00:00 H"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/transform_range.html","id":null,"dir":"Reference","previous_headings":"","what":"Transform Range — transform_range","title":"Transform Range — transform_range","text":"Transforms results source range target range. example, transforming source values 1, 2, 3, 4, 5 0, 25, 50, 75, 100.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/transform_range.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transform Range — transform_range","text":"","code":"transform_range(   source,   source_range,   target_range,   flip_direction = FALSE,   outside_range = \"NA\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/transform_range.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transform Range — transform_range","text":"source vector values transformed numeric vector expected. source_range permitted source range numeric vector containing two elements expected, representing lower upper bounds permitted source range. target_range target range numeric vector containing two elements expected, representing lower upper bounds target range. flip_direction Flip direction range? transformed values reversed within target range, e.g. within range 0 100, 25 reversed 75. Permitted Values: TRUE, FALSE outside_range Handling values outside source range Values outside source range (source_range) transformed NA. \"warning\" \"error\" specified, warning error issued source includes values outside source range. Permitted Values: \"NA\", \"warning\", \"error\"","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/transform_range.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transform Range — transform_range","text":"source linearly transformed target range","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/transform_range.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transform Range — transform_range","text":"Returns values source linearly transformed source range (source_range) target range (target_range). Values outside source range set NA.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/transform_range.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transform Range — transform_range","text":"","code":"transform_range(   source = c(1, 4, 3, 6, 5),   source_range = c(1, 5),   target_range = c(0, 100) ) #> [1]   0  75  50  NA 100  transform_range(   source = c(1, 4, 3, 6, 5),   source_range = c(1, 5),   target_range = c(0, 100),   flip_direction = TRUE ) #> [1] 100  25  50  NA   0"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/tte_source.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a tte_source Object — tte_source","title":"Create a tte_source Object — tte_source","text":"tte_source object used define events possible censorings.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/tte_source.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a tte_source Object — tte_source","text":"","code":"tte_source(dataset_name, filter = NULL, date, censor = 0, set_values_to = NULL)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/tte_source.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a tte_source Object — tte_source","text":"dataset_name name source dataset name refers dataset provided source_datasets parameter derive_param_tte(). filter unquoted condition selecting observations dataset events possible censoring time points. date variable expression providing date event censoring. date, datetime can specified. unquoted symbol expression expected. Refer derive_vars_dt() convert_dtc_to_dt() impute derive date date character vector date object. censor Censoring value CDISC strongly recommends using 0 events positive integers censoring. set_values_to named list returned exprs() defining variables set event censoring, e.g. exprs(EVENTDESC = \"DEATH\", SRCDOM = \"ADSL\", SRCVAR = \"DTHDT\"). values must symbol, character string, numeric value, expression, NA.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/tte_source.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a tte_source Object — tte_source","text":"object class tte_source","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/tte_source_objects.html","id":null,"dir":"Reference","previous_headings":"","what":"Pre-Defined Time-to-Event Source Objects — death_event","title":"Pre-Defined Time-to-Event Source Objects — death_event","text":"pre-defined tte_source objects can used input derive_param_tte().","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/tte_source_objects.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pre-Defined Time-to-Event Source Objects — death_event","text":"","code":"death_event  lastalive_censor  ae_event  ae_ser_event  ae_gr1_event  ae_gr2_event  ae_gr3_event  ae_gr4_event  ae_gr5_event  ae_gr35_event  ae_sev_event  ae_wd_event"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/tte_source_objects.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Pre-Defined Time-to-Event Source Objects — death_event","text":"see definition various objects simply print object R console, e.g. print(death_event). details use objects please refer derive_param_tte().","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/tte_source_objects.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pre-Defined Time-to-Event Source Objects — death_event","text":"","code":"# This shows the definition of all pre-defined `tte_source` objects that ship # with {admiral} for (obj in list_tte_source_objects()$object) {   cat(obj, \"\\n\")   print(get(obj))   cat(\"\\n\") } #> ae_gr3_event  #> <event_source> object #> dataset_name: \"adae\" #> filter: TRTEMFL == \"Y\" & ATOXGR == \"3\" #> date: ASTDT #> censor: 0 #> set_values_to: #>   EVNTDESC: \"GRADE 3 ADVERSE EVENT\" #>   SRCDOM: \"ADAE\" #>   SRCVAR: \"ASTDT\" #>   SRCSEQ: AESEQ #>  #> ae_wd_event  #> <event_source> object #> dataset_name: \"adae\" #> filter: TRTEMFL == \"Y\" & AEACN == \"DRUG WITHDRAWN\" #> date: ASTDT #> censor: 0 #> set_values_to: #>   EVNTDESC: \"ADVERSE EVENT LEADING TO DRUG WITHDRAWAL\" #>   SRCDOM: \"ADAE\" #>   SRCVAR: \"ASTDT\" #>   SRCSEQ: AESEQ #>  #> ae_gr35_event  #> <event_source> object #> dataset_name: \"adae\" #> filter: TRTEMFL == \"Y\" & ATOXGR %in% c(\"3\", \"4\", \"5\") #> date: ASTDT #> censor: 0 #> set_values_to: #>   EVNTDESC: \"GRADE 3-5 ADVERSE EVENT\" #>   SRCDOM: \"ADAE\" #>   SRCVAR: \"ASTDT\" #>   SRCSEQ: AESEQ #>  #> lastalive_censor  #> <censor_source> object #> dataset_name: \"adsl\" #> filter: NULL #> date: LSTALVDT #> censor: 1 #> set_values_to: #>   EVNTDESC: \"ALIVE\" #>   SRCDOM: \"ADSL\" #>   SRCVAR: \"LSTALVDT\" #>  #> ae_gr1_event  #> <event_source> object #> dataset_name: \"adae\" #> filter: TRTEMFL == \"Y\" & ATOXGR == \"1\" #> date: ASTDT #> censor: 0 #> set_values_to: #>   EVNTDESC: \"GRADE 1 ADVERSE EVENT\" #>   SRCDOM: \"ADAE\" #>   SRCVAR: \"ASTDT\" #>   SRCSEQ: AESEQ #>  #> ae_ser_event  #> <event_source> object #> dataset_name: \"adae\" #> filter: TRTEMFL == \"Y\" & AESER == \"Y\" #> date: ASTDT #> censor: 0 #> set_values_to: #>   EVNTDESC: \"SERIOUS ADVERSE EVENT\" #>   SRCDOM: \"ADAE\" #>   SRCVAR: \"ASTDT\" #>   SRCSEQ: AESEQ #>  #> ae_gr2_event  #> <event_source> object #> dataset_name: \"adae\" #> filter: TRTEMFL == \"Y\" & ATOXGR == \"2\" #> date: ASTDT #> censor: 0 #> set_values_to: #>   EVNTDESC: \"GRADE 2 ADVERSE EVENT\" #>   SRCDOM: \"ADAE\" #>   SRCVAR: \"ASTDT\" #>   SRCSEQ: AESEQ #>  #> ae_event  #> <event_source> object #> dataset_name: \"adae\" #> filter: TRTEMFL == \"Y\" #> date: ASTDT #> censor: 0 #> set_values_to: #>   EVNTDESC: \"ADVERSE EVENT\" #>   SRCDOM: \"ADAE\" #>   SRCVAR: \"ASTDT\" #>   SRCSEQ: AESEQ #>  #> ae_gr4_event  #> <event_source> object #> dataset_name: \"adae\" #> filter: TRTEMFL == \"Y\" & ATOXGR == \"4\" #> date: ASTDT #> censor: 0 #> set_values_to: #>   EVNTDESC: \"GRADE 4 ADVERSE EVENT\" #>   SRCDOM: \"ADAE\" #>   SRCVAR: \"ASTDT\" #>   SRCSEQ: AESEQ #>  #> ae_gr5_event  #> <event_source> object #> dataset_name: \"adae\" #> filter: TRTEMFL == \"Y\" & ATOXGR == \"5\" #> date: ASTDT #> censor: 0 #> set_values_to: #>   EVNTDESC: \"GRADE 5 ADVERSE EVENT\" #>   SRCDOM: \"ADAE\" #>   SRCVAR: \"ASTDT\" #>   SRCSEQ: AESEQ #>  #> ae_sev_event  #> <event_source> object #> dataset_name: \"adae\" #> filter: TRTEMFL == \"Y\" & AESEV == \"SEVERE\" #> date: ASTDT #> censor: 0 #> set_values_to: #>   EVNTDESC: \"SEVERE ADVERSE EVENT\" #>   SRCDOM: \"ADAE\" #>   SRCVAR: \"ASTDT\" #>   SRCSEQ: AESEQ #>  #> death_event  #> <event_source> object #> dataset_name: \"adsl\" #> filter: DTHFL == \"Y\" #> date: DTHDT #> censor: 0 #> set_values_to: #>   EVNTDESC: \"DEATH\" #>   SRCDOM: \"ADSL\" #>   SRCVAR: \"DTHDT\" #>"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/use_ad_template.html","id":null,"dir":"Reference","previous_headings":"","what":"Open an ADaM Template Script — use_ad_template","title":"Open an ADaM Template Script — use_ad_template","text":"Open ADaM Template Script","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/use_ad_template.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Open an ADaM Template Script — use_ad_template","text":"","code":"use_ad_template(   adam_name = \"adsl\",   save_path = paste0(\"./\", adam_name, \".R\"),   package = \"admiral\",   overwrite = FALSE,   open = interactive() )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/use_ad_template.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Open an ADaM Template Script — use_ad_template","text":"adam_name ADaM dataset name. can use available dataset names \"ADAE\", \"ADCM\", \"ADEG\", \"ADEX\", \"ADLB\", \"ADLBHY\", \"ADMH\", \"ADPC\", \"ADPP\", \"ADPPK\", \"ADSL\", \"ADVS\". dataset name case-insensitive. default dataset name \"ADSL\". save_path Path save script. package R package look templates. default \"admiral\". overwrite Whether overwrite existing file named save_path. open Whether open script right away.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/use_ad_template.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Open an ADaM Template Script — use_ad_template","text":"return values, called side effects","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/use_ad_template.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Open an ADaM Template Script — use_ad_template","text":"Running without arguments use_ad_template() auto-generates adsl.R current path. Use list_all_templates() discover templates available.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/use_ad_template.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Open an ADaM Template Script — use_ad_template","text":"","code":"if (interactive()) {   use_ad_template(\"adsl\") }"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/yn_to_numeric.html","id":null,"dir":"Reference","previous_headings":"","what":"Map ","title":"Map ","text":"Map \"Y\" \"N\" numeric values.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/yn_to_numeric.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Map ","text":"","code":"yn_to_numeric(arg)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/yn_to_numeric.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Map ","text":"arg Character vector","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/yn_to_numeric.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Map ","text":"1 arg equals \"Y\", 0 arg equals \"N\", NA_real_ otherwise","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/yn_to_numeric.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Map ","text":"","code":"yn_to_numeric(c(\"Y\", \"N\", NA_character_)) #> [1]  1  0 NA"}]
+[{"path":"https://pharmaverse.github.io/admiral/dev/CODE_OF_CONDUCT.html","id":null,"dir":"","previous_headings":"","what":"Contributor Code of Conduct","title":"Contributor Code of Conduct","text":"contributors maintainers project, pledge respect people contribute reporting issues, posting feature requests, updating documentation, submitting pull requests patches, activities. committed making participation project harassment-free experience everyone, regardless level experience, gender, gender identity expression, sexual orientation, disability, personal appearance, body size, race, ethnicity, age, religion. Examples unacceptable behavior participants include use sexual language imagery, derogatory comments personal attacks, trolling, public private harassment, insults, unprofessional conduct. Project maintainers right responsibility remove, edit, reject comments, commits, code, wiki edits, issues, contributions aligned Code Conduct. Project maintainers follow Code Conduct may removed project team. Instances abusive, harassing, otherwise unacceptable behavior may reported opening issue contacting one project maintainers. Code Conduct adapted Contributor Covenant (http://www.contributor-covenant.org), version 1.0.0, available http://www.contributor-covenant.org/version/1/0/0/","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/CONTRIBUTING.html","id":null,"dir":"","previous_headings":"","what":"Contribution to {admiral}","title":"Contribution to {admiral}","text":"outlines propose change admiral package. detailed info contributing {admiral}, pharmaverse packages, please see Contribution Guide well Developer Guides Articles section {admiraldev} website. Please note try align best practices used R packages’ development processes - veteran developers familiar processes. However, deviate slightly best practices advise new contributors review package documentation accordingly.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/CONTRIBUTING.html","id":"basics-of-contribution","dir":"","previous_headings":"","what":"Basics of Contribution","title":"Contribution to {admiral}","text":"🦋 new contribution, user creates issue issue tab GitHub put backlog. issues can range bug identification /fixes, enhancements functions, documentation, tests new features. 🦋 advise contact us issue created via Slack (don’t access, use link join). can discuss details align expectations familiar admiral philosophy programming strategy. team try review issues within next backlog meeting give initial feedback. Since 100% fully resourced software development team might issues take longer respond depending amount overall issues. 🦋 advise familiarize programming strategy, guidance GitHub usage unit testing. 🦋 newly created issues reviewed within next backlog meeting creator receive initial feedback via comment. Someone core development team triage new issues assigning appropriate labels (“user request” can easily identify new requests). 🦋 Issues meant taken users Pharma programming community exclusively admiral team GSK Roche.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/CONTRIBUTING.html","id":"type-1-contribution-without-code","dir":"","previous_headings":"","what":"Type 1: Contribution without Code","title":"Contribution to {admiral}","text":"🦋 User creates issue ideally contacts admiral team member via Slack. 🦋 admiral core development team contact issue creator soon possible discuss details.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/CONTRIBUTING.html","id":"type-2-contribution-with-code","dir":"","previous_headings":"","what":"Type 2: Contribution with Code","title":"Contribution to {admiral}","text":"🦋 First, user creates issue comments existing issue notify ’d like contribute code. 🦋 Follow development process step--step guide. 🦋 advise contact admiral core development team directly via Slack submitting code complex functionality.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/CONTRIBUTING.html","id":"detailed-development-process","dir":"","previous_headings":"","what":"Detailed Development Process","title":"Contribution to {admiral}","text":"decide contribute code ’re ready make first code contribution, detailed development process step--step guide help tie detailed vignettes together give simplest experience helping grow enhance codebase. Create new feature branch main branch following naming convention pull latest changes - detailed GitHub usage guide. Familiarize admiral programming strategy, make required code updates. code formatted according tidyverse style guide? create/add appropriate unit tests? removed/replaced function /function parameters, fully follow deprecation guidance? update [documentation]https://pharmaverse.github.io/admiraldev/articles/programming_strategy.html#function-header-documentation)? , remember run devtools::document() include updated NAMESPACE .Rd files man/. code update impact ADaM template R scripts stored inst/templates? code update impact vignettes stored vignettes? update Changelog NEWS.md? build admiral site pkgdown::build_site() check affected examples displayed correctly new functions occur “Reference” page? happy updates, make pull request merge main branch link issue closes successful merging. Check merge conflicts. , fix requesting review. See solving merge conflicts guidance. Check results automated R-CMD check lintr checks issues consult guide. Assign reviewer admiral core development team - anyone discussed issue previously via Slack GitHub. unsure, add comment pull request ready review add @pharmaverse/admiral tag . review completed, reviewer merge PR automatically delete feature branch. Finally, just note say core developers hugely appreciate taking time contribute admiral. Don’t offended review send requests back , expectations high can ensure admiral codebase remains robust consistent. best way learn always jump get involved, please don’t afraid ’ll make mistakes along way – continue , ’s reviews . Also ever get stuck don’t hesitate reach support via Slack channel. Welcome admiral community!","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"Apache License","title":"Apache License","text":"Version 2.0, January 2004 <http://www.apache.org/licenses/>","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/LICENSE.html","id":"id_1-definitions","dir":"","previous_headings":"Terms and Conditions for use, reproduction, and distribution","what":"1. Definitions","title":"Apache License","text":"“License” shall mean terms conditions use, reproduction, distribution defined Sections 1 9 document. “Licensor” shall mean copyright owner entity authorized copyright owner granting License. “Legal Entity” shall mean union acting entity entities control, controlled , common control entity. purposes definition, “control” means () power, direct indirect, cause direction management entity, whether contract otherwise, (ii) ownership fifty percent (50%) outstanding shares, (iii) beneficial ownership entity. “” (“”) shall mean individual Legal Entity exercising permissions granted License. “Source” form shall mean preferred form making modifications, including limited software source code, documentation source, configuration files. “Object” form shall mean form resulting mechanical transformation translation Source form, including limited compiled object code, generated documentation, conversions media types. “Work” shall mean work authorship, whether Source Object form, made available License, indicated copyright notice included attached work (example provided Appendix ). “Derivative Works” shall mean work, whether Source Object form, based (derived ) Work editorial revisions, annotations, elaborations, modifications represent, whole, original work authorship. purposes License, Derivative Works shall include works remain separable , merely link (bind name) interfaces , Work Derivative Works thereof. “Contribution” shall mean work authorship, including original version Work modifications additions Work Derivative Works thereof, intentionally submitted Licensor inclusion Work copyright owner individual Legal Entity authorized submit behalf copyright owner. purposes definition, “submitted” means form electronic, verbal, written communication sent Licensor representatives, including limited communication electronic mailing lists, source code control systems, issue tracking systems managed , behalf , Licensor purpose discussing improving Work, excluding communication conspicuously marked otherwise designated writing copyright owner “Contribution.” “Contributor” shall mean Licensor individual Legal Entity behalf Contribution received Licensor subsequently incorporated within Work.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/LICENSE.html","id":"id_2-grant-of-copyright-license","dir":"","previous_headings":"Terms and Conditions for use, reproduction, and distribution","what":"2. 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Accepting Warranty or Additional Liability","title":"Apache License","text":"redistributing Work Derivative Works thereof, may choose offer, charge fee , acceptance support, warranty, indemnity, liability obligations /rights consistent License. However, accepting obligations, may act behalf sole responsibility, behalf Contributor, agree indemnify, defend, hold Contributor harmless liability incurred , claims asserted , Contributor reason accepting warranty additional liability. END TERMS CONDITIONS","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/LICENSE.html","id":"appendix-how-to-apply-the-apache-license-to-your-work","dir":"","previous_headings":"","what":"APPENDIX: How to apply the Apache License to your work","title":"Apache License","text":"apply Apache License work, attach following boilerplate notice, fields enclosed brackets [] replaced identifying information. (Don’t include brackets!) text enclosed appropriate comment syntax file format. also recommend file class name description purpose included “printed page” copyright notice easier identification within third-party archives.","code":"Copyright 2021 F. Hoffmann-La Roche AG and GlaxoSmithKline LLC  Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at    http://www.apache.org/licenses/LICENSE-2.0  Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License."},{"path":"https://pharmaverse.github.io/admiral/dev/articles/admiral.html","id":"main-idea","dir":"Articles","previous_headings":"","what":"Main Idea","title":"Get Started","text":"main idea admiral ADaM dataset built sequence derivations. derivation adds one variables records processed dataset. modular approach makes easy adjust code adding, removing, modifying derivations. derivation function call. vignette explore different type derivation functions offered admiral, well argument conventions follow best start admiral script.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/admiral.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Get Started","text":"help examples showcased, load packages set example data manipulate later. dplyr, lubridate stringr packages tidyverse packages used heavily throughout vignette. admiral package also leverages pharmaversesdtm package example SDTM datasets CDISC Pilot Study. adsl advs objects now prepared showcase addition variables addition records later. Note: example , reading dummy R data pharmaversesdtm. However, using SAS datasets starting point, sure consult section Handling Missing Values Programming Concepts Conventions User Guide learn use function convert_blanks_to_na() process.","code":"# Uncomment line below if you need to install these packages # install.packages(c(\"dplyr\", \"lubridate\", \"stringr\", \"tibble\", \"pharmaversesdtm\", \"admiral\"))  library(dplyr, warn.conflicts = FALSE) library(lubridate) library(stringr) library(tibble) library(pharmaversesdtm) library(admiral)  # Read in SDTM datasets dm <- pharmaversesdtm::dm ds <- pharmaversesdtm::ds ex <- pharmaversesdtm::ex vs <- pharmaversesdtm::vs admiral_adsl <- admiral::admiral_adsl ex_ext <- ex %>%   derive_vars_dtm(     dtc = EXSTDTC,     new_vars_prefix = \"EXST\"   )  vs <- vs %>%   filter(     USUBJID %in% c(       \"01-701-1015\", \"01-701-1023\", \"01-703-1086\",       \"01-703-1096\", \"01-707-1037\", \"01-716-1024\"     ) &       VSTESTCD %in% c(\"SYSBP\", \"DIABP\") &       VSPOS == \"SUPINE\"   )  adsl <- admiral_adsl %>%   select(-TRTSDTM, -TRTSTMF)  advs <- vs %>%   mutate(     PARAM = VSTEST, PARAMCD = VSTESTCD, AVAL = VSSTRESN, AVALU = VSORRESU,     AVISIT = VISIT, AVISITN = VISITNUM   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/admiral.html","id":"derivation-functions","dir":"Articles","previous_headings":"","what":"Derivation Functions","title":"Get Started","text":"important functions admiral derivations. Derivations add variables observations/records input dataset. Existing variables observations input dataset changed. Derivation functions start derive_. first argument functions expects input dataset. allows us string together derivations using %>% operator. Functions derive dedicated variable start derive_var_ followed variable name, e.g., derive_var_trtdurd() derives TRTDURD variable. Functions can derive multiple variables start derive_vars_ followed variable name, e.g., derive_vars_dtm() can derive TRTSDTM TRTSTMF variables. Functions derive dedicated parameter start derive_param_ followed parameter name, e.g., derive_param_bmi() derives BMI parameter.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/admiral.html","id":"addvars","dir":"Articles","previous_headings":"Derivation Functions","what":"Example: Adding Variables","title":"Get Started","text":"can see example call one common derivation functions, derive_vars_merged(). function adds variable(s) input dataset based contents another dataset. example, add treatment start datetime corresponding imputation flag (EXSTTMF) adsl identifying first record ex non-missing Exposure Start Datetime (EXSTDTM) sorting EXSTDM EXSEQ.","code":"adsl <- adsl %>%   derive_vars_merged(     dataset_add = ex_ext,     filter_add = !is.na(EXSTDTM),     new_vars = exprs(TRTSDTM = EXSTDTM, TRTSTMF = EXSTTMF),     order = exprs(EXSTDTM, EXSEQ),     mode = \"first\",     by_vars = exprs(STUDYID, USUBJID)   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/admiral.html","id":"addrecs","dir":"Articles","previous_headings":"Derivation Functions","what":"Example: Adding Records","title":"Get Started","text":"Another common derivation function derive_param_computed(). function adds derived parameter input dataset. example , use derive Mean Arterial Pressure (MAP) Systolic Diastolic values blood pressure. parameters needed derivation specified parameters argument, within set_values_to set variable values new derived record. Note: users’ convenience, admiral actually provides derive_param_map() (wrapper derive_param_computed()) derive MAP. example serves illustrative purposes .","code":"advs <- advs %>%   derive_param_computed(     by_vars = exprs(USUBJID, AVISIT, AVISITN),     parameters = c(\"SYSBP\", \"DIABP\"),     set_values_to = exprs(       AVAL = (AVAL.SYSBP + 2 * AVAL.DIABP) / 3,       PARAMCD = \"MAP\",       PARAM = \"Mean Arterial Pressure (mmHg)\",       AVALU = \"mmHg\"     )   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/admiral.html","id":"other-types-of-functions","dir":"Articles","previous_headings":"","what":"Other Types of Functions","title":"Get Started","text":"Along derivation functions, admiral provides large arsenal functions help ADaM derivations. categories.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/admiral.html","id":"higher-order-functions","dir":"Articles","previous_headings":"Other Types of Functions","what":"Higher Order Functions","title":"Get Started","text":"Higher order functions admiral functions take functions input. enhance existing portfolio derivation functions allowing greater customization latter’s behavior. done allowing derivation function : call_derivation() - Called multiple times, varying input arguments. restrict_derivation() - Executed subset input dataset. slice_derivation() - Executed differently subsets input dataset. Higher order functions relatively advanced topic within admiral; can can read dedicated vignette Higher Order Functions.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/admiral.html","id":"computation-functions","dir":"Articles","previous_headings":"Other Types of Functions","what":"Computation Functions","title":"Get Started","text":"Computations expect vectors input return vector. Usually computation functions can used %>%. functions can used expressions like convert_dtc_to_dt() derivation Final Lab Visit Date (FINLABDT) example : Computations can also used inside mutate() statement (similar), can leveraged variable derivations require calls specific admiral functions:","code":"# Add the date of the final lab visit to ADSL adsl <- dm %>%   derive_vars_merged(     dataset_add = ds,     by_vars = exprs(USUBJID),     new_vars = exprs(FINLABDT = convert_dtc_to_dt(DSSTDTC)),     filter_add = DSDECOD == \"FINAL LAB VISIT\"   ) adsl <- adsl %>%   mutate(RFSTDT = convert_dtc_to_dt(RFSTDTC))"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/admiral.html","id":"filter-functions","dir":"Articles","previous_headings":"Other Types of Functions","what":"Filter Functions","title":"Get Started","text":"Filter functions admiral utilities filter input dataset different manners, instance returning records fit/don’t fit certain condition first/last observation group. functions form important internal backbone admiral functions, can also used explore manipulate dataset. instance, example use filter_extreme() extract recent MAP records advs:","code":"advs_lastmap <- advs %>%   filter(PARAMCD == \"MAP\") %>%   filter_extreme(     by_vars = exprs(USUBJID),     order = exprs(AVISITN, PARAMCD),     mode = \"last\"   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/admiral.html","id":"argument-conventions","dir":"Articles","previous_headings":"","what":"Argument Conventions","title":"Get Started","text":"Within admiral package, arguments expect variable names expressions variable names, symbols expressions must specified rather strings. arguments expect single variable name, name can specified without quotes quotation, e.g. new_var = TEMPBL arguments expect one variable names, list symbols expected, e.g. by_vars = exprs(PARAMCD, AVISIT) arguments expect single expression, expression needs passed “”, e.g. filter = PARAMCD == \"TEMP\" arguments expect one expressions, list expressions expected, e.g. order = exprs(AVISIT, desc(AESEV)) new expressions, consider reading Expressions Scripts section Concepts Conventions User Guide learn .","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/admiral.html","id":"starting-a-script","dir":"Articles","previous_headings":"","what":"Starting a Script","title":"Get Started","text":"ADaM data structures, overview flow example function calls common steps provided following vignettes: Creating ADSL Creating OCCDS ADaM Creating BDS Findings ADaM admiral also provides template R scripts starting point. can created calling use_ad_template(), e.g., list available templates can obtained list_all_templates():","code":"use_ad_template(   adam_name = \"adsl\",   save_path = \"./ad_adsl.R\" ) library(admiral) list_all_templates() #> Existing ADaM templates in package 'admiral': #> • ADAE #> • ADCM #> • ADEG #> • ADEX #> • ADLB #> • ADLBHY #> • ADMH #> • ADPC #> • ADPP #> • ADPPK #> • ADSL #> • ADVS"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/admiral.html","id":"support","dir":"Articles","previous_headings":"","what":"Support","title":"Get Started","text":"Support provided via admiral Slack channel. Additionally, please feel free raise issues GitHub repository.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/admiral.html","id":"see-also","dir":"Articles","previous_headings":"","what":"See also","title":"Get Started","text":"Template scripts Programming Concepts Conventions Programming Strategy","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/adsl.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Creating ADSL","text":"article describes creating ADSL ADaM. Examples currently presented tested using DM, EX , AE, LB DS SDTM domains. However, domains used. Note: examples assume CDISC SDTM /ADaM format input unless otherwise specified.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/adsl.html","id":"programming-flow","dir":"Articles","previous_headings":"","what":"Programming Flow","title":"Creating ADSL","text":"Read Data Derive Period, Subperiod, Phase Variables (e.g. APxxSDT, APxxEDT, …) Derive Treatment Variables (TRT0xP, TRT0xA) Derive/Impute Numeric Treatment Date/Time Duration (TRTSDT, TRTEDT, TRTDURD) Disposition Dates (e.g. EOSDT) Disposition Status (e.g. EOSTT) Disposition Reason(s) (e.g. DCSREAS, DCSREASP) Randomization Date (RANDDT) Death Date (DTHDT) Cause Death (DTHCAUS) Duration Relative Death Derive Last Known Date Alive (LSTALVDT) Grouping (e.g. AGEGR1 REGION1) Population Flags (e.g. SAFFL) Derive Variables Add Labels Attributes","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/adsl.html","id":"readdata","dir":"Articles","previous_headings":"Programming Flow","what":"Read in Data","title":"Creating ADSL","text":"start, data frames needed creation ADSL read environment. company specific process. data frames needed may DM, EX, DS, AE, LB. example purpose, CDISC Pilot SDTM datasets—included pharmaversesdtm—used. DM domain used basis ADSL:","code":"library(admiral) library(dplyr, warn.conflicts = FALSE) library(pharmaversesdtm) library(lubridate) library(stringr)  dm <- pharmaversesdtm::dm ds <- pharmaversesdtm::ds ex <- pharmaversesdtm::ex ae <- pharmaversesdtm::ae lb <- pharmaversesdtm::lb  dm <- convert_blanks_to_na(dm) ds <- convert_blanks_to_na(ds) ex <- convert_blanks_to_na(ex) ae <- convert_blanks_to_na(ae) lb <- convert_blanks_to_na(lb) adsl <- dm %>%   select(-DOMAIN)"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/adsl.html","id":"periodvars","dir":"Articles","previous_headings":"Programming Flow","what":"Derive Period, Subperiod, and Phase Variables (e.g. APxxSDT, APxxEDT, …)","title":"Creating ADSL","text":"See “Visit Period Variables” vignette information. variables derived based period reference dataset, may derived later point flow. example, phases like “Treatment Phase” “Follow ” derived based treatment start end date.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/adsl.html","id":"treatmentvar","dir":"Articles","previous_headings":"Programming Flow","what":"Derive Treatment Variables (TRT0xP, TRT0xA)","title":"Creating ADSL","text":"mapping treatment variables left ADaM programmer. example mapping study without periods may : studies periods see “Visit Period Variables” vignette.","code":"adsl <- dm %>%   mutate(TRT01P = ARM, TRT01A = ACTARM)"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/adsl.html","id":"trtdatetime","dir":"Articles","previous_headings":"Programming Flow","what":"Derive/Impute Numeric Treatment Date/Time and Duration (TRTSDTM, TRTEDTM, TRTDURD)","title":"Creating ADSL","text":"function derive_vars_merged() can used derive treatment start end date/times using ex domain. pre-processing step ex required convert variable EXSTDTC EXSTDTC datetime variables impute missing date time components. Conversion imputation done derive_vars_dtm(). Example calls: call returns original data frame column TRTSDTM, TRTSTMF, TRTEDTM, TRTETMF added. Exposure observations incomplete date zero doses non placebo treatments ignored. Missing time parts imputed first last start end date respectively. datetime variables returned can converted dates using derive_vars_dtm_to_dt() function. Now, TRTSDT TRTEDT derived, function derive_var_trtdurd() can used calculate Treatment duration (TRTDURD).","code":"# impute start and end time of exposure to first and last respectively, # do not impute date ex_ext <- ex %>%   derive_vars_dtm(     dtc = EXSTDTC,     new_vars_prefix = \"EXST\"   ) %>%   derive_vars_dtm(     dtc = EXENDTC,     new_vars_prefix = \"EXEN\",     time_imputation = \"last\"   )  adsl <- adsl %>%   derive_vars_merged(     dataset_add = ex_ext,     filter_add = (EXDOSE > 0 |       (EXDOSE == 0 &         str_detect(EXTRT, \"PLACEBO\"))) & !is.na(EXSTDTM),     new_vars = exprs(TRTSDTM = EXSTDTM, TRTSTMF = EXSTTMF),     order = exprs(EXSTDTM, EXSEQ),     mode = \"first\",     by_vars = exprs(STUDYID, USUBJID)   ) %>%   derive_vars_merged(     dataset_add = ex_ext,     filter_add = (EXDOSE > 0 |       (EXDOSE == 0 &         str_detect(EXTRT, \"PLACEBO\"))) & !is.na(EXENDTM),     new_vars = exprs(TRTEDTM = EXENDTM, TRTETMF = EXENTMF),     order = exprs(EXENDTM, EXSEQ),     mode = \"last\",     by_vars = exprs(STUDYID, USUBJID)   ) adsl <- adsl %>%   derive_vars_dtm_to_dt(source_vars = exprs(TRTSDTM, TRTEDTM)) adsl <- adsl %>%   derive_var_trtdurd()"},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/articles/adsl.html","id":"disposition_date","dir":"Articles","previous_headings":"Programming Flow > Derive Disposition Variables","what":"Disposition Dates (e.g. EOSDT)","title":"Creating ADSL","text":"functions derive_vars_dt() derive_vars_merged() can used derive disposition date. First character disposition date (DS.DSSTDTC) converted numeric date (DSSTDT) calling derive_vars_dt(). DS dataset extended DSSTDT variable date required derivations, e.g., RANDDT well. relevant disposition date selected adjusting filter_add argument. add End Study date (EOSDT) input dataset, call : ds_ext dataset: adsl dataset: derive_vars_dt() function allows impute partial dates well. imputation needed missing days imputed first month missing months first month year, set highest_imputation = \"M\".","code":"# convert character date to numeric date without imputation ds_ext <- derive_vars_dt(   ds,   dtc = DSSTDTC,   new_vars_prefix = \"DSST\" )  adsl <- adsl %>%   derive_vars_merged(     dataset_add = ds_ext,     by_vars = exprs(STUDYID, USUBJID),     new_vars = exprs(EOSDT = DSSTDT),     filter_add = DSCAT == \"DISPOSITION EVENT\" & DSDECOD != \"SCREEN FAILURE\"   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/adsl.html","id":"disposition_status","dir":"Articles","previous_headings":"Programming Flow > Derive Disposition Variables","what":"Disposition Status (e.g. EOSSTT)","title":"Creating ADSL","text":"function derive_vars_merged() can used derive End Study status (EOSSTT) based DSCAT DSDECOD DS. relevant observations selected adjusting filter_add argument. function mapping DSDECOD values EOSSTT values can defined used new_vars argument. mapping call \"COMPLETED\" DSDECOD == \"COMPLETED\" NA_character_ DSDECOD \"SCREEN FAILURE\" \"DISCONTINUED\" otherwise Example function format_eosstt(): customized mapping function format_eosstt() can now passed main function. subjects without disposition event end study status set \"ONGOING\" specifying missing_values argument. call return input dataset column EOSSTT added. derivation must changed, user can create /function map DSDECOD suitable EOSSTT value.","code":"format_eosstt <- function(x) {   case_when(     x %in% c(\"COMPLETED\") ~ \"COMPLETED\",     x %in% c(\"SCREEN FAILURE\") ~ NA_character_,     TRUE ~ \"DISCONTINUED\"   ) } adsl <- adsl %>%   derive_vars_merged(     dataset_add = ds,     by_vars = exprs(STUDYID, USUBJID),     filter_add = DSCAT == \"DISPOSITION EVENT\",     new_vars = exprs(EOSSTT = format_eosstt(DSDECOD)),     missing_values = exprs(EOSSTT = \"ONGOING\")   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/adsl.html","id":"disposition_reason","dir":"Articles","previous_headings":"Programming Flow > Derive Disposition Variables","what":"Disposition Reason(s) (e.g. DCSREAS, DCSREASP)","title":"Creating ADSL","text":"main reason discontinuation usually stored DSDECOD DSTERM provides additional details regarding subject’s discontinuation (e.g., description \"\"). function derive_vars_merged() can used derive disposition reason (along details, required) specific timepoint. relevant observations selected adjusting filter_add argument. derive End Study reason(s) (DCSREAS DCSREASP), function map DCSREAS DSDECOD, DCSREASP DSTERM DSDECOD \"COMPLETED\", \"SCREEN FAILURE\", NA, NA otherwise. call return input dataset column DCSREAS DCSREASP added. derivation must changed, user can define derivation filter_add argument function map DSDECOD DSTERM suitable DCSREAS/DCSREASP value. call maps DCSREAS DCREASP follows: DCSREAS DSDECOD DSDECOD \"COMPLETED\" NA, NA otherwise DCSREASP DSTERM DSDECOD equal , NA otherwise","code":"adsl <- adsl %>%   derive_vars_merged(     dataset_add = ds,     by_vars = exprs(USUBJID),     new_vars = exprs(DCSREAS = DSDECOD, DCSREASP = DSTERM),     filter_add = DSCAT == \"DISPOSITION EVENT\" &       !(DSDECOD %in% c(\"SCREEN FAILURE\", \"COMPLETED\", NA))   ) adsl <- adsl %>%   derive_vars_merged(     dataset_add = ds,     by_vars = exprs(USUBJID),     new_vars = exprs(DCSREAS = DSDECOD),     filter_add = DSCAT == \"DISPOSITION EVENT\" &       DSDECOD %notin% c(\"SCREEN FAILURE\", \"COMPLETED\", NA)   ) %>%   derive_vars_merged(     dataset_add = ds,     by_vars = exprs(USUBJID),     new_vars = exprs(DCSREASP = DSTERM),     filter_add = DSCAT == \"DISPOSITION EVENT\" & DSDECOD %in% \"OTHER\"   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/adsl.html","id":"randomization_date","dir":"Articles","previous_headings":"Programming Flow > Derive Disposition Variables","what":"Randomization Date (RANDDT)","title":"Creating ADSL","text":"function derive_vars_merged() can used derive randomization date variable. map Randomization Date (RANDDT), call : call return input dataset column RANDDT added.","code":"adsl <- adsl %>%   derive_vars_merged(     dataset_add = ds_ext,     filter_add = DSDECOD == \"RANDOMIZED\",     by_vars = exprs(STUDYID, USUBJID),     new_vars = exprs(RANDDT = DSSTDT)   )"},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/articles/adsl.html","id":"death_date","dir":"Articles","previous_headings":"Programming Flow > Derive Death Variables","what":"Death Date (DTHDT)","title":"Creating ADSL","text":"function derive_vars_dt() can used derive DTHDT. function allows user impute date well. Example calls: call return input dataset columns DTHDT added , default, associated date imputation flag (DTHDTF) populated controlled terminology outlined ADaM IG date imputations. imputation flag required, user must set argument flag_imputation \"none\". imputation needed date imputed first day month/year call : See also Date Time Imputation.","code":"adsl <- adsl %>%   derive_vars_dt(     new_vars_prefix = \"DTH\",     dtc = DTHDTC   ) adsl <- adsl %>%   derive_vars_dt(     new_vars_prefix = \"DTH\",     dtc = DTHDTC,     date_imputation = \"first\"   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/adsl.html","id":"death_cause","dir":"Articles","previous_headings":"Programming Flow > Derive Death Variables","what":"Cause of Death (DTHCAUS)","title":"Creating ADSL","text":"cause death DTHCAUS can derived using function derive_vars_extreme_event(). Since cause death collected/mapped different domains (e.g. DS, AE, DD), important user specifies right source(s) derive cause death . example, date death collected AE form AE Fatal, cause death set preferred term (AEDECOD) Fatal AE, date death collected DS form, cause death set disposition term (DSTERM). achieve , event() objects within derive_vars_extreme_event() must specified defined fit study requirement. example call derive_vars_extreme_event() : function also offers option add traceability variables (e.g. DTHDOM store domain date death collected, DTHSEQ store xxSEQ value domain). traceability variables added event() calls included new_vars parameter derive_vars_extreme_event(). Following derivation DTHCAUS related traceability variables, possible derive grouping variables death categories (DTHCGRx) using standard tidyverse code.","code":"adsl <- adsl %>%   derive_vars_extreme_event(     by_vars = exprs(STUDYID, USUBJID),     events = list(       event(         dataset_name = \"ae\",         condition = AEOUT == \"FATAL\",         set_values_to = exprs(DTHCAUS = AEDECOD),       ),       event(         dataset_name = \"ds\",         condition = DSDECOD == \"DEATH\" & grepl(\"DEATH DUE TO\", DSTERM),         set_values_to = exprs(DTHCAUS = DSTERM),       )     ),     source_datasets = list(ae = ae, ds = ds),     tmp_event_nr_var = event_nr,     order = exprs(event_nr),     mode = \"first\",     new_vars = exprs(DTHCAUS)   ) adsl <- adsl %>%   select(-DTHCAUS) %>% # remove it before deriving it again   derive_vars_extreme_event(     by_vars = exprs(STUDYID, USUBJID),     events = list(       event(         dataset_name = \"ae\",         condition = AEOUT == \"FATAL\",         set_values_to = exprs(DTHCAUS = AEDECOD, DTHDOM = \"AE\", DTHSEQ = AESEQ),       ),       event(         dataset_name = \"ds\",         condition = DSDECOD == \"DEATH\" & grepl(\"DEATH DUE TO\", DSTERM),         set_values_to = exprs(DTHCAUS = DSTERM, DTHDOM = \"DS\", DTHSEQ = DSSEQ),       )     ),     source_datasets = list(ae = ae, ds = ds),     tmp_event_nr_var = event_nr,     order = exprs(event_nr),     mode = \"first\",     new_vars = exprs(DTHCAUS, DTHDOM, DTHSEQ)   ) adsl <- adsl %>%   mutate(DTHCGR1 = case_when(     is.na(DTHDOM) ~ NA_character_,     DTHDOM == \"AE\" ~ \"ADVERSE EVENT\",     str_detect(DTHCAUS, \"(PROGRESSIVE DISEASE|DISEASE RELAPSE)\") ~ \"PROGRESSIVE DISEASE\",     TRUE ~ \"OTHER\"   ))"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/adsl.html","id":"death_other","dir":"Articles","previous_headings":"Programming Flow > Derive Death Variables","what":"Duration Relative to Death","title":"Creating ADSL","text":"function derive_vars_duration() can used derive duration relative death like Relative Day Death (DTHADY) numbers days last dose death (LDDTHELD). Example calls: Relative Day Death Elapsed Days Last Dose Death","code":"adsl <- adsl %>%   derive_vars_duration(     new_var = DTHADY,     start_date = TRTSDT,     end_date = DTHDT   ) adsl <- adsl %>%   derive_vars_duration(     new_var = LDDTHELD,     start_date = TRTEDT,     end_date = DTHDT,     add_one = FALSE   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/adsl.html","id":"lstalvdt","dir":"Articles","previous_headings":"Programming Flow","what":"Derive the Last Date Known Alive (LSTALVDT)","title":"Creating ADSL","text":"Similarly cause death (DTHCAUS), last known alive date (LSTALVDT) can derived multiples sources using derive_vars_extreme_event(). example (DTC dates converted numeric dates imputing missing day month first): Traceability variables can added specifying variables set_values_to parameter event() function.","code":"adsl <- adsl %>%   derive_vars_extreme_event(     by_vars = exprs(STUDYID, USUBJID),     events = list(       event(         dataset_name = \"ae\",         order = exprs(AESTDTC, AESEQ),         condition = !is.na(AESTDTC),         set_values_to = exprs(           LSTALVDT = convert_dtc_to_dt(AESTDTC, highest_imputation = \"M\"),           seq = AESEQ         ),       ),       event(         dataset_name = \"ae\",         order = exprs(AEENDTC, AESEQ),         condition = !is.na(AEENDTC),         set_values_to = exprs(           LSTALVDT = convert_dtc_to_dt(AEENDTC, highest_imputation = \"M\"),           seq = AESEQ         ),       ),       event(         dataset_name = \"lb\",         order = exprs(LBDTC, LBSEQ),         condition = !is.na(LBDTC),         set_values_to = exprs(           LSTALVDT = convert_dtc_to_dt(LBDTC, highest_imputation = \"M\"),           seq = LBSEQ         ),       ),       event(         dataset_name = \"adsl\",         condition = !is.na(TRTEDT),         set_values_to = exprs(LSTALVDT = TRTEDT, seq = 0),       )     ),     source_datasets = list(ae = ae, lb = lb, adsl = adsl),     tmp_event_nr_var = event_nr,     order = exprs(LSTALVDT, seq, event_nr),     mode = \"last\",     new_vars = exprs(LSTALVDT)   ) adsl <- adsl %>%   select(-LSTALVDT) %>% # created in the previous call   derive_vars_extreme_event(     by_vars = exprs(STUDYID, USUBJID),     events = list(       event(         dataset_name = \"ae\",         order = exprs(AESTDTC, AESEQ),         condition = !is.na(AESTDTC),         set_values_to = exprs(           LSTALVDT = convert_dtc_to_dt(AESTDTC, highest_imputation = \"M\"),           LALVSEQ = AESEQ,           LALVDOM = \"AE\",           LALVVAR = \"AESTDTC\"         ),       ),       event(         dataset_name = \"ae\",         order = exprs(AEENDTC, AESEQ),         condition = !is.na(AEENDTC),         set_values_to = exprs(           LSTALVDT = convert_dtc_to_dt(AEENDTC, highest_imputation = \"M\"),           LALVSEQ = AESEQ,           LALVDOM = \"AE\",           LALVVAR = \"AEENDTC\"         ),       ),       event(         dataset_name = \"lb\",         order = exprs(LBDTC, LBSEQ),         condition = !is.na(LBDTC),         set_values_to = exprs(           LSTALVDT = convert_dtc_to_dt(LBDTC, highest_imputation = \"M\"),           LALVSEQ = LBSEQ,           LALVDOM = \"LB\",           LALVVAR = \"LBDTC\"         ),       ),       event(         dataset_name = \"adsl\",         condition = !is.na(TRTEDT),         set_values_to = exprs(LSTALVDT = TRTEDT, LALVSEQ = NA_integer_, LALVDOM = \"ADSL\", LALVVAR = \"TRTEDTM\"),       )     ),     source_datasets = list(ae = ae, lb = lb, adsl = adsl),     tmp_event_nr_var = event_nr,     order = exprs(LSTALVDT, LALVSEQ, event_nr),     mode = \"last\",     new_vars = exprs(LSTALVDT, LALVSEQ, LALVDOM, LALVVAR)   )"},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/articles/adsl.html","id":"groupings_ex","dir":"Articles","previous_headings":"Programming Flow > Derive Groupings and Populations","what":"Grouping (e.g. AGEGR1 or REGION1)","title":"Creating ADSL","text":"Numeric categorical variables (AGE, RACE, COUNTRY, etc.) may need grouped perform required analysis. admiral provides derive_vars_cat() function create groups. function especially useful one variable needs created condition, e.g., AGEGR1 AGEGR1N. Additionally, one needs careful considering order conditions lookup table. category assigned based first match. means catch-conditions must come specific conditions, e.g. !.na(AGE) must come AGE < 18. Alternatively, can also solve task custom functions:","code":"# create lookup tables agegr1_lookup <- exprs(   ~condition,           ~AGEGR1,   AGE < 18,               \"<18\",   between(AGE, 18, 64), \"18-64\",   AGE > 64,               \">64\",   is.na(AGE),         \"Missing\" )  region1_lookup <- exprs(   ~condition,                          ~REGION1,   COUNTRY %in% c(\"CAN\", \"USA\"), \"North America\",   !is.na(COUNTRY),          \"Rest of the World\",   is.na(COUNTRY),                     \"Missing\" ) adsl <- adsl %>%   derive_vars_cat(     definition = agegr1_lookup   ) %>%   derive_vars_cat(     definition = region1_lookup   ) format_agegr1 <- function(var_input) {   case_when(     var_input < 18 ~ \"<18\",     between(var_input, 18, 64) ~ \"18-64\",     var_input > 64 ~ \">64\",     TRUE ~ \"Missing\"   ) } format_region1 <- function(var_input) {   case_when(     var_input %in% c(\"CAN\", \"USA\") ~ \"North America\",     !is.na(var_input) ~ \"Rest of the World\",     TRUE ~ \"Missing\"   ) }  adsl %>%   mutate(     AGEGR1 = format_agegr1(AGE),     REGION1 = format_region1(COUNTRY)   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/adsl.html","id":"popflag","dir":"Articles","previous_headings":"Programming Flow > Derive Groupings and Populations","what":"Population Flags (e.g. SAFFL)","title":"Creating ADSL","text":"Since populations flags mainly company/study specific dedicated functions provided, cases can easily derived using derive_var_merged_exist_flag. example implementation :","code":"adsl <- adsl %>%   derive_var_merged_exist_flag(     dataset_add = ex,     by_vars = exprs(STUDYID, USUBJID),     new_var = SAFFL,     condition = (EXDOSE > 0 | (EXDOSE == 0 & str_detect(EXTRT, \"PLACEBO\")))   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/adsl.html","id":"other","dir":"Articles","previous_headings":"Programming Flow","what":"Derive Other Variables","title":"Creating ADSL","text":"users can add specific code cover need analysis. following functions helpful many ADSL derivations: derive_vars_merged() - Merge Variables Dataset Input Dataset derive_var_merged_exist_flag() - Merge Existence Flag derive_var_merged_summary() - Merge Summary Variables See also Generic Functions.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/adsl.html","id":"attributes","dir":"Articles","previous_headings":"Programming Flow","what":"Add Labels and Attributes","title":"Creating ADSL","text":"Adding labels attributes SAS transport files supported following packages: metacore: establish common foundation use metadata within R session. metatools: enable use metacore objects. Metatools can used build datasets enhance columns existing datasets well checking datasets metadata. xportr: functionality associate metadata information local R data frame, perform data set level validation checks convert transport v5 file(xpt). NOTE: packages experimental phase, vision associated End End pipeline umbrella pharmaverse. example applying metadata perform associated checks can found pharmaverse E2E example.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_exposure.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Creating a BDS Exposure ADaM","text":"article describes creating Exposure ADaM using BDS structure. Examples currently presented using underlying EX domain EX domain represents data collected CRF ADEX ADaM output. However, examples can applied situations EC domain used input /ADEC another exposure ADaM created. many different approaches modeling exposure data. vignette gives examples creating PARAMCD AVAL combinations using exposure data. vignette meant guide standard structure exposure analysis datasets. Note: examples assume CDISC SDTM /ADaM format input unless otherwise specified.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_exposure.html","id":"programming-workflow","dir":"Articles","previous_headings":"","what":"Programming Workflow","title":"Creating a BDS Exposure ADaM","text":"Read Data Derive/Impute Numeric Date/Time Analysis Day (ADT, ADTM, ADY, ADTF, ATMF) Compute Duration Record Create 1:1 Mapping Records Create Summary Records Assign PARAMCD, PARAMN, etc. Reference Tables Derive Categorization Variables (AVALCATy) Assign ASEQ Add ADSL variables ASEQ Add Labels Attributes","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_exposure.html","id":"readdata","dir":"Articles","previous_headings":"Programming Workflow","what":"Read in Data","title":"Creating a BDS Exposure ADaM","text":"start, data frames needed creation ADEX read environment. company specific process. data frames needed may EX ADSL. example purpose, CDISC Pilot SDTM ADaM datasets—included pharmaversesdtm—used. step, may useful join ADSL EX domain well. ADSL variables used derivations selected step. rest relevant ADSL variables added later. CDISC pilot EX domain data contain dose adjustment flag planned dose information. demonstration purposes, added data.","code":"library(admiral) library(dplyr, warn.conflicts = FALSE) library(pharmaversesdtm) library(lubridate) library(stringr) library(tibble)  ex <- pharmaversesdtm::ex adsl <- admiral::admiral_adsl  ex <- convert_blanks_to_na(ex) adsl_vars <- exprs(TRTSDT, TRTSDTM, TRTEDT, TRTEDTM)  adex <- derive_vars_merged(   ex,   dataset_add = adsl,   new_vars = adsl_vars,   by_vars = get_admiral_option(\"subject_keys\") ) adex <- adex %>%   mutate(     EXADJ = case_when(       USUBJID == \"01-701-1028\" & VISIT %in% c(\"WEEK 2\") ~ \"ADVERSE EVENT\",       USUBJID == \"01-701-1148\" & VISIT %in% c(\"WEEK 2\", \"WEEK 24\") ~ \"MEDICATION ERROR\",       TRUE ~ NA_character_     ),     EXDOSE = case_when(       USUBJID == \"01-701-1028\" & VISIT %in% c(\"WEEK 2\") ~ 0,       USUBJID == \"01-701-1148\" & VISIT %in% c(\"WEEK 2\", \"WEEK 24\") ~ 0,       TRUE ~ EXDOSE     )   ) %>%   mutate(EXPLDOS = if_else(EXTRT == \"PLACEBO\", 0, 54))  distinct(adex, EXTRT, EXPLDOS) #> # A tibble: 2 × 2 #>   EXTRT      EXPLDOS #>   <chr>        <dbl> #> 1 PLACEBO          0 #> 2 XANOMELINE      54 count(adex, EXADJ) #> # A tibble: 1 × 2 #>   EXADJ     n #>   <chr> <int> #> 1 NA       13"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_exposure.html","id":"datetime","dir":"Articles","previous_headings":"Programming Workflow","what":"Derive/Impute Numeric Date/Time and Analysis Day (ADT, ADTM, ADY, ADTF, ATMF)","title":"Creating a BDS Exposure ADaM","text":"function derive_vars_dt() can used derive ADT. function allows user impute date well. Example calls: next examples demonstrates datetime imputation features available derive_vars_dtm() function, time imputed “00:00:00”: example imputes start date first first day month imputes end date last day month. Please see Date Time Imputation additional examples calculating imputing analysis dates. Next, analysis study days can derived:","code":"adex <- derive_vars_dt(adex, new_vars_prefix = \"AST\", dtc = EXSTDTC) adex <- derive_vars_dt(adex, new_vars_prefix = \"AEN\", dtc = EXENDTC) adex <- derive_vars_dtm(   adex,   dtc = EXSTDTC,   highest_imputation = \"M\",   new_vars_prefix = \"AST\" )  adex <- derive_vars_dtm(   adex,   dtc = EXENDTC,   highest_imputation = \"M\",   date_imputation = \"last\",   new_vars_prefix = \"AEN\" ) adex <-   derive_vars_dy(adex,     reference_date = TRTSDT,     source_vars = exprs(ASTDT, AENDT)   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_exposure.html","id":"duration","dir":"Articles","previous_headings":"Programming Workflow","what":"Compute duration for a record","title":"Creating a BDS Exposure ADaM","text":"compute duration treatment exposure record, derive_vars_duration() function can used. units calculated duration can also changed. example, duration output years: Please refer derive_vars_duration() documentation detailed information input parameters. may necessary calculate additional intermediate values. example, cumulative doses received cumulative planned doses may calculated : may additional interest turn single record containing dosing summary information set multiple single records, representing single dose interval specified summary record. another approach deriving total dose parameter EXDOSFRQ != . function create_single_dose_dataset() can used expand record containing start date, end date, dosing frequency corresponding set records representing one dose (.e. EXDOSFRQ == \"\").","code":"adex <- adex %>%   derive_vars_duration(     new_var = EXDURD,     start_date = ASTDT,     end_date = AENDT   ) adex <- adex %>%   derive_vars_duration(     new_var = EXDURDY,     out_unit = \"years\",     start_date = ASTDT,     end_date = AENDT   ) adex <- adex %>%   mutate(     DOSEO = EXDOSE * EXDURD,     PDOSEO = EXPLDOS * EXDURD   ) single_dose <- adex %>%   filter(USUBJID == \"01-701-1015\" & EXSTDY == 1) %>%   create_single_dose_dataset(keep_source_vars = exprs(USUBJID, EXDOSE, EXPLDOS, EXDOSFRQ, ASTDT, AENDT))"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_exposure.html","id":"onetoone","dir":"Articles","previous_headings":"Programming Workflow","what":"Create 1:1 mapping records","title":"Creating a BDS Exposure ADaM","text":"first set exposure records create records mapped 1:1 existing collected exposure record SDTM. records, AVAL AVALC calculated using columns exist data summarizing records necessary. records may used input summary records used individually summarization outputs. examples may exposure duration, dose administered, dose adjusted, etc. based one exposure record SDTM. records can derived using simple dplyr::mutate assignments combined:","code":"adex_durd <- adex %>%   mutate(     PARAMCD = \"DURD\",     AVAL = EXDURD   )  adex_dose <- adex %>%   mutate(     PARAMCD = \"DOSE\",     AVAL = DOSEO   )  adex_pldos <- adex %>%   mutate(     PARAMCD = \"PLDOSE\",     AVAL = PDOSEO   )  adex_adj <- adex %>%   mutate(     PARAMCD = \"ADJ\",     AVALC = if_else(!is.na(EXADJ), \"Y\", NA_character_)   )  adex_adjae <- adex %>%   mutate(     PARAMCD = \"ADJAE\",     AVALC = if_else(EXADJ == \"ADVERSE EVENT\", \"Y\", NA_character_)   )  adex <- bind_rows(   adex_durd,   adex_dose,   adex_pldos,   adex_adj,   adex_adjae ) %>%   mutate(PARCAT1 = \"INDIVIDUAL\")  count(adex, PARAMCD) #> # A tibble: 5 × 2 #>   PARAMCD     n #>   <chr>   <int> #> 1 ADJ        13 #> 2 ADJAE      13 #> 3 DOSE       13 #> 4 DURD       13 #> 5 PLDOSE     13"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_exposure.html","id":"summaryrec","dir":"Articles","previous_headings":"Programming Workflow","what":"Create Summary Records","title":"Creating a BDS Exposure ADaM","text":"Exposure commonly analyzed timing interval (e.g. APHASE, APERIOD, AVISIT, etc.). types calculations, derive_param_exposure() function may used. addition creating summarized AVAL, function also compute minimum maximum dates record. example, calculate total dose subject treatment, record PARAMCD == \"TDOSE\" created PARCAT1 set \"OVERALL\" using records ADEX PARAMCD == \"DOSE\" summing AVAL. addition, ASTDT, AENDT created minimum maximum date/times associated by_vars grouping. Note , addition PARAMCD, PARCAT1, AVAL, ASTDT AENDT, variables specified by_vars argument populated new records. Multiple parameters (records) may created one time using call_derivation() function: Dose intensity can calculated using function derive_param_doseint(). planned dose administered dose passed function new record created dose intensity calculation. , variables specified by_vars argument populated new record. default calculation dose intensity : Administered Doses / Planned Doses * 100. Please see derive_param_doseint() documentation see planned doses 0 NA handled.","code":"adex <- derive_param_exposure(   adex,   dataset_add = adex,   by_vars = c(get_admiral_option(\"subject_keys\"), adsl_vars),   input_code = \"DOSE\",   set_values_to = exprs(     PARAMCD = \"TDOSE\",     PARCAT1 = \"OVERALL\",     AVAL = sum(AVAL, na.rm = TRUE)   ) ) adex <- adex %>%   call_derivation(     derivation = derive_param_exposure,     variable_params = list(       params(         set_values_to = exprs(           PARAMCD = \"TDOSE\",           PARCAT1 = \"OVERALL\",           AVAL = sum(AVAL, na.rm = TRUE)         ),         input_code = \"DOSE\"       ),       params(         set_values_to = exprs(           PARAMCD = \"TPDOSE\",           PARCAT1 = \"OVERALL\",           AVAL = sum(AVAL, na.rm = TRUE)         ),         input_code = \"PLDOSE\"       ),       params(         set_values_to = exprs(           PARAMCD = \"TDURD\",           PARCAT1 = \"OVERALL\",           AVAL = sum(AVAL, na.rm = TRUE)         ),         input_code = \"DURD\"       ),       params(         set_values_to = exprs(           PARAMCD = \"TADJ\",           PARCAT1 = \"OVERALL\",           AVALC = if_else(sum(!is.na(AVALC)) > 0, \"Y\", NA_character_)         ),         input_code = \"ADJ\"       ),       params(         set_values_to = exprs(           PARAMCD = \"TADJAE\",           PARCAT1 = \"OVERALL\",           AVALC = if_else(sum(!is.na(AVALC)) > 0, \"Y\", NA_character_)         ),         input_code = \"ADJAE\"       )     ),     dataset_add = adex,     by_vars = c(get_admiral_option(\"subject_keys\"), adsl_vars)   )  count(adex, PARAMCD, PARCAT1) #> # A tibble: 10 × 3 #>    PARAMCD PARCAT1        n #>    <chr>   <chr>      <int> #>  1 ADJ     INDIVIDUAL    13 #>  2 ADJAE   INDIVIDUAL    13 #>  3 DOSE    INDIVIDUAL    13 #>  4 DURD    INDIVIDUAL    13 #>  5 PLDOSE  INDIVIDUAL    13 #>  6 TADJ    OVERALL        6 #>  7 TADJAE  OVERALL        6 #>  8 TDOSE   OVERALL        6 #>  9 TDURD   OVERALL        6 #> 10 TPDOSE  OVERALL        6 adex <- adex %>%   derive_param_doseint(     by_vars = c(get_admiral_option(\"subject_keys\"), adsl_vars),     set_values_to = exprs(PARAMCD = \"TNDOSINT\"),     tadm_code = \"TDOSE\",     tpadm_code = \"TPDOSE\"   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_exposure.html","id":"paramcd","dir":"Articles","previous_headings":"Programming Workflow","what":"Assign PARAMCD, PARAMN, etc. from Reference tables","title":"Creating a BDS Exposure ADaM","text":"assign parameter level values PARAM, PARAMN, PARCAT1, etc., lookup can created join source data. example, creating ADEX, lookup based ADaM PARAMCD value may created: Please note, example additional columns may needed join depending lookup/metadata table.","code":"adex <- derive_vars_merged(   adex,   dataset_add = param_lookup,   by_vars = exprs(PARAMCD) )  count(adex, PARAMCD, PARAM, PARAMN) #> # A tibble: 11 × 4 #>    PARAMCD  PARAM                                                   PARAMN     n #>    <chr>    <chr>                                                    <dbl> <int> #>  1 ADJ      Dose adjusted during constant dosing interval                4    13 #>  2 ADJAE    Dose adjusted  due to AE during constant dosing interv…      5    13 #>  3 DOSE     Dose administered during constant dosing interval (mg)       2    13 #>  4 DURD     Study drug duration during constant dosing interval (d…      1    13 #>  5 PLDOSE   Planned dose during constant dosing interval (mg)            3    13 #>  6 TADJ     Dose adjusted during study                                  10     6 #>  7 TADJAE   Dose adjusted during study due to AE                        11     6 #>  8 TDOSE    Total dose administered (mg)                                 7     6 #>  9 TDURD    Overall duration (days)                                      6     6 #> 10 TNDOSINT Overall dose intensity (%)                                  12     6 #> 11 TPDOSE   Total planned dose (mg)                                      9     6"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_exposure.html","id":"cat","dir":"Articles","previous_headings":"Programming Workflow","what":"Derive Categorization Variables (AVALCATy)","title":"Creating a BDS Exposure ADaM","text":"can use derive_vars_cat() function derive categorization variables.","code":"avalcax_lookup <- exprs(   ~PARAMCD,            ~condition,             ~AVALCAT1,   \"TDURD\",             AVAL >= 90,          \">= 90 days\",   \"TDURD\", AVAL >= 30 & AVAL < 90, \">= 30 and < 90 days\",   \"TDURD\",              AVAL < 30,           \"< 30 days\",   \"TDOSE\",            AVAL < 1000,           \"< 1000 mg\",   \"TDOSE\",           AVAL >= 1000,          \">= 1000 mg\",   \"TPDOSE\",           AVAL < 1000,           \"< 1000 mg\",   \"TPDOSE\",          AVAL >= 1000,          \">= 1000 mg\" )  adex <- adex %>%   derive_vars_cat(     definition = avalcax_lookup,     by_vars = exprs(PARAMCD)   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_exposure.html","id":"aseq","dir":"Articles","previous_headings":"Programming Workflow","what":"Assign ASEQ","title":"Creating a BDS Exposure ADaM","text":"admiral function derive_var_obs_number() can used derive ASEQ. example call :","code":"adex <- derive_var_obs_number(   adex,   new_var = ASEQ,   by_vars = get_admiral_option(\"subject_keys\"),   order = exprs(PARCAT1, ASTDT, VISIT, VISITNUM, EXSEQ, PARAMN),   check_type = \"error\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_exposure.html","id":"adsl_vars","dir":"Articles","previous_headings":"Programming Workflow","what":"Add the ADSL variables","title":"Creating a BDS Exposure ADaM","text":"needed, ADSL variables can now added:","code":"adex <- adex %>%   derive_vars_merged(     dataset_add = select(adsl, !!!negate_vars(adsl_vars)),     by_vars = get_admiral_option(\"subject_keys\")   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_exposure.html","id":"attributes","dir":"Articles","previous_headings":"Programming Workflow","what":"Add Labels and Attributes","title":"Creating a BDS Exposure ADaM","text":"Adding labels attributes SAS transport files supported following packages: metacore: establish common foundation use metadata within R session. metatools: enable use metacore objects. Metatools can used build datasets enhance columns existing datasets well checking datasets metadata. xportr: functionality associate metadata information local R data frame, perform data set level validation checks convert transport v5 file(xpt). NOTE: packages experimental phase, vision associated End End pipeline umbrella pharmaverse. example applying metadata perform associated checks can found pharmaverse E2E example.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_finding.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Creating a BDS Finding ADaM","text":"article describes creating BDS finding ADaM. Examples currently presented tested context ADVS. However, examples applied BDS Finding ADaMs ADEG, ADLB, etc. single result captured SDTM Finding domain single date /time. Note: examples assume CDISC SDTM /ADaM format input unless otherwise specified.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_finding.html","id":"programming-workflow","dir":"Articles","previous_headings":"","what":"Programming Workflow","title":"Creating a BDS Finding ADaM","text":"Read Data Derive/Impute Numeric Date/Time Analysis Day (ADT, ADTM, ADY, ADTF, ATMF) Assign PARAMCD, PARAM, PARAMN, PARCAT1 Derive Results (AVAL, AVALC) Derive Additional Parameters (e.g. BSA, BMI, MAP ADVS) Derive Timing Variables (e.g. APHASE, AVISIT, APERIOD) Timing Flag Variables (e.g. ONTRTFL) Assign Reference Range Indicator (ANRIND) Derive Baseline (BASETYPE, ABLFL, BASE, BASEC, BNRIND) Derive Change Baseline (CHG, PCHG) Derive Shift (e.g.SHIFT1) Derive Analysis Ratio (e.g. R2BASE) Derive Analysis Flags (e.g. ANL01FL) Assign Treatment (TRTA, TRTP) Assign ASEQ Derive Categorization Variables (AVALCATy) Derive Criterion Variables (CRITy, CRITyFL, CRITyFN) Add ADSL variables Derive New Rows Add Labels Attributes","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_finding.html","id":"readdata","dir":"Articles","previous_headings":"Programming Workflow","what":"Read in Data","title":"Creating a BDS Finding ADaM","text":"start, data frames needed creation ADVS read environment. company specific process. data frames needed may VS ADSL. example purpose, CDISC Pilot SDTM ADaM datasets—included pharmaversesdtm—used. step, may useful join ADSL VS domain. ADSL variables used derivations selected step. rest relevant ADSL variables added later.","code":"library(admiral) library(dplyr, warn.conflicts = FALSE) library(pharmaversesdtm) library(lubridate) library(stringr) library(tibble)  vs <- pharmaversesdtm::vs adsl <- admiral::admiral_adsl  vs <- convert_blanks_to_na(vs) adsl_vars <- exprs(TRTSDT, TRTEDT, TRT01A, TRT01P)  advs <- derive_vars_merged(   vs,   dataset_add = adsl,   new_vars = adsl_vars,   by_vars = exprs(STUDYID, USUBJID) )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_finding.html","id":"datetime","dir":"Articles","previous_headings":"Programming Workflow","what":"Derive/Impute Numeric Date/Time and Analysis Day (ADT, ADTM, ADY, ADTF, ATMF)","title":"Creating a BDS Finding ADaM","text":"function derive_vars_dt() can used derive ADT. function allows user impute date well. Example calls: imputation needed date imputed first month, call : Similarly, ADTM may created using function derive_vars_dtm(). Imputation may done date time components ADTM. default, variable ADTF derive_vars_dt() ADTF ATMF derive_vars_dtm() created populated controlled terminology outlined ADaM IG date imputations. See also Date Time Imputation. ADT derived, function derive_vars_dy() can used derive ADY. example assumes ADT TRTSDT exist data frame.","code":"advs <- derive_vars_dt(advs, new_vars_prefix = \"A\", dtc = VSDTC) advs <- derive_vars_dt(   advs,   new_vars_prefix = \"A\",   dtc = VSDTC,   highest_imputation = \"M\" ) # CDISC Pilot data does not contain times and the output of the derivation # ADTM is not presented. advs <- derive_vars_dtm(   advs,   new_vars_prefix = \"A\",   dtc = VSDTC,   highest_imputation = \"M\" ) advs <-   derive_vars_dy(advs, reference_date = TRTSDT, source_vars = exprs(ADT))"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_finding.html","id":"paramcd","dir":"Articles","previous_headings":"Programming Workflow","what":"Assign PARAMCD, PARAM, PARAMN, PARCAT1","title":"Creating a BDS Finding ADaM","text":"assign parameter level values PARAMCD, PARAM, PARAMN, PARCAT1, etc., lookup can created join source data. example, creating ADVS, lookup based SDTM --TESTCD value may created: lookup may now joined source data: stage, PARAMCD required perform derivations. Additional derived parameters may added, PARAMCD joined datasets point. variables related PARAMCD (e.g. PARAM, PARCAT1, …) added PARAMCD derived. Please note, may necessary include variables join. example, perhaps PARAMCD based VSTESTCD VSPOS, may necessary expand lookup create separate look PARAMCD. one lookup table, e.g., company parameter mappings project parameter mappings, available, consolidate_metadata() can used consolidate single lookup table.","code":"advs <- derive_vars_merged_lookup(   advs,   dataset_add = param_lookup,   new_vars = exprs(PARAMCD),   by_vars = exprs(VSTESTCD) ) #> All `VSTESTCD` are mapped."},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_finding.html","id":"aval","dir":"Articles","previous_headings":"Programming Workflow","what":"Derive Results (AVAL, AVALC)","title":"Creating a BDS Finding ADaM","text":"mapping AVAL AVALC left ADaM programmer. example mapping may : example, often case ADVS, AVAL values numeric without corresponding non-redundant text value AVALC. Per recommendation ADaMIG v1.3 map AVALC.","code":"advs <- mutate(   advs,   AVAL = VSSTRESN )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_finding.html","id":"derive_param","dir":"Articles","previous_headings":"Programming Workflow","what":"Derive Additional Parameters (e.g. BSA, BMI or MAP for ADVS)","title":"Creating a BDS Finding ADaM","text":"Optionally derive new parameters creating PARAMCD AVAL. Note variables specified by_vars argument populated newly created records. relevant functions derive_param_map, derive_param_bsa, derive_param_bmi, derive_param_qtc. example creating Mean Arterial Pressure ADVS, see also Example 3 section Derive New Rows alternative way creating new parameters. Likewise, function call , create parameter Body Surface Area (BSA) Body Mass Index (BMI) ADVS domain. Note height collected use constant_by_vars specify subject-level variable merge . Otherwise BSA BMI calculated visits collected. Similarly, ADEG, parameters QTCBF QTCBS QTCL can created function call. See example PARAMCD = QTCF. Similarly, ADLB, function derive_param_wbc_abs() can used create new parameter lab differentials converted absolute values. See example : PARAMCD derived added dataset, information look-table (PARAM, PARAMCAT1,…) added.","code":"advs <- derive_param_map(   advs,   by_vars = exprs(STUDYID, USUBJID, !!!adsl_vars, VISIT, VISITNUM, ADT, ADY, VSTPT, VSTPTNUM),   set_values_to = exprs(PARAMCD = \"MAP\"),   get_unit_expr = VSSTRESU,   filter = VSSTAT != \"NOT DONE\" | is.na(VSSTAT) ) advs <- derive_param_bsa(   advs,   by_vars = exprs(STUDYID, USUBJID, !!!adsl_vars, VISIT, VISITNUM, ADT, ADY, VSTPT, VSTPTNUM),   method = \"Mosteller\",   set_values_to = exprs(PARAMCD = \"BSA\"),   get_unit_expr = VSSTRESU,   filter = VSSTAT != \"NOT DONE\" | is.na(VSSTAT),   constant_by_vars = exprs(USUBJID) )  advs <- derive_param_bmi(   advs,   by_vars = exprs(STUDYID, USUBJID, !!!adsl_vars, VISIT, VISITNUM, ADT, ADY, VSTPT, VSTPTNUM),   set_values_to = exprs(PARAMCD = \"BMI\"),   get_unit_expr = VSSTRESU,   filter = VSSTAT != \"NOT DONE\" | is.na(VSSTAT),   constant_by_vars = exprs(USUBJID) ) adeg <- tibble::tribble(   ~USUBJID, ~EGSTRESU, ~PARAMCD, ~AVAL,          ~VISIT,   \"P01\",       \"msec\",     \"QT\",   350, \"CYCLE 1 DAY 1\",   \"P01\",       \"msec\",     \"QT\",   370, \"CYCLE 2 DAY 1\",   \"P01\",       \"msec\",     \"RR\",   842, \"CYCLE 1 DAY 1\",   \"P01\",       \"msec\",     \"RR\",   710, \"CYCLE 2 DAY 1\" )  adeg <- derive_param_qtc(   adeg,   by_vars = exprs(USUBJID, VISIT),   method = \"Fridericia\",   set_values_to = exprs(PARAMCD = \"QTCFR\"),   get_unit_expr = EGSTRESU ) adlb <- tibble::tribble(   ~USUBJID, ~PARAMCD, ~AVAL,                        ~PARAM,          ~VISIT,   \"P01\",       \"WBC\",    33,    \"Leukocyte Count (10^9/L)\", \"CYCLE 1 DAY 1\",   \"P01\",       \"WBC\",    38,    \"Leukocyte Count (10^9/L)\", \"CYCLE 2 DAY 1\",   \"P01\",     \"LYMLE\",  0.90, \"Lymphocytes (fraction of 1)\", \"CYCLE 1 DAY 1\",   \"P01\",     \"LYMLE\",  0.70, \"Lymphocytes (fraction of 1)\", \"CYCLE 2 DAY 1\" )  derive_param_wbc_abs(   dataset = adlb,   by_vars = exprs(USUBJID, VISIT),   set_values_to = exprs(     PARAMCD = \"LYMPH\",     PARAM = \"Lymphocytes Abs (10^9/L)\",     DTYPE = \"CALCULATION\"   ),   get_unit_expr = extract_unit(PARAM),   wbc_code = \"WBC\",   diff_code = \"LYMLE\",   diff_type = \"fraction\" ) # Derive PARAM and PARAMN advs <- derive_vars_merged(   advs,   dataset_add = select(param_lookup, -VSTESTCD),   by_vars = exprs(PARAMCD) )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_finding.html","id":"timing","dir":"Articles","previous_headings":"Programming Workflow","what":"Derive Timing Variables (e.g. APHASE, AVISIT, APERIOD)","title":"Creating a BDS Finding ADaM","text":"Categorical timing variables protocol analysis dependent. simple example. assigning visits based time windows deriving periods, subperiods, phase variables see “Visit Period Variables” vignette.","code":"advs <- advs %>%   mutate(     AVISIT = case_when(       str_detect(VISIT, \"SCREEN\") ~ NA_character_,       str_detect(VISIT, \"UNSCHED\") ~ NA_character_,       str_detect(VISIT, \"RETRIEVAL\") ~ NA_character_,       str_detect(VISIT, \"AMBUL\") ~ NA_character_,       !is.na(VISIT) ~ str_to_title(VISIT)     ),     AVISITN = as.numeric(case_when(       VISIT == \"BASELINE\" ~ \"0\",       str_detect(VISIT, \"WEEK\") ~ str_trim(str_replace(VISIT, \"WEEK\", \"\"))     )),     ATPT = VSTPT,     ATPTN = VSTPTNUM   )  count(advs, VISITNUM, VISIT, AVISITN, AVISIT) #> # A tibble: 15 × 5 #>    VISITNUM VISIT               AVISITN AVISIT       n #>       <dbl> <chr>                 <dbl> <chr>    <int> #>  1      1   SCREENING 1              NA NA         102 #>  2      2   SCREENING 2              NA NA          78 #>  3      3   BASELINE                  0 Baseline    96 #>  4      3.5 AMBUL ECG PLACEMENT      NA NA          65 #>  5      4   WEEK 2                    2 Week 2      96 #>  6      5   WEEK 4                    4 Week 4      80 #>  7      6   AMBUL ECG REMOVAL        NA NA          52 #>  8      7   WEEK 6                    6 Week 6      48 #>  9      8   WEEK 8                    8 Week 8      48 #> 10      9   WEEK 12                  12 Week 12     48 #> 11     10   WEEK 16                  16 Week 16     48 #> 12     11   WEEK 20                  20 Week 20     32 #> 13     12   WEEK 24                  24 Week 24     32 #> 14     13   WEEK 26                  26 Week 26     32 #> 15    201   RETRIEVAL                NA NA          26  count(advs, VSTPTNUM, VSTPT, ATPTN, ATPT) #> # A tibble: 4 × 5 #>   VSTPTNUM VSTPT                          ATPTN ATPT                           n #>      <dbl> <chr>                          <dbl> <chr>                      <int> #> 1      815 AFTER LYING DOWN FOR 5 MINUTES   815 AFTER LYING DOWN FOR 5 MI…   232 #> 2      816 AFTER STANDING FOR 1 MINUTE      816 AFTER STANDING FOR 1 MINU…   232 #> 3      817 AFTER STANDING FOR 3 MINUTES     817 AFTER STANDING FOR 3 MINU…   232 #> 4       NA NA                                NA NA                           187"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_finding.html","id":"timingflag","dir":"Articles","previous_headings":"Programming Workflow","what":"Timing Flag Variables (e.g. ONTRTFL)","title":"Creating a BDS Finding ADaM","text":"analyses, may necessary flag observation -treatment. admiral function derive_var_ontrtfl() can used. example, -treatment defined observation treatment start treatment end, flag may derived : function returns original data frame column ONTRTFL added. Additionally, function functionality handle window ref_end_date. example, -treatment defined treatment start treatment end plus 60 days, call : addition, function allow filter pre-treatment observations occurred start date. example, observations VSTPT == PRE considered -treatment observation date falls treatment start end date, user may specify using filter_pre_timepoint parameter: Lastly, function allow create -treatment flag based analysis needs. example, variable ONTR01FL needed, showing -treatment flag Period 01, need set new var = ONTR01FL. addition, Period 01 Start Date Period 01 End Date, need ref_start_date = AP01SDT ref_end_date = AP01EDT.","code":"advs <- derive_var_ontrtfl(   advs,   start_date = ADT,   ref_start_date = TRTSDT,   ref_end_date = TRTEDT ) advs <- derive_var_ontrtfl(   advs,   start_date = ADT,   ref_start_date = TRTSDT,   ref_end_date = TRTEDT,   ref_end_window = 60 ) advs <- derive_var_ontrtfl(   advs,   start_date = ADT,   ref_start_date = TRTSDT,   ref_end_date = TRTEDT,   filter_pre_timepoint = ATPT == \"AFTER LYING DOWN FOR 5 MINUTES\" ) advs <- derive_var_ontrtfl(   advs,   new_var = ONTR01FL,   start_date = ASTDT,   end_date = AENDT,   ref_start_date = AP01SDT,   ref_end_date = AP01EDT,   span_period = TRUE )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_finding.html","id":"referencerange","dir":"Articles","previous_headings":"Programming Workflow","what":"Assign Reference Range Indicator (ANRIND)","title":"Creating a BDS Finding ADaM","text":"admiral function derive_var_anrind() may used derive reference range indicator ANRIND. function requires reference range boundaries exist data frame (ANRLO, ANRHI) also accommodates additional boundaries A1LO A1HI. function called :","code":"advs <- derive_var_anrind(advs)"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_finding.html","id":"baseline","dir":"Articles","previous_headings":"Programming Workflow","what":"Derive Baseline (BASETYPE, ABLFL, BASE, BNRIND)","title":"Creating a BDS Finding ADaM","text":"BASETYPE derived using function derive_basetype_records(). parameter basetypes function requires named list expression detailing BASETYPE assigned. Note, record falls multiple expressions within basetypes expression, row produced BASETYPE. important derive BASETYPE first can utilized subsequent derivations. important data frame contains multiple values BASETYPE. Next, analysis baseline flag ABLFL can derived using admiral function derive_var_extreme_flag(). example, baseline defined last non-missing AVAL prior TRTSDT, function call ABLFL : Note: Additional examples derive_var_extreme_flag() function can found . Lastly, BASE, BNRIND columns can derived using admiral function derive_var_base(). Example calls :","code":"advs <- derive_basetype_records(   dataset = advs,   basetypes = exprs(     \"LAST: AFTER LYING DOWN FOR 5 MINUTES\" = ATPTN == 815,     \"LAST: AFTER STANDING FOR 1 MINUTE\" = ATPTN == 816,     \"LAST: AFTER STANDING FOR 3 MINUTES\" = ATPTN == 817,     \"LAST\" = is.na(ATPTN)   ) )  count(advs, ATPT, ATPTN, BASETYPE) #> # A tibble: 4 × 4 #>   ATPT                           ATPTN BASETYPE                                n #>   <chr>                          <dbl> <chr>                               <int> #> 1 AFTER LYING DOWN FOR 5 MINUTES   815 LAST: AFTER LYING DOWN FOR 5 MINUT…   232 #> 2 AFTER STANDING FOR 1 MINUTE      816 LAST: AFTER STANDING FOR 1 MINUTE     232 #> 3 AFTER STANDING FOR 3 MINUTES     817 LAST: AFTER STANDING FOR 3 MINUTES    232 #> 4 NA                                NA LAST                                  187 advs <- restrict_derivation(   advs,   derivation = derive_var_extreme_flag,   args = params(     by_vars = exprs(STUDYID, USUBJID, BASETYPE, PARAMCD),     order = exprs(ADT, ATPTN, VISITNUM),     new_var = ABLFL,     mode = \"last\"   ),   filter = (!is.na(AVAL) & ADT <= TRTSDT & !is.na(BASETYPE)) ) advs <- derive_var_base(   advs,   by_vars = exprs(STUDYID, USUBJID, PARAMCD, BASETYPE),   source_var = AVAL,   new_var = BASE )  advs <- derive_var_base(   advs,   by_vars = exprs(STUDYID, USUBJID, PARAMCD, BASETYPE),   source_var = ANRIND,   new_var = BNRIND )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_finding.html","id":"bchange","dir":"Articles","previous_headings":"Programming Workflow","what":"Derive Change from Baseline (CHG, PCHG)","title":"Creating a BDS Finding ADaM","text":"Change percent change baseline can derived using admiral functions derive_var_chg() derive_var_pchg(). functions expect AVAL BASE exist data frame. CHG simply AVAL - BASE PCHG (AVAL - BASE) / absolute value (BASE) * 100. Examples calls : variables derived records, e.g., post-baseline records , restrict_derivation() can used.","code":"advs <- derive_var_chg(advs)  advs <- derive_var_pchg(advs)"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_finding.html","id":"shift","dir":"Articles","previous_headings":"Programming Workflow","what":"Derive Shift (e.g. SHIFT1)","title":"Creating a BDS Finding ADaM","text":"Shift variables can derived using admiral function derive_var_shift(). function derives character shift variable concatenating shift values based user-defined pairing, e.g., shift baseline reference range BNRIND analysis reference range ANRIND. Examples calls : variables derived records, e.g., post-baseline records , restrict_derivation() can used.","code":"advs <- derive_var_shift(advs,   new_var = SHIFT1,   from_var = BNRIND,   to_var = ANRIND )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_finding.html","id":"analysisratio","dir":"Articles","previous_headings":"Programming Workflow","what":"Derive Analysis Ratio (R2BASE)","title":"Creating a BDS Finding ADaM","text":"Analysis ratio variables can derived using admiral function derive_var_analysis_ratio(). function derives ratio variable based user-specified pair. example, Ratio Baseline calculated AVAL / BASE function appends new variable R2BASE dataset. Examples calls : variables derived records, e.g., post-baseline records , restrict_derivation() can used.","code":"advs <- derive_var_analysis_ratio(advs,   numer_var = AVAL,   denom_var = BASE )  advs <- derive_var_analysis_ratio(advs,   numer_var = AVAL,   denom_var = ANRLO,   new_var = R01ANRLO )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_finding.html","id":"analysisrec","dir":"Articles","previous_headings":"Programming Workflow","what":"Derive Analysis Flags (e.g. ANL01FL)","title":"Creating a BDS Finding ADaM","text":"finding ADaMs, analysis flag derived identify appropriate observation(s) use particular analysis subject multiple observations within particular timing period. situation, analysis flag (e.g. ANLxxFL) may used choose appropriate record analysis. flag may derived using admiral function derive_var_extreme_flag(). example, assume like choose latest highest value USUBJID, PARAMCD, AVISIT, ATPT. Another common example flagging worst value subject, parameter, visit. example, assume 3 PARAMCD values (SYSBP, DIABP, RESP). also assume high worst SYSBP DIABP low worst RESP.","code":"advs <- restrict_derivation(   advs,   derivation = derive_var_extreme_flag,   args = params(     by_vars = exprs(STUDYID, USUBJID, BASETYPE, PARAMCD, AVISIT),     order = exprs(ADT, ATPTN, AVAL),     new_var = ANL01FL,     mode = \"last\"   ),   filter = !is.na(AVISITN) ) advs <- slice_derivation(   advs,   derivation = derive_var_extreme_flag,   args = params(     by_vars = exprs(STUDYID, USUBJID, BASETYPE, PARAMCD, AVISIT),     order = exprs(ADT, ATPTN),     new_var = WORSTFL,     mode = \"first\"   ),   derivation_slice(     filter = PARAMCD %in% c(\"SYSBP\", \"DIABP\") & (!is.na(AVISIT) & !is.na(AVAL))   ),   derivation_slice(     filter = PARAMCD %in% \"PULSE\" & (!is.na(AVISIT) & !is.na(AVAL)),     args = params(mode = \"last\")   ) ) %>%   arrange(STUDYID, USUBJID, BASETYPE, PARAMCD, AVISIT)"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_finding.html","id":"treatment","dir":"Articles","previous_headings":"Programming Workflow","what":"Assign Treatment (TRTA, TRTP)","title":"Creating a BDS Finding ADaM","text":"TRTA TRTP must match least one value character treatment variables ADSL (e.g., TRTxxA/TRTxxP, TRTSEQA/TRTSEQP, TRxxAGy/TRxxPGy). example simple implementation study without periods : studies periods see “Visit Period Variables” vignette.","code":"advs <- mutate(advs, TRTP = TRT01P, TRTA = TRT01A)  count(advs, TRTP, TRTA, TRT01P, TRT01A) #> # A tibble: 2 × 5 #>   TRTP                TRTA                TRT01P              TRT01A           n #>   <chr>               <chr>               <chr>               <chr>        <int> #> 1 Placebo             Placebo             Placebo             Placebo        640 #> 2 Xanomeline Low Dose Xanomeline Low Dose Xanomeline Low Dose Xanomeline …   243"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_finding.html","id":"aseq","dir":"Articles","previous_headings":"Programming Workflow","what":"Assign ASEQ","title":"Creating a BDS Finding ADaM","text":"admiral function derive_var_obs_number() can used derive ASEQ. example call :","code":"advs <- derive_var_obs_number(   advs,   new_var = ASEQ,   by_vars = exprs(STUDYID, USUBJID),   order = exprs(PARAMCD, ADT, AVISITN, VISITNUM, ATPTN),   check_type = \"error\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_finding.html","id":"cat","dir":"Articles","previous_headings":"Programming Workflow","what":"Derive Categorization Variables (AVALCATy)","title":"Creating a BDS Finding ADaM","text":"can use derive_vars_cat() function derive categorization variables.","code":"avalcat_lookup <- exprs(   ~PARAMCD,  ~condition,   ~AVALCAT1, ~AVALCA1N,   \"HEIGHT\",  AVAL > 140,   \">140 cm\",         1,   \"HEIGHT\", AVAL <= 140, \"<= 140 cm\",         2 ) advs <- advs %>%   derive_vars_cat(     definition = avalcat_lookup,     by_vars = exprs(PARAMCD)   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_finding.html","id":"crit_vars","dir":"Articles","previous_headings":"Programming Workflow","what":"Derive Criterion Variables (CRITy, CRITyFL, CRITyFN)","title":"Creating a BDS Finding ADaM","text":"deriving criterion variables (CRITy, CRITyFL, CRITyFN) admiral provides derive_vars_crit_flag(). ensures derived ADaM-compliant way (see documentation function details). cases criterion depends parameter. higher order functions restrict_derivation() slice_derivation() useful case. following example criterion flags systolic diastolic blood pressure ADaM IG derived. first criterion based AVAL derived systolic diastolic blood pressure. slice_derivation() us used specify condition description criterion depending parameter. second criterion based AVAL CHG derived systolic blood pressure . Thus restrict_derivation() used.","code":"advs <- advs %>%   slice_derivation(     derivation = derive_vars_crit_flag,     args = params(       values_yn = TRUE,       create_numeric_flag = TRUE     ),     derivation_slice(       filter = PARAMCD == \"SYSBP\",       args = params(         condition = AVAL > 160,         description = \"Systolic Pressure > 160\"       )     ),     derivation_slice(       filter = PARAMCD == \"DIABP\",       args = params(         condition = AVAL > 95,         description = \"Diastolic Pressure > 95\"       )     )   ) advs <- advs %>%   restrict_derivation(     derivation = derive_vars_crit_flag,     args = params(       condition = AVAL > 160 & CHG > 10,       description = \"Systolic Pressure > 160 and Change from Baseline in Systolic Pressure > 10\",       crit_nr = 2,       values_yn = TRUE,       create_numeric_flag = TRUE     ),     filter = PARAMCD == \"SYSBP\"   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_finding.html","id":"adsl_vars","dir":"Articles","previous_headings":"Programming Workflow","what":"Add ADSL variables","title":"Creating a BDS Finding ADaM","text":"needed, ADSL variables can now added. List ADSL variables already merged held vector adsl_vars","code":"advs <- advs %>%   derive_vars_merged(     dataset_add = select(adsl, !!!negate_vars(adsl_vars)),     by_vars = exprs(STUDYID, USUBJID)   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_finding.html","id":"additional","dir":"Articles","previous_headings":"Programming Workflow","what":"Derive New Rows","title":"Creating a BDS Finding ADaM","text":"deriving new rows data frame, essential programmer takes time insert derivation correct location code. location vary depending previous computations retained new record computations must done new records.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_finding.html","id":"example-1-creating-a-new-record","dir":"Articles","previous_headings":"Programming Workflow > Derive New Rows","what":"Example 1 (Creating a New Record):","title":"Creating a BDS Finding ADaM","text":"add new record based selection certain criterion (e.g. minimum, maximum) derive_extreme_records() can used. new records include variables selected records.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_finding.html","id":"adding-a-new-record-for-the-last-value","dir":"Articles","previous_headings":"Programming Workflow > Derive New Rows > Example 1 (Creating a New Record):","what":"Adding a New Record for the Last Value","title":"Creating a BDS Finding ADaM","text":"subject Vital Signs parameter, add record holding last valid observation end treatment. Set AVISIT \"End Treatment\" assign unique AVISITN value.","code":"advs_ex1 <- advs %>%   derive_extreme_records(     dataset_add = advs,     by_vars = exprs(STUDYID, USUBJID, PARAMCD),     order = exprs(ADT, AVISITN, ATPTN, AVAL),     mode = \"last\",     filter_add = (4 < AVISITN & AVISITN <= 12 & ANL01FL == \"Y\"),     set_values_to = exprs(       AVISIT = \"End of Treatment\",       AVISITN = 99,       DTYPE = \"LOV\"     )   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_finding.html","id":"adding-a-new-record-for-the-minimum-value","dir":"Articles","previous_headings":"Programming Workflow > Derive New Rows > Example 1 (Creating a New Record):","what":"Adding a New Record for the Minimum Value","title":"Creating a BDS Finding ADaM","text":"subject Vital Signs parameter, add record holding minimum value end treatment. minimum attained multiple observations first one selected. Set AVISIT \"Minimum Treatment\" assign unique AVISITN value.","code":"advs_ex1 <- advs %>%   derive_extreme_records(     dataset_add = advs,     by_vars = exprs(STUDYID, USUBJID, PARAMCD),     order = exprs(AVAL, ADT, AVISITN, ATPTN),     mode = \"first\",     filter_add = (4 < AVISITN & AVISITN <= 12 & ANL01FL == \"Y\" & !is.na(AVAL)),     set_values_to = exprs(       AVISIT = \"Minimum on Treatment\",       AVISITN = 98,       DTYPE = \"MINIMUM\"     )   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_finding.html","id":"example-2-deriving-a-summary-record","dir":"Articles","previous_headings":"Programming Workflow > Derive New Rows","what":"Example 2 (Deriving a Summary Record)","title":"Creating a BDS Finding ADaM","text":"adding new records based aggregating records derive_summary_records() can used. new records variables specified by_vars set_values_to populated. subject, Vital Signs parameter, visit, date add record holding average value observations date. Set DTYPE AVERAGE.","code":"advs_ex2 <- derive_summary_records(   advs,   dataset_add = advs,   by_vars = exprs(STUDYID, USUBJID, PARAMCD, VISITNUM, ADT),   set_values_to = exprs(     AVAL = mean(AVAL, na.rm = TRUE),     DTYPE = \"AVERAGE\"   ) )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_finding.html","id":"example-3-deriving-a-new-paramcd","dir":"Articles","previous_headings":"Programming Workflow > Derive New Rows","what":"Example 3 (Deriving a New PARAMCD)","title":"Creating a BDS Finding ADaM","text":"Use function derive_param_computed() create new PARAMCD. Note variables specified by_vars argument populated newly created records. example creating Mean Arterial Pressure (PARAMCD = MAP2) alternative formula.","code":"advs_ex3 <- derive_param_computed(   advs,   by_vars = exprs(USUBJID, VISIT, ATPT),   parameters = c(\"SYSBP\", \"DIABP\"),   set_values_to = exprs(     AVAL = (AVAL.SYSBP - AVAL.DIABP) / 3 + AVAL.DIABP,     PARAMCD = \"MAP2\",     PARAM = \"Mean Arterial Pressure 2 (mmHg)\"   ) )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_finding.html","id":"attributes","dir":"Articles","previous_headings":"Programming Workflow","what":"Add Labels and Attributes","title":"Creating a BDS Finding ADaM","text":"Adding labels attributes SAS transport files supported following packages: metacore: establish common foundation use metadata within R session. metatools: enable use metacore objects. Metatools can used build datasets enhance columns existing datasets well checking datasets metadata. xportr: functionality associate metadata information local R data frame, perform data set level validation checks convert transport v5 file(xpt). NOTE: packages experimental phase, vision associated End End pipeline umbrella pharmaverse. example applying metadata perform associated checks can found pharmaverse E2E example.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_tte.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Creating a BDS Time-to-Event ADaM","text":"article describes creating BDS time--event ADaM. main part programming time--event dataset definition events censoring times. admiral supports single events like death composite events like disease progression death. one source dataset can used definition event censoring times. Note: examples assume CDISC SDTM /ADaM format input unless otherwise specified.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_tte.html","id":"required-packages","dir":"Articles","previous_headings":"Introduction","what":"Required Packages","title":"Creating a BDS Time-to-Event ADaM","text":"examples vignette require following packages.","code":"library(admiral) library(dplyr, warn.conflicts = FALSE) library(pharmaversesdtm)"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_tte.html","id":"programming-workflow","dir":"Articles","previous_headings":"","what":"Programming Workflow","title":"Creating a BDS Time-to-Event ADaM","text":"Read Data Derive Parameters (CNSR, ADT, STARTDT) Derive Analysis Value (AVAL) Derive Analysis Sequence Number (ASEQ) Add ADSL Variables Add Labels Attributes","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_tte.html","id":"readdata","dir":"Articles","previous_headings":"Programming Workflow","what":"Read in Data","title":"Creating a BDS Time-to-Event ADaM","text":"start, datasets needed creation time--event dataset read environment. company specific process. example purpose, ADSL dataset—included admiral—SDTM datasets pharmaversesdtm used. following code creates minimally viable ADAE dataset used throughout following examples.","code":"ae <- pharmaversesdtm::ae adsl <- admiral::admiral_adsl  ae <- convert_blanks_to_na(ae) adae <- ae %>%   left_join(adsl, by = c(\"STUDYID\", \"USUBJID\")) %>%   derive_vars_dt(     new_vars_prefix = \"AST\",     dtc = AESTDTC,     highest_imputation = \"M\"   ) %>%   derive_vars_dt(     new_vars_prefix = \"AEN\",     dtc = AEENDTC,     highest_imputation = \"M\",     date_imputation = \"last\"   ) %>%   mutate(TRTEMFL = if_else(ASTDT >= TRTSDT &     AENDT <= TRTEDT + days(30), \"Y\", NA_character_))"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_tte.html","id":"parameters","dir":"Articles","previous_headings":"Programming Workflow","what":"Derive Parameters (CNSR, ADT, STARTDT)","title":"Creating a BDS Time-to-Event ADaM","text":"derive parameter dependent variables like CNSR, ADT, STARTDT, EVNTDESC, SRCDOM, PARAMCD, … derive_param_tte() function can used. adds one parameter input dataset one observation per subject. Usually called several times. subject determined event occurred. affirmative analysis date ADT set earliest event date. event occurred, analysis date set latest censoring date. events censorings defined event_source() censor_source() class respectively. defines observations (filter parameter) source dataset (dataset_name parameter) potential events censorings, value CNSR variable (censor parameter), variable provides date (date parameter). date can provided date (--DT variable) datetime (--DTM variable). CDISC strongly recommends CNSR = 0 events positive integers censorings. admiral enforces recommendation. Therefore censor parameter available censor_source() . defaulted 1. dataset_name parameter expects character value used identifier. actual data used derivation parameter provided via source_datasets parameter derive_param_tte(). expects named list datasets. names correspond identifiers specified dataset_name parameter. allows define events censoring independent data.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_tte.html","id":"pre-defined-time-to-event-source-objects","dir":"Articles","previous_headings":"Programming Workflow > Derive Parameters (CNSR, ADT, STARTDT)","what":"Pre-Defined Time-to-Event Source Objects","title":"Creating a BDS Time-to-Event ADaM","text":"table shows pre-defined tte_source objects cover common use cases. pre-defined objects can passed directly derive_param_tte() create new time--event parameter.","code":"adtte <- derive_param_tte(   dataset_adsl = adsl,   start_date = TRTSDT,   event_conditions = list(ae_ser_event),   censor_conditions = list(lastalive_censor),   source_datasets = list(adsl = adsl, adae = adae),   set_values_to = exprs(PARAMCD = \"TTAESER\", PARAM = \"Time to First Serious AE\") )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_tte.html","id":"single-event","dir":"Articles","previous_headings":"Programming Workflow > Derive Parameters (CNSR, ADT, STARTDT)","what":"Single Event","title":"Creating a BDS Time-to-Event ADaM","text":"example, overall survival time defined treatment start death. Patients alive lost follow-censored last alive date. following call defines death event based ADSL variables. corresponding censoring based last known alive date can defined following call. definitions can passed derive_param_tte() create new time--event parameter. Note practice efficacy parameters might use randomization date time event origin date.","code":"death <- event_source(   dataset_name = \"adsl\",   filter = DTHFL == \"Y\",   date = DTHDT ) lstalv <- censor_source(   dataset_name = \"adsl\",   date = LSTALVDT ) adtte <- derive_param_tte(   dataset_adsl = adsl,   source_datasets = list(adsl = adsl),   start_date = TRTSDT,   event_conditions = list(death),   censor_conditions = list(lstalv),   set_values_to = exprs(PARAMCD = \"OS\", PARAM = \"Overall Survival\") )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_tte.html","id":"add-additional-information-for-events-and-censoring-evntdesc-srcvar","dir":"Articles","previous_headings":"Programming Workflow > Derive Parameters (CNSR, ADT, STARTDT)","what":"Add Additional Information for Events and Censoring (EVNTDESC, SRCVAR, …)","title":"Creating a BDS Time-to-Event ADaM","text":"add additional information like event censoring description (EVNTDESC) source variable (SRCVAR) set_values_to parameter can specified event/censoring definition.","code":"# define death event # death <- event_source(   dataset_name = \"adsl\",   filter = DTHFL == \"Y\",   date = DTHDT,   set_values_to = exprs(     EVNTDESC = \"DEATH\",     SRCDOM = \"ADSL\",     SRCVAR = \"DTHDT\"   ) )  # define censoring at last known alive date # lstalv <- censor_source(   dataset_name = \"adsl\",   date = LSTALVDT,   set_values_to = exprs(     EVNTDESC = \"LAST KNOWN ALIVE DATE\",     SRCDOM = \"ADSL\",     SRCVAR = \"LSTALVDT\"   ) )  # derive time-to-event parameter # adtte <- derive_param_tte(   dataset_adsl = adsl,   source_datasets = list(adsl = adsl),   event_conditions = list(death),   censor_conditions = list(lstalv),   set_values_to = exprs(PARAMCD = \"OS\", PARAM = \"Overall Survival\") )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_tte.html","id":"handling-subjects-without-assessment","dir":"Articles","previous_headings":"Programming Workflow > Derive Parameters (CNSR, ADT, STARTDT)","what":"Handling Subjects Without Assessment","title":"Creating a BDS Time-to-Event ADaM","text":"subject event record meeting censoring rule, included output dataset. order record subject output dataset, another censoring_source() object created specify patients censored. Therefore start censoring defined achieve subjects without data adrs censored start date. ADaM IG requires computed date must accompanied imputation flags. Thus, function detects --DTF /--TMF variable corresponding start_date STARTDTF STARTTMF set automatically values variables. date variable one event censoring source datasets imputed, imputation flag can specified set_values_to parameter event_source() censor_source() (see definition start censoring ). CDISC pilot contain RS dataset, following example progression free survival uses manually created datasets. event progression free survival occurs progression disease observed subject dies. Therefore two event_source() objects defined: pd progression disease death death. subjects may experience events. case first one selected derive_param_tte(). Subjects without event must censored last tumor assessment. censoring lastvisit object defined tumor assessments. Please note necessary select last one exclude assessments resulted progression disease. handled within derive_param_tte(). Patients without tumor assessment censored start date. Therefore start object defined treatment start date censoring date. necessary exclude patient tumor assessment definition start derive_param_tte() selects last date across censor_source() objects censoring date.","code":"View(adsl) View(adrs) # progressive disease event # pd <- event_source(   dataset_name = \"adrs\",   filter = AVALC == \"PD\",   date = ADT,   set_values_to = exprs(     EVNTDESC = \"PD\",     SRCDOM = \"ADRS\",     SRCVAR = \"ADT\",     SRCSEQ = ASEQ   ) )  # death event # death <- event_source(   dataset_name = \"adsl\",   filter = DTHFL == \"Y\",   date = DTHDT,   set_values_to = exprs(     EVNTDESC = \"DEATH\",     SRCDOM = \"ADSL\",     SRCVAR = \"DTHDT\"   ) ) # last tumor assessment censoring (CNSR = 1 by default) # lastvisit <- censor_source(   dataset_name = \"adrs\",   date = ADT,   set_values_to = exprs(     EVNTDESC = \"LAST TUMOR ASSESSMENT\",     SRCDOM = \"ADRS\",     SRCVAR = \"ADT\"   ) ) # start date censoring (for patients without tumor assessment) (CNSR = 2) # start <- censor_source(   dataset_name = \"adsl\",   date = TRTSDT,   censor = 2,   set_values_to = exprs(     EVNTDESC = \"TREATMENT START\",     SRCDOM = \"ADSL\",     SRCVAR = \"TRTSDT\",     ADTF = TRTSDTF   ) )  # derive time-to-event parameter # adtte <- derive_param_tte(   dataset_adsl = adsl,   source_datasets = list(adsl = adsl, adrs = adrs),   start_date = TRTSDT,   event_conditions = list(pd, death),   censor_conditions = list(lastvisit, start),   set_values_to = exprs(PARAMCD = \"PFS\", PARAM = \"Progression Free Survival\") )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_tte.html","id":"deriving-a-series-of-time-to-event-parameters","dir":"Articles","previous_headings":"Programming Workflow > Derive Parameters (CNSR, ADT, STARTDT)","what":"Deriving a Series of Time-to-Event Parameters","title":"Creating a BDS Time-to-Event ADaM","text":"several similar time--event parameters need derived call_derivation() function useful. following example parameters time first AE, time first serious AE, time first related AE derived. censoring three. definition event differs.","code":"# define censoring # observation_end <- censor_source(   dataset_name = \"adsl\",   date = pmin(TRTEDT + days(30), EOSDT),   censor = 1,   set_values_to = exprs(     EVNTDESC = \"END OF TREATMENT\",     SRCDOM = \"ADSL\",     SRCVAR = \"TRTEDT\"   ) )  # define time to first AE # tt_ae <- event_source(   dataset_name = \"ae\",   date = ASTDT,   set_values_to = exprs(     EVNTDESC = \"ADVERSE EVENT\",     SRCDOM = \"AE\",     SRCVAR = \"AESTDTC\"   ) )  # define time to first serious AE # tt_ser_ae <- event_source(   dataset_name = \"ae\",   filter = AESER == \"Y\",   date = ASTDT,   set_values_to = exprs(     EVNTDESC = \"SERIOUS ADVERSE EVENT\",     SRCDOM = \"AE\",     SRCVAR = \"AESTDTC\"   ) )  # define time to first related AE # tt_rel_ae <- event_source(   dataset_name = \"ae\",   filter = AEREL %in% c(\"PROBABLE\", \"POSSIBLE\", \"REMOTE\"),   date = ASTDT,   set_values_to = exprs(     EVNTDESC = \"RELATED ADVERSE EVENT\",     SRCDOM = \"AE\",     SRCVAR = \"AESTDTC\"   ) )  # derive all three time to event parameters # adaette <- call_derivation(   derivation = derive_param_tte,   variable_params = list(     params(       event_conditions = list(tt_ae),       set_values_to = exprs(PARAMCD = \"TTAE\")     ),     params(       event_conditions = list(tt_ser_ae),       set_values_to = exprs(PARAMCD = \"TTSERAE\")     ),     params(       event_conditions = list(tt_rel_ae),       set_values_to = exprs(PARAMCD = \"TTRELAE\")     )   ),   dataset_adsl = adsl,   source_datasets = list(     adsl = adsl,     ae = filter(adae, TRTEMFL == \"Y\")   ),   censor_conditions = list(observation_end) )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_tte.html","id":"deriving-time-to-event-parameters-using-by-groups","dir":"Articles","previous_headings":"Programming Workflow > Derive Parameters (CNSR, ADT, STARTDT)","what":"Deriving Time-to-Event Parameters Using By Groups","title":"Creating a BDS Time-to-Event ADaM","text":"time--event parameters need derived group source dataset, by_vars parameter can specified. time--event parameter derived group. Please note CDISC requires separate parameters (PARAMCD, PARAM) groups. Therefore variables specified by_vars parameter included output dataset. PARAMCD variable specified set_value_to parameter using expression right hand side results unique value group. values variables included output dataset, can stored PARCATn variables. following example time--event parameter preferred term AE dataset derived.","code":"View(adsl) View(ae) # define time to first adverse event event # ttae <- event_source(   dataset_name = \"ae\",   date = AESTDT,   set_values_to = exprs(     EVNTDESC = \"AE\",     SRCDOM = \"AE\",     SRCVAR = \"AESTDTC\",     SRCSEQ = AESEQ   ) )  # define censoring at end of study # eos <- censor_source(   dataset_name = \"adsl\",   date = EOSDT,   set_values_to = exprs(     EVNTDESC = \"END OF STUDY\",     SRCDOM = \"ADSL\",     SRCVAR = \"EOSDT\"   ) )  # derive time-to-event parameter # adtte <- derive_param_tte(   dataset_adsl = adsl,   by_vars = exprs(AEDECOD),   start_date = TRTSDT,   event_conditions = list(ttae),   censor_conditions = list(eos),   source_datasets = list(adsl = adsl, ae = ae),   set_values_to = exprs(     PARAMCD = paste0(\"TTAE\", as.numeric(as.factor(AEDECOD))),     PARAM = paste(\"Time to First\", AEDECOD, \"Adverse Event\"),     PARCAT1 = \"TTAE\",     PARCAT2 = AEDECOD   ) )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_tte.html","id":"aval","dir":"Articles","previous_headings":"Programming Workflow","what":"Derive Analysis Value (AVAL)","title":"Creating a BDS Time-to-Event ADaM","text":"analysis value (AVAL) can derived calling derive_vars_duration(). example derives time event days. units can requested specifying out_unit parameter.","code":"adtte <- derive_vars_duration(   adtte,   new_var = AVAL,   start_date = STARTDT,   end_date = ADT )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_tte.html","id":"aseq","dir":"Articles","previous_headings":"Programming Workflow","what":"Derive Analysis Sequence Number (ASEQ)","title":"Creating a BDS Time-to-Event ADaM","text":"admiral function derive_var_obs_number() can used derive ASEQ:","code":"adtte <- derive_var_obs_number(   adtte,   by_vars = exprs(STUDYID, USUBJID),   order = exprs(PARAMCD),   check_type = \"error\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_tte.html","id":"adslvars","dir":"Articles","previous_headings":"Programming Workflow","what":"Add ADSL Variables","title":"Creating a BDS Time-to-Event ADaM","text":"Variables ADSL required time--event analyses, e.g., treatment variables covariates can added using derive_vars_merged().","code":"adtte <- derive_vars_merged(   adtte,   dataset_add = adsl,   new_vars = exprs(ARMCD, ARM, ACTARMCD, ACTARM, AGE, SEX),   by_vars = exprs(STUDYID, USUBJID) )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/bds_tte.html","id":"attributes","dir":"Articles","previous_headings":"Programming Workflow","what":"Add Labels and Attributes","title":"Creating a BDS Time-to-Event ADaM","text":"Adding labels attributes SAS transport files supported following packages: metacore: establish common foundation use metadata within R session. metatools: enable use metacore objects. Metatools can used build datasets enhance columns existing datasets well checking datasets metadata. xportr: functionality associate metadata information local R data frame, perform data set level validation checks convert transport v5 file(xpt). NOTE: packages experimental phase, vision associated End End pipeline umbrella pharmaverse. example applying metadata perform associated checks can found pharmaverse E2E example.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/concepts_conventions.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Programming Concepts and Conventions","text":"vignette aims discuss common programming concepts conventions adopted within admiral family packages. intended user-facing version Programming Strategy vignette, users can also read latter becoming familiar package expand topics interest. common admiral FAQ, visit corresponding FAQ page provided drop menu vignette.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/concepts_conventions.html","id":"input-and-output","dir":"Articles","previous_headings":"","what":"Input and Output","title":"Programming Concepts and Conventions","text":"expected input dataset grouped. Otherwise error issued. output dataset ungrouped. observations ordered dedicated way. particular, order observations input dataset may preserved.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/concepts_conventions.html","id":"admiral-functions-and-options","dir":"Articles","previous_headings":"","what":"{admiral} Functions and Options","title":"Programming Concepts and Conventions","text":"general principle, behavior admiral functions determined input, global object, .e. inputs like datasets, variable names, options, etc. must provided function arguments. Correspondingly, general functions side-effects like creating modifying global objects, printing, writing files, etc. exception principle found approach package options (see get_admiral_option() set_admiral_options()), allow user-defined defaults commonly used function arguments. instance, option subject_keys currently pre-defined exprs(STUDYID, USUBJID), can modified using set_admiral_options(subject_keys = exprs(...)) top script. full discussion admiral Inputs, Outputs Options, see section developer-facing Programming Strategy.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/concepts_conventions.html","id":"missing","dir":"Articles","previous_headings":"","what":"Handling of Missing Values","title":"Programming Concepts and Conventions","text":"using haven package read SAS datasets R, SAS-style character missing values, .e. \"\", converted proper R NA values. Rather kept . problematic downstream data processing R handles \"\" just string. Thus, data manipulation performed SAS blanks converted R NAs using admiral’s convert_blanks_to_na() function, e.g. Note logical operator applied NA value always returns NA rather TRUE FALSE. exception .na() returns TRUE input NA. Thus, filter visits \"Baseline\" following condition need used. Also note aggregation functions, like mean() max(), also return NA element input vector missing. avoid behavior one explicitly set na.rm = TRUE. important keep mind using admiral’s aggregation functions derive_summary_records(). handling NAs sorting variables see Sort Order.","code":"dm <- haven::read_sas(\"dm.sas7bdat\") %>%    convert_blanks_to_na() visits <- c(\"Baseline\", NA, \"Screening\", \"Week 1 Day 7\") visits != \"Baseline\" #> [1] FALSE    NA  TRUE  TRUE is.na(visits) #> [1] FALSE  TRUE FALSE FALSE visits != \"Baseline\" | is.na(visits) #> [1] FALSE  TRUE  TRUE  TRUE mean(c(1, NA, 2)) #> [1] NA mean(c(1, NA, 2), na.rm = TRUE) #> [1] 1.5"},{"path":[]},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/articles/concepts_conventions.html","id":"expr-and-exprs","dir":"Articles","previous_headings":"Expressions in Scripts > Quoting and Unquoting: Introducing expr(), exprs(), !! and !!!","what":"expr() and exprs()","title":"Programming Concepts and Conventions","text":"expr() function rlang package, used create expression. expression evaluated - rather, passed derivation function evaluates environment. exprs() plural version expr(), accepts multiple comma-separated items returns list expressions. used within contest admiral derivation function, expr() exprs() allow function evaluate expressions context input dataset. example, expr() exprs() allow users pass variable names datasets function without wrapping quotation marks. expressions framework powerful users able intuitively “inject code” admiral functions (function parameters) using similar syntax writing open code, exception possibly outer exprs() wrapper. instance, derive_vars_merged() call , user merging adsl ex able filter ex prior merge using expression passed filter_add parameter. filter_add accepts expressions, user full power filter dataset please. vein, user able create new variables wish merge using new_vars argument, pass list expressions containing “standard” R code.","code":"library(rlang)  adae <- data.frame(USUBJID = \"XXX-1\", AEDECOD = \"HEADACHE\")  # Return the adae object adae #>   USUBJID  AEDECOD #> 1   XXX-1 HEADACHE  # Return an expression expr(adae) #> adae derive_vars_merged(   adsl,   dataset_add = ex,   filter_add = !is.na(EXENDTM),   by_vars = exprs(STUDYID, USUBJID),   new_vars = exprs(     TRTEDTM = EXENDTM,     TRTETMF = EXENTMF,     COMPTRT = if_else(!is.na(EXENDTM), \"Y\", \"N\")   ),   order = exprs(EXENDTM),   mode = \"last\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/concepts_conventions.html","id":"unquoting","dir":"Articles","previous_headings":"Expressions in Scripts > Quoting and Unquoting: Introducing expr(), exprs(), !! and !!!","what":"Bang-Bang (!!) and Bang-Bang-Bang (!!!)","title":"Programming Concepts and Conventions","text":"Sometimes may want construct expression using , pre-existing expressions. However, ’s immediately clear achieve expressions inherently pause evaluation code ’s executed: !! (bang-bang) comes : provided rlang package, allows inject contents expression another expression, meaning using !! can modify code inside expression R evaluates . using !! unquoting expression, .e. evaluating pass onwards. can see example !! comes handy within admiral code Common Pitfall 1, contents expression unquoted can passed derive_vars_merged(). !!! (bang-bang-bang) plural version !! can used unquote list expressions: Within admiral, operator can useful need unquote list variables (stored expressions) use inside admiral even dplyr call. One example admiral subject keys: want use subject keys stored within admiral option subset dataset, need use !!! unquote list. Let’s construct dummy example illustrate point: can see another example !!! action line admiral ADEX template script, used dynamically control variables passed admiral function.","code":"a <- expr(2) b <- expr(3)  expr(a + b) #> a + b # NOT 2 + 3 expr(!!a + !!b) #> 2 + 3 exprs(!!!list(a, b)) #> [[1]] #> [1] 2 #>  #> [[2]] #> [1] 3 get_admiral_option(\"subject_keys\") #> [[1]] #> STUDYID #>  #> [[2]] #> USUBJID adcm <- data.frame(STUDYID = \"XXX\", USUBJID = \"XXX-1\", CMTRT = \"ASPIRIN\") adcm #>   STUDYID USUBJID   CMTRT #> 1     XXX   XXX-1 ASPIRIN  # This doesn't work as we are not unquoting the subject keys adcm %>% select(get_admiral_option(\"subject_keys\")) #> Error in `select()`: #> ! Can't select columns with `get_admiral_option(\"subject_keys\")`. #> ✖ `get_admiral_option(\"subject_keys\")` must be numeric or character, not a list.  # This works because we are unquoting the subject keys adcm %>% select(!!!get_admiral_option(\"subject_keys\")) #>   STUDYID USUBJID #> 1     XXX   XXX-1"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/concepts_conventions.html","id":"summary","dir":"Articles","previous_headings":"Expressions in Scripts > Quoting and Unquoting: Introducing expr(), exprs(), !! and !!!","what":"Summary","title":"Programming Concepts and Conventions","text":"summary, although expressions framework may seem slightly clunky mysterious begin , allows power flexibility forms key part admiral package. comprehensive treatment expressions, see Chapter 18 Chapter 19 Advanced R textbook. Chapter 19 specifically covers much detail concept unquoting.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/concepts_conventions.html","id":"common-pitfalls","dir":"Articles","previous_headings":"Expressions in Scripts","what":"Common pitfalls","title":"Programming Concepts and Conventions","text":"Expressions powerful, can also lead misunderstandings functionality. Let’s set dummy data explore common issues new (experienced!) programmers may encounter dealing expressions.","code":"library(dplyr, warn.conflicts = FALSE) library(admiral)  vs <- tribble(   ~USUBJID, ~VSTESTCD, ~VISIT, ~VSSTRESN, ~VSSTRESU, ~VSDTC,   \"01-1301\", \"WEIGHT\", \"SCREENING\", 82.1, \"kg\", \"2013-08-29\",   \"01-1301\", \"WEIGHT\", \"WEEK 2\", 81.19, \"kg\", \"2013-09-15\",   \"01-1301\", \"WEIGHT\", \"WEEK 4\", 82.56, \"kg\", \"2013-09-24\",   \"01-1302\", \"BMI\", \"SCREENING\", 20.1, \"kg/m2\", \"2013-08-29\",   \"01-1302\", \"BMI\", \"WEEK 2\", 20.2, \"kg/m2\", \"2013-09-15\",   \"01-1302\", \"BMI\", \"WEEK 4\", 19.9, \"kg/m2\", \"2013-09-24\" )  dm <- tribble(   ~USUBJID, ~AGE,   \"01-1301\", 18 )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/concepts_conventions.html","id":"pitfall1","dir":"Articles","previous_headings":"Expressions in Scripts > Common pitfalls","what":"1. Mistakenly passing something that isn’t an expression to an argument","title":"Programming Concepts and Conventions","text":"writing complex admiral code can easy mistakenly pass wrong input argument expects expression. example, code fails my_expression expression - name object global environment containing expression. fix code, need unquote my_expression expression holding passed correctly derive_vars_merged():","code":"my_expression <- expr(VSTESTCD == \"WEIGHT\" & VISIT == \"SCREENING\")  derive_vars_merged(   dm,   dataset_add = select(vs, USUBJID, VSTESTCD, VISIT),   by_vars = exprs(USUBJID),   filter_add = my_expression ) #> Error in `derive_vars_merged()`: #> ! Argument `filter_add` must be a filter condition, but is a symbol derive_vars_merged(   dm,   dataset_add = select(vs, USUBJID, VSTESTCD, VISIT),   by_vars = exprs(USUBJID),   filter_add = !!my_expression ) #> # A tibble: 1 × 4 #>   USUBJID   AGE VSTESTCD VISIT     #>   <chr>   <dbl> <chr>    <chr>     #> 1 01-1301    18 WEIGHT   SCREENING"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/concepts_conventions.html","id":"forgetting-that-expressions-must-be-evaluable-in-the-dataset","dir":"Articles","previous_headings":"Expressions in Scripts > Common pitfalls","what":"2. Forgetting that expressions must be evaluable in the dataset","title":"Programming Concepts and Conventions","text":"similar vein , even actual expression passed argument, must make sure can evaluated within dataset interest. may seem trivial, common pitfall expressions delay evaluation code can delay identification issues. instance, consider example: second call fails hidden within expression mention VSTPT, dropped vs filter_vs_and_merge().","code":"filter_vs_and_merge <- function(my_expression) {   derive_vars_merged(     dm,     dataset_add = select(vs, USUBJID, VSTESTCD, VISIT),     by_vars = exprs(USUBJID),     filter_add = !!my_expression   ) }  # This works filter_vs_and_merge(expr(VSTESTCD == \"WEIGHT\" & VISIT == \"SCREENING\")) #> # A tibble: 1 × 4 #>   USUBJID   AGE VSTESTCD VISIT     #>   <chr>   <dbl> <chr>    <chr>     #> 1 01-1301    18 WEIGHT   SCREENING  # This fails filter_vs_and_merge(expr(VSTESTCD == \"WEIGHT\" & VISIT == \"SCREENING\" & VSTPT == \"PREDOSE\")) #> Error in `filter()`: #> ℹ In argument: `VSTESTCD == \"WEIGHT\" & VISIT == \"SCREENING\" & VSTPT == #>   \"PREDOSE\"`. #> Caused by error: #> ! object 'VSTPT' not found"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/concepts_conventions.html","id":"see-also","dir":"Articles","previous_headings":"","what":"See also","title":"Programming Concepts and Conventions","text":"Programming Strategy","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/faq.html","id":"what-is-admiral","dir":"Articles","previous_headings":"","what":"What is admiral?","title":"FAQ","text":"block stand alone purpose (function provides specific functionality) Data Scientists can create blocks (create R functions) Constructing ADaM dataset become like building blocks based admiral modular functions user created modular functions.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/faq.html","id":"why-did-we-decide-to-start-admiral","dir":"Articles","previous_headings":"","what":"Why did we decide to start admiral?","title":"FAQ","text":"Data analysis challenges clinical trials vary depending scientific goals, therapeutic areas, indications, data sources data quality. face challenge limit company-level adoption crowd-sourcing create ADaM datasets? Build ADaMs via collaboration co-creation Early engagement like-minded companies moving towards R lead solution shared open source framework contribution across-industry Building ADaMs like modular building blocks, everyone can contribute module clear input output enable re-usable solutions Users can “slot ” modules address specific company/TA/Molecule/Study requirements TA specific requirements can open sourced transformed common ADaM approach analysis long-term gain consistent way producing ADaM wider community across-industry developers contributing grow codebase cover infinite array possibilities Contributors: option make name Pharma open-source community & avenue collaborate like-minded people across industry Imagine ADaMs built consistent manner code openly maintained functions impact Health Authorities, readable code, QC, talent flow","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/faq.html","id":"why-did-we-use-r-as-a-programming-language","dir":"Articles","previous_headings":"","what":"Why did we use R as a programming language?","title":"FAQ","text":"R isolated software product, everyone can contribute (open source principal) People University/Statistical talent pipeline likely come R skills rather proprietary language seems strong data science/analytics R community FDA open accepting R submissions heavy users Top line visualization/graphics - R-Shiny interactive data displays also R Markdown offers great report writing functionality R popular among statisticians new statistical methods likely implemented R language might equally suited programming languages - however stage make decision :)","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/faq.html","id":"admiral-offers-a-toolbox-of-functions-to-facilitate-adam--what-does-that-mean","dir":"Articles","previous_headings":"","what":"Admiral offers a toolbox of functions to facilitate ADaM. What does that mean?","title":"FAQ","text":"e.g. derive_vars_aage parameterized start end-date unit. Depending parameters results may vary specification. Functions serve toolbox user can create ADaM according requirements. principles, programming strategy documentation admiral considered framework users contribute.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/faq.html","id":"how-does-a-user-know-what-a-function-does-exactly","dir":"Articles","previous_headings":"","what":"How does a user know what a function does exactly?","title":"FAQ","text":"Function details purpose, requirements, parameters, dependencies examples documented header function. Complex functions potentially vignette admiral homepage provide details. admiral provide link explicit specification define.xml.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/faq.html","id":"where-a-user-can-quickly-find-some-references-or-advice-to-use-a-function","dir":"Articles","previous_headings":"","what":"Where a user can quickly find some references or advice to use a function?","title":"FAQ","text":"Cheat Sheet available, providing examples many admiral functions. detailed description, please refer Reference section.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/faq.html","id":"would-admiral-create-a-whole-adam-dataset","dir":"Articles","previous_headings":"","what":"Would {admiral} create a whole ADaM dataset?","title":"FAQ","text":"admiral meant toolbox enable Data Scientists build ADaMs according varying analysis needs admiral meant “click button, comes ADaM” tool admiral webpage, example scripts provided can used starting point create ADaM (see end vignette)","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/faq.html","id":"in-which-order-does-a-user-need-to-execute-the-functions","dir":"Articles","previous_headings":"","what":"In which order does a user need to execute the functions?","title":"FAQ","text":"Guidance provided ADSL, BDS OCCDS ADaM structure including template scripts.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/faq.html","id":"is-the-admiral-package-validated","dir":"Articles","previous_headings":"","what":"Is the {admiral} package validated?","title":"FAQ","text":"functions reviewed tested (see provided around function testing?) ensure work described documentation. Test cases function part R package. Users can add tests provide additional feedback. testing admiral team function replace QC validation process company. GitHub action (using open source packages) exists generate validation report R package, option company use. example report using earlier version admiral exists illustration.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/faq.html","id":"what-will-be-provided-around-function-testing","dir":"Articles","previous_headings":"","what":"What will be provided around function testing?","title":"FAQ","text":"Unit tests reliability function - available part open source release integration testing done ensure functions can called together create ADaM (e.g. even via internal testing teams) validation able use package company-specific SCE GxP purposes associated audit evidence strategy use admiral fits company-specific quality assurance process (double programming comparison versus company-specific legacy ADaM solution appropriate confidence builds) see guidance unit testing","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/faq.html","id":"will-admiral-provide-harmonized-define-xml-or-submittable-specifications-for-functions","dir":"Articles","previous_headings":"","what":"Will admiral provide harmonized define.xml or submittable specifications for functions?","title":"FAQ","text":". functions documented via programming headers, define.xml responsibility end user. Functions usually generalized specific. (see Admiral offers toolbox functions facilitate ADaM. mean?) users responsible make sure use functions parameters right way ensure alignment define.xml","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/faq.html","id":"will-admiral-provide-adam-ig-cdisc-compliant-datasets","dir":"Articles","previous_headings":"","what":"Will {admiral} provide ADaM IG CDISC compliant datasets?","title":"FAQ","text":"Although admiral follows CDISC standards claim dataset resulting calling admiral functions ADaM compliant. ensured user.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/faq.html","id":"how-much-of-the-adam-ig-is-covered-by-admiral","dir":"Articles","previous_headings":"","what":"How much of the ADaM IG is covered by admiral?","title":"FAQ","text":"ADaM IG standard framework without specific number datasets variables, used specific baseline answer question. provide guidance ADaM dataset structure (ADSL, OCCDS BDS) highlight functionality admiral covers. (see order user need execute functions?) guidance also highlight gaps filled user (e.g. timing, ranges). standard ADaM datasets (ADAE, ADCM, …) can provide estimated coverage based early adopters Roche/GSK ADaM implementation","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/faq.html","id":"will-there-be-a-usercontribution-guide","dir":"Articles","previous_headings":"","what":"Will there be a user/contribution guide?","title":"FAQ","text":"programming strategy serves framework users create functions. Please see contribution model website detailed description contribute","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/faq.html","id":"how-has-admiral-been-tested-externally-to-rochegsk","dir":"Articles","previous_headings":"","what":"How has {admiral} been tested externally to Roche/GSK?","title":"FAQ","text":"Sept/Oct 2021, limited release testing conducted 18 companies (>50 individuals) order assess compatibility admiral toolkit different company standards implementations test usability functions, e.g. clarity, reliability, robustness, flexibility. foundational version admiral achieved 7.9 / 10 average score survey respondents >75% said ’d advocate using admiral ADaM transformations R. “Extremely easy learn get , well thought planned. Plenty minor functions instead aiming create large”jack trades” framework. toolkit attempt become large one-button ADaM generator (fantastic).” “huge advantage Pharma companies common functions common stuff develop. easier authorities foundation ADaM programs. development goes faster every one develop across companies, bug-fixing faster many using package likely find potential bugs.” “huge proponent shared solutions within Pharma. Overall impressed admiral project – design, development, documentation, validation details available teams readily adopt.”","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/faq.html","id":"are-there-any-presentations-available-about-admiral","dir":"Articles","previous_headings":"","what":"Are there any presentations available about {admiral}?","title":"FAQ","text":"full collection admiral conference presentations years, please travel Presentation Archive.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/generic.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Generic Derivations","text":"vignette provides overview important admiral functions, generic derivations. derive specific variable parameter specific dataset can used many different derivations many different datasets. important concepts examples presented . full details examples see documentation functions.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/generic.html","id":"required-packages","dir":"Articles","previous_headings":"Introduction","what":"Required Packages","title":"Generic Derivations","text":"examples vignette require following packages.","code":"library(admiral) library(tibble)"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/generic.html","id":"characterization-of-derivations","dir":"Articles","previous_headings":"","what":"Characterization of Derivations","title":"Generic Derivations","text":"generic functions can characterized following three properties: Variables Records/Parameters single source dataset Multiple source datasets Selection: new values derived selecting records, e.g., baseline records, last exposure record, … Summary: new values derived summarizing values, e.g., sum, average, concatenation, … Computation: new values derived computation one value input, e.g., deriving BMI BSA height weight","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/generic.html","id":"overview-of-derivations","dir":"Articles","previous_headings":"Characterization of Derivations","what":"Overview of Derivations","title":"Generic Derivations","text":"Using three properties makes easy find appropriate function particular derivation. following interactive table lists generic functions properties.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/generic.html","id":"source-data","dir":"Articles","previous_headings":"","what":"Source Data","title":"Generic Derivations","text":"derivation functions expect single source dataset. multiple source datasets can specified. cases way specify source datasets across generic functions.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/generic.html","id":"single-source-dataset","dir":"Articles","previous_headings":"Source Data","what":"Single Source Dataset","title":"Generic Derivations","text":"functions expecting single source dataset data provided dataset_add argument. mandatory argument. data provided dataset argument used1. dataset_add argument provided, data dataset used (derive_var_extreme_flag()).","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/generic.html","id":"multiple-source-datasets","dir":"Articles","previous_headings":"Source Data","what":"Multiple Source Datasets","title":"Generic Derivations","text":"functions expecting multiple source datasets data provided source_datasets argument. datasets referred dataset_name element source objects. example, consider derivation response parameter. three possible responses defined event() objects. objects define events include data. Instead dataset_name field set (character) id. id used source_datasets argument derivation function link data events. .e., first two events (complete_response partial_response) dataset adrs_ovr used last event dataset myadsl used. allows define source objects independent data. .e., source object can used different source datasets. example, parameter derived data second reporter just changing source_dataset: source objects dataset_name element optional, e.g., event(). specified, input dataset (dataset) used.","code":"complete_response <- event(   description = \"Define complete response\",   dataset_name = \"ovr\",   condition = AVALC == \"CR\",   set_values_to = exprs(AVALC = \"COMPLETE RESPONSE\") )  partial_response <- event(   description = \"Define partial response\",   dataset_name = \"ovr\",   condition = AVALC == \"PR\",   set_values_to = exprs(AVALC = \"PARTIAL RESPONSE\") )  no_response <- event(   description = \"Define no response for all patients in adsl\",   dataset_name = \"adsl\",   condition = TRUE,   set_values_to = exprs(AVALC = \"NO RESPONSE\") )  derive_extreme_event(   ...   events = list(complete_response, partial_response, no_response),   source_datasets = list(ovr = adrs_ovr, adsl = myadsl),   ... ) derive_extreme_event(   ...   events = list(complete_response, partial_response, no_response),   source_datasets = list(ovr = adrs_ovr_reporter2, adsl = myadsl),   ... )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/generic.html","id":"methods","dir":"Articles","previous_headings":"","what":"Methods","title":"Generic Derivations","text":"generic derivations use three different methods deriving values new variables records. Usually derivation applies method several times, group input source data. groups defined by_vars argument, e.g., by_vars = exprs(USUBJID) “subject” by_vars = exprs(USUBJID, PARAMCD) “subject parameter”.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/generic.html","id":"selection","dir":"Articles","previous_headings":"Methods","what":"Selection","title":"Generic Derivations","text":"common method selection method. selects record source dataset(s) adds information selected record input dataset. information just flag indicating record exists, one variables selected records, new variables created selected record.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/generic.html","id":"options-for-selection","dir":"Articles","previous_headings":"Methods > Selection","what":"Options for Selection","title":"Generic Derivations","text":"simplest case record selected condition. condition specified filter_add filter argument. following example baseline weight added ADSL. Sometimes possible select record interest condition, e.g., first, last, best, worst, lowest, highest, … value derived. case mode order argument can specified select first last record respect variables specified order. day last valid dose added ADSL. also possible select record based records input source dataset. type selection admiral provides functions derive_vars_joined(), derive_var_joined_exist_flag(), derive_extreme_event(). provide filter_join argument accepts conditions variables input dataset (dataset) additional dataset (dataset_add). example consider deriving day dose last study treatment adverse event: filter_join condition applied temporary dataset created left joining input dataset additional dataset (restricted filter_add): “joined” function can also used condition selecting depends previous subsequent records dataset. case dataset specified dataset dataest_add. Consider following example \"HIGH\" results flagged confirmed second \"HIGH\" result least ten days later. join_type argument set \"\" restrict joined records subsequent results. variables included dataset dataset_add, dataset_add renamed adding suffix “.join”. variables dataset_add used filter_join must specified join_vars. temporary dataset applying filter_join : possible use summary functions like () () filter_join. Assume previous example records flagged results flagged record confirmation record \"HIGH\". can achieved specifying first_cond_upper argument set condition confirmation. first_cond_upper argument specified records joined dataset restricted first records condition fulfilled: Thereafter filter_join applied restricted joined dataset. .e., () function considers results confirmation records ignores subsequent results. Note: principle, actually achieve every result derive_vars_merged() derive_var_merged_exist_flag() using derive_vars_joined() derive_var_joined_exist_flag() respectively. However, “joined” functions require much resources (time memory), hence recommended use really required, .e., condition selecting records depends variables datasets.","code":"adsl <- tribble(   ~USUBJID,   \"1\",   \"2\",   \"3\" )  advs <- tribble(   ~USUBJID, ~PARAMCD, ~AVISIT,    ~ABLFL, ~AVAL, ~AVALU,   \"1\",      \"WEIGHT\", \"BASELINE\", \"Y\",     58.7, \"kg\",   \"1\",      \"HEIGHT\", \"BASELINE\", \"Y\",    169.2, \"cm\",   \"1\",      \"WEIGHT\", \"WEEK 3\",   NA,      59.3, \"kg\",   \"2\",      \"WEIGHT\", \"BASELINE\", \"Y\",     72.5, \"kg\",   \"2\",      \"WEIGHT\", \"WEKK 3\",   NA,      71.9, \"kg\", )  derive_vars_merged(   adsl,   dataset_add = advs,   by_vars = exprs(USUBJID),   filter_add = PARAMCD == \"WEIGHT\" & ABLFL == \"Y\",   new_vars = exprs(WGTBL = AVAL) ) #> # A tibble: 3 × 2 #>   USUBJID WGTBL #>   <chr>   <dbl> #> 1 1        58.7 #> 2 2        72.5 #> 3 3        NA adsl <- tribble(   ~USUBJID,   \"1\",   \"2\",   \"3\" )  ex <- tribble(   ~USUBJID, ~EXSTDY, ~EXDOSE,   \"1\",            1,      50,   \"1\",            7,      70,   \"1\",           14,       0,   \"2\",            1,      75,   \"2\",            9,      70 )  derive_vars_merged(   adsl,   dataset_add = ex,   by_vars = exprs(USUBJID),   filter_add = EXDOSE > 0,   order = exprs(EXSTDY),   mode = \"last\",   new_vars = exprs(TRTEDY = EXSTDY) ) #> # A tibble: 3 × 2 #>   USUBJID TRTEDY #>   <chr>    <dbl> #> 1 1            7 #> 2 2            9 #> 3 3           NA adae <- tribble(   ~USUBJID, ~ASTDY, ~AESEQ,   \"1\",           3,      1,   \"1\",           3,      2,   \"1\",          15,      3 )  ex <- tribble(   ~USUBJID, ~EXSTDY, ~EXDOSE,   \"1\",            1,      50,   \"1\",            7,      70,   \"1\",           14,       0,   \"2\",            1,      75,   \"2\",            9,      70 )  derive_vars_joined(   adae,   dataset_add = ex,   by_vars = exprs(USUBJID),   filter_add = EXDOSE > 0,   filter_join = EXSTDY <= ASTDY,   join_type = \"all\",   order = exprs(EXSTDY),   mode = \"last\",   new_vars = exprs(LSTDOSDY = EXSTDY, LASTDOS = EXDOSE) ) #> # A tibble: 3 × 5 #>   USUBJID ASTDY AESEQ LSTDOSDY LASTDOS #>   <chr>   <dbl> <dbl>    <dbl>   <dbl> #> 1 1           3     1        1      50 #> 2 1           3     2        1      50 #> 3 1          15     3        7      70 #> # A tibble: 6 × 5 #>   USUBJID ASTDY AESEQ EXSTDY EXDOSE #>   <chr>   <dbl> <dbl>  <dbl>  <dbl> #> 1 1           3     1      1     50 #> 2 1           3     1      7     70 #> 3 1           3     2      1     50 #> 4 1           3     2      7     70 #> 5 1          15     3      1     50 #> 6 1          15     3      7     70 adlb <- tribble(   ~USUBJID, ~PARAMCD, ~ADY, ~ANRIND,   \"1\",      \"AST\",       1, \"HIGH\",   \"1\",      \"AST\",       7, \"HIGH\",   \"1\",      \"AST\",      14, \"NORMAL\",   \"1\",      \"ALT\",       1, \"HIGH\",   \"1\",      \"ALT\",       7, \"NORMAL\",   \"1\",      \"ALT\",      14, \"HIGH\",   \"2\",      \"AST\",       1, \"HIGH\",   \"2\",      \"AST\",      15, \"HIGH\",   \"2\",      \"AST\",      22, \"NORMAL\",   \"2\",      \"ALT\",       1, \"HIGH\" )  derive_var_joined_exist_flag(   adlb,   dataset_add = adlb,   by_vars = exprs(USUBJID, PARAMCD),   order = exprs(ADY),   join_vars = exprs(ADY, ANRIND),   join_type = \"after\",   filter_join = ANRIND == \"HIGH\" & ANRIND.join == \"HIGH\" & ADY.join > ADY + 10,   new_var = HICONFFL ) #> # A tibble: 10 × 5 #>    USUBJID PARAMCD   ADY ANRIND HICONFFL #>    <chr>   <chr>   <dbl> <chr>  <chr>    #>  1 1       AST         1 HIGH   NA       #>  2 1       AST         7 HIGH   NA       #>  3 1       AST        14 NORMAL NA       #>  4 1       ALT         1 HIGH   Y        #>  5 1       ALT         7 NORMAL NA       #>  6 1       ALT        14 HIGH   NA       #>  7 2       AST         1 HIGH   Y        #>  8 2       AST        15 HIGH   NA       #>  9 2       AST        22 NORMAL NA       #> 10 2       ALT         1 HIGH   NA #> # A tibble: 9 × 6 #>   USUBJID PARAMCD   ADY ANRIND ADY.join ANRIND.join #>   <chr>   <chr>   <dbl> <chr>     <dbl> <chr>       #> 1 1       ALT         1 HIGH          7 NORMAL      #> 2 1       ALT         1 HIGH         14 HIGH        #> 3 1       ALT         7 NORMAL       14 HIGH        #> 4 1       AST         1 HIGH          7 HIGH        #> 5 1       AST         1 HIGH         14 NORMAL      #> 6 1       AST         7 HIGH         14 NORMAL      #> 7 2       AST         1 HIGH         15 HIGH        #> 8 2       AST         1 HIGH         22 NORMAL      #> 9 2       AST        15 HIGH         22 NORMAL derive_var_joined_exist_flag(   adlb,   dataset_add = adlb,   by_vars = exprs(USUBJID, PARAMCD),   order = exprs(ADY),   join_vars = exprs(ADY, ANRIND),   join_type = \"after\",   first_cond_upper = ANRIND.join == \"HIGH\" & ADY.join > ADY + 10,   filter_join = ANRIND == \"HIGH\" & all(ANRIND.join == \"HIGH\"),   new_var = HICONFFL ) #> # A tibble: 10 × 5 #>    USUBJID PARAMCD   ADY ANRIND HICONFFL #>    <chr>   <chr>   <dbl> <chr>  <chr>    #>  1 1       AST         1 HIGH   NA       #>  2 1       AST         7 HIGH   NA       #>  3 1       AST        14 NORMAL NA       #>  4 1       ALT         1 HIGH   NA       #>  5 1       ALT         7 NORMAL NA       #>  6 1       ALT        14 HIGH   NA       #>  7 2       AST         1 HIGH   Y        #>  8 2       AST        15 HIGH   NA       #>  9 2       AST        22 NORMAL NA       #> 10 2       ALT         1 HIGH   NA #> # A tibble: 3 × 6 #>   USUBJID PARAMCD   ADY ANRIND ADY.join ANRIND.join #>   <chr>   <chr>   <dbl> <chr>     <dbl> <chr>       #> 1 1       ALT         1 HIGH          7 NORMAL      #> 2 1       ALT         1 HIGH         14 HIGH        #> 3 2       AST         1 HIGH         15 HIGH"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/generic.html","id":"sort_order","dir":"Articles","previous_headings":"Methods > Selection","what":"Sort Order","title":"Generic Derivations","text":"admiral functions use dplyr::arrange() sorting, .e., NAs always sorted end (regardless whether desc() used ). Consider example following derivation last visit flag. record AVISITN == NA flagged NA sorted end. order argument accepts expressions. allows specify NAs handled. example, following sorts NA start. Thus AVISITN == 3 record flagged. can achieved following, also works character variables.","code":"advs <- tribble(   ~USUBJID, ~PARAMCD, ~AVISITN, ~AVAL,   \"1\",      \"WEIGHT\",       NA,  62.1,   \"1\",      \"WEIGHT\",        1,  62.3,   \"1\",      \"WEIGHT\",        2,  62.5,   \"1\",      \"WEIGHT\",        3,  62.4 )  derive_var_extreme_flag(   advs,   by_vars = exprs(USUBJID, PARAMCD),   order = exprs(AVISITN),   mode = \"last\",   new_var = LSTVISFL ) #> # A tibble: 4 × 5 #>   USUBJID PARAMCD AVISITN  AVAL LSTVISFL #>   <chr>   <chr>     <dbl> <dbl> <chr>    #> 1 1       WEIGHT        1  62.3 NA       #> 2 1       WEIGHT        2  62.5 NA       #> 3 1       WEIGHT        3  62.4 NA       #> 4 1       WEIGHT       NA  62.1 Y derive_var_extreme_flag(   advs,   by_vars = exprs(USUBJID, PARAMCD),   order = exprs(if_else(is.na(AVISITN), -Inf, AVISITN)),   mode = \"last\",   new_var = LSTVISFL ) #> # A tibble: 4 × 5 #>   USUBJID PARAMCD AVISITN  AVAL LSTVISFL #>   <chr>   <chr>     <dbl> <dbl> <chr>    #> 1 1       WEIGHT       NA  62.1 NA       #> 2 1       WEIGHT        1  62.3 NA       #> 3 1       WEIGHT        2  62.5 NA       #> 4 1       WEIGHT        3  62.4 Y derive_var_extreme_flag(   advs,   by_vars = exprs(USUBJID, PARAMCD),   order = exprs(!is.na(AVISITN), AVISITN),   mode = \"last\",   new_var = LSTVISFL ) #> # A tibble: 4 × 5 #>   USUBJID PARAMCD AVISITN  AVAL LSTVISFL #>   <chr>   <chr>     <dbl> <dbl> <chr>    #> 1 1       WEIGHT       NA  62.1 NA       #> 2 1       WEIGHT        1  62.3 NA       #> 3 1       WEIGHT        2  62.5 NA       #> 4 1       WEIGHT        3  62.4 Y"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/generic.html","id":"new-values","dir":"Articles","previous_headings":"Methods > Selection","what":"New Values","title":"Generic Derivations","text":"(new) variables set depends whether variables, flag, records added derivation. flag needs added, flag functions (derive_var_merged_exist_flag(), derive_var_joined_exist_flag()) can used. name new variable specified new_var argument values flag true_value false_value. new variables selected record needs added, name new variables values specified new_vars argument. addition flag added, exist_flag argument true_value false_value argument can used. new records added, variables values defined set_values_to argument.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/generic.html","id":"summary","dir":"Articles","previous_headings":"Methods","what":"Summary","title":"Generic Derivations","text":"new values derived summarizing values, e.g., sum, average, concatenation, …, functions derive_summary_records() derive_var_merged_summary() can used. example, adding average dose parameter ADEX can done following: summary function, source variable, new variable can specified set_values_to argument. Variables specified set_values_to by_vars set NA new records. average dose added variable ADSL, consider following: summary function, source variable, new variable specified new_vars argument. subjects without exposure observations value new variable can defined missing_values argument.","code":"adex <- tribble(   ~USUBJID, ~ASTDY, ~AVAL, ~PARAMCD,   \"1\",           1,    50, \"DOSE\",   \"1\",           7,    70, \"DOSE\",   \"1\",          14,     0, \"DOSE\",   \"2\",           1,    75, \"DOSE\",   \"2\",           9,    70, \"DOSE\" )  derive_summary_records(   adex,   dataset_add = adex,   filter_add = AVAL > 0,   by_vars = exprs(USUBJID),   set_values_to = exprs(     AVAL = mean(AVAL),     PARAMCD = \"AVERAGE DOSE\"   ) ) #> # A tibble: 7 × 4 #>   USUBJID ASTDY  AVAL PARAMCD      #>   <chr>   <dbl> <dbl> <chr>        #> 1 1           1  50   DOSE         #> 2 1           7  70   DOSE         #> 3 1          14   0   DOSE         #> 4 2           1  75   DOSE         #> 5 2           9  70   DOSE         #> 6 1          NA  60   AVERAGE DOSE #> 7 2          NA  72.5 AVERAGE DOSE adsl <- tribble(   ~USUBJID,   \"1\",   \"2\",   \"3\" )  derive_var_merged_summary(   adsl,   dataset_add = adex,   filter_add = AVAL > 0,   by_vars = exprs(USUBJID),   new_vars = exprs(     AVERDOSE = mean(AVAL)   ),   missing_values = exprs(AVERDOSE = 0) ) #> # A tibble: 3 × 2 #>   USUBJID AVERDOSE #>   <chr>      <dbl> #> 1 1           60   #> 2 2           72.5 #> 3 3            0"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/generic.html","id":"computed","dir":"Articles","previous_headings":"Methods","what":"Computed","title":"Generic Derivations","text":"new values computed different parameters source dataset, derive_param_computed() can used. computed value can specified set_values_to argument. values source variable parameter can referred temporary variables form <variable name>.<parameter name>. common computations like BMI admiral offers computation functions. previous example compute_bmi() used instead formula BMI:","code":"advs <- tribble(   ~USUBJID, ~AVISIT,    ~PARAMCD, ~AVAL, ~AVALU,   \"1\",      \"BASELINE\", \"WEIGHT\",  32.6, \"kg\",   \"1\",      \"BASELINE\", \"HEIGHT\", 155.4, \"cm\",   \"1\",      \"MONTH 6\",  \"WEIGHT\",  33.2, \"kg\",   \"1\",      \"MONTH 6\",  \"HEIGHT\", 155.8, \"cm\",   \"2\",      \"BASELINE\", \"WEIGHT\",  44.2, \"kg\",   \"2\",      \"BASELINE\", \"HEIGHT\", 145.3, \"cm\",   \"2\",      \"MONTH 6\",  \"WEIGHT\",  42.0, \"kg\",   \"2\",      \"MONTH 6\",  \"HEIGHT\", 146.4, \"cm\" )  derive_param_computed(   advs,   by_vars = exprs(USUBJID, AVISIT),   parameters = c(\"WEIGHT\", \"HEIGHT\"),   set_values_to = exprs(     AVAL = AVAL.WEIGHT / (AVAL.HEIGHT / 100)^2,     PARAMCD = \"BMI\",     AVALU = \"kg/m^2\"   ) ) #> # A tibble: 12 × 5 #>    USUBJID AVISIT   PARAMCD  AVAL AVALU  #>    <chr>   <chr>    <chr>   <dbl> <chr>  #>  1 1       BASELINE WEIGHT   32.6 kg     #>  2 1       BASELINE HEIGHT  155.  cm     #>  3 1       MONTH 6  WEIGHT   33.2 kg     #>  4 1       MONTH 6  HEIGHT  156.  cm     #>  5 2       BASELINE WEIGHT   44.2 kg     #>  6 2       BASELINE HEIGHT  145.  cm     #>  7 2       MONTH 6  WEIGHT   42   kg     #>  8 2       MONTH 6  HEIGHT  146.  cm     #>  9 1       BASELINE BMI      13.5 kg/m^2 #> 10 1       MONTH 6  BMI      13.7 kg/m^2 #> 11 2       BASELINE BMI      20.9 kg/m^2 #> 12 2       MONTH 6  BMI      19.6 kg/m^2 derive_param_computed(   advs,   by_vars = exprs(USUBJID, AVISIT),   parameters = c(\"WEIGHT\", \"HEIGHT\"),   set_values_to = exprs(     AVAL = compute_bmi(weight = AVAL.WEIGHT, height = AVAL.HEIGHT),     PARAMCD = \"BMI\",     AVALU = \"kg/m^2\"   ) ) #> # A tibble: 12 × 5 #>    USUBJID AVISIT   PARAMCD  AVAL AVALU  #>    <chr>   <chr>    <chr>   <dbl> <chr>  #>  1 1       BASELINE WEIGHT   32.6 kg     #>  2 1       BASELINE HEIGHT  155.  cm     #>  3 1       MONTH 6  WEIGHT   33.2 kg     #>  4 1       MONTH 6  HEIGHT  156.  cm     #>  5 2       BASELINE WEIGHT   44.2 kg     #>  6 2       BASELINE HEIGHT  145.  cm     #>  7 2       MONTH 6  WEIGHT   42   kg     #>  8 2       MONTH 6  HEIGHT  146.  cm     #>  9 1       BASELINE BMI      13.5 kg/m^2 #> 10 1       MONTH 6  BMI      13.7 kg/m^2 #> 11 2       BASELINE BMI      20.9 kg/m^2 #> 12 2       MONTH 6  BMI      19.6 kg/m^2"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/higher_order.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Higher Order Functions","text":"vignette explains advanced options admiral related higher order functions. higher order function function takes another function input. introducing higher order functions, intend give user greater power derivation functions, whilst trying negate need adding additional admiral functions arguments, user needing many separate steps. functions covered : call_derivation(): Call single derivation multiple times arguments fixed across iterations others varying restrict_derivation(): Execute single derivation subset input dataset slice_derivation(): input dataset split slices (subsets) slice single derivation called separately. arguments derivation may vary depending slice.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/higher_order.html","id":"required-packages","dir":"Articles","previous_headings":"Introduction","what":"Required Packages","title":"Higher Order Functions","text":"examples vignette require following packages. example purpose, ADSL dataset—included admiral—SDTM datasets pharmaversesdtm used. following code creates minimally viable ADAE dataset used needed following examples.","code":"library(admiral) library(pharmaversesdtm) library(dplyr, warn.conflicts = FALSE)  ae <- pharmaversesdtm::ae vs <- pharmaversesdtm::vs adsl <- admiral::admiral_adsl  ae <- convert_blanks_to_na(ae) vs <- convert_blanks_to_na(vs) adae <- ae %>%   left_join(adsl, by = c(\"STUDYID\", \"USUBJID\")) %>%   derive_vars_dt(     new_vars_prefix = \"AST\",     dtc = AESTDTC,     highest_imputation = \"M\"   ) %>%   mutate(TRTEMFL = if_else(ASTDT >= TRTSDT, \"Y\", NA_character_))"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/higher_order.html","id":"call-derivation","dir":"Articles","previous_headings":"","what":"Call Derivation","title":"Higher Order Functions","text":"function exists purely convenience save user repeating numerous similar derivation function calls. best used multiple derived variables similar specifications slight variations. example, imagine case parameters BDS ADaM required highest value flag lowest value flag. example achieve without using call_derivation(): example achieve using call_derivation(), different arguments passed using params(): example, can see higher order functions, derivation user supplies name derivation function apply, trailing parentheses required. variable_params used pass list different arguments needed derived variable. advantage higher order function highlighted examples two variable derivations similar needs, case multiple time AE parameters derived one call. Note example relies pre-defined tte_source objects, explained Creating BDS Time--Event ADaM. Developing ADaM scripts way using call_derivation() give following benefits: code becomes efficient readable maintenance eased case specification changes downstream quality checking require less effort","code":"vs_without <- vs %>%   derive_var_extreme_flag(     by_vars = exprs(USUBJID, VSTESTCD),     order = exprs(VSORRES, VSSEQ),     new_var = AHIFL,     mode = \"last\"   ) %>%   derive_var_extreme_flag(     by_vars = exprs(USUBJID, VSTESTCD),     order = exprs(VSORRES, VSSEQ),     new_var = ALOFL,     mode = \"first\"   ) vs_with <- vs %>%   call_derivation(     derivation = derive_var_extreme_flag,     variable_params = list(       params(new_var = AHIFL, mode = \"last\"),       params(new_var = ALOFL, mode = \"first\")     ),     by_vars = exprs(USUBJID, VSTESTCD),     order = exprs(VSORRES, VSSEQ)   ) adaette <- call_derivation(   derivation = derive_param_tte,   variable_params = list(     params(       event_conditions = list(ae_event),       set_values_to = exprs(PARAMCD = \"TTAE\")     ),     params(       event_conditions = list(ae_ser_event),       set_values_to = exprs(PARAMCD = \"TTSERAE\")     ),     params(       event_conditions = list(ae_sev_event),       set_values_to = exprs(PARAMCD = \"TTSEVAE\")     ),     params(       event_conditions = list(ae_wd_event),       set_values_to = exprs(PARAMCD = \"TTWDAE\")     )   ),   dataset_adsl = adsl,   source_datasets = list(adsl = adsl, adae = adae),   censor_conditions = list(lastalive_censor) )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/higher_order.html","id":"restrict-derivation","dir":"Articles","previous_headings":"","what":"Restrict Derivation","title":"Higher Order Functions","text":"idea behind function sometimes want apply derivation certain records input dataset. Introducing restrict_derivation() therefore gives users ability achieve across function, without function needing argument allow . example wanted flag first occurring AE highest severity patient, wanted records occurring study day 1. achieve using restrict_derivation(), function arguments passed using params() restriction criteria given using filter:","code":"ae <- ae %>%   mutate(TEMP_AESEVN = as.integer(factor(AESEV, levels = c(\"SEVERE\", \"MODERATE\", \"MILD\")))) %>%   restrict_derivation(     derivation = derive_var_extreme_flag,     args = params(       new_var = AHSEVFL,       by_vars = exprs(USUBJID),       order = exprs(TEMP_AESEVN, AESTDY, AESEQ),       mode = \"first\"     ),     filter = AESTDY >= 1   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/higher_order.html","id":"slice-derivation","dir":"Articles","previous_headings":"","what":"Slice Derivation","title":"Higher Order Functions","text":"function way combines features two. allows single derivation applied different arguments different slices (subsets) records input dataset. separate restrict_derivation() calls different set records, slice_derivation() allows achieve one call. example wanted achieve derivation records occurring study day 1, pre-treatment AEs wanted flag last occurring AE. achieve using slice_derivation(), function arguments passed using params() via different slices controlled filter: can see example, derivation_slice ordering important. addressed AEs study day 1 first, used filter = TRUE option catch remaining records (case pre-treatment AEs). ordering perhaps shown even look example three slices taken. Remember observations match one slice considered first matching slice. case ’re creating flag patient record first severe AE, first moderate AE, finally flagging last occurring AE severe moderate. order important slices mutually exclusive, case moderate AE slice severe AE slice, example, difference result. However third slice come last check remaining (.e. severe moderate) records .","code":"ae <- ae %>%   slice_derivation(     derivation = derive_var_extreme_flag,     args = params(       new_var = AHSEV2FL,       by_vars = exprs(USUBJID)     ),     derivation_slice(       filter = AESTDY >= 1,       args = params(order = exprs(TEMP_AESEVN, AESTDY, AESEQ), mode = \"first\")     ),     derivation_slice(       filter = TRUE,       args = params(order = exprs(AESTDY, AESEQ), mode = \"last\")     )   ) ae <- ae %>%   slice_derivation(     derivation = derive_var_extreme_flag,     args = params(       new_var = AHSEV3FL,       by_vars = exprs(USUBJID)     ),     derivation_slice(       filter = AESEV == \"SEVERE\",       args = params(order = exprs(AESTDY, AESEQ), mode = \"first\")     ),     derivation_slice(       filter = AESEV == \"MODERATE\",       args = params(order = exprs(AESTDY, AESEQ), mode = \"first\")     ),     derivation_slice(       filter = TRUE,       args = params(order = exprs(AESTDY, AESEQ), mode = \"last\")     )   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/hys_law.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Hy's Law Implementation","text":"drug development process, clinical trials often required assess potential experimental drug can cause severe liver injury, known drug induced liver injury (DILI) Drug-Induced Liver Injury: Premarketing Clinical Evaluation. multiple criteria need evaluated determine classify DILI “Event”. Hy’s Law, common rule thumb DILI Event , usually comprised three parts: Elevated alanine aminotransferase (ALT) aspartate aminotransferase (AST) 3-times greater upper limit normal. Elevated serum total bilirubin (BILI) 2-times greater within window time, ~14 days elevated ALT/AST event. reason explain increased lab values like preexisting liver disease.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/hys_law.html","id":"required-packages","dir":"Articles","previous_headings":"Introduction","what":"Required Packages","title":"Hy's Law Implementation","text":"examples vignette require following packages.","code":"library(admiral) library(dplyr, warn.conflicts = FALSE)"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/hys_law.html","id":"programming-workflow","dir":"Articles","previous_headings":"","what":"Programming Workflow","title":"Hy's Law Implementation","text":"Read Data Flagging Elevated Values (CRITy, CRITyFL) Subsetting LBTESTCD Joining Potential Events Create New Parameters Rows Conclusion","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/hys_law.html","id":"readdata","dir":"Articles","previous_headings":"Programming Workflow","what":"Read in Data","title":"Hy's Law Implementation","text":"assume ADLB dataset available 1. First read ADLB parameters required Hy’s Law parameters:","code":"adlb <- admiral::admiral_adlb %>%   filter(PARAMCD %in% c(\"AST\", \"ALT\", \"BILI\") & is.na(DTYPE))"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/hys_law.html","id":"columns","dir":"Articles","previous_headings":"Programming Workflow","what":"Flagging Elevated Values (CRITy, CRITyFL)","title":"Hy's Law Implementation","text":"standard convention ADLBHY datasets, various CRITy CRITyFL columns describe conditions necessary reach particular criterion Hy’s Law actual flag indicate whether condition reached. Using mutate(), call_derivation() derive_var_merged_exist_flag(), can create columns indicate 3-fold greater upper limit normal ALT/AST 2-fold greater upper limit normal BILI. increase visibility simplicity, retain columns relevant Hy’s Law analysis now.","code":"adlb_annotated <- adlb %>%   slice_derivation(     derive_vars_crit_flag,     args = params(       values_yn = TRUE     ),     derivation_slice(       filter = PARAMCD %in% c(\"AST\", \"ALT\"),       args = params(         condition = AVAL / ANRHI >= 3,         description = paste(PARAMCD, \">=3xULN\")       )     ),     derivation_slice(       filter = PARAMCD == \"BILI\",       args = params(         condition = AVAL / ANRHI >= 2,         description = \"BILI >= 2xULN\"       )     )   ) %>%   select(STUDYID, USUBJID, TRT01A, PARAMCD, LBSEQ, ADT, AVISIT, ADY, AVAL, ANRHI, CRIT1, CRIT1FL)"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/hys_law.html","id":"joins","dir":"Articles","previous_headings":"Programming Workflow","what":"Subsetting by LBTESTCD and Joining by Potential Events","title":"Hy's Law Implementation","text":"elevated ALT/AST event reaches threshold Hy’s Law, need search elevated BILI events within certain time-window, usually 14 days elevated ALT/AST event (window may vary organization). , Splitting dataset ALT/AST BILI subsets, respectively, Joining two datasets using derive_vars_joined() using filter_join argument join together relevant flagged BILI records corresponding flagged ALT/AST record (prior 14 days may vary trial/organization) indicate potential Hy’s Law event, resulting dataset helpful deriving additional parameters. dataset may also prove useful listing display two lab-records one row showcase potential event.","code":"altast_records <- adlb_annotated %>%   filter(PARAMCD %in% c(\"AST\", \"ALT\"))  bili_records <- adlb_annotated %>%   filter(PARAMCD %in% c(\"BILI\"))  hylaw_records <- derive_vars_joined(   dataset = altast_records,   dataset_add = bili_records,   by_vars = exprs(STUDYID, USUBJID),   order = exprs(ADY),   join_type = \"all\",   filter_join = 0 <= ADT.join - ADT & ADT.join - ADT <= 14 & CRIT1FL == \"Y\" & CRIT1FL.join == \"Y\",   new_vars = exprs(BILI_DT = ADT, BILI_CRITFL = CRIT1FL),   mode = \"first\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/hys_law.html","id":"newparams","dir":"Articles","previous_headings":"Programming Workflow","what":"How to Create New Parameters and Rows","title":"Hy's Law Implementation","text":"Using derive_param_exist_flag() can create variety parameters final dataset AVAL = 1/0 specific Hy’s Law analysis. example indicate potential Hy’s Law event, PARAMCD set \"HYSLAW\" PARAM set \"ALT/AST >= 3xULN BILI >= 2xULN\" patient using flags prior dataset. method allows flexibility well, parameters visit desired, add AVISIT ADT select() by_vars lines denoted following code. Additional modifications can made : Parameter indicate worsening condition sort baseline/post-baseline based analysis Flags lab values like ALP modified last step binding rows back whatever previous dataset appropriate based data specifications, case, best suited bind back adlb_annotated object.","code":"hylaw_records_pts_visits <- hylaw_records %>%   select(STUDYID, USUBJID, TRT01A) %>% # add AVISIT, ADT for by visit   distinct()  hylaw_records_fls <- hylaw_records %>%   select(STUDYID, USUBJID, TRT01A, CRIT1FL, BILI_CRITFL) %>% # add AVISIT, ADT for by visit   distinct()  hylaw_params <- derive_param_exist_flag(   dataset_ref = hylaw_records_pts_visits,   dataset_add = hylaw_records_fls,   condition = CRIT1FL == \"Y\" & BILI_CRITFL == \"Y\",   false_value = \"N\",   missing_value = \"N\",   by_vars = exprs(STUDYID, USUBJID, TRT01A), # add AVISIT, ADT for by visit   set_values_to = exprs(     PARAMCD = \"HYSLAW\",     PARAM = \"ALT/AST >= 3xULN and BILI >= 2xULN\",     AVAL = yn_to_numeric(AVALC)   ) )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/hys_law.html","id":"conclusion","dir":"Articles","previous_headings":"Programming Workflow","what":"Conclusion","title":"Hy's Law Implementation","text":"demonstrated base-case may asked trial programmer. reality Hy’s Law assessing potential DILI events can get rather complex quite quickly. Differences assessment across organizations specific trials might require modifications, may include: additional CRITy CRITyFL columns different cutoffs like 5xULN, 10xULN, 20xULN checking elevated values additional labs like alkaline phosphatase (ALP) appearance certain adverse events associated elevated lab-values different criteria cutoffs depend baseline values characteristics parameters worsening condition hope demonstrating flexibility admiral functions using general workflow create necessary parameters ADLBHY, creating final dataset becomes simplified easily scalable. Ideally, ready organization’s standard macros previous code TLFs outputs well. first attempt breaking summarizing topic. welcome feedback ideas improve guide!","code":"adlbhy <- adlb_annotated %>%   bind_rows(hylaw_params)"},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/articles/imputation.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Date and Time Imputation","text":"vignette broken three major sections. first section briefly explores imputation rules used admiral. second section focuses imputation functions work vectors lots small examples explore imputation rules. vector-based functions form backbone admiral’s powerful functions derive_vars_dt() derive_vars_dtm() building ADaM dataset. final section moves detailed examples user might face working ADaMs need ---DT ---DTM variables.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/imputation.html","id":"required-packages","dir":"Articles","previous_headings":"Introduction","what":"Required Packages","title":"Date and Time Imputation","text":"examples vignette require following packages.","code":"library(admiral) library(lubridate) library(tibble) library(dplyr, warn.conflicts = FALSE)"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/imputation.html","id":"imputation-rules","dir":"Articles","previous_headings":"","what":"Imputation Rules","title":"Date and Time Imputation","text":"Date time collected SDTM character values using extended ISO 8601 format. example, \"2019-10-9T13:42:00\". allows parts date time missing, e.g., \"2019-10\" day time unknown. ADaM timing variables like ADTM (Analysis Datetime) ADY (Analysis Relative Day) numeric variables. can derived date datetime complete. Therefore admiral provides imputation functions fill missing date time parts according certain imputation rules. admiral users primarily use two functions derive_vars_dt() derive_vars_dtm() date datetime imputations respectively. functions dates can passed argument, expect full dates datetimes (unless otherwise specified), possibility partials functions used first step make required imputation. functions need date/time imputation follow rule called Highest Imputation, corresponding argument functions called highest_imputation. rule best explained working examples , put briefly, rule allows user control components DTC value imputed missing. default imputation _dtm() functions, e.g. impute_dtc_dtm(), derive_vars_dtm(), “h” (hours). user can specify imputation done setting highest_imputation = n. However, _dt() functions, e.g. impute_dtc_dt(), derive_vars_dt() default imputation already set highest_imputation = \"n\". Care must taken deciding level imputation. component higher level highest imputation level missing, NA_character_ returned. example, highest_imputation = \"D\" \"2020\" results NA_character_ month missing. encourage readers explore detail highest_imputation options _dtm() _dt() function documentations examples .","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/imputation.html","id":"imputation-on-a-vector","dir":"Articles","previous_headings":"Imputation Rules","what":"Imputation on a Vector","title":"Date and Time Imputation","text":"first example, make use impute_dtc_dtm() 2019-10 setting highest_imputation = \"M\". argument date_imputation time_imputation given expressed inputs imputation like see done. Next impute using 2019-02, done naively can result invalid dates, e.g., Therefore keywords \"first\" \"last\" can specified date_imputation request missing parts replaced first last possible value - giving us valid date! dates, additional option use keyword \"mid\" impute missing day 15 missing day month 06-30, note different behavior depending preserve argument case month missing: wanted achieve similar result replacing missing part date fixed value 06-15, also possible, note difference days cases month missing:","code":"impute_dtc_dtm(   \"2019-10\",   highest_imputation = \"M\",   date_imputation = \"01-01\",   time_imputation = \"00:00:00\" ) #> [1] \"2019-10-01T00:00:00\" impute_dtc_dtm(   \"2019-02\",   highest_imputation = \"M\",   date_imputation = \"02-31\",   time_imputation = \"00:00:00\" ) #> [1] \"2019-02-31T00:00:00\" impute_dtc_dtm(   \"2019-02\",   highest_imputation = \"M\",   date_imputation = \"last\",   time_imputation = \"00:00:00\" ) #> [1] \"2019-02-28T00:00:00\" dates <- c(   \"2019-02\",   \"2019\",   \"2019---01\" ) impute_dtc_dtm(   dates,   highest_imputation = \"M\",   date_imputation = \"mid\",   time_imputation = \"00:00:00\",   preserve = FALSE ) #> [1] \"2019-02-15T00:00:00\" \"2019-06-30T00:00:00\" \"2019-06-30T00:00:00\" impute_dtc_dtm(   dates,   highest_imputation = \"M\",   date_imputation = \"mid\",   time_imputation = \"00:00:00\",   preserve = TRUE ) #> [1] \"2019-02-15T00:00:00\" \"2019-06-30T00:00:00\" \"2019-06-01T00:00:00\" dates <- c(   \"2019-02\",   \"2019\",   \"2019---01\" ) impute_dtc_dtm(   dates,   highest_imputation = \"M\",   date_imputation = \"06-15\",   time_imputation = \"00:00:00\" ) #> [1] \"2019-02-15T00:00:00\" \"2019-06-15T00:00:00\" \"2019-06-15T00:00:00\""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/imputation.html","id":"imputation-level","dir":"Articles","previous_headings":"Imputation Rules","what":"Imputation Level","title":"Date and Time Imputation","text":"imputation level, .e., components imputed missing, controlled highest_imputation argument. components specified level imputed. imputation years (highest_imputation = \"Y\") see next section.","code":"dates <- c(   \"2019-02-03T12:30:15\",   \"2019-02-03T12:30\",   \"2019-02-03\",   \"2019-02\",   \"2019\" )  # Do not impute impute_dtc_dtm(   dates,   highest_imputation = \"n\" ) #> [1] \"2019-02-03T12:30:15\" NA                    NA                    #> [4] NA                    NA  # Impute seconds only impute_dtc_dtm(   dates,   highest_imputation = \"s\" ) #> [1] \"2019-02-03T12:30:15\" \"2019-02-03T12:30:00\" NA                    #> [4] NA                    NA  # Impute time (hours, minutes, seconds) only impute_dtc_dtm(   dates,   highest_imputation = \"h\" ) #> [1] \"2019-02-03T12:30:15\" \"2019-02-03T12:30:00\" \"2019-02-03T00:00:00\" #> [4] NA                    NA  # Impute days and time impute_dtc_dtm(   dates,   highest_imputation = \"D\" ) #> [1] \"2019-02-03T12:30:15\" \"2019-02-03T12:30:00\" \"2019-02-03T00:00:00\" #> [4] \"2019-02-01T00:00:00\" NA  # Impute date (months and days) and time impute_dtc_dtm(   dates,   highest_imputation = \"M\" ) #> [1] \"2019-02-03T12:30:15\" \"2019-02-03T12:30:00\" \"2019-02-03T00:00:00\" #> [4] \"2019-02-01T00:00:00\" \"2019-01-01T00:00:00\""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/imputation.html","id":"minimummaximum-dates","dir":"Articles","previous_headings":"Imputation Rules","what":"Minimum/Maximum Dates","title":"Date and Time Imputation","text":"scenarios imputed date certain dates. example imputed date data cut date death date desirable. admiral imputation functions provide min_dates max_dates argument specify dates. example: ensured imputed date specified dates. dates range possible dates DTC value considered. possible dates defined missing parts DTC date, .e., “2019-02” possible dates range “2019-02-01” “2019-02-28”. Thus “2019-01-14” ignored. ensures non-missing parts DTC date changed. min_dates max_dates argument specified, also possible impute completely missing dates. date_imputation = \"first\" min_dates argument must specified date_imputation = \"last\" max_dates argument. imputation rules imputing year possible.","code":"impute_dtc_dtm(   \"2019-02\",   highest_imputation = \"M\",   date_imputation = \"last\",   time_imputation = \"last\",   max_dates = list(ymd(\"2019-01-14\"), ymd(\"2019-02-25\")) ) #> [1] \"2019-02-25T23:59:59\" # Impute year to first impute_dtc_dtm(   c(\"2019-02\", NA),   highest_imputation = \"Y\",   min_dates = list(     ymd(\"2019-01-14\", NA),     ymd(\"2019-02-25\", \"2020-01-01\")   ) ) #> [1] \"2019-02-25T00:00:00\" \"2020-01-01T00:00:00\"  # Impute year to last impute_dtc_dtm(   c(\"2019-02\", NA),   highest_imputation = \"Y\",   date_imputation = \"last\",   time_imputation = \"last\",   max_dates = list(     ymd(\"2019-01-14\", NA),     ymd(\"2019-02-25\", \"2020-01-01\")   ) ) #> [1] \"2019-02-25T23:59:59\" \"2020-01-01T23:59:59\""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/imputation.html","id":"imputation-flags","dir":"Articles","previous_headings":"","what":"Imputation Flags","title":"Date and Time Imputation","text":"ADaM requires date datetime variables imputation used accompanied date /time imputation flag variables (*DTF *TMF, e.g., ADTF ATMF ADTM). variables indicate highest level imputed, e.g., minutes seconds imputed, imputation flag set \"M\". admiral functions derive imputed variables also adding corresponding imputation flag variables. Note: admiral datetime imputation function provides ignore_seconds_flag argument can set TRUE cases seconds never collected. due following ADaM IG: given SDTM DTC variable, hours minutes ever collected, seconds imputed *DTM 00, necessary set *TMF \"S\".","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/imputation.html","id":"imputation-functions","dir":"Articles","previous_headings":"","what":"Imputation Functions","title":"Date and Time Imputation","text":"admiral provides following functions imputation: derive_vars_dt(): Adds date variable date imputation flag variable (optional) based –DTC variable imputation rules. derive_vars_dtm(): Adds datetime variable, date imputation flag variable, time imputation flag variable (optional) based –DTC variable imputation rules. impute_dtc_dtm(): Returns complete ISO 8601 datetime NA based partial ISO 8601 datetime imputation rules. impute_dtc_dt(): Returns complete ISO 8601 date (without time) NA based partial ISO 8601 date(time) imputation rules. convert_dtc_to_dt(): Returns date input ISO 8601 date complete. Otherwise, NA returned. convert_dtc_to_dtm(): Returns datetime input ISO 8601 date complete (missing time replaced \"00:00:00\" default). Otherwise, NA returned. compute_dtf(): Returns date imputation flag. compute_tmf(): Returns time imputation flag.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/articles/imputation.html","id":"create-an-imputed-datetime-and-date-variable-and-imputation-flag-variables","dir":"Articles","previous_headings":"Examples","what":"Create an Imputed Datetime and Date Variable and Imputation Flag Variables","title":"Date and Time Imputation","text":"derive_vars_dtm() function derives imputed datetime variable corresponding date time imputation flags. imputed date variable can derived using derive_vars_dtm_to_dt() function. necessary advisable perform imputation date variable already done datetime variable. CDISC considers datetime date variable two representations date. Thus imputation must imputation flags valid datetime date variable.","code":"ae <- tribble(   ~AESTDTC,   \"2019-08-09T12:34:56\",   \"2019-04-12\",   \"2010-09\",   NA_character_ ) %>%   derive_vars_dtm(     dtc = AESTDTC,     new_vars_prefix = \"AST\",     highest_imputation = \"M\",     date_imputation = \"first\",     time_imputation = \"first\"   ) %>%   derive_vars_dtm_to_dt(exprs(ASTDTM))"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/imputation.html","id":"create-an-imputed-date-variable-and-imputation-flag-variable","dir":"Articles","previous_headings":"Examples","what":"Create an Imputed Date Variable and Imputation Flag Variable","title":"Date and Time Imputation","text":"imputed date variable without corresponding datetime variable required, can derived derive_vars_dt() function.","code":"ae <- tribble(   ~AESTDTC,   \"2019-08-09T12:34:56\",   \"2019-04-12\",   \"2010-09\",   NA_character_ ) %>%   derive_vars_dt(     dtc = AESTDTC,     new_vars_prefix = \"AST\",     highest_imputation = \"M\",     date_imputation = \"first\"   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/imputation.html","id":"impute-time-without-imputing-date","dir":"Articles","previous_headings":"Examples","what":"Impute Time without Imputing Date","title":"Date and Time Imputation","text":"time imputed date, highest_imputation argument set \"h\". results NA date partial. date imputed date imputation flag created.","code":"ae <- tribble(   ~AESTDTC,   \"2019-08-09T12:34:56\",   \"2019-04-12\",   \"2010-09\",   NA_character_ ) %>%   derive_vars_dtm(     dtc = AESTDTC,     new_vars_prefix = \"AST\",     highest_imputation = \"h\",     time_imputation = \"first\"   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/imputation.html","id":"avoid-imputed-dates-before-a-particular-date","dir":"Articles","previous_headings":"Examples","what":"Avoid Imputed Dates Before a Particular Date","title":"Date and Time Imputation","text":"Usually adverse event start date imputed earliest date possible dates filling missing parts. result may date treatment start date. desirable adverse event considered treatment emergent excluded adverse event summaries. can avoided specifying treatment start date variable (TRTSDTM) min_dates argument. Please note TRTSDTM used imputed date non missing date time parts AESTDTC coincide TRTSDTM. Therefore 2019-10 imputed 2019-11-11 12:34:56. ensures collected information changed imputation.","code":"ae <- tribble(   ~AESTDTC,              ~TRTSDTM,   \"2019-08-09T12:34:56\", ymd_hms(\"2019-11-11T12:34:56\"),   \"2019-10\",             ymd_hms(\"2019-11-11T12:34:56\"),   \"2019-11\",             ymd_hms(\"2019-11-11T12:34:56\"),   \"2019-12-04\",          ymd_hms(\"2019-11-11T12:34:56\") ) %>%   derive_vars_dtm(     dtc = AESTDTC,     new_vars_prefix = \"AST\",     highest_imputation = \"M\",     date_imputation = \"first\",     time_imputation = \"first\",     min_dates = exprs(TRTSDTM)   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/imputation.html","id":"avoid-imputed-dates-after-a-particular-date","dir":"Articles","previous_headings":"Examples","what":"Avoid Imputed Dates After a Particular Date","title":"Date and Time Imputation","text":"date imputed latest date possible dates filling missing parts, result dates data cut death. can achieved specifying dates max_dates argument. Please note non missing date parts changed. Thus 2019-12-04 imputed 2019-12-04 23:59:59 although data cut date. may make sense replace data cut date part imputation. done separate data cleaning data cut step.","code":"ae <- tribble(   ~AEENDTC,              ~DTHDT,            ~DCUTDT,   \"2019-08-09T12:34:56\", ymd(\"2019-11-11\"), ymd(\"2019-12-02\"),   \"2019-11\",             ymd(\"2019-11-11\"), ymd(\"2019-12-02\"),   \"2019-12\",             NA,                ymd(\"2019-12-02\"),   \"2019-12-04\",          NA,                ymd(\"2019-12-02\") ) %>%   derive_vars_dtm(     dtc = AEENDTC,     new_vars_prefix = \"AEN\",     highest_imputation = \"M\",     date_imputation = \"last\",     time_imputation = \"last\",     max_dates = exprs(DTHDT, DCUTDT)   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/imputation.html","id":"imputation-without-creating-a-new-variable","dir":"Articles","previous_headings":"Examples","what":"Imputation Without Creating a New Variable","title":"Date and Time Imputation","text":"imputation required without creating new variable convert_dtc_to_dt() function can called obtain vector imputed dates. can used example conditions:","code":"mh <- tribble(   ~MHSTDTC,     ~TRTSDT,   \"2019-04\",    ymd(\"2019-04-15\"),   \"2019-04-01\", ymd(\"2019-04-15\"),   \"2019-05\",    ymd(\"2019-04-15\"),   \"2019-06-21\", ymd(\"2019-04-15\") ) %>%   filter(     convert_dtc_to_dt(       MHSTDTC,       highest_imputation = \"M\",       date_imputation = \"first\"     ) < TRTSDT   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/imputation.html","id":"using-more-than-one-imputation-rule-for-a-variable","dir":"Articles","previous_headings":"Examples","what":"Using More Than One Imputation Rule for a Variable","title":"Date and Time Imputation","text":"Using different imputation rules depending observation can done using slice_derivation().","code":"vs <- tribble(   ~VSDTC,                ~VSTPT,   \"2019-08-09T12:34:56\", NA,   \"2019-10-12\",          \"PRE-DOSE\",   \"2019-11-10\",          NA,   \"2019-12-04\",          NA ) %>%   slice_derivation(     derivation = derive_vars_dtm,     args = params(       dtc = VSDTC,       new_vars_prefix = \"A\"     ),     derivation_slice(       filter = VSTPT == \"PRE-DOSE\",       args = params(time_imputation = \"first\")     ),     derivation_slice(       filter = TRUE,       args = params(time_imputation = \"last\")     )   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/lab_grading.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Lab Grading","text":"Within ADLB ADaM data set concept lab grading, set criteria particular lab tests grade severity abnormality lab value. grades 0 4, grade 0 can viewed generally “NORMAL” value. higher grade severe abnormal lab value . several sets lab grading criteria, currently admiral implemented NCI-CTCAEv4, NCI-CTCAEv5 DAIDS grading criteria. future releases admiral may look implement grading criteria. NCI-CTCAE version 4 5 grading criteria can found : https://ctep.cancer.gov/protocoldevelopment/electronic_applications/ctc.htm . NCI-CTCAEv4 criteria can found heading Common Terminology Criteria Adverse Events (CTCAE)v4.0 NCI-CTCAEv5 criteria can found heading Common Terminology Criteria Adverse Events (CTCAE)v5.0 DAIDS grading criteria can found : https://rsc.niaid.nih.gov/clinical-research-sites/daids-adverse-event-grading-tables . DAIDS criteria can found heading DAIDS Table Grading Severity Adult Pediatric Adverse Events Corrected Version 2.1","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/lab_grading.html","id":"grading-metadata","dir":"Articles","previous_headings":"","what":"Grading metadata","title":"Lab Grading","text":"admiral store metadata data set set grading criteria data folder admiral. Currently, atoxgr_criteria_ctcv4() NCI-CTCAEv4, atoxgr_criteria_ctcv5() NCI-CTCAEv5 atoxgr_criteria_daids() DAIDS. metadata data set required variables optional variables, optional variables purely transparency, contain detailed information grading criteria. required variables used admiral create grade.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/lab_grading.html","id":"structure-of-metadata-set","dir":"Articles","previous_headings":"Grading metadata","what":"Structure of metadata set","title":"Lab Grading","text":"metadata data set following structure required variables: metadata data set following structure optional variables:","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/lab_grading.html","id":"handling-floating-points-when-comparing-numeric-values","dir":"Articles","previous_headings":"","what":"Handling floating points when comparing numeric values","title":"Lab Grading","text":"comparing numeric values, example AVAL > 1.1*ANRHI, unexpected results can occur due floating point issues. solve issue {admiral} used signif() function side equation, number significant digits used compare passed function derive_var_atoxgr_dir() via argument signif_dig. Please see documentation function details blog post admiral handles floating points context.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/articles/lab_grading.html","id":"mapping-adlb-to-the-term-variable-in-the-admiral-metadata-data-set","dir":"Articles","previous_headings":"Creating the Lab Grade","what":"Mapping ADLB to the TERM variable in the {admiral} metadata data set","title":"Lab Grading","text":"company needs map lab test term describes criteria applied. list terms defined admiral metadata implement NCI-CTCAEv4 :  Likewise, list terms defined admiral metadata implement NCI-CTCAEv5 : (Terms identical NCI-CTCAEv4, except Hyperglycemia, Hyperglycemia (Fasting) Hypophosphatemia) present NCI-CTCAEv5.  Finally, list terms defined admiral metadata implement DAIDS : Using CDISC data lab tests can mapped correct terms, firstly create PARAMCD, PARAM, AVAL, ANRLO ANRHI, also lab grading criteria require BASE PCHG, also need created running derive_var_atoxgr_dir() function. Another look-table used add ATOXDSCL ATOXDSCH using PARAMCD. ATOXDSCL holds terms grading low lab values, ATOXDSCH holds terms grading high lab values. names variables can user-defined. ATOXDSCL ATOXDSCH link ADLB data admiral metadata holds grading criteria. now straightforward create grade, low lab values grade held ATOXGRL high lab values grade held ATOXGRH. Note: NCICTCAEv5 grading, update meta_criteria parameter atoxgr_criteria_ctcv5 DAIDS grading update meta_criteria parameter atoxgr_criteria_daids Note: admiral grade ‘Anemia’ ‘Hemoglobin Increased’ metadata based SI unit ‘g/L’, however CDISC data SI unit ‘mmol/L’. Please see SI_UNIT_CHECK variable admiral metadata atoxgr_criteria_ctcv4() atoxgr_criteria_ctcv5() atoxgr_criteria_daids(), metadata data folder admiral.  admiral also gives option combine ATOXGRL ATOXGRH one variable, namely ATOXGR. Grades held ATOXGRL given negative value ATOXGR distinguish low high values.","code":"library(admiral) library(pharmaversesdtm) library(dplyr, warn.conflicts = FALSE) library(stringr) library(tibble)  lb <- pharmaversesdtm::lb adsl <- admiral::admiral_adsl  lb <- convert_blanks_to_na(lb) # Look-up tables ----  # Assign PARAMCD, PARAM, and PARAMN param_lookup <- tibble::tribble(   ~LBTESTCD, ~PARAMCD,  ~PARAM,                                             ~PARAMN,   \"ALB\",     \"ALB\",     \"Albumin (g/L)\",                                    1,   \"ALP\",     \"ALKPH\",   \"Alkaline Phosphatase (U/L)\",                       2,   \"ALT\",     \"ALT\",     \"Alanine Aminotransferase (U/L)\",                   3,   \"ANISO\",   \"ANISO\",   \"Anisocytes\",                                       4,   \"AST\",     \"AST\",     \"Aspartate Aminotransferase (U/L)\",                 5,   \"BASO\",    \"BASO\",    \"Basophils (10^9/L)\",                               6,   \"BASOLE\",  \"BASOLE\",  \"Basophils/Leukocytes (FRACTION)\",                  7,   \"BILI\",    \"BILI\",    \"Bilirubin (umol/L)\",                               8,   \"BUN\",     \"BUN\",     \"Blood Urea Nitrogen (mmol/L)\",                     9,   \"CA\",      \"CA\",      \"Calcium (mmol/L)\",                                 10,   \"CHOL\",    \"CHOLES\",  \"Cholesterol (mmol/L)\",                             11,   \"CK\",      \"CK\",      \"Creatinine Kinase (U/L)\",                          12,   \"CL\",      \"CL\",      \"Chloride (mmol/L)\",                                13,   \"COLOR\",   \"COLOR\",   \"Color\",                                            14,   \"CREAT\",   \"CREAT\",   \"Creatinine (umol/L)\",                              15,   \"EOS\",     \"EOS\",     \"Eosinophils (10^9/L)\",                             16,   \"EOSLE\",   \"EOSLE\",   \"Eosinophils/Leukocytes (FRACTION)\",                17,   \"GGT\",     \"GGT\",     \"Gamma Glutamyl Transferase (U/L)\",                 18,   \"GLUC\",    \"GLUC\",    \"Glucose (mmol/L)\",                                 19,   \"HBA1C\",   \"HBA1C\",   \"Hemoglobin A1C (1)\",                               20,   \"HCT\",     \"HCT\",     \"Hematocrit (1)\",                                   21,   \"HGB\",     \"HGB\",     \"Hemoglobin (mmol/L)\",                              22,   \"K\",       \"POTAS\",   \"Potassium (mmol/L)\",                               23,   \"KETONES\", \"KETON\",   \"Ketones\",                                          24,   \"LYM\",     \"LYMPH\",   \"Lymphocytes (10^9/L)\",                             25,   \"LYMLE\",   \"LYMPHLE\", \"Lymphocytes/Leukocytes (FRACTION)\",                26,   \"MACROCY\", \"MACROC\",  \"Macrocytes\",                                       27,   \"MCH\",     \"MCH\",     \"Ery. Mean Corpuscular Hemoglobin (fmol(Fe))\",      28,   \"MCHC\",    \"MCHC\",    \"Ery. Mean Corpuscular HGB Concentration (mmol/L)\", 29,   \"MCV\",     \"MCV\",     \"Ery. Mean Corpuscular Volume (f/L)\",               30,   \"MICROCY\", \"MICROC\",  \"Microcytes\",                                       31,   \"MONO\",    \"MONO\",    \"Monocytes (10^9/L)\",                               32,   \"MONOLE\",  \"MONOLE\",  \"Monocytes/Leukocytes (FRACTION)\",                  33,   \"PH\",      \"PH\",      \"pH\",                                               34,   \"PHOS\",    \"PHOS\",    \"Phosphate (mmol/L)\",                               35,   \"PLAT\",    \"PLAT\",    \"Platelet (10^9/L)\",                                36,   \"POIKILO\", \"POIKIL\",  \"Poikilocytes\",                                     37,   \"POLYCHR\", \"POLYCH\",  \"Polychromasia\",                                    38,   \"PROT\",    \"PROT\",    \"Protein (g/L)\",                                    39,   \"RBC\",     \"RBC\",     \"Erythrocytes (TI/L)\",                              40,   \"SODIUM\",  \"SODIUM\",  \"Sodium (mmol/L)\",                                  41,   \"SPGRAV\",  \"SPGRAV\",  \"Specific Gravity\",                                 42,   \"TSH\",     \"TSH\",     \"Thyrotropin (mU/L)\",                               43,   \"URATE\",   \"URATE\",   \"Urate (umol/L)\",                                   44,   \"UROBIL\",  \"UROBIL\",  \"Urobilinogen\",                                     45,   \"VITB12\",  \"VITB12\",  \"Vitamin B12 (pmol/L)\",                             46,   \"WBC\",     \"WBC\",     \"Leukocytes (10^9/L)\",                              47 )  adlb <- lb %>%   ## Add PARAMCD PARAM and PARAMN - from LOOK-UP table   derive_vars_merged_lookup(     dataset_add = param_lookup,     new_vars = exprs(PARAMCD, PARAM, PARAMN),     by_vars = exprs(LBTESTCD)   ) %>%   ## Calculate PARCAT1 AVAL AVALC ANRLO ANRHI   ## Dummy the values for BASE   mutate(     PARCAT1 = LBCAT,     AVAL = LBSTRESN,     AVALC = ifelse(       is.na(LBSTRESN) | as.character(LBSTRESN) != LBSTRESC,       LBSTRESC,       NA     ),     ANRLO = LBSTNRLO,     ANRHI = LBSTNRHI,     BASE = AVAL - 10   ) #> All `LBTESTCD` are mapped. # Assign ATOXDSCL and ATOXDSCH to hold lab grading terms # ATOXDSCL and ATOXDSCH hold terms defined by NCI-CTCAEv4. grade_lookup <- tibble::tribble(   ~PARAMCD, ~ATOXDSCL,                    ~ATOXDSCH,   \"ALB\",    \"Hypoalbuminemia\",            NA_character_,   \"ALKPH\",  NA_character_,                \"Alkaline phosphatase increased\",   \"ALT\",    NA_character_,                \"Alanine aminotransferase increased\",   \"AST\",    NA_character_,                \"Aspartate aminotransferase increased\",   \"BILI\",   NA_character_,                \"Blood bilirubin increased\",   \"CA\",     \"Hypocalcemia\",               \"Hypercalcemia\",   \"CHOLES\", NA_character_,                \"Cholesterol high\",   \"CK\",     NA_character_,                \"CPK increased\",   \"CREAT\",  NA_character_,                \"Creatinine increased\",   \"GGT\",    NA_character_,                \"GGT increased\",   \"GLUC\",   \"Hypoglycemia\",               \"Hyperglycemia\",   \"HGB\",    \"Anemia\",                     \"Hemoglobin increased\",   \"POTAS\",  \"Hypokalemia\",                \"Hyperkalemia\",   \"LYMPH\",  \"CD4 lymphocytes decreased\",  NA_character_,   \"PHOS\",   \"Hypophosphatemia\",           NA_character_,   \"PLAT\",   \"Platelet count decreased\",   NA_character_,   \"SODIUM\", \"Hyponatremia\",               \"Hypernatremia\",   \"WBC\",    \"White blood cell decreased\", \"Leukocytosis\", )  adlb <- adlb %>%   derive_vars_merged(     dataset_add = grade_lookup,     by_vars = exprs(PARAMCD),   ) adlb <- adlb %>%   derive_var_atoxgr_dir(     new_var = ATOXGRL,     tox_description_var = ATOXDSCL,     meta_criteria = atoxgr_criteria_ctcv4,     criteria_direction = \"L\",     get_unit_expr = extract_unit(PARAM)   ) %>%   derive_var_atoxgr_dir(     new_var = ATOXGRH,     tox_description_var = ATOXDSCH,     meta_criteria = atoxgr_criteria_ctcv4,     criteria_direction = \"H\",     get_unit_expr = extract_unit(PARAM)   ) adlb <- adlb %>%   derive_var_atoxgr()"},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/articles/lab_grading.html","id":"terms-graded","dir":"Articles","previous_headings":"NCI-CTCAEV4 implementation","what":"Terms graded","title":"Lab Grading","text":"Grading implemented lab tests lab value included grading definition, admiral try read data determine grade, ADLB dataset used. following CTCAE v4.0 SOC values identified grading, “Investigations”, “Metabolism nutrition disorders” “Blood lymphatic system disorders”. SOC values following terms criteria implemented admiral SOC = “Investigations” 21 CTCAE v4.0 Terms: Activated partial thromboplastin time prolonged Alanine aminotransferase increased Alkaline phosphatase increased Aspartate aminotransferase increased Blood bilirubin increased CD4 lymphocytes decreased Cholesterol high CPK increased Creatinine increased Fibrinogen decreased GGT increased Haptoglobin decreased Hemoglobin increased INR increased Lipase increased Lymphocyte count decreased Lymphocyte count increased Neutrophil count decreased Platelet count decreased Serum amylase increased White blood cell decreased SOC = “Metabolism nutrition disorders” 14 CTCAE v4.0 Terms: Hypercalcemia Hyperglycemia Hyperkalemia Hypermagnesemia Hypernatremia Hypertriglyceridemia Hyperuricemia Hypoalbuminemia Hypocalcemia Hypoglycemia Hypokalemia Hypomagnesemia Hyponatremia Hypophosphatemia SOC = “Blood lymphatic system disorders” 2 CTCAE v4.0 Terms: Anemia Leukocytosis","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/lab_grading.html","id":"updates-made-to-term","dir":"Articles","previous_headings":"NCI-CTCAEV4 implementation","what":"Updates made to TERM","title":"Lab Grading","text":"terms “Hypocalcemia” “Hypercalcemia” criteria provided Calcium Ionized Calcium, therefore admiral created row metadata, noted COMMENT variable metadata:  Similarly, criteria applicable Fasting Glucose well non-Fasting Glucose “Hyperglycemia” split 2 rows, noted COMMENT variable. Note “Hypoglycemia” require split 2 rows:","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/lab_grading.html","id":"assumptions-made-when-grading","dir":"Articles","previous_headings":"NCI-CTCAEV4 implementation","what":"Assumptions made when grading","title":"Lab Grading","text":"term “INR Increased” following criteria:   admiral assumed worst case used parts criteria grading, comparing lab value ULN also BASE. decision made put COMMENT field.   TERM “Hyperuricemia”, criteria Grade 1 Grade 3 respect lab value, worse case assumed grade 3. decision made put COMMENT field.   similar approach taken TERM “Hypokalemia” Grade 1 Grade 2 criteria respect lab value, worse case assumed grade 2. decision made put COMMENT field.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/articles/lab_grading.html","id":"terms-graded-1","dir":"Articles","previous_headings":"NCI-CTCAEV5 implementation","what":"Terms graded","title":"Lab Grading","text":"Grading implemented lab tests lab value included grading definition, admiral try read data determine grade, ADLB dataset used. following CTCAE v5.0 SOC values identified grading, “Investigations”, “Metabolism nutrition disorders” “Blood lymphatic system disorders”. SOC values following terms criteria implemented admiral SOC = “Investigations” 21 CTCAE v5.0 Terms: Activated partial thromboplastin time prolonged Alanine aminotransferase increased Alkaline phosphatase increased Aspartate aminotransferase increased Blood bilirubin increased CD4 lymphocytes decreased Cholesterol high CPK increased Creatinine increased Fibrinogen decreased GGT increased Haptoglobin decreased Hemoglobin increased INR increased Lipase increased Lymphocyte count decreased Lymphocyte count increased Neutrophil count decreased Platelet count decreased Serum amylase increased White blood cell decreased Note: terms identified NCI-CTCAEv4. SOC = “Metabolism nutrition disorders” 12 CTCAE v4.0 Terms: Hypercalcemia Hyperkalemia Hypermagnesemia Hypernatremia Hypertriglyceridemia Hyperuricemia Hypoalbuminemia Hypocalcemia Hypoglycemia Hypokalemia Hypomagnesemia Hyponatremia Note: terms identified NCI-CTCAEv4, except “Hypophosphatemia” “Hyperglycemia” NCICTCAEv5 grading criteria. SOC = “Blood lymphatic system disorders” 2 CTCAE v4.0 Terms: Anemia Leukocytosis Note: terms identified NCI-CTCAEv4.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/lab_grading.html","id":"updates-made-to-term-1","dir":"Articles","previous_headings":"NCI-CTCAEV5 implementation","what":"Updates made to TERM","title":"Lab Grading","text":"terms “Hypocalcemia” “Hypercalcemia” criteria provided Calcium Ionized Calcium, therefore admiral created row metadata, noted COMMENT variable metadata:","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/lab_grading.html","id":"assumptions-made-when-grading-1","dir":"Articles","previous_headings":"NCI-CTCAEV5 implementation","what":"Assumptions made when grading","title":"Lab Grading","text":"terms “Alanine aminotransferase increased”, “Alkaline phosphatase increased”, “Aspartate aminotransferase increased”, “Blood bilirubin increased” “GGT increased” criteria dependent Baseline Value normal abnormal. use variable BNRIND determine , users can pass value(s) BNRIND indicate baseline abnormal via argument abnormal_indicator function derive_var_atoxgr_dir(). example, setting abnormal_indicator = \"HIGH\" mean observations BNRIND = \"HIGH\" deemed abnormal Baseline Value. Note: can pass multiple values, abnormal_indicator = c(\"H\", \"HIGH\") 5 terms, means BNRIND abnormal grade always zero baseline observation. term “INR Increased” following criteria:   admiral assumed worst case used parts criteria grading, comparing lab value ULN also BASE. decision made put COMMENT field.  Similarly, terms “Lipase Increased” “Serum amylase increased” following criteria:   admiral assumed worst case implemented highest grade possible. decision made put COMMENT field.   TERM “Hyperuricemia”, criteria Grade 1 Grade 3 respect lab value, worse case assumed grade 3. decision made put COMMENT field.   similar approach taken TERM “Hypokalemia” “Hyponatremia”. “Hypokalemia”, Grade 1 Grade 2 criteria respect lab value, worse case assumed grade 2. “Hyponatremia”, Grade 2 Grade 2 criteria respect lab value, worse case assumed grade 3. decision made put COMMENT field.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/articles/lab_grading.html","id":"terms-graded-2","dir":"Articles","previous_headings":"DAIDS implementation","what":"Terms graded","title":"Lab Grading","text":"Grading implemented lab tests lab value included grading definition, admiral try read data determine grade, ADLB dataset used. following DAIDS SOC values identified grading, “Chemistries” “Hematology”. SOC values following terms criteria implemented admiral SOC = “Chemistries” 31 DAIDS Terms: Acidosis Albumin, Low Alkaline Phosphatase, High Alkalosis ALT, High Amylase, High AST, High Bicarbonate, Low Direct Bilirubin, High Total Bilirubin, High Calcium, High Calcium (Ionized), High Calcium, Low Calcium (Ionized), Low Creatine Kinase, High Creatinine, High Glucose Fasting, High Glucose Nonfasting, High Glucose, Low Lactate, High Lipase, High Cholesterol, Fasting, High LDL, Fasting, High Triglycerides, Fasting, High Magnesium, Low Phosphate, Low Potassium, High Potassium, Low Sodium, High Sodium, Low Uric Acid, High Note: {admiral} grade TERM = “Total Bilirubin, High” AGE <= 28 days, criteria Appendix DAIDS Table Grading Severity Adult Pediatric Adverse Events Corrected Version 2.1. SOC = “Hematology” 11 DAIDS Terms: Absolute CD4+ Count, Low Absolute Lymphocyte Count, Low Absolute Neutrophil Count (ANC), Low Fibrinogen Decreased Hemoglobin, Low INR, High Methemoglobin PTT, High Platelets, Decreased PT, High WBC, Decreased","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/lab_grading.html","id":"terms-with-age-or-sex-dependent-grading-criteria","dir":"Articles","previous_headings":"DAIDS implementation","what":"Terms with age or sex dependent grading criteria","title":"Lab Grading","text":"terms defined DAIDS age sex dependent grading criteria, {admiral} handles variable FILTER metadata. use {admiral} function compute_duration calculate age, see TERM = “Cholesterol, Fasting, High”:  Note: possible values must covered TERM defined, TERM = “Absolute Lymphocyte Count, Low” “Absolute CD4+ Count, Low” grading criteria defined age > 5 years. Therefore, add another row age <= 5 years set grade missing. Similarly, TERM = “LDL, Fasting, High” grading criteria defined age > 2 years. Therefore, add another row age <= 2 years set grade missing.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/lab_grading.html","id":"assumptions-made-when-grading-2","dir":"Articles","previous_headings":"DAIDS implementation","what":"Assumptions made when grading","title":"Lab Grading","text":"terms “INR, High”, “PT, High” “PTT, High”, criteria based subjects “anticoagulation therapy”, captured COMMENT field.   Similarly, terms “Absolute CD4+ Count, Low” “Absolute Lymphocyte Count, Low”, criteria based subjects “HIV infected”, captured COMMENT field.   term “Acidosis”, “Alkalosis” “Direct Bilirubin, High (> 28 days age)”, {admiral} grades high possible, assumes worst case subject “life-threatening consequences”. captured COMMENT field.  Similarly, term “Lactate, High”, {admiral} grade 1 2, following criteria:   admiral assumed worst case assume “without acidosis”. decision made put COMMENT field.   TERM “Direct Bilirubin, High (<= 28 days age)” “Uric Acid, High” criteria given SI unit. conversion SI unit comment field.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/lab_grading.html","id":"conclusion","dir":"Articles","previous_headings":"","what":"Conclusion","title":"Lab Grading","text":"NCI-CTCAEv4, NCI-CTCAEv5 DAIDS grading now implemented, {admiral} may look implement industry standard grading criteria. Providing tools users easily interact metadata update criteria, based companies needs also looked . Ideally, users able create metadata company specific grading schemes.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/occds.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Creating an OCCDS ADaM","text":"article describes creating OCCDS ADaM. Examples currently presented tested context ADAE. However, examples applied OCCDS ADaMs ADCM, ADMH, ADDV, etc. Note: examples assume CDISC SDTM /ADaM format input unless otherwise specified.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/occds.html","id":"programming-workflow","dir":"Articles","previous_headings":"","what":"Programming Workflow","title":"Creating an OCCDS ADaM","text":"Read Data Derive/Impute End Start Analysis Date/time Relative Day Derive Durations Derive ATC variables Derive Planned Actual Treatment Derive Date/Date-time Last Dose Derive Severity, Causality, Toxicity Grade Derive Treatment Emergent Flag Derive Occurrence Flags Derive Query Variables Add ADSL variables Derive Analysis Sequence Number Add Labels Attributes","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/occds.html","id":"readdata","dir":"Articles","previous_headings":"Programming Workflow","what":"Read in Data","title":"Creating an OCCDS ADaM","text":"start, data frames needed creation ADAE read environment. company specific process. data frames needed may AE ADSL example purpose, CDISC Pilot SDTM ADaM datasets —included pharmaversesdtm— used. step, may useful join ADSL AE domain well. ADSL variables used derivations selected step. rest relevant ADSL variables added later.","code":"library(admiral) library(dplyr, warn.conflicts = FALSE) library(pharmaversesdtm) library(lubridate)  ae <- pharmaversesdtm::ae adsl <- admiral::admiral_adsl ex_single <- admiral::ex_single  ae <- convert_blanks_to_na(ae) adsl_vars <- exprs(TRTSDT, TRTEDT, TRT01A, TRT01P, DTHDT, EOSDT)  adae <- derive_vars_merged(   ae,   dataset_add = adsl,   new_vars = adsl_vars,   by = exprs(STUDYID, USUBJID) )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/occds.html","id":"datetime","dir":"Articles","previous_headings":"Programming Workflow","what":"Derive/Impute End and Start Analysis Date/time and Relative Day","title":"Creating an OCCDS ADaM","text":"part derives ASTDTM, ASTDT, ASTDY, AENDTM, AENDT, AENDY. function derive_vars_dtm() can used derive ASTDTM AENDTM ASTDTM company-specific. ASTDT AENDT can derived ASTDTM AENDTM, respectively, using function derive_vars_dtm_to_dt(). derive_vars_dy() can used create ASTDY AENDY. See also Date Time Imputation.","code":"adae <- adae %>%   derive_vars_dtm(     dtc = AESTDTC,     new_vars_prefix = \"AST\",     highest_imputation = \"M\",     min_dates = exprs(TRTSDT)   ) %>%   derive_vars_dtm(     dtc = AEENDTC,     new_vars_prefix = \"AEN\",     highest_imputation = \"M\",     date_imputation = \"last\",     time_imputation = \"last\",     max_dates = exprs(DTHDT, EOSDT)   ) %>%   derive_vars_dtm_to_dt(exprs(ASTDTM, AENDTM)) %>%   derive_vars_dy(     reference_date = TRTSDT,     source_vars = exprs(ASTDT, AENDT)   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/occds.html","id":"duration","dir":"Articles","previous_headings":"Programming Workflow","what":"Derive Durations","title":"Creating an OCCDS ADaM","text":"function derive_vars_duration() can used create variables ADURN ADURU.","code":"adae <- adae %>%   derive_vars_duration(     new_var = ADURN,     new_var_unit = ADURU,     start_date = ASTDT,     end_date = AENDT   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/occds.html","id":"atc","dir":"Articles","previous_headings":"Programming Workflow","what":"Derive ATC variables","title":"Creating an OCCDS ADaM","text":"function derive_vars_atc() can used derive ATC Class Variables. helps add Anatomical Therapeutic Chemical class variables FACM ADCM. expected result input dataset ATC variables added.","code":"cm <- tibble::tribble(   ~STUDYID,  ~USUBJID,       ~CMGRPID, ~CMREFID,  ~CMDECOD,   \"STUDY01\", \"BP40257-1001\", \"14\",     \"1192056\", \"PARACETAMOL\",   \"STUDY01\", \"BP40257-1001\", \"18\",     \"2007001\", \"SOLUMEDROL\",   \"STUDY01\", \"BP40257-1002\", \"19\",     \"2791596\", \"SPIRONOLACTONE\" ) facm <- tibble::tribble(   ~STUDYID,  ~USUBJID,       ~FAGRPID, ~FAREFID,  ~FATESTCD,  ~FASTRESC,   \"STUDY01\", \"BP40257-1001\", \"1\",      \"1192056\", \"CMATC1CD\", \"N\",   \"STUDY01\", \"BP40257-1001\", \"1\",      \"1192056\", \"CMATC2CD\", \"N02\",   \"STUDY01\", \"BP40257-1001\", \"1\",      \"1192056\", \"CMATC3CD\", \"N02B\",   \"STUDY01\", \"BP40257-1001\", \"1\",      \"1192056\", \"CMATC4CD\", \"N02BE\",   \"STUDY01\", \"BP40257-1001\", \"1\",      \"2007001\", \"CMATC1CD\", \"D\",   \"STUDY01\", \"BP40257-1001\", \"1\",      \"2007001\", \"CMATC2CD\", \"D10\",   \"STUDY01\", \"BP40257-1001\", \"1\",      \"2007001\", \"CMATC3CD\", \"D10A\",   \"STUDY01\", \"BP40257-1001\", \"1\",      \"2007001\", \"CMATC4CD\", \"D10AA\",   \"STUDY01\", \"BP40257-1001\", \"2\",      \"2007001\", \"CMATC1CD\", \"D\",   \"STUDY01\", \"BP40257-1001\", \"2\",      \"2007001\", \"CMATC2CD\", \"D07\",   \"STUDY01\", \"BP40257-1001\", \"2\",      \"2007001\", \"CMATC3CD\", \"D07A\",   \"STUDY01\", \"BP40257-1001\", \"2\",      \"2007001\", \"CMATC4CD\", \"D07AA\",   \"STUDY01\", \"BP40257-1001\", \"3\",      \"2007001\", \"CMATC1CD\", \"H\",   \"STUDY01\", \"BP40257-1001\", \"3\",      \"2007001\", \"CMATC2CD\", \"H02\",   \"STUDY01\", \"BP40257-1001\", \"3\",      \"2007001\", \"CMATC3CD\", \"H02A\",   \"STUDY01\", \"BP40257-1001\", \"3\",      \"2007001\", \"CMATC4CD\", \"H02AB\",   \"STUDY01\", \"BP40257-1002\", \"1\",      \"2791596\", \"CMATC1CD\", \"C\",   \"STUDY01\", \"BP40257-1002\", \"1\",      \"2791596\", \"CMATC2CD\", \"C03\",   \"STUDY01\", \"BP40257-1002\", \"1\",      \"2791596\", \"CMATC3CD\", \"C03D\",   \"STUDY01\", \"BP40257-1002\", \"1\",      \"2791596\", \"CMATC4CD\", \"C03DA\" )  derive_vars_atc(cm, dataset_facm = facm, id_vars = exprs(FAGRPID)) #> # A tibble: 5 × 9 #>   STUDYID USUBJID      CMGRPID CMREFID CMDECOD       ATC1CD ATC2CD ATC3CD ATC4CD #>   <chr>   <chr>        <chr>   <chr>   <chr>         <chr>  <chr>  <chr>  <chr>  #> 1 STUDY01 BP40257-1001 14      1192056 PARACETAMOL   N      N02    N02B   N02BE  #> 2 STUDY01 BP40257-1001 18      2007001 SOLUMEDROL    D      D10    D10A   D10AA  #> 3 STUDY01 BP40257-1001 18      2007001 SOLUMEDROL    D      D07    D07A   D07AA  #> 4 STUDY01 BP40257-1001 18      2007001 SOLUMEDROL    H      H02    H02A   H02AB  #> 5 STUDY01 BP40257-1002 19      2791596 SPIRONOLACTO… C      C03    C03D   C03DA"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/occds.html","id":"trtpa","dir":"Articles","previous_headings":"Programming Workflow","what":"Derive Planned and Actual Treatment","title":"Creating an OCCDS ADaM","text":"TRTA TRTP must match least one value character treatment variables ADSL (e.g., TRTxxA/TRTxxP, TRTSEQA/TRTSEQP, TRxxAGy/TRxxPGy). example simple implementation study without periods : studies periods see “Visit Period Variables” vignette.","code":"adae <- mutate(adae, TRTP = TRT01P, TRTA = TRT01A)  count(adae, TRTP, TRTA, TRT01P, TRT01A) #> # A tibble: 2 × 5 #>   TRTP                TRTA                TRT01P              TRT01A           n #>   <chr>               <chr>               <chr>               <chr>        <int> #> 1 Placebo             Placebo             Placebo             Placebo         10 #> 2 Xanomeline Low Dose Xanomeline Low Dose Xanomeline Low Dose Xanomeline …     6"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/occds.html","id":"last_dose","dir":"Articles","previous_headings":"Programming Workflow","what":"Derive Date/Date-time of Last Dose","title":"Creating an OCCDS ADaM","text":"function derive_vars_joined() can used derive last dose date start event.","code":"ex_single <- derive_vars_dtm(   ex_single,   dtc = EXSTDTC,   new_vars_prefix = \"EXST\",   flag_imputation = \"none\" )  adae <- derive_vars_joined(   adae,   ex_single,   by_vars = exprs(STUDYID, USUBJID),   new_vars = exprs(LDOSEDTM = EXSTDTM),   join_vars = exprs(EXSTDTM),   join_type = \"all\",   order = exprs(EXSTDTM),   filter_add = (EXDOSE > 0 | (EXDOSE == 0 & grepl(\"PLACEBO\", EXTRT))) & !is.na(EXSTDTM),   filter_join = EXSTDTM <= ASTDTM,   mode = \"last\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/occds.html","id":"severity","dir":"Articles","previous_headings":"Programming Workflow","what":"Derive Severity, Causality, and Toxicity Grade","title":"Creating an OCCDS ADaM","text":"variables ASEV, AREL, ATOXGR can added using simple dplyr::mutate() assignments, imputation required.","code":"adae <- adae %>%   mutate(     ASEV = AESEV,     AREL = AEREL   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/occds.html","id":"trtflag","dir":"Articles","previous_headings":"Programming Workflow","what":"Derive Treatment Emergent Flag","title":"Creating an OCCDS ADaM","text":"derive treatment emergent flag TRTEMFL, one can call derive_var_trtemfl(). example , use 30 days flag derivation. derive -treatment flag (ONTRTFL) ADaM dataset single occurrence date, use derive_var_ontrtfl(). expected result input dataset additional column named ONTRTFL value \"Y\" NA. want also check end date, add end_date argument. Note scenario set span_period = TRUE want occurrences started prior drug intake, ongoing ended time considered -treatment.","code":"adae <- adae %>%   derive_var_trtemfl(     trt_start_date = TRTSDT,     trt_end_date = TRTEDT,     end_window = 30   ) bds1 <- tibble::tribble(   ~USUBJID, ~ADT,              ~TRTSDT,           ~TRTEDT,   \"P01\",    ymd(\"2020-02-24\"), ymd(\"2020-01-01\"), ymd(\"2020-03-01\"),   \"P02\",    ymd(\"2020-01-01\"), ymd(\"2020-01-01\"), ymd(\"2020-03-01\"),   \"P03\",    ymd(\"2019-12-31\"), ymd(\"2020-01-01\"), ymd(\"2020-03-01\") ) derive_var_ontrtfl(   bds1,   start_date = ADT,   ref_start_date = TRTSDT,   ref_end_date = TRTEDT ) #> # A tibble: 3 × 5 #>   USUBJID ADT        TRTSDT     TRTEDT     ONTRTFL #>   <chr>   <date>     <date>     <date>     <chr>   #> 1 P01     2020-02-24 2020-01-01 2020-03-01 Y       #> 2 P02     2020-01-01 2020-01-01 2020-03-01 Y       #> 3 P03     2019-12-31 2020-01-01 2020-03-01 NA  bds2 <- tibble::tribble(   ~USUBJID, ~ADT,              ~TRTSDT,           ~TRTEDT,   \"P01\",    ymd(\"2020-07-01\"), ymd(\"2020-01-01\"), ymd(\"2020-03-01\"),   \"P02\",    ymd(\"2020-04-30\"), ymd(\"2020-01-01\"), ymd(\"2020-03-01\"),   \"P03\",    ymd(\"2020-03-15\"), ymd(\"2020-01-01\"), ymd(\"2020-03-01\") ) derive_var_ontrtfl(   bds2,   start_date = ADT,   ref_start_date = TRTSDT,   ref_end_date = TRTEDT,   ref_end_window = 60 ) #> # A tibble: 3 × 5 #>   USUBJID ADT        TRTSDT     TRTEDT     ONTRTFL #>   <chr>   <date>     <date>     <date>     <chr>   #> 1 P01     2020-07-01 2020-01-01 2020-03-01 NA      #> 2 P02     2020-04-30 2020-01-01 2020-03-01 Y       #> 3 P03     2020-03-15 2020-01-01 2020-03-01 Y  bds3 <- tibble::tribble(   ~ADTM,              ~TRTSDTM,           ~TRTEDTM,           ~TPT,   \"2020-01-02T12:00\", \"2020-01-01T12:00\", \"2020-03-01T12:00\", NA,   \"2020-01-01T12:00\", \"2020-01-01T12:00\", \"2020-03-01T12:00\", \"PRE\",   \"2019-12-31T12:00\", \"2020-01-01T12:00\", \"2020-03-01T12:00\", NA ) %>%   mutate(     ADTM = ymd_hm(ADTM),     TRTSDTM = ymd_hm(TRTSDTM),     TRTEDTM = ymd_hm(TRTEDTM)   ) derive_var_ontrtfl(   bds3,   start_date = ADTM,   ref_start_date = TRTSDTM,   ref_end_date = TRTEDTM,   filter_pre_timepoint = TPT == \"PRE\" ) #> # A tibble: 3 × 5 #>   ADTM                TRTSDTM             TRTEDTM             TPT   ONTRTFL #>   <dttm>              <dttm>              <dttm>              <chr> <chr>   #> 1 2020-01-02 12:00:00 2020-01-01 12:00:00 2020-03-01 12:00:00 NA    Y       #> 2 2020-01-01 12:00:00 2020-01-01 12:00:00 2020-03-01 12:00:00 PRE   NA      #> 3 2019-12-31 12:00:00 2020-01-01 12:00:00 2020-03-01 12:00:00 NA    NA"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/occds.html","id":"occflag","dir":"Articles","previous_headings":"Programming Workflow","what":"Derive Occurrence Flags","title":"Creating an OCCDS ADaM","text":"function derive_var_extreme_flag() can help derive variables AOCCIFL, AOCCPIFL, AOCCSIFL, AOCCzzFL. grades collected, following can used flag first occurrence maximum toxicity grade. Similarly, ASEV can also used derive occurrence flags, severity collected. case, variable need recoded numeric variable. Flag first occurrence severe adverse event:","code":"adae <- adae %>%   restrict_derivation(     derivation = derive_var_extreme_flag,     args = params(       by_vars = exprs(USUBJID),       order = exprs(desc(ATOXGR), ASTDTM, AESEQ),       new_var = AOCCIFL,       mode = \"first\"     ),     filter = TRTEMFL == \"Y\"   ) adae <- adae %>%   restrict_derivation(     derivation = derive_var_extreme_flag,     args = params(       by_vars = exprs(USUBJID),       order = exprs(         as.integer(factor(           ASEV,           levels = c(\"DEATH THREATENING\", \"SEVERE\", \"MODERATE\", \"MILD\")         )),         ASTDTM, AESEQ       ),       new_var = AOCCIFL,       mode = \"first\"     ),     filter = TRTEMFL == \"Y\"   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/occds.html","id":"query","dir":"Articles","previous_headings":"Programming Workflow","what":"Derive Query Variables","title":"Creating an OCCDS ADaM","text":"deriving query variables SMQzzNAM, SMQzzCD, SMQzzSC, SMQzzSCN, CQzzNAM derive_vars_query() function can used. input expects queries dataset, provides definition queries. See Queries dataset documentation detailed description queries dataset. create_query_data() function can used create queries datasets. following example shows derive query variables Standardized MedDRA Queries (SMQs) ADAE. Similarly SMQ, derive_vars_query() function can used derive Standardized Drug Groupings (SDG).","code":"queries <- admiral::queries adae1 <- tibble::tribble(   ~USUBJID, ~ASTDTM, ~AETERM, ~AESEQ, ~AEDECOD, ~AELLT, ~AELLTCD,   \"01\", \"2020-06-02 23:59:59\", \"ALANINE AMINOTRANSFERASE ABNORMAL\",   3, \"Alanine aminotransferase abnormal\", NA_character_, NA_integer_,   \"02\", \"2020-06-05 23:59:59\", \"BASEDOW'S DISEASE\",   5, \"Basedow's disease\", NA_character_, 1L,   \"03\", \"2020-06-07 23:59:59\", \"SOME TERM\",   2, \"Some query\", \"Some term\", NA_integer_,   \"05\", \"2020-06-09 23:59:59\", \"ALVEOLAR PROTEINOSIS\",   7, \"Alveolar proteinosis\", NA_character_, NA_integer_ )  adae_query <- derive_vars_query(dataset = adae1, dataset_queries = queries) sdg <- tibble::tribble(   ~PREFIX, ~GRPNAME,          ~GRPID, ~SCOPE,  ~SCOPEN, ~SRCVAR,   ~TERMCHAR,          ~TERMNUM,   \"SDG01\", \"Diuretics\",           11, \"BROAD\", 1,       \"CMDECOD\", \"Diuretic 1\",       NA,   \"SDG01\", \"Diuretics\",           11, \"BROAD\", 1,       \"CMDECOD\", \"Diuretic 2\",       NA,   \"SDG02\", \"Costicosteroids\",     12, \"BROAD\", 1,       \"CMDECOD\", \"Costicosteroid 1\", NA,   \"SDG02\", \"Costicosteroids\",     12, \"BROAD\", 1,       \"CMDECOD\", \"Costicosteroid 2\", NA,   \"SDG02\", \"Costicosteroids\",     12, \"BROAD\", 1,       \"CMDECOD\", \"Costicosteroid 3\", NA, ) adcm <- tibble::tribble(   ~USUBJID, ~ASTDTM,               ~CMDECOD,   \"01\",     \"2020-06-02 23:59:59\", \"Diuretic 1\",   \"02\",     \"2020-06-05 23:59:59\", \"Diuretic 1\",   \"03\",     \"2020-06-07 23:59:59\", \"Costicosteroid 2\",   \"05\",     \"2020-06-09 23:59:59\", \"Diuretic 2\" ) adcm_query <- derive_vars_query(adcm, sdg)"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/occds.html","id":"adsl_vars","dir":"Articles","previous_headings":"Programming Workflow","what":"Add the ADSL variables","title":"Creating an OCCDS ADaM","text":"needed, ADSL variables can now added:","code":"adae <- adae %>%   derive_vars_merged(     dataset_add = select(adsl, !!!negate_vars(adsl_vars)),     by_vars = exprs(STUDYID, USUBJID)   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/occds.html","id":"aseq","dir":"Articles","previous_headings":"Programming Workflow","what":"Derive Analysis Sequence Number","title":"Creating an OCCDS ADaM","text":"function derive_var_obs_number() can used deriving ASEQ variable ensure uniqueness subject records within dataset. example, can multiple records present ADCM single subject ASTDTM CMSEQ variables. records still differ ATC level:","code":"adcm <- tibble::tribble(   ~USUBJID,       ~ASTDTM,          ~CMSEQ, ~CMDECOD,         ~ATC1CD, ~ATC2CD, ~ATC3CD, ~ATC4CD,   \"BP40257-1001\", \"2013-07-05 UTC\", \"14\",   \"PARACETAMOL\",    \"N\",     \"N02\",   \"N02B\",  \"N02BE\",   \"BP40257-1001\", \"2013-08-15 UTC\", \"18\",   \"SOLUMEDROL\",     \"D\",     \"D10\",   \"D10A\",  \"D10AA\",   \"BP40257-1001\", \"2013-08-15 UTC\", \"18\",   \"SOLUMEDROL\",     \"D\",     \"D07\",   \"D07A\",  \"D07AA\",   \"BP40257-1001\", \"2013-08-15 UTC\", \"18\",   \"SOLUMEDROL\",     \"H\",     \"H02\",   \"H02A\",  \"H02AB\",   \"BP40257-1002\", \"2012-12-15 UTC\", \"19\",   \"SPIRONOLACTONE\", \"C\",     \"C03\",   \"C03D\",  \"C03DA\" )  adcm_aseq <- adcm %>%   derive_var_obs_number(     by_vars    = exprs(USUBJID),     order      = exprs(ASTDTM, CMSEQ, ATC1CD, ATC2CD, ATC3CD, ATC4CD),     new_var    = ASEQ,     check_type = \"error\"   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/occds.html","id":"attributes","dir":"Articles","previous_headings":"Programming Workflow","what":"Add Labels and Attributes","title":"Creating an OCCDS ADaM","text":"Adding labels attributes SAS transport files supported following packages: metacore: establish common foundation use metadata within R session. metatools: enable use metacore objects. Metatools can used build datasets enhance columns existing datasets well checking datasets metadata. xportr: functionality associate metadata information local R data frame, perform data set level validation checks convert transport v5 file(xpt). NOTE: packages experimental phase, vision associated End End pipeline umbrella pharmaverse. example applying metadata perform associated checks can found pharmaverse E2E example.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/articles/pk_adnca.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Creating a PK NCA or Population PK ADaM","text":"article describes creating Pharmacokinetics (PK) Non-compartmental analysis (NCA) ADaM (ADNCA/ADPC) Population PK ADaM (ADPPK). first part article describes NCA file creation second part describes Population PK. initial steps files similar combined one script desired.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/pk_adnca.html","id":"programming-pk-nca-adpcadnca-analysis-data","dir":"Articles","previous_headings":"","what":"Programming PK NCA (ADPC/ADNCA) Analysis Data","title":"Creating a PK NCA or Population PK ADaM","text":"Non-compartmental analysis (NCA) ADaM uses CDISC Implementation Guide (https://www.cdisc.org/standards/foundational/adam/adamig-non-compartmental-analysis-input-data-v1-0). example presented uses underlying EX PC domains EX PC domains represent data collected ADPC ADaM output. However, example can applied situations EC domain used input instead EX /ADNCA another ADaM created. One important aspects dataset derivation relative timing variables. variables consist nominal actual times, refer time first dose time recent reference dose. reference dose pre-dose records may upcoming dose. CDISC Implementation Guide makes use duplicated records analysis, allows record used respect previous dose next upcoming dose. illustrated later vignette. relative time variables use. correspond names CDISC Implementation Guide. Note: examples assume CDISC SDTM /ADaM format input unless otherwise specified.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/pk_adnca.html","id":"adpc-programming-workflow","dir":"Articles","previous_headings":"","what":"ADPC Programming Workflow","title":"Creating a PK NCA or Population PK ADaM","text":"Read Data Expand Dosing Records Find First Dose Find Reference Dose Dates Corresponding PK Records Combine PC EX Records Derive Relative Time Variables Derive Analysis Variables Create Duplicated Records Analysis Combine ADPC data Duplicated Records Calculate Change Baseline Assign ASEQ Add Additional Baseline Variables Add ADSL variables Add Labels Attributes","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/pk_adnca.html","id":"readdata","dir":"Articles","previous_headings":"ADPC Programming Workflow","what":"Read in Data","title":"Creating a PK NCA or Population PK ADaM","text":"start, data frames needed creation ADPC read environment. company specific process. data frames needed may PC, EX, ADSL. Additional domains VS LB may used additional baseline variables needed. may come either SDTM ADaM source. purpose example, CDISC Pilot SDTM ADaM datasets—included pharmaversesdtm—used. step, may useful join ADSL PC EX domains well. ADSL variables used derivations selected step. rest relevant ADSL variables added later. case keep TRTSDT/TRTSDTM day derivation TRT01P/TRT01A planned actual treatments. segment use derive_vars_merged() join ADSL variables following admiral functions derive analysis dates, times days: derive_vars_dtm(), derive_vars_dtm_to_dt(), derive_vars_dtm_to_tm(), derive_vars_dy(). also create NFRLT PC data based PCTPTNUM. create event ID (EVID) 0 concentration records 1 dosing records. traditional variable provide handy tool identify records dropped final dataset example. Next also join ADSL data EX derive dates/times. section uses admiral functions derive_vars_merged(), derive_vars_dtm(), derive_vars_dtm_to_dt(). Time imputed 00:00:00 reasons specific sample data. imputation times may used based study details. create NFRLT EX data based VISITDY using dplyr::mutate().","code":"library(dplyr, warn.conflicts = FALSE) library(admiral) library(pharmaversesdtm) library(lubridate) library(stringr) library(tibble)  ex <- pharmaversesdtm::ex pc <- pharmaversesdtm::pc vs <- pharmaversesdtm::vs lb <- pharmaversesdtm::lb adsl <- admiral::admiral_adsl  ex <- convert_blanks_to_na(ex) pc <- convert_blanks_to_na(pc) vs <- convert_blanks_to_na(vs) lb <- convert_blanks_to_na(lb) %>%   filter(LBBLFL == \"Y\")  # ---- Lookup tables ---- param_lookup <- tibble::tribble(   ~PCTESTCD, ~PARAMCD, ~PARAM, ~PARAMN,   \"XAN\", \"XAN\", \"Pharmacokinetic concentration of Xanomeline\", 1,   \"DOSE\", \"DOSE\", \"Xanomeline Patch Dose\", 2, ) adsl_vars <- exprs(TRTSDT, TRTSDTM, TRT01P, TRT01A)  pc_dates <- pc %>%   # Join ADSL with PC (need TRTSDT for ADY derivation)   derive_vars_merged(     dataset_add = adsl,     new_vars = adsl_vars,     by_vars = exprs(STUDYID, USUBJID)   ) %>%   # Derive analysis date/time   # Impute missing time to 00:00:00   derive_vars_dtm(     new_vars_prefix = \"A\",     dtc = PCDTC,     time_imputation = \"00:00:00\"   ) %>%   # Derive dates and times from date/times   derive_vars_dtm_to_dt(exprs(ADTM)) %>%   derive_vars_dtm_to_tm(exprs(ADTM)) %>%   derive_vars_dy(reference_date = TRTSDT, source_vars = exprs(ADT)) %>%   # Derive event ID and nominal relative time from first dose (NFRLT)   mutate(     EVID = 0,     DRUG = PCTEST,     NFRLT = if_else(PCTPTNUM < 0, 0, PCTPTNUM), .after = USUBJID   ) # ---- Get dosing information ----  ex_dates <- ex %>%   derive_vars_merged(     dataset_add = adsl,     new_vars = adsl_vars,     by_vars = exprs(STUDYID, USUBJID)   ) %>%   # Keep records with nonzero dose   filter(EXDOSE > 0) %>%   # Add time and set missing end date to start date   # Impute missing time to 00:00:00   # Note all times are missing for dosing records in this example data   # Derive Analysis Start and End Dates   derive_vars_dtm(     new_vars_prefix = \"AST\",     dtc = EXSTDTC,     time_imputation = \"00:00:00\"   ) %>%   derive_vars_dtm(     new_vars_prefix = \"AEN\",     dtc = EXENDTC,     time_imputation = \"00:00:00\"   ) %>%   # Derive event ID and nominal relative time from first dose (NFRLT)   mutate(     EVID = 1,     NFRLT = case_when(       VISITDY == 1 ~ 0,       TRUE ~ 24 * VISITDY     )   ) %>%   # Set missing end dates to start date   mutate(AENDTM = case_when(     is.na(AENDTM) ~ ASTDTM,     TRUE ~ AENDTM   )) %>%   # Derive dates from date/times   derive_vars_dtm_to_dt(exprs(ASTDTM)) %>%   derive_vars_dtm_to_dt(exprs(AENDTM))"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/pk_adnca.html","id":"expand","dir":"Articles","previous_headings":"ADPC Programming Workflow","what":"Expand Dosing Records","title":"Creating a PK NCA or Population PK ADaM","text":"function create_single_dose_dataset() can used expand dosing records start date end date. nominal time also expanded based values EXDOSFRQ, example “QD” result nominal time incremented 24 hours “BID” result nominal time incremented 12 hours. new feature create_single_dose_dataset(). Dates times derived expansion using derive_vars_dtm_to_dt() derive_vars_dtm_to_tm(). example study define analysis visit (AVISIT) based nominal day value NFRLT give format, “Day 1”, “Day 2”, “Day 3”, etc. important creating BASETYPE variable later. DRUG created EXTRT . useful linking treatment data concentration data multiple drugs /analytes, variable also dropped final dataset example.","code":"# ---- Expand dosing records between start and end dates ----  ex_exp <- ex_dates %>%   create_single_dose_dataset(     dose_freq = EXDOSFRQ,     start_date = ASTDT,     start_datetime = ASTDTM,     end_date = AENDT,     end_datetime = AENDTM,     nominal_time = NFRLT,     lookup_table = dose_freq_lookup,     lookup_column = CDISC_VALUE,     keep_source_vars = exprs(       STUDYID, USUBJID, EVID, EXDOSFRQ, EXDOSFRM,       NFRLT, EXDOSE, EXDOSU, EXTRT, ASTDT, ASTDTM, AENDT, AENDTM,       VISIT, VISITNUM, VISITDY, TRT01A, TRT01P, DOMAIN, EXSEQ, !!!adsl_vars     )   ) %>%   # Derive AVISIT based on nominal relative time   # Derive AVISITN to nominal time in whole days using integer division   # Define AVISIT based on nominal day   mutate(     AVISITN = NFRLT %/% 24 + 1,     AVISIT = paste(\"Day\", AVISITN),     ADTM = ASTDTM,     DRUG = EXTRT,   ) %>%   # Derive dates and times from datetimes   derive_vars_dtm_to_dt(exprs(ADTM)) %>%   derive_vars_dtm_to_tm(exprs(ADTM)) %>%   derive_vars_dtm_to_tm(exprs(ASTDTM)) %>%   derive_vars_dtm_to_tm(exprs(AENDTM)) %>%   derive_vars_dy(reference_date = TRTSDT, source_vars = exprs(ADT))"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/pk_adnca.html","id":"firstdose","dir":"Articles","previous_headings":"ADPC Programming Workflow","what":"Find First Dose","title":"Creating a PK NCA or Population PK ADaM","text":"section find first dose subject drug, using derive_vars_merged(). also create analysis visit (AVISIT) based NFRLT. first dose datetime analyte FANLDTM calculated minimum ADTM dosing records subject drug.","code":"# ---- Find first dose per treatment per subject ---- # ---- Join with ADPC data and keep only subjects with dosing ----  adpc_first_dose <- pc_dates %>%   derive_vars_merged(     dataset_add = ex_exp,     filter_add = (EXDOSE > 0 & !is.na(ADTM)),     new_vars = exprs(FANLDTM = ADTM),     order = exprs(ADTM, EXSEQ),     mode = \"first\",     by_vars = exprs(STUDYID, USUBJID, DRUG)   ) %>%   filter(!is.na(FANLDTM)) %>%   # Derive AVISIT based on nominal relative time   # Derive AVISITN to nominal time in whole days using integer division   # Define AVISIT based on nominal day   mutate(     AVISITN = NFRLT %/% 24 + 1,     AVISIT = paste(\"Day\", AVISITN)   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/pk_adnca.html","id":"dosedates","dir":"Articles","previous_headings":"ADPC Programming Workflow","what":"Find Reference Dose Dates Corresponding to PK Records","title":"Creating a PK NCA or Population PK ADaM","text":"Use derive_vars_joined() find previous dose data. join expanded EX data ADPC based analysis date ADTM. Note filter_join parameter. addition date previous dose (ADTM_prev), also keep actual dose amount EXDOSE_prev analysis visit dose AVISIT_prev. Similarly, find next dose information using derive_vars_joined() filter_join parameter ADTM <= ADTM.join. keep next dose analysis date ADTM_next, next actual dose EXDOSE_next, next analysis visit AVISIT_next. Use method find previous next nominal times. Note data sorted nominal time rather actual time. tell us previous dose next dose supposed occur. Sometimes differ actual times study. keep previous nominal dose time NFRLT_prev next nominal dose time NFRLT_next. Note filter_join parameter uses nominal relative times, e.g. NFRLT > NFRLT.join.","code":"# ---- Find previous dose  ----  adpc_prev <- adpc_first_dose %>%   derive_vars_joined(     dataset_add = ex_exp,     by_vars = exprs(USUBJID),     order = exprs(ADTM),     new_vars = exprs(       ADTM_prev = ADTM, EXDOSE_prev = EXDOSE, AVISIT_prev = AVISIT,       AENDTM_prev = AENDTM     ),     join_vars = exprs(ADTM),     join_type = \"all\",     filter_add = NULL,     filter_join = ADTM > ADTM.join,     mode = \"last\",     check_type = \"none\"   ) # ---- Find next dose  ----  adpc_next <- adpc_prev %>%   derive_vars_joined(     dataset_add = ex_exp,     by_vars = exprs(USUBJID),     order = exprs(ADTM),     new_vars = exprs(       ADTM_next = ADTM, EXDOSE_next = EXDOSE, AVISIT_next = AVISIT,       AENDTM_next = AENDTM     ),     join_vars = exprs(ADTM),     join_type = \"all\",     filter_add = NULL,     filter_join = ADTM <= ADTM.join,     mode = \"first\",     check_type = \"none\"   ) # ---- Find previous nominal time ----  adpc_nom_prev <- adpc_next %>%   derive_vars_joined(     dataset_add = ex_exp,     by_vars = exprs(USUBJID),     order = exprs(NFRLT),     new_vars = exprs(NFRLT_prev = NFRLT),     join_vars = exprs(NFRLT),     join_type = \"all\",     filter_add = NULL,     filter_join = NFRLT > NFRLT.join,     mode = \"last\",     check_type = \"none\"   )  # ---- Find next nominal time ----  adpc_nom_next <- adpc_nom_prev %>%   derive_vars_joined(     dataset_add = ex_exp,     by_vars = exprs(USUBJID),     order = exprs(NFRLT),     new_vars = exprs(NFRLT_next = NFRLT),     join_vars = exprs(NFRLT),     join_type = \"all\",     filter_add = NULL,     filter_join = NFRLT <= NFRLT.join,     mode = \"first\",     check_type = \"none\"   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/pk_adnca.html","id":"relative","dir":"Articles","previous_headings":"ADPC Programming Workflow","what":"Combine PC and EX Records and Derive Relative Time Variables","title":"Creating a PK NCA or Population PK ADaM","text":"Combine PC EX records derive additional relative time variables. Often NCA data keep dosing concentration records. keep . Sometimes see ADPC concentration records. desired, dosing records can dropped saving final dataset. use admiral function derive_vars_duration() calculate actual relative time first dose (AFRLT) actual relative time recent dose (ARRLT). Note use parameter add_one = FALSE . also create variable representing actual time next dose (AXRLT) kept, used create duplicated records analysis pre-dose records. now, update missing values ARRLT corresponding pre-dose records AXRLT, dosing records set zero. also calculate reference dates FANLDTM (First Datetime Dose Analyte) PCRFTDTM (Reference Datetime Dose Analyte) corresponding date time variables. calculate maximum date concentration records keep dosing records date. nominal relative times calculate NRRLT generally NFRLT - NFRLT_prev NXRLT NFRLT - NFRLT_next.","code":"# ---- Combine ADPC and EX data ---- # Derive Relative Time Variables  adpc_arrlt <- bind_rows(adpc_nom_next, ex_exp) %>%   group_by(USUBJID, DRUG) %>%   mutate(     FANLDTM = min(FANLDTM, na.rm = TRUE),     min_NFRLT = min(NFRLT_prev, na.rm = TRUE),     maxdate = max(ADT[EVID == 0], na.rm = TRUE), .after = USUBJID   ) %>%   arrange(USUBJID, ADTM) %>%   ungroup() %>%   filter(ADT <= maxdate) %>%   # Derive Actual Relative Time from First Dose (AFRLT)   derive_vars_duration(     new_var = AFRLT,     start_date = FANLDTM,     end_date = ADTM,     out_unit = \"hours\",     floor_in = FALSE,     add_one = FALSE   ) %>%   # Derive Actual Relative Time from Reference Dose (ARRLT)   derive_vars_duration(     new_var = ARRLT,     start_date = ADTM_prev,     end_date = ADTM,     out_unit = \"hours\",     floor_in = FALSE,     add_one = FALSE   ) %>%   # Derive Actual Relative Time from Next Dose (AXRLT not kept)   derive_vars_duration(     new_var = AXRLT,     start_date = ADTM_next,     end_date = ADTM,     out_unit = \"hours\",     floor_in = FALSE,     add_one = FALSE   ) %>%   mutate(     ARRLT = case_when(       EVID == 1 ~ 0,       is.na(ARRLT) ~ AXRLT,       TRUE ~ ARRLT     ),      # Derive Reference Dose Date     PCRFTDTM = case_when(       EVID == 1 ~ ADTM,       is.na(ADTM_prev) ~ ADTM_next,       TRUE ~ ADTM_prev     )   ) %>%   # Derive dates and times from datetimes   derive_vars_dtm_to_dt(exprs(FANLDTM)) %>%   derive_vars_dtm_to_tm(exprs(FANLDTM)) %>%   derive_vars_dtm_to_dt(exprs(PCRFTDTM)) %>%   derive_vars_dtm_to_tm(exprs(PCRFTDTM)) adpc_nrrlt <- adpc_arrlt %>%   # Derive Nominal Relative Time from Reference Dose (NRRLT)   mutate(     NRRLT = case_when(       EVID == 1 ~ 0,       is.na(NFRLT_prev) ~ NFRLT - min_NFRLT,       TRUE ~ NFRLT - NFRLT_prev     ),     NXRLT = case_when(       EVID == 1 ~ 0,       TRUE ~ NFRLT - NFRLT_next     )   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/pk_adnca.html","id":"analysis","dir":"Articles","previous_headings":"ADPC Programming Workflow","what":"Derive Analysis Variables","title":"Creating a PK NCA or Population PK ADaM","text":"Using dplyr::mutate derive number analysis variables including analysis value (AVAL), analysis time point (ATPT) analysis timepoint reference (ATPTREF) baseline type (BASETYPE). set ATPT PCTPT concentration records “Dose” dosing records. analysis timepoint reference ATPTREF correspond dosing visit. use AVISIT_prev AVISIT_next derive. baseline type concatenation ATPTREF “Baseline” values “Day 1 Baseline”, “Day 2 Baseline”, etc. baseline flag ABLFL set “Y” pre-dose records. Analysis value AVAL example comes PCSTRESN concentration records. addition including dose value EXDOSE dosing records setting BLQ (Limit Quantitation) records 0 first dose 1/2 LLOQ (Lower Limit Quantitation) records first dose. (Additional tests whether 1/3 records BLQ may required done example.) also create listing-ready variable AVALCAT1 includes “BLQ” record indicator formats numeric values three significant digits. derive actual dose DOSEA based EXDOSE_prev EXDOSE_next planned dose DOSEP based planned treatment TRT01P. addition add units dose variables relative time variables.","code":"# ---- Derive Analysis Variables ---- # Derive ATPTN, ATPT, ATPTREF, ABLFL and BASETYPE # Derive planned dose DOSEP, actual dose DOSEA and units # Derive PARAMCD and relative time units # Derive AVAL, AVALU and AVALCAT1  adpc_aval <- adpc_nrrlt %>%   mutate(     PARCAT1 = PCSPEC,     ATPTN = case_when(       EVID == 1 ~ 0,       TRUE ~ PCTPTNUM     ),     ATPT = case_when(       EVID == 1 ~ \"Dose\",       TRUE ~ PCTPT     ),     ATPTREF = case_when(       EVID == 1 ~ AVISIT,       is.na(AVISIT_prev) ~ AVISIT_next,       TRUE ~ AVISIT_prev     ),     # Derive baseline flag for pre-dose records     ABLFL = case_when(       ATPT == \"Pre-dose\" ~ \"Y\",       TRUE ~ NA_character_     ),     # Derive BASETYPE     BASETYPE = paste(ATPTREF, \"Baseline\"),      # Derive Actual Dose     DOSEA = case_when(       EVID == 1 ~ EXDOSE,       is.na(EXDOSE_prev) ~ EXDOSE_next,       TRUE ~ EXDOSE_prev     ),     # Derive Planned Dose     DOSEP = case_when(       TRT01P == \"Xanomeline High Dose\" ~ 81,       TRT01P == \"Xanomeline Low Dose\" ~ 54     ),     DOSEU = \"mg\",   ) %>%   # Derive relative time units   mutate(     FRLTU = \"h\",     RRLTU = \"h\",     # Derive PARAMCD     PARAMCD = coalesce(PCTESTCD, \"DOSE\"),     ALLOQ = PCLLOQ,     # Derive AVAL     AVAL = case_when(       EVID == 1 ~ EXDOSE,       PCSTRESC == \"<BLQ\" & NFRLT == 0 ~ 0,       PCSTRESC == \"<BLQ\" & NFRLT > 0 ~ 0.5 * ALLOQ,       TRUE ~ PCSTRESN     ),     AVALU = case_when(       EVID == 1 ~ EXDOSU,       TRUE ~ PCSTRESU     ),     AVALCAT1 = if_else(PCSTRESC == \"<BLQ\", PCSTRESC, prettyNum(signif(AVAL, digits = 3))),   ) %>%   # Add SRCSEQ   mutate(     SRCDOM = DOMAIN,     SRCVAR = \"SEQ\",     SRCSEQ = coalesce(PCSEQ, EXSEQ)   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/pk_adnca.html","id":"copy","dir":"Articles","previous_headings":"ADPC Programming Workflow","what":"Create Duplicated Records for Analysis","title":"Creating a PK NCA or Population PK ADaM","text":"mentioned , CDISC ADaM Implementation Guide Non-compartmental Analysis uses duplicated records analysis record needs used one way. example 24 hour post-dose record also used pre-dose record “Day 2” dose. addition 24 hour post-dose records, situations may include pre-dose records “Cycle 2 Day 1”, etc. general, select records interest update relative time variables duplicated records. case select nominal relative time next dose zero. (Note need duplicate first dose record since prior dose.) DTYPE set “COPY” duplicated records original PCSEQ value retained. case change “24h Post-dose” “Pre-dose”. ABLFL set “Y” since records serve baseline “Day 2” dose. DOSEA set EXDOSE_next PCRFTDTM set ADTM_next.","code":"# ---- Create DTYPE copy records ----  dtype <- adpc_aval %>%   filter(NFRLT > 0 & NXRLT == 0 & EVID == 0 & !is.na(AVISIT_next)) %>%   select(-PCRFTDT, -PCRFTTM) %>%   # Re-derive variables in for DTYPE copy records   mutate(     ABLFL = NA_character_,     ATPTREF = AVISIT_next,     ARRLT = AXRLT,     NRRLT = NXRLT,     PCRFTDTM = ADTM_next,     DOSEA = EXDOSE_next,     BASETYPE = paste(AVISIT_next, \"Baseline\"),     ATPT = \"Pre-dose\",     ATPTN = -0.5,     ABLFL = \"Y\",     DTYPE = \"COPY\"   ) %>%   derive_vars_dtm_to_dt(exprs(PCRFTDTM)) %>%   derive_vars_dtm_to_tm(exprs(PCRFTDTM))"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/pk_adnca.html","id":"combine","dir":"Articles","previous_headings":"ADPC Programming Workflow","what":"Combine ADPC data with Duplicated Records","title":"Creating a PK NCA or Population PK ADaM","text":"Now duplicated records combined original records. also derive modified relative time reference dose MRRLT. case, negative values ARRLT set zero. also opportunity derive analysis flags e.g. ANL01FL , ANL02FL etc. example ANL01FL set “Y” records ANL02FL set “Y” records except duplicated records DTYPE = “COPY”. Additional flags may used select full profile records /select records included tables figures, etc.","code":"# ---- Combine original records and DTYPE copy records ----  adpc_dtype <- bind_rows(adpc_aval, dtype) %>%   arrange(STUDYID, USUBJID, BASETYPE, ADTM, NFRLT) %>%   mutate(     # Derive MRRLT, ANL01FL and ANL02FL     MRRLT = if_else(ARRLT < 0, 0, ARRLT),     ANL01FL = \"Y\",     ANL02FL = if_else(is.na(DTYPE), \"Y\", NA_character_),   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/pk_adnca.html","id":"aseq","dir":"Articles","previous_headings":"ADPC Programming Workflow","what":"Calculate Change from Baseline and Assign ASEQ","title":"Creating a PK NCA or Population PK ADaM","text":"admiral function derive_var_base() used derive BASE function derive_var_chg() used derive change baseline CHG. also now derive ASEQ using derive_var_obs_number() drop intermediate variables ending “_prev” “_next”. Finally derive PARAM PARAMN lookup table.","code":"# ---- Derive BASE and Calculate Change from Baseline ----  adpc_base <- adpc_dtype %>%   # Derive BASE   derive_var_base(     by_vars = exprs(STUDYID, USUBJID, PARAMCD, PARCAT1, BASETYPE),     source_var = AVAL,     new_var = BASE,     filter = ABLFL == \"Y\"   )  # Calculate CHG for post-baseline records # The decision on how to populate pre-baseline and baseline values of CHG is left to producer choice adpc_chg <- restrict_derivation(   adpc_base,   derivation = derive_var_chg,   filter = AVISITN > 0 )  # ---- Add ASEQ ----  adpc_aseq <- adpc_chg %>%   # Calculate ASEQ   derive_var_obs_number(     new_var = ASEQ,     by_vars = exprs(STUDYID, USUBJID),     order = exprs(ADTM, BASETYPE, EVID, AVISITN, ATPTN, PARCAT1, DTYPE),     check_type = \"error\"   ) %>%   # Remove temporary variables   select(     -DOMAIN, -PCSEQ, -starts_with(\"orig\"), -starts_with(\"min\"),     -starts_with(\"max\"), -starts_with(\"EX\"), -ends_with(\"next\"),     -ends_with(\"prev\"), -DRUG, -EVID, -AXRLT, -NXRLT, -VISITDY   ) %>%   # Derive PARAM and PARAMN   derive_vars_merged(     dataset_add = select(param_lookup, -PCTESTCD), by_vars = exprs(PARAMCD)   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/pk_adnca.html","id":"baselines","dir":"Articles","previous_headings":"ADPC Programming Workflow","what":"Add Additional Baseline Variables","title":"Creating a PK NCA or Population PK ADaM","text":"derive additional baseline values VS baseline height HTBL weight WTBL compute body mass index (BMI) compute_bmi(). values also obtained ADVS available. Baseline lab values also derived LB ADLB similar manner.","code":"# Derive additional baselines from VS adpc_baselines <- adpc_aseq %>%   derive_vars_merged(     dataset_add = vs,     filter_add = VSTESTCD == \"HEIGHT\",     by_vars = exprs(STUDYID, USUBJID),     new_vars = exprs(HTBL = VSSTRESN, HTBLU = VSSTRESU)   ) %>%   derive_vars_merged(     dataset_add = vs,     filter_add = VSTESTCD == \"WEIGHT\" & VSBLFL == \"Y\",     by_vars = exprs(STUDYID, USUBJID),     new_vars = exprs(WTBL = VSSTRESN, WTBLU = VSSTRESU)   ) %>%   mutate(     BMIBL = compute_bmi(height = HTBL, weight = WTBL),     BMIBLU = \"kg/m^2\"   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/pk_adnca.html","id":"adsl_vars","dir":"Articles","previous_headings":"ADPC Programming Workflow","what":"Add the ADSL variables","title":"Creating a PK NCA or Population PK ADaM","text":"needed, ADSL variables can now added: Adding attributes ADPC file discussed . now turn Population PK example.","code":"# Add all ADSL variables adpc <- adpc_baselines %>%   derive_vars_merged(     dataset_add = select(adsl, !!!negate_vars(adsl_vars)),     by_vars = exprs(STUDYID, USUBJID)   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/pk_adnca.html","id":"programming-population-pk-adppk-analysis-data","dir":"Articles","previous_headings":"","what":"Programming Population PK (ADPPK) Analysis Data","title":"Creating a PK NCA or Population PK ADaM","text":"Population PK Analysis Data (ADPPK) follows CDISC Implementation Guide (https://www.cdisc.org/standards/foundational/adam/basic-data-structure-adam-poppk-implementation-guide-v1-0). programming workflow Population PK (ADPPK) Analysis Data similar NCA Programming flow key differences. Population PK models generally make use nonlinear mixed effects models require numeric variables. data used models include dosing concentration records, relative time variables, numeric covariate variables. DV dependent variable often expected. equivalent ADaM AVAL variable included addition AVAL ADPPK. ADPPK file duplicated records analysis found NCA. relative time variables use ADPPK data. correspond names forthcoming CDISC Implementation Guide. ADPPK require numeric Event ID (EVID) defined ADPC keep.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/pk_adnca.html","id":"adppk-programming-workflow","dir":"Articles","previous_headings":"","what":"ADPPK Programming Workflow","title":"Creating a PK NCA or Population PK ADaM","text":"Read Data (ADPC) Expand Dosing Records (ADPC) Find First Dose Find Previous Dose Combine PC EX Records ADPPK Derive Analysis Variables Dependent Variable DV Add ASEQ Remove Temporary Variables Derive Numeric Covariates Derive Additional Covariates VS LB Combine Covariates ADPPK Data","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/pk_adnca.html","id":"ppkfirst","dir":"Articles","previous_headings":"ADPPK Programming Workflow","what":"Find First Dose ADPPK","title":"Creating a PK NCA or Population PK ADaM","text":"initial programming steps ADPPK follow sequence ADPC. includes reading pharmaversesdtm data, deriving analysis dates, defining nominal relative time first dose NFRLT, expanding dosing records. detail see steps (Read Data). pick stage find first dose concentration records. use derive_vars_merged() ADPC.","code":"# ---- Find first dose per treatment per subject ---- # ---- Join with ADPC data and keep only subjects with dosing ----  adppk_first_dose <- pc_dates %>%   derive_vars_merged(     dataset_add = ex_exp,     filter_add = (!is.na(ADTM)),     new_vars = exprs(FANLDTM = ADTM, EXDOSE_first = EXDOSE),     order = exprs(ADTM, EXSEQ),     mode = \"first\",     by_vars = exprs(STUDYID, USUBJID, DRUG)   ) %>%   filter(!is.na(FANLDTM)) %>%   # Derive AVISIT based on nominal relative time   # Derive AVISITN to nominal time in whole days using integer division   # Define AVISIT based on nominal day   mutate(     AVISITN = NFRLT %/% 24 + 1,     AVISIT = paste(\"Day\", AVISITN),   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/pk_adnca.html","id":"prevdose","dir":"Articles","previous_headings":"ADPPK Programming Workflow","what":"Find Previous Dose","title":"Creating a PK NCA or Population PK ADaM","text":"ADPPK find previous dose respect actual time nominal time. use derive_vars_joined() ADPC, note need find next dose ADPC.","code":"# ---- Find previous dose  ----  adppk_prev <- adppk_first_dose %>%   derive_vars_joined(     dataset_add = ex_exp,     by_vars = exprs(USUBJID),     order = exprs(ADTM),     new_vars = exprs(       ADTM_prev = ADTM, EXDOSE_prev = EXDOSE, AVISIT_prev = AVISIT,       AENDTM_prev = AENDTM     ),     join_vars = exprs(ADTM),     join_type = \"all\",     filter_add = NULL,     filter_join = ADTM > ADTM.join,     mode = \"last\",     check_type = \"none\"   )  # ---- Find previous nominal dose ----  adppk_nom_prev <- adppk_prev %>%   derive_vars_joined(     dataset_add = ex_exp,     by_vars = exprs(USUBJID),     order = exprs(NFRLT),     new_vars = exprs(NFRLT_prev = NFRLT),     join_vars = exprs(NFRLT),     join_type = \"all\",     filter_add = NULL,     filter_join = NFRLT > NFRLT.join,     mode = \"last\",     check_type = \"none\"   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/pk_adnca.html","id":"aprlt","dir":"Articles","previous_headings":"ADPPK Programming Workflow","what":"Combine PC and EX Records for ADPPK","title":"Creating a PK NCA or Population PK ADaM","text":"ADPC now combine PC EX records. derive relative time variables AFRLT (Actual Relative Time First Dose), APRLT (Actual Relative Time Previous Dose), NPRLT (Nominal Relative Time Previous Dose). Use derive_vars_duration() derive AFRLT APRLT. Note defined EVID values 0 observation records 1 dosing records.","code":"# ---- Combine ADPPK and EX data ---- # Derive Relative Time Variables  adppk_aprlt <- bind_rows(adppk_nom_prev, ex_exp) %>%   group_by(USUBJID, DRUG) %>%   mutate(     FANLDTM = min(FANLDTM, na.rm = TRUE),     min_NFRLT = min(NFRLT, na.rm = TRUE),     maxdate = max(ADT[EVID == 0], na.rm = TRUE), .after = USUBJID   ) %>%   arrange(USUBJID, ADTM) %>%   ungroup() %>%   filter(ADT <= maxdate) %>%   # Derive Actual Relative Time from First Dose (AFRLT)   derive_vars_duration(     new_var = AFRLT,     start_date = FANLDTM,     end_date = ADTM,     out_unit = \"hours\",     floor_in = FALSE,     add_one = FALSE   ) %>%   # Derive Actual Relative Time from Reference Dose (APRLT)   derive_vars_duration(     new_var = APRLT,     start_date = ADTM_prev,     end_date = ADTM,     out_unit = \"hours\",     floor_in = FALSE,     add_one = FALSE   ) %>%   # Derive APRLT   mutate(     APRLT = case_when(       EVID == 1 ~ 0,       is.na(APRLT) ~ AFRLT,       TRUE ~ APRLT     ),     NPRLT = case_when(       EVID == 1 ~ 0,       is.na(NFRLT_prev) ~ NFRLT - min_NFRLT,       TRUE ~ NFRLT - NFRLT_prev     )   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/pk_adnca.html","id":"dv","dir":"Articles","previous_headings":"ADPPK Programming Workflow","what":"Derive Analysis Variables and Dependent Variable DV","title":"Creating a PK NCA or Population PK ADaM","text":"expected analysis variable ADPPK DV dependent variable. example DV set numeric concentration value PCSTRESN. also include AVAL equivalent DV consistency CDISC ADaM standards. MDV missing dependent variable also included.","code":"# ---- Derive Analysis Variables ---- # Derive actual dose DOSEA and planned dose DOSEP, # Derive AVAL and DV  adppk_aval <- adppk_aprlt %>%   mutate(     # Derive Actual Dose     DOSEA = case_when(       EVID == 1 ~ EXDOSE,       is.na(EXDOSE_prev) ~ EXDOSE_first,       TRUE ~ EXDOSE_prev     ),     # Derive Planned Dose     DOSEP = case_when(       TRT01P == \"Xanomeline High Dose\" ~ 81,       TRT01P == \"Xanomeline Low Dose\" ~ 54,       TRT01P == \"Placebo\" ~ 0     ),     # Derive PARAMCD     PARAMCD = case_when(       EVID == 1 ~ \"DOSE\",       TRUE ~ PCTESTCD     ),     ALLOQ = PCLLOQ,     # Derive CMT     CMT = case_when(       EVID == 1 ~ 1,       PCSPEC == \"PLASMA\" ~ 2,       TRUE ~ 3     ),     # Derive BLQFL/BLQFN     BLQFL = case_when(       PCSTRESC == \"<BLQ\" ~ \"Y\",       TRUE ~ \"N\"     ),     BLQFN = case_when(       PCSTRESC == \"<BLQ\" ~ 1,       TRUE ~ 0     ),     AMT = case_when(       EVID == 1 ~ EXDOSE,       TRUE ~ NA_real_     ),     # Derive DV and AVAL     DV = PCSTRESN,     AVAL = DV,     DVL = case_when(       DV != 0 ~ log(DV),       TRUE ~ NA_real_     ),     # Derive MDV     MDV = case_when(       EVID == 1 ~ 1,       is.na(DV) ~ 1,       TRUE ~ 0     ),     AVALU = case_when(       EVID == 1 ~ NA_character_,       TRUE ~ PCSTRESU     ),     UDTC = format_ISO8601(ADTM),     II = if_else(EVID == 1, 1, 0),     SS = if_else(EVID == 1, 1, 0)   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/pk_adnca.html","id":"ppkaseq","dir":"Articles","previous_headings":"ADPPK Programming Workflow","what":"Add ASEQ and Remove Temporary Variables","title":"Creating a PK NCA or Population PK ADaM","text":"derive ASEQ using derive_var_obs_number(). add PROJID based drug include numeric version PROJIDN, drop drop intermediate variables ending “_prev”.","code":"# ---- Add ASEQ ----  adppk_aseq <- adppk_aval %>%   # Calculate ASEQ   derive_var_obs_number(     new_var = ASEQ,     by_vars = exprs(STUDYID, USUBJID),     order = exprs(AFRLT, EVID, CMT),     check_type = \"error\"   ) %>%   # Derive PARAM and PARAMN   derive_vars_merged(dataset_add = select(param_lookup, -PCTESTCD), by_vars = exprs(PARAMCD)) %>%   mutate(     PROJID = DRUG,     PROJIDN = 1   ) %>%   # Remove temporary variables   select(     -DOMAIN, -starts_with(\"min\"), -starts_with(\"max\"), -starts_with(\"EX\"),     -starts_with(\"PC\"), -ends_with(\"first\"), -ends_with(\"prev\"),     -ends_with(\"DTM\"), -ends_with(\"DT\"), -ends_with(\"TM\"), -starts_with(\"VISIT\"),     -starts_with(\"AVISIT\"), -ends_with(\"TMF\"), -starts_with(\"TRT\"),     -starts_with(\"ATPT\"), -DRUG   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/pk_adnca.html","id":"covar","dir":"Articles","previous_headings":"ADPPK Programming Workflow","what":"Derive Numeric Covariates","title":"Creating a PK NCA or Population PK ADaM","text":"key feature Population PK modeling presence numeric covariates. create numeric versions many standard CDISC demographic variables including STUDYIDN, USUBJIDN, SEXN, RACEN, ETHNICN.","code":"#---- Derive Covariates ---- # Include numeric values for STUDYIDN, USUBJIDN, SEXN, RACEN etc.  covar <- adsl %>%   derive_vars_merged(     dataset_add = country_code_lookup,     new_vars = exprs(COUNTRYN = country_number, COUNTRYL = country_name),     by_vars = exprs(COUNTRY = country_code),   ) %>%   mutate(     STUDYIDN = as.numeric(word(USUBJID, 1, sep = fixed(\"-\"))),     SITEIDN = as.numeric(word(USUBJID, 2, sep = fixed(\"-\"))),     USUBJIDN = as.numeric(word(USUBJID, 3, sep = fixed(\"-\"))),     SUBJIDN = as.numeric(SUBJID),     SEXN = case_when(       SEX == \"M\" ~ 1,       SEX == \"F\" ~ 2,       TRUE ~ 3     ),     RACEN = case_when(       RACE == \"AMERICAN INDIAN OR ALASKA NATIVE\" ~ 1,       RACE == \"ASIAN\" ~ 2,       RACE == \"BLACK OR AFRICAN AMERICAN\" ~ 3,       RACE == \"NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER\" ~ 4,       RACE == \"WHITE\" ~ 5,       TRUE ~ 6     ),     ETHNICN = case_when(       ETHNIC == \"HISPANIC OR LATINO\" ~ 1,       ETHNIC == \"NOT HISPANIC OR LATINO\" ~ 2,       TRUE ~ 3     ),     ARMN = case_when(       ARM == \"Placebo\" ~ 0,       ARM == \"Xanomeline Low Dose\" ~ 1,       ARM == \"Xanomeline High Dose\" ~ 2,       TRUE ~ 3     ),     ACTARMN = case_when(       ACTARM == \"Placebo\" ~ 0,       ACTARM == \"Xanomeline Low Dose\" ~ 1,       ACTARM == \"Xanomeline High Dose\" ~ 2,       TRUE ~ 3     ),     COHORT = ARMN,     COHORTC = ARM,     ROUTE = unique(ex$EXROUTE),     ROUTEN = case_when(       ROUTE == \"TRANSDERMAL\" ~ 3,       TRUE ~ NA_real_     ),     FORM = unique(ex$EXDOSFRM),     FORMN = case_when(       FORM == \"PATCH\" ~ 3,       TRUE ~ 4     )   ) %>%   select(     STUDYID, STUDYIDN, SITEID, SITEIDN, USUBJID, USUBJIDN,     SUBJID, SUBJIDN, AGE, SEX, SEXN, COHORT, COHORTC, ROUTE, ROUTEN,     RACE, RACEN, ETHNIC, ETHNICN, FORM, FORMN, COUNTRY, COUNTRYN, COUNTRYL   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/pk_adnca.html","id":"addcovar","dir":"Articles","previous_headings":"ADPPK Programming Workflow","what":"Derive Additional Covariates from VS and LB","title":"Creating a PK NCA or Population PK ADaM","text":"add additional covariates baseline height HTBL weight WTBL VS select baseline lab values CREATBL, ALTBL, ASTBL TBILBL LB. calculate BMI BSA height weight using compute_bmi() compute_bsa(). calculate creatinine clearance CRCLBL estimated glomerular filtration rate (eGFR) EGFRBL using compute_egfr() function.","code":"#---- Derive additional baselines from VS and LB ----  labsbl <- lb %>%   filter(LBBLFL == \"Y\" & LBTESTCD %in% c(\"CREAT\", \"ALT\", \"AST\", \"BILI\")) %>%   mutate(LBTESTCDB = paste0(LBTESTCD, \"BL\")) %>%   select(STUDYID, USUBJID, LBTESTCDB, LBSTRESN)  covar_vslb <- covar %>%   derive_vars_merged(     dataset_add = vs,     filter_add = VSTESTCD == \"HEIGHT\",     by_vars = exprs(STUDYID, USUBJID),     new_vars = exprs(HTBL = VSSTRESN)   ) %>%   derive_vars_merged(     dataset_add = vs,     filter_add = VSTESTCD == \"WEIGHT\" & VSBLFL == \"Y\",     by_vars = exprs(STUDYID, USUBJID),     new_vars = exprs(WTBL = VSSTRESN)   ) %>%   derive_vars_transposed(     dataset_merge = labsbl,     by_vars = exprs(STUDYID, USUBJID),     key_var = LBTESTCDB,     value_var = LBSTRESN   ) %>%   mutate(     BMIBL = compute_bmi(height = HTBL, weight = WTBL),     BSABL = compute_bsa(       height = HTBL,       weight = HTBL,       method = \"Mosteller\"     ),     # Derive CRCLBL and EGFRBL using new function     CRCLBL = compute_egfr(       creat = CREATBL, creatu = \"SI\", age = AGE, weight = WTBL, sex = SEX,       method = \"CRCL\"     ),     EGFRBL = compute_egfr(       creat = CREATBL, creatu = \"SI\", age = AGE, weight = WTBL, sex = SEX,       method = \"CKD-EPI\"     )   ) %>%   rename(TBILBL = BILIBL)"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/pk_adnca.html","id":"final","dir":"Articles","previous_headings":"ADPPK Programming Workflow","what":"Combine Covariates with ADPPK Data","title":"Creating a PK NCA or Population PK ADaM","text":"Finally, combine covariates ADPPK data.","code":"# Combine covariates with APPPK data  adppk <- adppk_aseq %>%   derive_vars_merged(     dataset_add = covar_vslb,     by_vars = exprs(STUDYID, USUBJID)   ) %>%   arrange(STUDYIDN, USUBJIDN, AFRLT, EVID) %>%   mutate(RECSEQ = row_number())"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/pk_adnca.html","id":"attributes","dir":"Articles","previous_headings":"","what":"Add Labels and Attributes","title":"Creating a PK NCA or Population PK ADaM","text":"Adding labels attributes SAS transport files supported following packages: metacore: establish common foundation use metadata within R session. metatools: enable use metacore objects. Metatools can used build datasets enhance columns existing datasets well checking datasets metadata. xportr: functionality associate metadata information local R data frame, perform data set level validation checks convert transport v5 file(xpt). NOTE: packages experimental phase, vision associated End End pipeline umbrella pharmaverse. example applying metadata perform associated checks can found pharmaverse E2E example.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/articles/queries_dataset.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Queries Dataset Documentation","text":"support safety analysis, quite common define specific grouping events. One common ways group events medications specific medical concept Standard MedDRA Queries (SMQs) -Drug Standardized Drug Groupings (SDGs). help derivation variables, {admiral} function derive_vars_query() can used. function takes input dataset (dataset) grouping must occur (e.g ADAE) dataset containing required information perform derivation grouping variables (dataset_queries). dataset passed dataset_queries argument derive_vars_query() function can created create_query_data() function. SMQs SDGs company-specific functions accessing SMQ SDG database need passed create_query_data() function (see description get_terms_fun argument details). vignette describes expected structure content dataset passed dataset_queries argument derive_vars_query() function.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/articles/queries_dataset.html","id":"variables","dir":"Articles","previous_headings":"Structure of the Query Dataset","what":"Variables","title":"Queries Dataset Documentation","text":"Bold variables required dataset_queries: error issued variables missing. TERMCHAR REQUIRED character variable named SRCVAR. TERMNUM REQUIRED numeric variable named SRCVAR. SRCVAR contains character numeric variables, TERMCHAR TERMNUM required. variables optional. VERSION variable used derive_vars_query() can used check dictionary version queries dataset analysis dataset line.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/queries_dataset.html","id":"required-content","dir":"Articles","previous_headings":"Structure of the Query Dataset","what":"Required Content","title":"Queries Dataset Documentation","text":"row must unique within dataset. described , variables PREFIX, GRPNAME, SRCVAR, TERMCHAR TERMNUM required. combination variables allow creation grouping variable.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/queries_dataset.html","id":"input","dir":"Articles","previous_headings":"Structure of the Query Dataset > Required Content","what":"Input","title":"Queries Dataset Documentation","text":"PREFIX must character string starting 2 3 letters, followed 2-digits number (e.g. “CQ01”). GRPNAME must non missing character string must unique within PREFIX. SRCVAR must non missing character string. value SRCVAR represents variable dataset used define grouping variables (e.g. AEDECOD,AEBODSYS, AELLTCD). function derive_vars_query() check value given SRCVAR corresponding variable input dataset issue error otherwise. Different SRCVAR variables may specified within PREFIX. TERMCHAR must character string. must populated TERMNUM missing. TERMNUM must integer. must populated TERMCHAR missing.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/queries_dataset.html","id":"output","dir":"Articles","previous_headings":"Structure of the Query Dataset > Required Content","what":"Output","title":"Queries Dataset Documentation","text":"PREFIX used create grouping variable appending suffix “NAM”. variable now referred ABCzzNAM: name grouping variable. E.g. PREFIX == \"SMQ01\" create SMQ01NAM variable. PREFIX, new ABCzzNAM variable created dataset. GRPNAME used populate corresponding ABCzzNAM variable. SRCVAR used identify variables dataset used perform grouping (e.g. AEDECOD,AEBODSYS, AELLTCD). TERMCHAR (character variables), TERMNUM (numeric variables) used identify records meeting criteria dataset based variable defined SRCVAR. Result: record dataset, variable defined SRCVAR match term TERMCHAR (character variables) TERMNUM (numeric variables) datasets_queries, ABCzzNAM populated GRPNAME. Note: type (numeric character) variable defined SRCVAR checked dataset. variable character variable (e.g. AEDECOD), expected TERMCHAR populated, numeric variable (e.g. AEBDSYCD), expected TERMNUM populated, otherwise error issued.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/queries_dataset.html","id":"example","dir":"Articles","previous_headings":"Structure of the Query Dataset > Required Content","what":"Example","title":"Queries Dataset Documentation","text":"example, one standard MedDRA query (PREFIX = \"SMQ01\") one customized query (PREFIX = \"CQ02\") defined analyze adverse events. standard MedDRA query variable SMQ01NAM [PREFIX] populated “Standard Query 1” [GRPNAME] preferred term (AEDECOD) [SRCVAR] dataset equal “AE1” “AE2” [TERMCHAR] customized query (CQ02NAM) [PREFIX] populated “Query 2” [GRPNAME] Low Level Term Code (AELLTCD) [SRCVAR] dataset equal 10 [TERMNUM] preferred term (AEDECOD) [SRCVAR] dataset equal “AE4” [TERMCHAR].","code":""},{"path":[]},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/articles/queries_dataset.html","id":"output-dataset","dir":"Articles","previous_headings":"Structure of the Query Dataset > Required Content > Example","what":"Output Dataset","title":"Queries Dataset Documentation","text":"Generated calling derive_vars_query(dataset = ae, dataset_queries = ds_query). Subject 0001 one event meeting Standard Query 1 criteria (AEDECOD = \"AE1\") two events meeting customized query (AELLTCD = 10 AEDECOD = \"AE4\").","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/queries_dataset.html","id":"optional-content","dir":"Articles","previous_headings":"Structure of the Query Dataset","what":"Optional Content","title":"Queries Dataset Documentation","text":"standardized MedDRA Queries added dataset, expected name query (ABCzzNAM) populated along number code (ABCzzCD), Broad Narrow scope (ABCzzSC). following variables can added queries_datset derive information.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/queries_dataset.html","id":"input-1","dir":"Articles","previous_headings":"Structure of the Query Dataset > Optional Content","what":"Input","title":"Queries Dataset Documentation","text":"GRPID must integer. SCOPE must character string. Possible values : “BROAD”, “NARROW” NA. SCOPEN must integer. Possible values : 1, 2 NA.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/queries_dataset.html","id":"output-1","dir":"Articles","previous_headings":"Structure of the Query Dataset > Optional Content","what":"Output","title":"Queries Dataset Documentation","text":"GRPID, SCOPE SCOPEN used way GRPNAME (see ) help creation ABCzzCD, ABCzzSC ABCzzSCN variables.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/queries_dataset.html","id":"output-variables","dir":"Articles","previous_headings":"Structure of the Query Dataset > Optional Content","what":"Output Variables","title":"Queries Dataset Documentation","text":"variables optional populated dataset_queries, corresponding output variable created:","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/questionnaires.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Creating Questionnaire ADaMs","text":"article describes creating questionnaire ADaMs. Although questionnaire data collected single SDTM dataset (QS), usually make sense create single ADQS dataset questionnaire analyses. example, univariate analysis scores visit requires different variables time--event analysis. Therefore vignette provide programming workflow complete dataset, provides examples deriving common types questionnaire parameters. moment, admiral provide functions metadata specific questionnaires functionality handling vast amount questionnaires related parameters, e.g. metadata structure storing parameter definitions functions reading metadata. plan provide future releases. Note: examples assume CDISC SDTM /ADaM format input unless otherwise specified.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/questionnaires.html","id":"required-packages","dir":"Articles","previous_headings":"Introduction","what":"Required Packages","title":"Creating Questionnaire ADaMs","text":"examples vignette require following packages.","code":"library(dplyr) library(tidyr) library(tibble) library(admiral)"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/questionnaires.html","id":"example-data","dir":"Articles","previous_headings":"Introduction","what":"Example Data","title":"Creating Questionnaire ADaMs","text":"vignette use example data CDISC ADaM Supplements (Generalized Anxiety Disorder 7-Item Version 2 (GAD-7), Geriatric Depression Scale Short Form (GDS-SF))1:","code":"qs <- admiral::example_qs adsl <- tribble(   ~STUDYID, ~USUBJID, ~SITEID, ~ITTFL, ~TRTSDT,                      ~DTHCAUS,   \"STUDYX\",  \"P0001\",     13L,    \"Y\", lubridate::ymd(\"2012-11-16\"), NA_character_,   \"STUDYX\",  \"P0002\",     11L,    \"Y\", lubridate::ymd(\"2012-11-16\"), \"PROGRESSIVE DISEASE\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/questionnaires.html","id":"original-items","dir":"Articles","previous_headings":"","what":"Original Items","title":"Creating Questionnaire ADaMs","text":"original items, .e. answers questionnaire questions, can handled way BDS finding ADaM. example: handle unscheduled visits normal visits. deriving visits based time-windows, see Visit Period Variables. flagging values used analysis, see derive_var_extreme_flag().","code":"adqs <- qs %>%   # Add ADSL variables   derive_vars_merged(     dataset_add = adsl,     new_vars = exprs(TRTSDT, DTHCAUS),     by_vars = exprs(STUDYID, USUBJID)   ) %>%   # Add analysis parameter variables   mutate(     PARAMCD = QSTESTCD,     PARAM = QSTEST,     PARCAT1 = QSCAT,     AVALC = QSORRES,     AVAL = QSSTRESN   ) %>%   # Add timing variables   derive_vars_dt(new_vars_prefix = \"A\", dtc = QSDTC) %>%   derive_vars_dy(reference_date = TRTSDT, source_vars = exprs(ADT)) %>%   mutate(     AVISIT = if_else(ADT <= TRTSDT, \"BASELINE\", VISIT),     AVISITN = if_else(ADT <= TRTSDT, 0, VISITNUM)   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/questionnaires.html","id":"transformed-items","dir":"Articles","previous_headings":"","what":"Transformed Items","title":"Creating Questionnaire ADaMs","text":"Please note example data, numeric values answers mapped SDTM (QSSTRESN) can used deriving scores. Depending question, QSORRES == \"YES\" mapped QSSTRESN = 0 QSSTRESN = 1. QSSTRESN values ready used deriving scores require transformation, recommended QSSTRESN kept ADaM dataset traceability, transformed value stored AVAL, since ’s used score calculation. may also necessary transform range numeric values original items. example scale derived average range contributing items varies. case values linearly transformed unified range like [0, 100]. computation function transform_range() can used transformation.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/questionnaires.html","id":"scales-and-scores","dir":"Articles","previous_headings":"","what":"Scales and Scores","title":"Creating Questionnaire ADaMs","text":"Scales Scores often derived sum average across subset items. GAD-7 questionnaire, total score derived sum. derive_summary_records() function sum() can used derive new parameter. selecting parameters summarized, regular expressions like example may helpful. example derive separate ADaM dataset questionnaire. Depending analysis needs, also possible ADaM contains one questionnaire questionnaires. GDS-SF questionnaire, total score defined average item values transformed range [0, 15] rounded next integer. five items missing, total score considered missing. parameter can derived compute_scale() derive_summary_records(): deriving scores visit, baseline change baseline variables can derived:","code":"adgad7 <- adqs %>%   # Select records to keep in the GAD-7 ADaM   filter(PARCAT1 == \"GAD-7 V2\") %>%   derive_summary_records(     dataset = .,     dataset_add = .,     by_vars = exprs(STUDYID, USUBJID, AVISIT, ADT, ADY, TRTSDT, DTHCAUS),     # Select records contributing to total score     filter_add = str_detect(PARAMCD, \"GAD020[1-7]\"),     set_values_to = exprs(       AVAL = sum(AVAL, na.rm = TRUE),       PARAMCD = \"GAD02TS\",       PARAM = \"GAD02-Total Score - Analysis\"     )   ) adgdssf <- adqs %>%   # Select records to keep in the GDS-SF ADaM   filter(PARCAT1 == \"GDS SHORT FORM\") %>%   derive_summary_records(     dataset = .,     dataset_add = .,     by_vars = exprs(STUDYID, USUBJID, AVISIT, ADT, ADY, TRTSDT, DTHCAUS),     # Select records contributing to total score     filter_add = str_detect(PARAMCD, \"GDS02[01][0-9]\"),     set_values_to = exprs(       AVAL = compute_scale(         AVAL,         source_range = c(0, 1),         target_range = c(0, 15),         min_n = 10       ) %>%         ceiling(),       PARAMCD = \"GDS02TS\",       PARAM = \"GDS02- Total Score - Analysis\"     )   ) adgdssf <- adgdssf %>%   # Flag baseline records (last before treatement start)   restrict_derivation(     derivation = derive_var_extreme_flag,     args = params(       by_vars = exprs(STUDYID, USUBJID, PARAMCD),       order = exprs(ADT),       new_var = ABLFL,       mode = \"last\"     ),     filter = !is.na(AVAL) & ADT <= TRTSDT   ) %>%   # Derive baseline and change from baseline variables   derive_var_base(     by_vars = exprs(STUDYID, USUBJID, PARAMCD),     source_var = AVAL,     new_var = BASE   ) %>%   # Calculate CHG for post-baseline records   # The decision on how to populate pre-baseline and baseline values of CHG is left to producer choice   restrict_derivation(     derivation = derive_var_chg,     filter = AVISITN > 0   ) %>%   # Calculate PCHG for post-baseline records   # The decision on how to populate pre-baseline and baseline values of PCHG is left to producer choice   restrict_derivation(     derivation = derive_var_pchg,     filter = AVISITN > 0   ) %>%   # Derive sequence number   derive_var_obs_number(     by_vars = exprs(STUDYID, USUBJID),     order = exprs(PARAMCD, ADT),     check_type = \"error\"   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/questionnaires.html","id":"timetodeterioration","dir":"Articles","previous_headings":"","what":"Time to Deterioration/Improvement","title":"Creating Questionnaire ADaMs","text":"time event parameters require specific variables like CNSR, STARTDT, EVNTDESC, makes sense create separate time event dataset . However, might useful create flags categorization variables ADQS. example: time deterioration parameter can derived :","code":"# Create AVALCATx lookup table avalcat_lookup <- exprs(   ~PARAMCD, ~condition, ~AVALCAT1, ~AVALCAT1N,   \"GDS02TS\", AVAL <= 5, \"Normal\", 0L,   \"GDS02TS\", AVAL <= 10 & AVAL > 5, \"Possible Depression\", 1L,   \"GDS02TS\", AVAL > 10, \"Likely Depression\", 2L ) # Create CHGCAT1 lookup table chgcat_lookup <- exprs(   ~condition, ~CHGCAT1,   AVALCAT1N > BASECA1N, \"WORSENED\",   AVALCAT1N == BASECA1N, \"NO CHANGE\",   AVALCAT1N < BASECA1N, \"IMPROVED\" )  adgdssf <- adgdssf %>%   derive_vars_cat(     definition = avalcat_lookup,     by_vars = exprs(PARAMCD)   ) %>%   derive_var_base(     by_vars = exprs(STUDYID, USUBJID, PARAMCD),     source_var = AVALCAT1,     new_var = BASECAT1   ) %>%   derive_var_base(     by_vars = exprs(STUDYID, USUBJID, PARAMCD),     source_var = AVALCAT1N,     new_var = BASECA1N   ) %>%   derive_vars_cat(     definition = chgcat_lookup   ) # Define event deterioration_event <- event_source(   dataset_name = \"adqs\",   filter = PARAMCD == \"GDS02TS\" & CHGCAT1 == \"WORSENED\",   date = ADT,   set_values_to = exprs(     EVNTDESC = \"DEPRESSION WORSENED\",     SRCDOM = \"ADQS\",     SRCVAR = \"ADT\",     SRCSEQ = ASEQ   ) )  # Define censoring at last assessment last_valid_assessment <- censor_source(   dataset_name = \"adqs\",   filter = PARAMCD == \"GDS02TS\" & !is.na(CHGCAT1),   date = ADT,   set_values_to = exprs(     EVNTDESC = \"LAST ASSESSMENT\",     SRCDOM = \"ADQS\",     SRCVAR = \"ADT\",     SRCSEQ = ASEQ   ) )  # Define censoring at treatment start (for subjects without assessment) start <- censor_source(   dataset_name = \"adsl\",   date = TRTSDT,   set_values_to = exprs(     EVNTDESC = \"TREATMENT START\",     SRCDOM = \"ADSL\",     SRCVAR = \"TRTSDT\"   ) )  adgdstte <- derive_param_tte(   dataset_adsl = adsl,   source_datasets = list(adsl = adsl, adqs = adgdssf),   start_date = TRTSDT,   event_conditions = list(deterioration_event),   censor_conditions = list(last_valid_assessment, start),   set_values_to = exprs(     PARAMCD = \"TTDEPR\",     PARAM = \"Time to depression\"   ) ) %>%   derive_vars_duration(     new_var = AVAL,     start_date = STARTDT,     end_date = ADT   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/questionnaires.html","id":"time-to-confirmeddefinitive-deteriorationimprovement","dir":"Articles","previous_headings":"","what":"Time to Confirmed/Definitive Deterioration/Improvement","title":"Creating Questionnaire ADaMs","text":"derivation confirmed/definitive deterioration/improvement parameters similar unconfirmed deterioration parameters except event based CHGCATy, confirmation flag variable. confirmation flag can derived derive_var_joined_exist_flag(). example, flagging deteriorations, confirmed second assessment least seven days later: flagging deteriorations two consecutive assessments considering death due progression last assessment confirmation, tmp_obs_nr_var argument helpful: definitive deterioration (deterioration following assessments), parameter summary functions like () can used filter condition: time--event parameter can derived way unconfirmed parameters (see Time Deterioration/Improvement).","code":"adgdssf <- adgdssf %>%   derive_var_joined_exist_flag(     dataset_add = adgdssf,     by_vars = exprs(USUBJID, PARAMCD),     order = exprs(ADT),     new_var = CDETFL,     join_vars = exprs(CHGCAT1, ADY),     join_type = \"after\",     filter_join = CHGCAT1 == \"WORSENED\" &       CHGCAT1.join == \"WORSENED\" &       ADY.join >= ADY + 7   ) # Flagging deterioration at two consecutive assessments adgdssf <- adgdssf %>%   derive_var_joined_exist_flag(     dataset_add = adgdssf,     by_vars = exprs(USUBJID, PARAMCD),     order = exprs(ADT),     new_var = CONDETFL,     join_vars = exprs(CHGCAT1),     join_type = \"after\",     tmp_obs_nr_var = tmp_obs_nr,     filter_join = CHGCAT1 == \"WORSENED\" &       CHGCAT1.join == \"WORSENED\" &       tmp_obs_nr.join == tmp_obs_nr + 1   ) %>%   # Flagging deterioration confirmed by   # - a second deterioration at least 7 days later or   # - deterioration at the last assessment and death due to progression   derive_var_joined_exist_flag(     .,     dataset_add = .,     by_vars = exprs(USUBJID, PARAMCD),     order = exprs(ADT),     new_var = CDTDTHFL,     join_vars = exprs(CHGCAT1, ADY),     join_type = \"all\",     tmp_obs_nr_var = tmp_obs_nr,     filter_join = CHGCAT1 == \"WORSENED\" & (       CHGCAT1.join == \"WORSENED\" & ADY.join >= ADY + 7 |         tmp_obs_nr == max(tmp_obs_nr.join) & DTHCAUS == \"PROGRESSIVE DISEASE\")   ) adgdssf <- adgdssf %>%   derive_var_joined_exist_flag(     dataset_add = adgdssf,     by_vars = exprs(USUBJID, PARAMCD),     order = exprs(ADT),     new_var = DEFDETFL,     join_vars = exprs(CHGCAT1),     join_type = \"after\",     filter_join = CHGCAT1 == \"WORSENED\" & all(CHGCAT1.join == \"WORSENED\")   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/questionnaires.html","id":"worstbest-answer","dir":"Articles","previous_headings":"","what":"Worst/Best Answer","title":"Creating Questionnaire ADaMs","text":"class parameters can used worst answer set yes/answers selected. example, yes/answers “sleep”, “Waking three times”, “30 minutes fall asleep” collected, parameter worst sleeping problems derived. example, “sleeping problems” assumed questions answered “”.","code":"adsp <- adqs %>%   filter(PARCAT1 == \"SLEEPING PROBLEMS\") %>%   derive_extreme_event(     by_vars = exprs(USUBJID, AVISIT),     tmp_event_nr_var = event_nr,     order = exprs(event_nr, ADY, QSSEQ),     mode = \"first\",     events = list(       event(         condition = PARAMCD == \"SP0101\" & AVALC == \"YES\",         set_values_to = exprs(           AVALC = \"No sleep\",           AVAL = 1         )       ),       event(         condition = PARAMCD == \"SP0102\" & AVALC == \"YES\",         set_values_to = exprs(           AVALC = \"Waking up more than three times\",           AVAL = 2         )       ),       event(         condition = PARAMCD == \"SP0103\" & AVALC == \"YES\",         set_values_to = exprs(           AVALC = \"More than 30 mins to fall asleep\",           AVAL = 3         )       ),       event(         condition = all(AVALC == \"NO\"),         set_values_to = exprs(           AVALC = \"No sleeping problems\",           AVAL = 4         )       ),       event(         condition = TRUE,         set_values_to = exprs(           AVALC = \"Missing\",           AVAL = 99         )       )     ),     set_values_to = exprs(       PARAMCD = \"SP01WSP\",       PARAM = \"Worst Sleeping Problems\"     )   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/questionnaires.html","id":"completion","dir":"Articles","previous_headings":"","what":"Completion","title":"Creating Questionnaire ADaMs","text":"Parameters completion, like “least 90% questions answered”, can derived derive_summary_records(). Please note denominator may depend answers questions. example, given questionnaire might direct someone go question #4 directly question #8 based response question #4, questions #5, #6 #7 apply case. missed visits need taken account, expected records can added input dataset calling derive_expected_records():","code":"adgdssf <- adgdssf %>%   derive_summary_records(     dataset_add = adgdssf,     filter_add = str_detect(PARAMCD, \"GDS02[01][0-9]\"),     by_vars = exprs(USUBJID, AVISIT),     set_values_to = exprs(       AVAL = sum(!is.na(AVAL)) / 15 >= 0.9,       PARAMCD = \"COMPL90P\",       PARAM = \"Completed at least 90% of questions?\",       AVALC = if_else(AVAL == 1, \"YES\", \"NO\")     )   ) # Create dataset with expected visits and parameters (GDS0201 - GDS0215) parm_visit_ref <- crossing(   tribble(     ~AVISIT,    ~AVISITN,     \"BASELINE\",        0,     \"VISIT 2\",         2,     \"VISIT 3\",         3,     \"VISIT 4\",         4,     \"VISIT 5\",         5   ),   tibble(PARAMCD = sprintf(\"GDS02%02d\", seq(1, 15))) )  adgdssf <- adgdssf %>%   derive_expected_records(     dataset_ref = parm_visit_ref,     by_vars = exprs(USUBJID),     set_values_to = exprs(       filled_in = 1     )   ) %>%   derive_summary_records(     dataset = .,     dataset_add = .,     filter_add = str_detect(PARAMCD, \"GDS02[01][0-9]\"),     by_vars = exprs(USUBJID, AVISIT),     set_values_to = exprs(       AVAL = all(!is.na(AVAL)),       PARAMCD = \"COMPLALL\",       PARAM = \"Completed all questions?\",       AVALC = if_else(AVAL == 1, \"YES\", \"NO\")     )   ) %>%   filter(is.na(filled_in)) %>%   select(-filled_in)"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/visits_periods.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Visit and Period Variables","text":"derivation visit variables like AVISIT, AVISITN, AWLO, AWHI, … period, subperiod, phase variables like APERIOD, TRT01A, TRT02A, ASPER, PHSDTM, PHEDTM, … highly study-specific. Therefore admiral provide functions derive variables. However, common scenarios like visit assignments based time windows deriving BDS period variables ADSL period variables, functions provided support derivations.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/visits_periods.html","id":"required-packages","dir":"Articles","previous_headings":"Introduction","what":"Required Packages","title":"Visit and Period Variables","text":"examples vignette require following packages.","code":"library(admiral) #> Error in get(paste0(generic, \".\", class), envir = get_method_env()) :  #>   object 'type_sum.accel' not found library(tibble) library(dplyr, warn.conflicts = FALSE) library(lubridate)"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/visits_periods.html","id":"visits","dir":"Articles","previous_headings":"","what":"Visit variables (AVISIT, AVISITN, AWLO, AWHI, …)","title":"Visit and Period Variables","text":"common ways deriving AVISIT AVISITN : variables set collected visits (VISIT VISITNUM). variables set based time windows. former can achieved simply calling mutate(), like vignettes template scripts. latter (study-specific) reference dataset needs created provides visit start end day (AWLO AWHI) values visit related variables (AVISIT, AVISITN, AWTARGET, …). visits can assigned based analysis day (ADY) calling derive_vars_joined():","code":"windows <- tribble(   ~AVISIT,    ~AWLO, ~AWHI, ~AVISITN, ~AWTARGET,   \"BASELINE\",   -30,     1,        0,         1,   \"WEEK 1\",       2,     7,        1,         5,   \"WEEK 2\",       8,    15,        2,        11,   \"WEEK 3\",      16,    22,        3,        19,   \"WEEK 4\",      23,    30,        4,        26 ) adbds <- tribble(   ~USUBJID, ~ADY,   \"1\",       -33,   \"1\",        -2,   \"1\",         3,   \"1\",        24,   \"2\",        NA, )  adbds1 <- adbds %>%   derive_vars_joined(     dataset_add = windows,     filter_join = AWLO <= ADY & ADY <= AWHI,     join_type = \"all\",   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/visits_periods.html","id":"period-subperiod-and-phase-variables","dir":"Articles","previous_headings":"","what":"Period, Subperiod, and Phase Variables","title":"Visit and Period Variables","text":"periods, subperiods, phases used, simpler ADaM applications corresponding variables consistent across datasets. can achieved defining periods, subperiods, phases using definition datasets. definition can stored ADSL separate dataset. vignette’s examples, separate dataset called period/phase reference dataset depending data contains. Note periods, subperiods, phases can defined differently across datasets (instance, may different across safety efficacy analyses) case start/stop dates defined individual datasets, instead ADSL. However, vignette cover scenario.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/visits_periods.html","id":"a-note-on-study-specific-code","dir":"Articles","previous_headings":"Period, Subperiod, and Phase Variables","what":"A Note on Study Specific Code","title":"Visit and Period Variables","text":"sections showcase available tools admiral work period, subperiod phase variables. However, point study specific code always required. two options: Study specific code used first derive variables PxxSwSDT PxxSwEDT ADSL. create_period_dataset() derive_vars_joined() can used derive period/subperiod variables like ASPER ASPRSDT BDS OCCDS datasets. See example dataset . Study specific code used derive dataset one observation per patient, period, subperiod (see period reference dataset). derive_vars_period() can used derive PxxSwSDT PxxSwEDT ADSL derive_vars_joined() can used derive period/subperiod variables like ASPER ASPRSDT BDS OCCDS datasets. depends specific definition periods/subperiods option works best. definition based ADSL variables, first option work best. definition based vertically structured data like exposure data (EX dataset), second option used. vignette contains examples workflows.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/visits_periods.html","id":"reference","dir":"Articles","previous_headings":"Period, Subperiod, and Phase Variables","what":"The Period/Phase Reference Dataset","title":"Visit and Period Variables","text":"admiral functions expect separate reference datasets periods, subperiods, phases. periods numeric variable APERIOD expected, subperiods numeric variables APERIOD ASPER, phases numeric variable APHASEN. period/phase reference dataset created according design ADaM needs study question. contain one observation per subject period, subperiod, phase. See next section example dataset.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/visits_periods.html","id":"example_period","dir":"Articles","previous_headings":"Period, Subperiod, and Phase Variables","what":"Example Creation of the Period/Phase Reference Dataset","title":"Visit and Period Variables","text":"Consider simple crossover study following design:  Given design, option split study two periods: Period 1 (Day 1 Week 16); Period 2 (Week 16 End Study). Alternatively (additionally) one split two phases: Phase 1 - Treatment (Day 1 28 days Week 28); Phase 2 - Follow-(29 days Week 28 End Study). , present two example workflows: one period reference dataset created exposure dataset EX, phase reference dataset created using ADSL variables.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/visits_periods.html","id":"creating-the-periodphase-reference-dataset-from-ex","dir":"Articles","previous_headings":"Period, Subperiod, and Phase Variables > Example Creation of the Period/Phase Reference Dataset","what":"Creating the Period/Phase Reference Dataset from EX","title":"Visit and Period Variables","text":"create period reference dataset starting exposure dataset EX. Consider following exposure dataset: create period reference dataset, code like (similar) suffice: workflow populates Period End Date APEREDT periods except last one. value last period populated End Study Date (EOSDT) ADSL, obtain:","code":"ex <- tribble(   ~STUDYID, ~USUBJID,  ~VISIT,    ~EXTRT,   ~EXSTDTC,   \"xyz\",    \"1\",       \"Day 1\",   \"Drug X\", \"2022-01-02\",   \"xyz\",    \"1\",       \"Week 4\",  \"Drug X\", \"2022-02-05\",   \"xyz\",    \"1\",       \"Week 8\",  \"Drug X\", \"2022-03-01\",   \"xyz\",    \"1\",       \"Week 12\", \"Drug X\", \"2022-04-03\",   \"xyz\",    \"1\",       \"Week 16\", \"Drug Y\", \"2022-05-03\",   \"xyz\",    \"1\",       \"Week 20\", \"Drug Y\", \"2022-06-02\",   \"xyz\",    \"1\",       \"Week 24\", \"Drug Y\", \"2022-07-01\",   \"xyz\",    \"1\",       \"Week 28\", \"Drug Y\", \"2022-08-04\",   \"xyz\",    \"2\",       \"Day 1\",   \"Drug Y\", \"2023-10-20\",   \"xyz\",    \"2\",       \"Week 4\",  \"Drug Y\", \"2023-11-21\",   \"xyz\",    \"2\",       \"Week 8\",  \"Drug Y\", \"2023-12-19\",   \"xyz\",    \"2\",       \"Week 12\", \"Drug Y\", \"2024-01-19\",   \"xyz\",    \"2\",       \"Week 16\", \"Drug X\", \"2024-02-20\",   \"xyz\",    \"2\",       \"Week 20\", \"Drug X\", \"2024-03-17\",   \"xyz\",    \"2\",       \"Week 24\", \"Drug X\", \"2024-04-22\",   \"xyz\",    \"2\",       \"Week 28\", \"Drug X\", \"2024-05-21\" ) period_ref <- ex %>%   # Select visits marking the start of each period   filter(VISIT %in% c(\"Day 1\", \"Week 16\")) %>%   # Create APERIOD, APERSDT, TRTA based on SDTM counterparts   mutate(     APERIOD = case_when(       VISIT == \"Day 1\" ~ 1,       VISIT == \"Week 16\" ~ 2     ),     TRTA = EXTRT,     APERSDT = convert_dtc_to_dt(EXSTDTC)   ) %>%   # Create APEREDT based on start date of next period   arrange(USUBJID, APERSDT) %>%   group_by(USUBJID) %>%   mutate(     APEREDT = lead(APERSDT) - 1 # one day before start of next period   ) %>%   # Tidy up   ungroup() %>%   select(-starts_with(\"EX\"), -VISIT) adsl <- tribble(   ~STUDYID,  ~USUBJID,  ~TRTSDT,           ~TRTEDT,           ~EOSDT,   \"xyz\",     \"1\",       ymd(\"2022-01-02\"), ymd(\"2022-08-04\"), ymd(\"2022-09-10\"),   \"xyz\",     \"2\",       ymd(\"2023-10-20\"), ymd(\"2024-05-21\"), ymd(\"2024-06-30\") )  period_ref <- period_ref %>%   left_join(adsl, by = c(\"STUDYID\", \"USUBJID\")) %>%   mutate(APEREDT = case_when(     APERIOD == \"1\" ~ APEREDT,     APERIOD == \"2\" ~ EOSDT   )) %>%   select(-EOSDT, -TRTSDT, -TRTEDT)"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/visits_periods.html","id":"from_adsl","dir":"Articles","previous_headings":"Period, Subperiod, and Phase Variables > Example Creation of the Period/Phase Reference Dataset","what":"Creating the Period/Phase Reference Dataset from ADSL","title":"Visit and Period Variables","text":"treatment start end dates already included ADSL, can derive phase variables directly ADSL create phase reference dataset employing create_period_dataset(). example command achieve goal:","code":"adsl1 <- adsl %>%   mutate(     PH1SDT = TRTSDT,     PH1EDT = TRTEDT + 28,     APHASE1 = \"TREATMENT\",     PH2SDT = TRTEDT + 29,     PH2EDT = EOSDT,     APHASE2 = \"FUP\"   )  phase_ref <- create_period_dataset(   adsl1,   new_vars = exprs(PHSDT = PHwSDT, PHEDT = PHwEDT, APHASE = APHASEw) )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/visits_periods.html","id":"periods_adsl","dir":"Articles","previous_headings":"Period, Subperiod, and Phase Variables","what":"Creating ADSL Period, Subperiod, or Phase Variables","title":"Visit and Period Variables","text":"period/phase reference dataset available, ADSL variables periods, subperiods, phases can created dataset calling derive_vars_period(). example period reference dataset previous section can used add period variables (APxxSDT, APxxEDT) ADSL:","code":"adsl2 <- derive_vars_period(   adsl,   dataset_ref = period_ref,   new_vars = exprs(APxxSDT = APERSDT, APxxEDT = APEREDT) )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/visits_periods.html","id":"periods_bds","dir":"Articles","previous_headings":"Period, Subperiod, and Phase Variables","what":"Creating BDS and OCCDS Period, Subperiod, or Phase Variables","title":"Visit and Period Variables","text":"period/phase reference dataset available, BDS OCCDS variables periods, subperiods, phases can created calling derive_vars_joined(). example variables APHASEN, PHSDT, PHEDT, APHASE can derived phase reference dataset defined .","code":"adae <- tribble(   ~STUDYID, ~USUBJID, ~ASTDT,   \"xyz\",    \"1\",      \"2022-01-31\",   \"xyz\",    \"1\",      \"2022-05-02\",   \"xyz\",    \"1\",      \"2022-09-03\",   \"xyz\",    \"1\",      \"2022-09-09\",   \"xyz\",    \"2\",      \"2023-12-25\",   \"xyz\",    \"2\",      \"2024-06-19\", ) %>%   mutate(ASTDT = ymd(ASTDT))  adae1 <- adae %>%   derive_vars_joined(     dataset_add = phase_ref,     by_vars = exprs(STUDYID, USUBJID),     filter_join = PHSDT <= ASTDT & ASTDT <= PHEDT,     join_type = \"all\"   )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/visits_periods.html","id":"treatment-variables-trtxxp-trtxxa-trtp-trta","dir":"Articles","previous_headings":"","what":"Treatment Variables (TRTxxP, TRTxxA, TRTP, TRTA, …)","title":"Visit and Period Variables","text":"studies multiple periods treatment can differ period, e.g. crossover trial - see previous section example design showcasing . CDISC defines variables planned actual treatments ADSL (TRTxxP, TRTxxA, TRxxPGy, TRxxAGy, …) corresponding variables BDS OCCDS datasets (TRTP, TRTA, TRTPGy, TRTAGy, …). can derived way (step) period, subperiod, phase variables.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/articles/visits_periods.html","id":"treatment_adsl","dir":"Articles","previous_headings":"Treatment Variables (TRTxxP, TRTxxA, TRTP, TRTA, …)","what":"Creating ADSL Treatment Variables","title":"Visit and Period Variables","text":"treatment information included period/phase reference dataset, treatment ADSL variables can created calling derive_vars_period(). showcased using period reference dataset previous sections.","code":"adsl <- derive_vars_period(   adsl,   dataset_ref = period_ref,   new_vars = exprs(     APxxSDT = APERSDT,     APxxEDT = APEREDT,     TRTxxA = TRTA   ) )"},{"path":"https://pharmaverse.github.io/admiral/dev/articles/visits_periods.html","id":"treatment_bds","dir":"Articles","previous_headings":"Treatment Variables (TRTxxP, TRTxxA, TRTP, TRTA, …)","what":"Creating BDS and OCCDS Treatment Variables","title":"Visit and Period Variables","text":"period/phase reference dataset available, BDS OCCDS variables treatment can created calling derive_vars_joined(). example variables APERIOD TRTA can derived period reference dataset defined . period/phase reference dataset available period/phase variables ADSL, former can created ADSL calling create_period_dataset(), showcased . time, calling create_period_dataset() just need make sure include TRTA = TRTxxA part new_vars argument create treatment variables well.","code":"adae <- tribble(   ~STUDYID, ~USUBJID, ~ASTDT,   \"xyz\",    \"1\",      \"2022-01-31\",   \"xyz\",    \"1\",      \"2022-05-02\",   \"xyz\",    \"1\",      \"2022-08-24\",   \"xyz\",    \"1\",      \"2022-09-09\",   \"xyz\",    \"2\",      \"2023-12-25\",   \"xyz\",    \"2\",      \"2024-06-07\", ) %>%   mutate(ASTDT = ymd(ASTDT))  adae2 <- adae %>%   derive_vars_joined(     dataset_add = period_ref,     by_vars = exprs(STUDYID, USUBJID),     new_vars = exprs(APERIOD, TRTA),     join_vars = exprs(APERSDT, APEREDT),     join_type = \"all\",     filter_join = APERSDT <= ASTDT & ASTDT <= APEREDT   ) period_ref1 <- adsl %>%   create_period_dataset(     new_vars = exprs(APERSDT = APxxSDT, APEREDT = APxxEDT, TRTA = TRTxxA)   )"},{"path":"https://pharmaverse.github.io/admiral/dev/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Ben Straub. Author, maintainer. Stefan Bundfuss. Author. Jeffrey Dickinson. Author. Ross Farrugia. Author. Fanny Gautier. Author. G Gayatri. Author. Asha Chakma. Author. Dinakar Kulkarni. Author. Edoardo Mancini. Author. Sadchla Mascary. Author. Gordon Miller. Author. Lina Patil. Author. Jim Rothstein. Author. Sophie Shapcott. Author. Eric Simms. Author. Daniel Sjoberg. Author. Stefan Thoma. Author. Phillip Webster. Author. Kangjie Zhang. Author. Zelos Zhu. Author. F. Hoffmann-La Roche AG. Copyright holder, funder. GlaxoSmithKline LLC. Copyright holder, funder.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Straub B, Bundfuss S, Dickinson J, Farrugia R, Gautier F, Gayatri G, Chakma , Kulkarni D, Mancini E, Mascary S, Miller G, Patil L, Rothstein J, Shapcott S, Simms E, Sjoberg D, Thoma S, Webster P, Zhang K, Zhu Z (2024). admiral: ADaM R Asset Library. R package version 1.1.1.9046, https://github.com/pharmaverse/admiral, https://pharmaverse.github.io/admiral/.","code":"@Manual{,   title = {admiral: ADaM in R Asset Library},   author = {Ben Straub and Stefan Bundfuss and Jeffrey Dickinson and Ross Farrugia and Fanny Gautier and G Gayatri and Asha Chakma and Dinakar Kulkarni and Edoardo Mancini and Sadchla Mascary and Gordon Miller and Lina Patil and Jim Rothstein and Sophie Shapcott and Eric Simms and Daniel Sjoberg and Stefan Thoma and Phillip Webster and Kangjie Zhang and Zelos Zhu},   year = {2024},   note = {R package version 1.1.1.9046,     https://github.com/pharmaverse/admiral},   url = {https://pharmaverse.github.io/admiral/}, }"},{"path":"https://pharmaverse.github.io/admiral/dev/index.html","id":"admiral-","dir":"","previous_headings":"","what":"ADaM in R Asset Library","title":"ADaM in R Asset Library","text":"ADaM R Asset Library","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/index.html","id":"purpose","dir":"","previous_headings":"","what":"Purpose","title":"ADaM in R Asset Library","text":"provide open source, modularized toolbox enables pharmaceutical programming community develop ADaM datasets R.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"ADaM in R Asset Library","text":"package available CRAN can installed : install development version package GitHub run:","code":"install.packages(\"admiral\") # install.packages(\"devtools\") devtools::install_github(\"pharmaverse/admiral\")"},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/index.html","id":"release-schedule","dir":"","previous_headings":"","what":"Release Schedule","title":"ADaM in R Asset Library","text":"admiral family several downstream upstream dependencies releases done two Phases: Phase 1 release {admiraldev}, {pharmaversesdtm}, {admiral} core Phase 2 release extension packages, e.g. {admiralonco}, {admiralophtha}, {admiralvaccine} {pharmaverseadam}.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/index.html","id":"main-goal","dir":"","previous_headings":"","what":"Main Goal","title":"ADaM in R Asset Library","text":"Provide users open source, modularized toolbox create ADaM datasets R. opposed “run 1 line ADaM appears” black-box solution attempt automate ADaM. One key aspects admiral development users users. gives entry point collaborate, co-create contribute harmonized approach developing ADaMs R across pharmaceutical industry.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/index.html","id":"scope","dir":"","previous_headings":"","what":"Scope","title":"ADaM in R Asset Library","text":"set expectations: target admiral ever provide possible solutions ADaM datasets outside study specific needs. depends user’s collaboration contribution help grow time asset library robust, easy use across-industry focus. see coverage 100% ADaM derivations ever achievable—ADaM endless. provide: toolbox re-usable functions utilities create ADaM datasets using R scripts modular manner (“opinionated” design strategy). Pharmaceutical communities companies encouraged contribute admiral following provided programming strategy modular approach Functions comprehensively documented tested, including example calls—listed Reference section. Vignettes create ADSL, BDS OCCDS datasets, including example scripts. Vignettes ADaM dataset specific functionality (.e. dictionary coding, date imputation, SMQs …).","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/index.html","id":"the-admiral-family-of-packages","dir":"","previous_headings":"","what":"The {admiral} Family of Packages","title":"ADaM in R Asset Library","text":"three types packages admiral family: Core package—one package containing core functions required create ADaMs, usable company (.e. general derivations, utility functions checks ADSL, OCCDS BDS). TA (Therapeutic Area) package extensions—one package per TA functions specific algorithms requirements particular TA (e.g. {admiralonco}). Company package extensions—specific needs plug-ins company, access metadata (e.g. {admiralroche} {admiralgsk}).","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/index.html","id":"related-packages","dir":"","previous_headings":"","what":"Related Packages","title":"ADaM in R Asset Library","text":"Related data packages include: {pharmaversesdtm}—contains test SDTM data sourced CDISC pilot project constructed ad-hoc admiral team. prerequisite package admiral. {pharmaverseadam}—contains test ADaM data automatically generated running ADaM admiral TA package extensions templates {pharmaversesdtm} data. packages developed admiral team, can used across pharmaverse common, open-source test SDTM ADaM data. following packages also useful working ADaM datasets: {metacore} {metatools}—enable users manipulate work dataset metadata. {xportr}—provides functionality get xpt files ready transport.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/index.html","id":"admiral-manifesto","dir":"","previous_headings":"","what":"Admiral Manifesto","title":"ADaM in R Asset Library","text":"admiral extension packages, prioritize providing users simple adopt toolkit enables produce readable easily constructible ADaM programs. following explains philosophy, try adhere across admiral family packages. isn’t always clear single, straightforward rule, guiding principles adhere admiral. manifesto helps show considerations developers making decisions. four design principles achieve main goal:","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/index.html","id":"usability","dir":"","previous_headings":"Admiral Manifesto","what":"Usability","title":"ADaM in R Asset Library","text":"admiral functions easy use. Documentation absolute priority. function reference page cover purpose, descriptions argument permitted values, expected input output, clear real-life examples—users don’t need dig code find answers. Vignettes complement functional documentation help users see best functions can applied achieve ADaM requirements. Functions written structured way users able read, re-use extend study specific purposes needed (see Readability ).","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/index.html","id":"simplicity","dir":"","previous_headings":"Admiral Manifesto","what":"Simplicity","title":"ADaM in R Asset Library","text":"admiral functions clear purpose. try ever design single functions achieve numerous different derivations. example user pick function >10 different arguments chances going difficult understand function applied specific need. intention arguments/parameters can influence output function calculated, change purpose function. try combine similar tasks algorithms one function applicable reduce amount repetitive functions similar algorithms group together similar functionality increase usability (e.g. one study day calculation rather function per variable). strive design functions general trying fulfill multiple, complex purposes. Functions allow expressions arguments used code snippets function calls. recommend avoid copy paste complex computational algorithms repetitive code like checks advise wrap function. However also like avoid multi-layered functional nesting, needs considered carefully keep nesting 3-4 functions exception rather rule.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/index.html","id":"findability","dir":"","previous_headings":"Admiral Manifesto","what":"Findability","title":"ADaM in R Asset Library","text":"admiral functions easily findable. growing code base, across family packages, make every effort make functions easily findable. use consistent naming conventions across functions, provide vignettes ADaM templates help users get started build familiarity. admiral family package website searchable. avoid repetitive functions similar tasks (explained study day example). package extension kept focused specific scope, e.g. features relevant across multiple extension packages moved core admiral package.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/index.html","id":"readability","dir":"","previous_headings":"Admiral Manifesto","what":"Readability","title":"ADaM in R Asset Library","text":"admiral functions follow Programming Strategy developers contributors must follow, code high degree consistency readability. encourage use tidyverse (e.g. dplyr) similar functionality existing base R. sections code perform actual derivations (e.g. besides assertions basic utilities), try limit nesting many dependencies functions. Modularity focus—don’t try achieve many steps one. code well commented. recognize user Health Authority reviewer may wish delve code base (especially given open source setting), users may need extend/adapt code study specific needs. therefore want module understandable , admiral developers.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/index.html","id":"references-and-documentation","dir":"","previous_headings":"","what":"References and Documentation","title":"ADaM in R Asset Library","text":"Please go Get Started section start using admiral. Please see pharmaverse YouTube channel videos related admiral. Please see Programming Strategy understand functions created. Please see FAQ frequent questions. Please see Contribution Model get involved making contributions. Please see FAQ: R Package Versions develop certain R package versions.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/index.html","id":"pharmaverse-blog","dir":"","previous_headings":"","what":"Pharmaverse Blog","title":"ADaM in R Asset Library","text":"interested R Clinical Reporting, visit pharmaverse blog. contains regular, bite-sized posts showcasing admiral packages pharmaverse can used realize vision full end--end Clinical Reporting R. also always looking keen admiral users publish blog posts use package. , feel free make issue GitHub repo get started!","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/index.html","id":"recent-conference-presentations","dir":"","previous_headings":"","what":"Recent Conference Presentations","title":"ADaM in R Asset Library","text":"full collection admiral conference presentations years, please travel Presentation Archive.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/index.html","id":"contact","dir":"","previous_headings":"","what":"Contact","title":"ADaM in R Asset Library","text":"use following support communications user developer community: Slack—informal discussions, Q&building user community. don’t access, use link join pharmaverse Slack workspace. GitHub Issues—direct feedback, enhancement requests raising bugs.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/index.html","id":"acknowledgments","dir":"","previous_headings":"","what":"Acknowledgments","title":"ADaM in R Asset Library","text":"Along authors contributors, thanks following people work package: Jaxon Abercrombie, Mahdi , Teckla Akinyi, James Black, Claudia Carlucci, Asha Chakma, Bill Denney, Kamila Duniec, Alice Ehmann, Romain Francois, Ania Golab, Alana Harris, Declan Hodges, Anthony Howard, Shimeng Huang, Samia Kabi, James Kim, John Kirkpatrick, Leena Khatri, Robin Koeger, Konstantina Koukourikou, Pavan Kumar, Pooja Kumari, Shan Lee, Wenyi Liu, Iain McCay, Jack McGavigan, Jordanna Morrish, Syed Mubasheer, Thomas Neitmann, Yohann Omnes, Barbara O’Reilly, Hamza Rahal, Nick Ramirez, Tom Ratford, Jim Rothstein, Sukalpo Saha, Tamara Senior, Sophie Shapcott, Ondrej Slama, Andrew Smith, Daniil Stefonishin, Vignesh Thanikachalam, Michael Thorpe, Annie Yang, Ojesh Upadhyay, Franciszek Walkowiak Vladyslav Shuliar.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/admiral-package.html","id":null,"dir":"Reference","previous_headings":"","what":"admiral: ADaM in R Asset Library — admiral-package","title":"admiral: ADaM in R Asset Library — admiral-package","text":"toolbox programming Clinical Data Interchange Standards Consortium (CDISC) compliant Analysis Data Model (ADaM) datasets R. ADaM datasets mandatory part New Drug Biologics License Application submitted United States Food Drug Administration (FDA). Analysis derivations implemented accordance \"Analysis Data Model Implementation Guide\" (CDISC Analysis Data Model Team, 2021, https://www.cdisc.org/standards/foundational/adam).","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/admiral-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"admiral: ADaM in R Asset Library — admiral-package","text":"Maintainer: Ben Straub ben.x.straub@gsk.com Authors: Stefan Bundfuss (ORCID) Jeffrey Dickinson Ross Farrugia Fanny Gautier G Gayatri Asha Chakma Dinakar Kulkarni Edoardo Mancini (ORCID) Sadchla Mascary Gordon Miller Lina Patil Jim Rothstein (ORCID) Sophie Shapcott Eric Simms Daniel Sjoberg (ORCID) Stefan Thoma (ORCID) Phillip Webster Kangjie Zhang Zelos Zhu contributors: F. Hoffmann-La Roche AG [copyright holder, funder] GlaxoSmithKline LLC [copyright holder, funder]","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/admiral_adlb.html","id":null,"dir":"Reference","previous_headings":"","what":"Lab Analysis Dataset — admiral_adlb","title":"Lab Analysis Dataset — admiral_adlb","text":"example lab analysis dataset","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/admiral_adlb.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Lab Analysis Dataset — admiral_adlb","text":"","code":"admiral_adlb"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/admiral_adlb.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Lab Analysis Dataset — admiral_adlb","text":"object class tbl_df (inherits tbl, data.frame) 3779 rows 111 columns.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/admiral_adlb.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Lab Analysis Dataset — admiral_adlb","text":"Derived adlb template, filtered due dataset size following USUBJIDs: 01-701-1015, 01-701-1023, 01-701-1028, 01-701-1033, 01-701-1034, 01-701-1047, 01-701-1097, 01-705-1186, 01-705-1292, 01-705-1310, 01-708-1286","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/admiral_adsl.html","id":null,"dir":"Reference","previous_headings":"","what":"Subject Level Analysis Dataset — admiral_adsl","title":"Subject Level Analysis Dataset — admiral_adsl","text":"example subject level analysis dataset","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/admiral_adsl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Subject Level Analysis Dataset — admiral_adsl","text":"","code":"admiral_adsl"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/admiral_adsl.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Subject Level Analysis Dataset — admiral_adsl","text":"object class tbl_df (inherits tbl, data.frame) 306 rows 54 columns.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/admiral_adsl.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Subject Level Analysis Dataset — admiral_adsl","text":"Derived dm ds datasets using {admiral} (https://github.com/pharmaverse/admiral/blob/main/inst/templates/ad_adsl.R)","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/assert_parameters_argument.html","id":null,"dir":"Reference","previous_headings":"","what":"Asserts parameters Argument and Converts to List of Expressions — assert_parameters_argument","title":"Asserts parameters Argument and Converts to List of Expressions — assert_parameters_argument","text":"function asserts argument character vector list expressions. character vector, converts list symbols.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/assert_parameters_argument.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Asserts parameters Argument and Converts to List of Expressions — assert_parameters_argument","text":"","code":"assert_parameters_argument(parameters, optional = TRUE)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/assert_parameters_argument.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Asserts parameters Argument and Converts to List of Expressions — assert_parameters_argument","text":"parameters argument check optional checked argument optional? set FALSE parameters NULL error thrown.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/assert_parameters_argument.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Asserts parameters Argument and Converts to List of Expressions — assert_parameters_argument","text":"parameters argument (converted list symbol, character vector)","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/atoxgr_criteria_ctcv4.html","id":null,"dir":"Reference","previous_headings":"","what":"Metadata Holding Grading Criteria for NCI-CTCAEv4 — atoxgr_criteria_ctcv4","title":"Metadata Holding Grading Criteria for NCI-CTCAEv4 — atoxgr_criteria_ctcv4","text":"Metadata Holding Grading Criteria NCI-CTCAEv4","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/atoxgr_criteria_ctcv4.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Metadata Holding Grading Criteria for NCI-CTCAEv4 — atoxgr_criteria_ctcv4","text":"","code":"atoxgr_criteria_ctcv4"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/atoxgr_criteria_ctcv4.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Metadata Holding Grading Criteria for NCI-CTCAEv4 — atoxgr_criteria_ctcv4","text":"object class tbl_df (inherits tbl, data.frame) 40 rows 13 columns.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/atoxgr_criteria_ctcv4.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Metadata Holding Grading Criteria for NCI-CTCAEv4 — atoxgr_criteria_ctcv4","text":"metadata origin ADLB Grading Spec Excel file ships {admiral} can accessed using system.file(\"adlb_grading/adlb_grading_spec.xlsx\", package = \"admiral\") sheet = \"NCICTCAEv4\". dataset contained following columns: SOC: variable hold SOC lab test criteria. TERM: variable hold term describing criteria applied particular lab test, eg. 'Anemia' 'INR Increased'. Note: variable case insensitive. Grade 1: Criteria defining lab value Grade 1. Grade 2: Criteria defining lab value Grade 2. Grade 3: Criteria defining lab value Grade 3. Grade 4: Criteria defining lab value Grade 4. Grade 5: Criteria defining lab value Grade 5. Definition: Holds definition lab test abnormality. GRADE_CRITERIA_CODE: variable hold code creates grade based defined criteria. SI_UNIT_CHECK: variable hold unit particular lab test. Used check input data criteria based absolute values. VAR_CHECK: List variables required implement lab grade criteria. Use check input data. DIRECTION: variable hold direction abnormality particular lab test value. 'L' LOW values, 'H' HIGH values. Note: variable case insensitive. COMMENT: Holds information regarding rationale behind implementation grading criteria. Note: Variables SOC, TERM, Grade 1, Grade 2,Grade 3,Grade 4,Grade 5, Definition source document NCI-CTC website defining grading criteria. Common Terminology Criteria Adverse Events (CTCAE)v4.0 variables 'TERM' used {admiral} code, rest information traceability .","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/atoxgr_criteria_ctcv5.html","id":null,"dir":"Reference","previous_headings":"","what":"Metadata Holding Grading Criteria for NCI-CTCAEv5 — atoxgr_criteria_ctcv5","title":"Metadata Holding Grading Criteria for NCI-CTCAEv5 — atoxgr_criteria_ctcv5","text":"Metadata Holding Grading Criteria NCI-CTCAEv5","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/atoxgr_criteria_ctcv5.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Metadata Holding Grading Criteria for NCI-CTCAEv5 — atoxgr_criteria_ctcv5","text":"","code":"atoxgr_criteria_ctcv5"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/atoxgr_criteria_ctcv5.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Metadata Holding Grading Criteria for NCI-CTCAEv5 — atoxgr_criteria_ctcv5","text":"object class tbl_df (inherits tbl, data.frame) 37 rows 13 columns.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/atoxgr_criteria_ctcv5.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Metadata Holding Grading Criteria for NCI-CTCAEv5 — atoxgr_criteria_ctcv5","text":"metadata origin ADLB Grading Spec Excel file ships {admiral} can accessed using system.file(\"adlb_grading/adlb_grading_spec.xlsx\", package = \"admiral\") sheet = \"NCICTCAEv5\". dataset contained following columns: SOC: variable hold SOC lab test criteria. TERM: variable hold term describing criteria applied particular lab test, eg. 'Anemia' 'INR Increased'. Note: variable case insensitive. Grade 1: Criteria defining lab value Grade 1. Grade 2: Criteria defining lab value Grade 2. Grade 3: Criteria defining lab value Grade 3. Grade 4: Criteria defining lab value Grade 4. Grade 5: Criteria defining lab value Grade 5. Definition: Holds definition lab test abnormality. GRADE_CRITERIA_CODE: variable hold code creates grade based defined criteria. SI_UNIT_CHECK: variable hold unit particular lab test. Used check input data criteria based absolute values. VAR_CHECK: List variables required implement lab grade criteria. Use check input data. DIRECTION: variable hold direction abnormality particular lab test value. 'L' LOW values, 'H' HIGH values. Note: variable case insensitive. COMMENT: Holds information regarding rationale behind implementation grading criteria. Note: Variables SOC, TERM, Grade 1, Grade 2,Grade 3,Grade 4,Grade 5, Definition source document NCI-CTC website defining grading criteria. Common Terminology Criteria Adverse Events (CTCAE)v5.0 variables 'TERM' used {admiral} code, rest information traceability .","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/atoxgr_criteria_daids.html","id":null,"dir":"Reference","previous_headings":"","what":"Metadata Holding Grading Criteria for DAIDs — atoxgr_criteria_daids","title":"Metadata Holding Grading Criteria for DAIDs — atoxgr_criteria_daids","text":"Metadata Holding Grading Criteria DAIDs","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/atoxgr_criteria_daids.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Metadata Holding Grading Criteria for DAIDs — atoxgr_criteria_daids","text":"","code":"atoxgr_criteria_daids"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/atoxgr_criteria_daids.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Metadata Holding Grading Criteria for DAIDs — atoxgr_criteria_daids","text":"object class tbl_df (inherits tbl, data.frame) 63 rows 15 columns.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/atoxgr_criteria_daids.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Metadata Holding Grading Criteria for DAIDs — atoxgr_criteria_daids","text":"metadata origin ADLB Grading Spec Excel file ships {admiral} can accessed using system.file(\"adlb_grading/adlb_grading_spec.xlsx\", package = \"admiral\") sheet = \"DAIDS\". dataset contained following columns: SOC: variable hold SOC lab test criteria. TERM: variable hold term describing criteria applied particular lab test, eg. 'Anemia' 'INR Increased'. Note: variable case insensitive. SUBGROUP : Description sub-group subjects grading applied (.e. >= 18 years) Grade 1: Criteria defining lab value Grade 1. Grade 2: Criteria defining lab value Grade 2. Grade 3: Criteria defining lab value Grade 3. Grade 4: Criteria defining lab value Grade 4. Grade 5: Criteria defining lab value Grade 5. Definition: Holds definition lab test abnormality. FILTER : admiral code apply filter based SUBGROUP column. GRADE_CRITERIA_CODE: variable hold code creates grade based defined criteria. SI_UNIT_CHECK: variable hold unit particular lab test. Used check input data criteria based absolute values. VAR_CHECK: List variables required implement lab grade criteria. Use check input data. DIRECTION: variable hold direction abnormality particular lab test value. 'L' LOW values, 'H' HIGH values. Note: variable case insensitive. COMMENT: Holds information regarding rationale behind implementation grading criteria. Note: Variables SOC, TERM, SUBGROUP, Grade 1, Grade 2,Grade 3,Grade 4,Grade 5, Definition source document DAIDS website defining grading criteria. [Division AIDS (DAIDS) Table Grading Severity Adult Pediatric Adverse Events variables 'TERM' used {admiral} code, rest information traceability .","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/basket_select.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a basket_select object — basket_select","title":"Create a basket_select object — basket_select","text":"Create basket_select object","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/basket_select.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a basket_select object — basket_select","text":"","code":"basket_select(name = NULL, id = NULL, scope = NULL, type, ...)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/basket_select.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a basket_select object — basket_select","text":"name Name query used select definition query company database. id Identifier query used select definition query company database. scope Scope query used select definition query company database. Permitted Values: \"BROAD\", \"NARROW\", NA_character_ type type argument expects character scalar. passed company specific get_terms() function function can determine sort basket requested ... number named function arguments. Can used pass company specific conditions flags used user-defined function passed argument get_terms_fun function create_query_data().","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/basket_select.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a basket_select object — basket_select","text":"object class basket_select.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/basket_select.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create a basket_select object — basket_select","text":"Exactly one name id must specified.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/call_derivation.html","id":null,"dir":"Reference","previous_headings":"","what":"Call a Single Derivation Multiple Times — call_derivation","title":"Call a Single Derivation Multiple Times — call_derivation","text":"Call single derivation multiple times parameters/arguments fixed across iterations others varying.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/call_derivation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Call a Single Derivation Multiple Times — call_derivation","text":"","code":"call_derivation(dataset = NULL, derivation, variable_params, ...)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/call_derivation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Call a Single Derivation Multiple Times — call_derivation","text":"dataset Input dataset derivation derivation function call function performs specific derivation expected. derivation adds variables observations dataset. first argument derivation must expect dataset derivation must return dataset. function must provide dataset argument arguments specified params() objects passed variable_params ... argument. Please note possible specify {dplyr} functions like mutate() summarize(). variable_params list function arguments different across iterations. set function arguments must created using params(). ... number named function arguments stay across iterations. function argument specified inside variable_params ... value variable_params overwrites one ...","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/call_derivation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Call a Single Derivation Multiple Times — call_derivation","text":"input dataset additional records/variables added depending derivation used.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/call_derivation.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Call a Single Derivation Multiple Times — call_derivation","text":"","code":"library(dplyr, warn.conflicts = FALSE) adsl <- tribble(   ~STUDYID,   ~USUBJID,      ~TRTSDT,      ~TRTEDT,   \"PILOT01\", \"01-1307\",           NA,           NA,   \"PILOT01\", \"05-1377\", \"2014-01-04\", \"2014-01-25\",   \"PILOT01\", \"06-1384\", \"2012-09-15\", \"2012-09-24\",   \"PILOT01\", \"15-1085\", \"2013-02-16\", \"2013-08-18\",   \"PILOT01\", \"16-1298\", \"2013-04-08\", \"2013-06-28\" ) %>%   mutate(     across(TRTSDT:TRTEDT, as.Date)   )  ae <- tribble(   ~STUDYID,  ~DOMAIN,  ~USUBJID,     ~AESTDTC,     ~AEENDTC,   \"PILOT01\",    \"AE\", \"06-1384\", \"2012-09-15\", \"2012-09-29\",   \"PILOT01\",    \"AE\", \"06-1384\", \"2012-09-15\", \"2012-09-29\",   \"PILOT01\",    \"AE\", \"06-1384\", \"2012-09-23\", \"2012-09-29\",   \"PILOT01\",    \"AE\", \"06-1384\", \"2012-09-23\", \"2012-09-29\",   \"PILOT01\",    \"AE\", \"06-1384\", \"2012-09-15\", \"2012-09-29\",   \"PILOT01\",    \"AE\", \"06-1384\", \"2012-09-15\", \"2012-09-29\",   \"PILOT01\",    \"AE\", \"06-1384\", \"2012-09-15\", \"2012-09-29\",   \"PILOT01\",    \"AE\", \"06-1384\", \"2012-09-15\", \"2012-09-29\",   \"PILOT01\",    \"AE\", \"06-1384\", \"2012-09-23\", \"2012-09-29\",   \"PILOT01\",    \"AE\", \"06-1384\", \"2012-09-23\", \"2012-09-29\",   \"PILOT01\",    \"AE\", \"16-1298\", \"2013-06-08\", \"2013-07-06\",   \"PILOT01\",    \"AE\", \"16-1298\", \"2013-06-08\", \"2013-07-06\",   \"PILOT01\",    \"AE\", \"16-1298\", \"2013-04-22\", \"2013-07-06\",   \"PILOT01\",    \"AE\", \"16-1298\", \"2013-04-22\", \"2013-07-06\",   \"PILOT01\",    \"AE\", \"16-1298\", \"2013-04-22\", \"2013-07-06\",   \"PILOT01\",    \"AE\", \"16-1298\", \"2013-04-22\", \"2013-07-06\" )  adae <- ae %>%   derive_vars_merged(     dataset_add = adsl,     new_vars = exprs(TRTSDT, TRTEDT),     by_vars = exprs(USUBJID)   )  ## While `derive_vars_dt()` can only add one variable at a time, using `call_derivation()` ## one can add multiple variables in one go call_derivation(   dataset = adae,   derivation = derive_vars_dt,   variable_params = list(     params(dtc = AESTDTC, date_imputation = \"first\", new_vars_prefix = \"AST\"),     params(dtc = AEENDTC, date_imputation = \"last\", new_vars_prefix = \"AEN\")   ),   min_dates = exprs(TRTSDT),   max_dates = exprs(TRTEDT) ) #> # A tibble: 16 × 9 #>    STUDYID DOMAIN USUBJID AESTDTC    AEENDTC    TRTSDT     TRTEDT     ASTDT      #>    <chr>   <chr>  <chr>   <chr>      <chr>      <date>     <date>     <date>     #>  1 PILOT01 AE     06-1384 2012-09-15 2012-09-29 2012-09-15 2012-09-24 2012-09-15 #>  2 PILOT01 AE     06-1384 2012-09-15 2012-09-29 2012-09-15 2012-09-24 2012-09-15 #>  3 PILOT01 AE     06-1384 2012-09-23 2012-09-29 2012-09-15 2012-09-24 2012-09-23 #>  4 PILOT01 AE     06-1384 2012-09-23 2012-09-29 2012-09-15 2012-09-24 2012-09-23 #>  5 PILOT01 AE     06-1384 2012-09-15 2012-09-29 2012-09-15 2012-09-24 2012-09-15 #>  6 PILOT01 AE     06-1384 2012-09-15 2012-09-29 2012-09-15 2012-09-24 2012-09-15 #>  7 PILOT01 AE     06-1384 2012-09-15 2012-09-29 2012-09-15 2012-09-24 2012-09-15 #>  8 PILOT01 AE     06-1384 2012-09-15 2012-09-29 2012-09-15 2012-09-24 2012-09-15 #>  9 PILOT01 AE     06-1384 2012-09-23 2012-09-29 2012-09-15 2012-09-24 2012-09-23 #> 10 PILOT01 AE     06-1384 2012-09-23 2012-09-29 2012-09-15 2012-09-24 2012-09-23 #> 11 PILOT01 AE     16-1298 2013-06-08 2013-07-06 2013-04-08 2013-06-28 2013-06-08 #> 12 PILOT01 AE     16-1298 2013-06-08 2013-07-06 2013-04-08 2013-06-28 2013-06-08 #> 13 PILOT01 AE     16-1298 2013-04-22 2013-07-06 2013-04-08 2013-06-28 2013-04-22 #> 14 PILOT01 AE     16-1298 2013-04-22 2013-07-06 2013-04-08 2013-06-28 2013-04-22 #> 15 PILOT01 AE     16-1298 2013-04-22 2013-07-06 2013-04-08 2013-06-28 2013-04-22 #> 16 PILOT01 AE     16-1298 2013-04-22 2013-07-06 2013-04-08 2013-06-28 2013-04-22 #> # ℹ 1 more variable: AENDT <date>  ## The above call using `call_derivation()` is equivalent to the following adae %>%   derive_vars_dt(     new_vars_prefix = \"AST\",     dtc = AESTDTC,     date_imputation = \"first\",     min_dates = exprs(TRTSDT),     max_dates = exprs(TRTEDT)   ) %>%   derive_vars_dt(     new_vars_prefix = \"AEN\",     dtc = AEENDTC,     date_imputation = \"last\",     min_dates = exprs(TRTSDT),     max_dates = exprs(TRTEDT)   ) #> # A tibble: 16 × 9 #>    STUDYID DOMAIN USUBJID AESTDTC    AEENDTC    TRTSDT     TRTEDT     ASTDT      #>    <chr>   <chr>  <chr>   <chr>      <chr>      <date>     <date>     <date>     #>  1 PILOT01 AE     06-1384 2012-09-15 2012-09-29 2012-09-15 2012-09-24 2012-09-15 #>  2 PILOT01 AE     06-1384 2012-09-15 2012-09-29 2012-09-15 2012-09-24 2012-09-15 #>  3 PILOT01 AE     06-1384 2012-09-23 2012-09-29 2012-09-15 2012-09-24 2012-09-23 #>  4 PILOT01 AE     06-1384 2012-09-23 2012-09-29 2012-09-15 2012-09-24 2012-09-23 #>  5 PILOT01 AE     06-1384 2012-09-15 2012-09-29 2012-09-15 2012-09-24 2012-09-15 #>  6 PILOT01 AE     06-1384 2012-09-15 2012-09-29 2012-09-15 2012-09-24 2012-09-15 #>  7 PILOT01 AE     06-1384 2012-09-15 2012-09-29 2012-09-15 2012-09-24 2012-09-15 #>  8 PILOT01 AE     06-1384 2012-09-15 2012-09-29 2012-09-15 2012-09-24 2012-09-15 #>  9 PILOT01 AE     06-1384 2012-09-23 2012-09-29 2012-09-15 2012-09-24 2012-09-23 #> 10 PILOT01 AE     06-1384 2012-09-23 2012-09-29 2012-09-15 2012-09-24 2012-09-23 #> 11 PILOT01 AE     16-1298 2013-06-08 2013-07-06 2013-04-08 2013-06-28 2013-06-08 #> 12 PILOT01 AE     16-1298 2013-06-08 2013-07-06 2013-04-08 2013-06-28 2013-06-08 #> 13 PILOT01 AE     16-1298 2013-04-22 2013-07-06 2013-04-08 2013-06-28 2013-04-22 #> 14 PILOT01 AE     16-1298 2013-04-22 2013-07-06 2013-04-08 2013-06-28 2013-04-22 #> 15 PILOT01 AE     16-1298 2013-04-22 2013-07-06 2013-04-08 2013-06-28 2013-04-22 #> 16 PILOT01 AE     16-1298 2013-04-22 2013-07-06 2013-04-08 2013-06-28 2013-04-22 #> # ℹ 1 more variable: AENDT <date>"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/call_user_fun.html","id":null,"dir":"Reference","previous_headings":"","what":"Calls a Function Provided by the User — call_user_fun","title":"Calls a Function Provided by the User — call_user_fun","text":"Calls function provided user adds function call error message call fails.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/call_user_fun.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calls a Function Provided by the User — call_user_fun","text":"","code":"call_user_fun(call)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/call_user_fun.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calls a Function Provided by the User — call_user_fun","text":"call Call executed","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/call_user_fun.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calls a Function Provided by the User — call_user_fun","text":"return value function call","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/call_user_fun.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calls a Function Provided by the User — call_user_fun","text":"","code":"call_user_fun(compute_bmi(   height = 172,   weight = 60 )) #> [1] 20.28123  try(call_user_fun(compute_bmi(   height = 172,   weight = \"hallo\" ))) #> Error in call_user_fun(compute_bmi(height = 172, weight = \"hallo\")) :  #>   Calling `compute_bmi(height = 172, weight = \"hallo\")` caused the #> following error: #> Argument `weight` must be a numeric vector, but it is a string."},{"path":"https://pharmaverse.github.io/admiral/dev/reference/censor_source.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a censor_source Object — censor_source","title":"Create a censor_source Object — censor_source","text":"censor_source objects used define censorings input derive_param_tte() function. Note: wrapper function generic tte_source().","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/censor_source.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a censor_source Object — censor_source","text":"","code":"censor_source(   dataset_name,   filter = NULL,   date,   censor = 1,   set_values_to = NULL )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/censor_source.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a censor_source Object — censor_source","text":"dataset_name name source dataset name refers dataset provided source_datasets parameter derive_param_tte(). filter unquoted condition selecting observations dataset events possible censoring time points. date variable expression providing date event censoring. date, datetime can specified. unquoted symbol expression expected. Refer derive_vars_dt() convert_dtc_to_dt() impute derive date date character vector date object. censor Censoring value CDISC strongly recommends using 0 events positive integers censoring. set_values_to named list returned exprs() defining variables set event censoring, e.g. exprs(EVENTDESC = \"DEATH\", SRCDOM = \"ADSL\", SRCVAR = \"DTHDT\"). values must symbol, character string, numeric value, expression, NA.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/censor_source.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a censor_source Object — censor_source","text":"object class censor_source, inheriting class tte_source","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/censor_source.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a censor_source Object — censor_source","text":"","code":"# Last study date known alive censor  censor_source(   dataset_name = \"adsl\",   date = LSTALVDT,   set_values_to = exprs(     EVNTDESC = \"ALIVE\",     SRCDOM = \"ADSL\",     SRCVAR = \"LSTALVDT\"   ) ) #> <censor_source> object #> dataset_name: \"adsl\" #> filter: NULL #> date: LSTALVDT #> censor: 1 #> set_values_to: #>   EVNTDESC: \"ALIVE\" #>   SRCDOM: \"ADSL\" #>   SRCVAR: \"LSTALVDT\""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/chr2vars.html","id":null,"dir":"Reference","previous_headings":"","what":"Turn a Character Vector into a List of Expressions — chr2vars","title":"Turn a Character Vector into a List of Expressions — chr2vars","text":"Turn character vector list expressions","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/chr2vars.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Turn a Character Vector into a List of Expressions — chr2vars","text":"","code":"chr2vars(chr)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/chr2vars.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Turn a Character Vector into a List of Expressions — chr2vars","text":"chr character vector","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/chr2vars.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Turn a Character Vector into a List of Expressions — chr2vars","text":"list expressions returned exprs()","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/chr2vars.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Turn a Character Vector into a List of Expressions — chr2vars","text":"","code":"chr2vars(c(\"USUBJID\", \"AVAL\")) #> [[1]] #> USUBJID #>  #> [[2]] #> AVAL #>"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_age_years.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Age in Years — compute_age_years","title":"Compute Age in Years — compute_age_years","text":"Converts set age values specified time unit years.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_age_years.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Age in Years — compute_age_years","text":"","code":"compute_age_years(age, age_unit)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_age_years.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Age in Years — compute_age_years","text":"age ages convert. numeric vector expected. age_unit Age unit. Either string containing time unit ages age character vector containing time units age age expected. Note permitted values cases insensitive (e.g. \"YEARS\" treated \"years\" \"Years\"). Permitted Values: \"years\", \"months\", \"weeks\", \"days\", \"hours\", \"minutes\", \"seconds\", NA_character_.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_age_years.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute Age in Years — compute_age_years","text":"ages contained age converted years.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_age_years.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute Age in Years — compute_age_years","text":"Returns numeric vector ages years doubles. Note passing NA_character_ unit result NA value outputted age. Also note, underlying computations assume equal number days year (365.25).","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_age_years.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute Age in Years — compute_age_years","text":"","code":"compute_age_years(   age = c(240, 360, 480),   age_unit = \"MONTHS\" ) #> [1] 20 30 40  compute_age_years(   age = c(10, 520, 3650, 1000),   age_unit = c(\"YEARS\", \"WEEKS\", \"DAYS\", NA_character_) ) #> [1] 10.000000  9.965777  9.993155        NA"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_bmi.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Body Mass Index (BMI) — compute_bmi","title":"Compute Body Mass Index (BMI) — compute_bmi","text":"Computes BMI height weight","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_bmi.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Body Mass Index (BMI) — compute_bmi","text":"","code":"compute_bmi(height, weight)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_bmi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Body Mass Index (BMI) — compute_bmi","text":"height HEIGHT value expected HEIGHT cm. Permitted Values: numeric vector weight WEIGHT value expected WEIGHT kg. Permitted Values: numeric vector","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_bmi.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute Body Mass Index (BMI) — compute_bmi","text":"BMI (Body Mass Index Area) kg/m^2.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_bmi.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute Body Mass Index (BMI) — compute_bmi","text":"Usually computation function can used %>%.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_bmi.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute Body Mass Index (BMI) — compute_bmi","text":"","code":"compute_bmi(height = 170, weight = 75) #> [1] 25.95156"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_bsa.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Body Surface Area (BSA) — compute_bsa","title":"Compute Body Surface Area (BSA) — compute_bsa","text":"Computes BSA height weight making use specified derivation method","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_bsa.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Body Surface Area (BSA) — compute_bsa","text":"","code":"compute_bsa(height = height, weight = weight, method)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_bsa.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Body Surface Area (BSA) — compute_bsa","text":"height HEIGHT value expected HEIGHT cm. Permitted Values: numeric vector weight WEIGHT value expected WEIGHT kg. Permitted Values: numeric vector method Derivation method use: Mosteller: sqrt(height * weight / 3600) DuBois-DuBois: 0.007184 * height ^ 0.725 * weight ^ 0.425 Haycock: 0.024265 * height ^ 0.3964 * weight ^ 0.5378 Gehan-George: 0.0235 * height ^ 0.42246 * weight ^ 0.51456 Boyd: 0.0003207 * (height ^ 0.3) * (1000 * weight) ^ (0.7285 - (0.0188 * log10(1000 * weight))) Fujimoto: 0.008883 * height ^ 0.663 * weight ^ 0.444 Takahira: 0.007241 * height ^ 0.725 * weight ^ 0.425 Permitted Values: character value","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_bsa.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute Body Surface Area (BSA) — compute_bsa","text":"BSA (Body Surface Area) m^2.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_bsa.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute Body Surface Area (BSA) — compute_bsa","text":"Usually computation function can used %>%.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_bsa.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute Body Surface Area (BSA) — compute_bsa","text":"","code":"# Derive BSA by the Mosteller method compute_bsa(   height = 170,   weight = 75,   method = \"Mosteller\" ) #> [1] 1.881932  # Derive BSA by the DuBois & DuBois method compute_bsa(   height = c(170, 185),   weight = c(75, 90),   method = \"DuBois-DuBois\" ) #> [1] 1.863558 2.141011"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_dtf.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive the Date Imputation Flag — compute_dtf","title":"Derive the Date Imputation Flag — compute_dtf","text":"Derive date imputation flag ('--DTF') comparing date character vector ('--DTC') Date vector ('--DT').","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_dtf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive the Date Imputation Flag — compute_dtf","text":"","code":"compute_dtf(dtc, dt)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_dtf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive the Date Imputation Flag — compute_dtf","text":"dtc date character vector ('--DTC'). character date expected format like yyyy-mm-ddThh:mm:ss (partial complete). dt  Date vector compare. date object expected.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_dtf.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive the Date Imputation Flag — compute_dtf","text":"date imputation flag ('--DTF') (character value 'D', 'M' , 'Y' NA)","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_dtf.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Derive the Date Imputation Flag — compute_dtf","text":"Usually computation function can used %>%.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_dtf.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive the Date Imputation Flag — compute_dtf","text":"","code":"compute_dtf(dtc = \"2019-07\", dt = as.Date(\"2019-07-18\")) #> [1] \"D\" compute_dtf(dtc = \"2019\", dt = as.Date(\"2019-07-18\")) #> [1] \"M\" compute_dtf(dtc = \"--06-01T00:00\", dt = as.Date(\"2022-06-01\")) #> [1] \"Y\" compute_dtf(dtc = \"2022-06--T00:00\", dt = as.Date(\"2022-06-01\")) #> [1] \"D\" compute_dtf(dtc = \"2022---01T00:00\", dt = as.Date(\"2022-06-01\")) #> [1] \"M\" compute_dtf(dtc = \"2022----T00:00\", dt = as.Date(\"2022-06-01\")) #> [1] \"M\""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_duration.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Duration — compute_duration","title":"Compute Duration — compute_duration","text":"Compute duration two dates, e.g., duration adverse event, relative day, age, ...","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_duration.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Duration — compute_duration","text":"","code":"compute_duration(   start_date,   end_date,   in_unit = \"days\",   out_unit = \"days\",   floor_in = TRUE,   add_one = TRUE,   trunc_out = FALSE,   type = \"duration\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_duration.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Duration — compute_duration","text":"start_date start date date date-time object expected. Refer derive_vars_dt() impute derive date date character vector date object. Refer convert_dtc_to_dt() obtain vector imputed dates. end_date end date date date-time object expected. Refer derive_vars_dt() impute derive date date character vector date object. Refer convert_dtc_to_dt() obtain vector imputed dates. in_unit Input unit See floor_in add_one parameter details. Permitted Values (case-insensitive): years: \"year\", \"years\", \"yr\", \"yrs\", \"y\" months: \"month\", \"months\", \"mo\", \"mos\" days: \"day\", \"days\", \"d\" hours: \"hour\", \"hours\", \"hr\", \"hrs\", \"h\" minutes: \"minute\", \"minutes\", \"min\", \"mins\" seconds: \"second\", \"seconds\", \"sec\", \"secs\", \"s\" out_unit Output unit duration derived specified unit Permitted Values (case-insensitive): years: \"year\", \"years\", \"yr\", \"yrs\", \"y\" months: \"month\", \"months\", \"mo\", \"mos\" weeks: \"week\", \"weeks\", \"wk\", \"wks\", \"w\" days: \"day\", \"days\", \"d\" hours: \"hour\", \"hours\", \"hr\", \"hrs\", \"h\" minutes: \"minute\", \"minutes\", \"min\", \"mins\" seconds: \"second\", \"seconds\", \"sec\", \"secs\", \"s\" floor_in Round input dates? input dates round respect input unit, e.g., input unit 'days', time input dates ignored. Default: TRUE Permitted Values: TRUE, FALSE add_one Add one input unit? duration non-negative, one input unit added. .e., duration can zero. Default: TRUE Permitted Values: TRUE, FALSE trunc_out Return integer part fractional part duration (output unit) removed, .e., integer part returned. Default: FALSE Permitted Values: TRUE, FALSE type lubridate duration type. See details. Default: \"duration\" Permitted Values: \"duration\", \"interval\"","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_duration.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute Duration — compute_duration","text":"duration two date specified unit","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_duration.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute Duration — compute_duration","text":"output numeric vector providing duration time start end date specified unit. end date start date, duration negative.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_duration.html","id":"duration-type","dir":"Reference","previous_headings":"","what":"Duration Type","title":"Compute Duration — compute_duration","text":"lubridate package calculates two types spans two dates: duration interval. calculations largely , unit time period month year result can slightly different. difference arises ambiguity length \"1 month\" \"1 year\". Months may 31, 30, 28, 29 days, years 365 days 366 leap years. Durations intervals help solve ambiguity measures. interval 2000-02-01 2000-03-01 1 (.e. one month). duration two dates 0.95, accounts fact year 2000 leap year, February 29 days, average month length 30.4375, .e. 29 / 30.4375 = 0.95. additional details, review lubridate time span reference page.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_duration.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute Duration — compute_duration","text":"","code":"library(lubridate) #>  #> Attaching package: ‘lubridate’ #> The following objects are masked from ‘package:base’: #>  #>     date, intersect, setdiff, union  # Derive duration in days (integer), i.e., relative day compute_duration(   start_date = ymd_hms(\"2020-12-06T15:00:00\"),   end_date = ymd_hms(\"2020-12-24T08:15:00\") ) #> [1] 19  # Derive duration in days (float) compute_duration(   start_date = ymd_hms(\"2020-12-06T15:00:00\"),   end_date = ymd_hms(\"2020-12-24T08:15:00\"),   floor_in = FALSE,   add_one = FALSE ) #> [1] 17.71875  # Derive age in years compute_duration(   start_date = ymd(\"1984-09-06\"),   end_date = ymd(\"2020-02-24\"),   trunc_out = TRUE,   out_unit = \"years\",   add_one = FALSE ) #> [1] 35  # Derive duration in hours compute_duration(   start_date = ymd_hms(\"2020-12-06T9:00:00\"),   end_date = ymd_hms(\"2020-12-06T13:30:00\"),   out_unit = \"hours\",   floor_in = FALSE,   add_one = FALSE, ) #> [1] 4.5"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_egfr.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Estimated Glomerular Filtration Rate (eGFR) for Kidney Function — compute_egfr","title":"Compute Estimated Glomerular Filtration Rate (eGFR) for Kidney Function — compute_egfr","text":"Compute Kidney Function Tests: Estimated Creatinine Clearance (CRCL) Cockcroft-Gault equation Estimated Glomerular Filtration Rate (eGFR) CKD-EPI MDRD equations","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_egfr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Estimated Glomerular Filtration Rate (eGFR) for Kidney Function — compute_egfr","text":"","code":"compute_egfr(creat, creatu = \"SI\", age, weight, sex, race = NULL, method)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_egfr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Estimated Glomerular Filtration Rate (eGFR) for Kidney Function — compute_egfr","text":"creat Creatinine numeric vector expected. creatu Creatinine Units character vector expected. Default: \"SI\" Expected Values: \"SI\", \"CV\", \"umol/L\", \"mg/dL\" age Age (years) numeric vector expected. weight Weight (kg) numeric vector expected method = \"CRCL\" sex Gender character vector expected. Expected Values: \"M\", \"F\" race Race character vector expected method = \"MDRD\" Expected Values: \"BLACK AFRICAN AMERICAN\" others method Method character vector expected. Expected Values: \"CRCL\", \"CKD-EPI\", \"MDRD\"","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_egfr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute Estimated Glomerular Filtration Rate (eGFR) for Kidney Function — compute_egfr","text":"numeric vector egfr values","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_egfr.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute Estimated Glomerular Filtration Rate (eGFR) for Kidney Function — compute_egfr","text":"Calculates estimate Glomerular Filtration Rate (eGFR) CRCL Creatinine Clearance (Cockcroft-Gault) Creatinine umol/L: $$\\frac{(140 - age) \\times weight(kg) \\times constant}{Serum\\:Creatinine(\\mu mol/L)}$$ $$Constant = 1.04\\:\\:females, 1.23\\:\\:males$$ Creatinine mg/dL: $$\\frac{(140 - age) \\times weight(kg) \\times (0.85\\:\\:female)}{72 \\times Serum\\:Creatinine(mg/dL)}$$ units = mL/min CKD-EPI Chronic Kidney Disease Epidemiology Collaboration formula $$eGFR = 142 \\times min(SCr/{\\kappa}, 1)^{\\alpha} \\times max(SCr/{\\kappa}, 1)^{-1.200} \\times 0.9938^{Age} \\times 1.012 [\\:female]$$ SCr = standardized serum creatinine mg/dL (Note SCr(mg/dL) = Creat(umol/L) / 88.42) $$\\kappa$$ = 0.7 (females) 0.9 (males) $$\\alpha$$ = -0.241 (female) -0.302 (male) units = mL/min/1.73 m2 MDRD Modification Diet Renal Disease formula $$eGFR = 175 \\times (SCr)^{-1.154} \\times (age)^{-0.203} \\times 0.742 [\\:female] \\times 1.212 [\\:Black]$$ SCr = standardized serum creatinine mg/dL (Note SCr(mg/dL) = Creat(umol/L) / 88.42) units = mL/min/1.73 m2","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_egfr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute Estimated Glomerular Filtration Rate (eGFR) for Kidney Function — compute_egfr","text":"","code":"compute_egfr(   creat = 90, creatu = \"umol/L\", age = 53, weight = 85, sex = \"M\", method = \"CRCL\" ) #> [1] 101.065  compute_egfr(   creat = 90, creatu = \"umol/L\", age = 53, sex = \"M\", race = \"ASIAN\", method = \"MDRD\" ) #> [1] 76.58319  compute_egfr(   creat = 70, creatu = \"umol/L\", age = 52, sex = \"F\", race = \"BLACK OR AFRICAN AMERICAN\",   method = \"MDRD\" ) #> [1] 92.40002  compute_egfr(   creat = 90, creatu = \"umol/L\", age = 53, sex = \"M\", method = \"CKD-EPI\" ) #> [1] 88.10399   base <- tibble::tribble(   ~STUDYID, ~USUBJID, ~AGE, ~SEX, ~RACE, ~WTBL, ~CREATBL, ~CREATBLU,   \"P01\", \"P01-1001\", 55, \"M\", \"WHITE\", 90.7, 96.3, \"umol/L\",   \"P01\", \"P01-1002\", 52, \"F\", \"BLACK OR AFRICAN AMERICAN\", 68.5, 70, \"umol/L\",   \"P01\", \"P01-1003\", 67, \"M\", \"BLACK OR AFRICAN AMERICAN\", 85.0, 77, \"umol/L\",   \"P01\", \"P01-1004\", 76, \"F\", \"ASIAN\", 60.7, 65, \"umol/L\", )  base %>%   dplyr::mutate(     CRCL_CG = compute_egfr(       creat = CREATBL, creatu = CREATBLU, age = AGE, weight = WTBL, sex = SEX,       method = \"CRCL\"     ),     EGFR_EPI = compute_egfr(       creat = CREATBL, creatu = CREATBLU, age = AGE, weight = WTBL, sex = SEX,       method = \"CKD-EPI\"     ),     EGFR_MDRD = compute_egfr(       creat = CREATBL, creatu = CREATBLU, age = AGE, weight = WTBL, sex = SEX,       race = RACE, method = \"MDRD\"     ),   ) #> # A tibble: 4 × 11 #>   STUDYID USUBJID    AGE SEX   RACE       WTBL CREATBL CREATBLU CRCL_CG EGFR_EPI #>   <chr>   <chr>    <dbl> <chr> <chr>     <dbl>   <dbl> <chr>      <dbl>    <dbl> #> 1 P01     P01-1001    55 M     WHITE      90.7    96.3 umol/L      98.5     80.2 #> 2 P01     P01-1002    52 F     BLACK OR…  68.5    70   umol/L      89.6     89.7 #> 3 P01     P01-1003    67 M     BLACK OR…  85      77   umol/L      99.1     94.5 #> 4 P01     P01-1004    76 F     ASIAN      60.7    65   umol/L      62.2     84.5 #> # ℹ 1 more variable: EGFR_MDRD <dbl>"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_framingham.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Framingham Heart Study Cardiovascular Disease 10-Year Risk Score — compute_framingham","title":"Compute Framingham Heart Study Cardiovascular Disease 10-Year Risk Score — compute_framingham","text":"Computes Framingham Heart Study Cardiovascular Disease 10-Year Risk Score (FCVD101) based systolic blood pressure, total serum cholesterol (mg/dL), HDL serum cholesterol (mg/dL), sex, smoking status, diabetic status, treated hypertension flag.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_framingham.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Framingham Heart Study Cardiovascular Disease 10-Year Risk Score — compute_framingham","text":"","code":"compute_framingham(sysbp, chol, cholhdl, age, sex, smokefl, diabetfl, trthypfl)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_framingham.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Framingham Heart Study Cardiovascular Disease 10-Year Risk Score — compute_framingham","text":"sysbp Systolic blood pressure numeric vector expected. chol Total serum cholesterol (mg/dL) numeric vector expected. cholhdl HDL serum cholesterol (mg/dL) numeric vector expected. age Age (years) numeric vector expected. sex Gender character vector expected. Expected Values: 'M' 'F' smokefl Smoking Status character vector  expected. Expected Values: 'Y' 'N' diabetfl Diabetic Status character vector expected. Expected Values: 'Y' 'N' trthypfl Treated hypertension status character vector expected. Expected Values: 'Y' 'N'","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_framingham.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute Framingham Heart Study Cardiovascular Disease 10-Year Risk Score — compute_framingham","text":"numeric vector Framingham values","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_framingham.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute Framingham Heart Study Cardiovascular Disease 10-Year Risk Score — compute_framingham","text":"predicted probability cardiovascular disease (CVD) within 10-years according Framingham formula.  See AHA Journal article General Cardiovascular Risk Profile Use Primary Care reference. Women: Men: equation calculating risk: $$RiskFactors = (log(Age) * AgeFactor) + (log(TotalChol) * TotalCholFactor) + (log(CholHDL) * CholHDLFactor) \\\\ + (log(SysBP) * SysBPFactor) + Smoker + Diabetes Present - AvgRisk$$ $$Risk = 100 * (1 - RiskPeriodFactor ^ exp(RiskFactors))$$","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_framingham.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute Framingham Heart Study Cardiovascular Disease 10-Year Risk Score — compute_framingham","text":"","code":"compute_framingham(   sysbp = 133, chol = 216.16, cholhdl = 54.91, age = 53,   sex = \"M\", smokefl = \"N\", diabetfl = \"N\", trthypfl = \"N\" ) #> [1] 10.37514  compute_framingham(   sysbp = 161, chol = 186.39, cholhdl = 64.19, age = 52,   sex = \"F\", smokefl = \"Y\", diabetfl = \"N\", trthypfl = \"Y\" ) #> [1] 16.40353"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_map.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Mean Arterial Pressure (MAP) — compute_map","title":"Compute Mean Arterial Pressure (MAP) — compute_map","text":"Computes mean arterial pressure (MAP) based diastolic systolic blood pressure. Optionally heart rate can used well.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_map.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Mean Arterial Pressure (MAP) — compute_map","text":"","code":"compute_map(diabp, sysbp, hr = NULL)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_map.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Mean Arterial Pressure (MAP) — compute_map","text":"diabp Diastolic blood pressure numeric vector expected. sysbp Systolic blood pressure numeric vector expected. hr Heart rate numeric vector NULL expected.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_map.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute Mean Arterial Pressure (MAP) — compute_map","text":"numeric vector MAP values","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_map.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute Mean Arterial Pressure (MAP) — compute_map","text":"$$\\frac{2DIABP + SYSBP}{3}$$ based diastolic systolic blood pressure $$DIABP + 0.01 e^{4.14 - \\frac{40.74}{HR}} (SYSBP - DIABP)$$ based diastolic, systolic blood pressure, heart rate. Usually computation function can used %>%.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_map.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute Mean Arterial Pressure (MAP) — compute_map","text":"","code":"# Compute MAP based on diastolic and systolic blood pressure compute_map(diabp = 51, sysbp = 121) #> [1] 74.33333  # Compute MAP based on diastolic and systolic blood pressure and heart rate compute_map(diabp = 51, sysbp = 121, hr = 59) #> [1] 73.03907"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_qtc.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Corrected QT — compute_qtc","title":"Compute Corrected QT — compute_qtc","text":"Computes corrected QT using Bazett's, Fridericia's Sagie's formula.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_qtc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Corrected QT — compute_qtc","text":"","code":"compute_qtc(qt, rr, method)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_qtc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Corrected QT — compute_qtc","text":"qt QT interval numeric vector expected. expected QT measured ms msec. rr RR interval numeric vector expected. expected RR measured ms msec. method Method used QT correction Permitted Values: \"Bazett\", \"Fridericia\", \"Sagie\"","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_qtc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute Corrected QT — compute_qtc","text":"QT interval ms","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_qtc.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute Corrected QT — compute_qtc","text":"Depending chosen method one following formulae used. Bazett: $$\\frac{QT}{\\sqrt{\\frac{RR}{1000}}}$$ Fridericia: $$\\frac{QT}{\\sqrt[3]{\\frac{RR}{1000}}}$$ Sagie: $$1000\\left(\\frac{QT}{1000} + 0.154\\left(1 - \\frac{RR}{1000}\\right)\\right)$$ Usually computation function can used %>%.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_qtc.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute Corrected QT — compute_qtc","text":"","code":"compute_qtc(qt = 350, rr = 857, method = \"Bazett\") #> [1] 378.0747  compute_qtc(qt = 350, rr = 857, method = \"Fridericia\") #> [1] 368.4748  compute_qtc(qt = 350, rr = 857, method = \"Sagie\") #> [1] 372.022"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_qual_imputation.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to Impute Values When Qualifier Exists in Character Result — compute_qual_imputation","title":"Function to Impute Values When Qualifier Exists in Character Result — compute_qual_imputation","text":"Derive imputed value","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_qual_imputation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to Impute Values When Qualifier Exists in Character Result — compute_qual_imputation","text":"","code":"compute_qual_imputation(character_value, imputation_type = 1, factor = 0)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_qual_imputation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to Impute Values When Qualifier Exists in Character Result — compute_qual_imputation","text":"character_value Character version value imputed imputation_type (default value=1) Valid Values: 1: Strip <, >, = convert numeric. 2: imputation_type=1 character value contains < >, number decimals associated character value found factor 1/10^(number decimals + 1) added/subtracted numeric value. decimals exists, factor 1/10 added/subtracted value. factor Numeric value (default=0), using imputation_type = 1, value can added subtracted qualifier removed.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_qual_imputation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to Impute Values When Qualifier Exists in Character Result — compute_qual_imputation","text":"imputed value","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_qual_imputation.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to Impute Values When Qualifier Exists in Character Result — compute_qual_imputation","text":"","code":"compute_qual_imputation(\"<40\") #> [1] 40"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_qual_imputation_dec.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Factor for Value Imputations When Character Value Contains < or > — compute_qual_imputation_dec","title":"Compute Factor for Value Imputations When Character Value Contains < or > — compute_qual_imputation_dec","text":"Function compute factor value imputation character value contains < >.  factor calculated using number decimals. decimals, factor 1, otherwise factor = 1/10^decimal place.  example, factor 100 = 1, factor 5.4 = 1/10^1, factor 5.44 = 1/10^2.  results additional false precision added value.  intermediate function.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_qual_imputation_dec.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Factor for Value Imputations When Character Value Contains < or > — compute_qual_imputation_dec","text":"","code":"compute_qual_imputation_dec(character_value_decimal)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_qual_imputation_dec.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Factor for Value Imputations When Character Value Contains < or > — compute_qual_imputation_dec","text":"character_value_decimal Character value determine decimal precision","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_qual_imputation_dec.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute Factor for Value Imputations When Character Value Contains < or > — compute_qual_imputation_dec","text":"Decimal precision value add subtract","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_qual_imputation_dec.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute Factor for Value Imputations When Character Value Contains < or > — compute_qual_imputation_dec","text":"Derive imputed value","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_qual_imputation_dec.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute Factor for Value Imputations When Character Value Contains < or > — compute_qual_imputation_dec","text":"","code":"compute_qual_imputation_dec(\"<40.1\") #> [1] 0.1"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_rr.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute RR Interval From Heart Rate — compute_rr","title":"Compute RR Interval From Heart Rate — compute_rr","text":"Computes RR interval heart rate.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_rr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute RR Interval From Heart Rate — compute_rr","text":"","code":"compute_rr(hr)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_rr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute RR Interval From Heart Rate — compute_rr","text":"hr Heart rate numeric vector expected. expected heart rate measured beats/min.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_rr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute RR Interval From Heart Rate — compute_rr","text":"RR interval ms: $$\\frac{60000}{HR}$$","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_rr.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute RR Interval From Heart Rate — compute_rr","text":"Usually computation function can used %>%.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_rr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute RR Interval From Heart Rate — compute_rr","text":"","code":"compute_rr(hr = 70.14) #> [1] 855.432"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_scale.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Scale Parameters — compute_scale","title":"Compute Scale Parameters — compute_scale","text":"Computes average set source values transforms result source range target range. example, calculating average set questionnaire response scores re-coding average response obtain subscale score.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_scale.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Scale Parameters — compute_scale","text":"","code":"compute_scale(   source,   source_range = NULL,   target_range = NULL,   flip_direction = FALSE,   min_n = 1 )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_scale.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Scale Parameters — compute_scale","text":"source vector values scaled numeric vector expected. source_range permitted source range numeric vector containing two elements expected, representing lower upper bounds permitted source range. Alternatively, argument specified source_range target_range, transformation performed. target_range target range numeric vector containing two elements expected, representing lower upper bounds target range. Alternatively, argument specified source_range target_range, transformation performed. flip_direction Flip direction scale? transformed values reversed within target range, e.g. within range 0 100, 25 reversed 75. argument ignored source_range target_range specified. Default: FALSE Permitted Values: TRUE, FALSE min_n Minimum number values computation minimum number non-missing values source computation carried . number non-missing values min_n, result set missing, .e. NA. positive integer expected. Default: 1","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_scale.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute Scale Parameters — compute_scale","text":"average source transformed target range NA source contain min_n values.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_scale.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute Scale Parameters — compute_scale","text":"Returns numeric value. source contains less min_n values, result set NA. source_range target_range specified, mean computed without transformation performed.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_scale.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute Scale Parameters — compute_scale","text":"","code":"compute_scale(   source = c(1, 4, 3, 5),   source_range = c(1, 5),   target_range = c(0, 100),   flip_direction = TRUE,   min_n = 3 ) #> [1] 43.75"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_tmf.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive the Time Imputation Flag — compute_tmf","title":"Derive the Time Imputation Flag — compute_tmf","text":"Derive time imputation flag ('--TMF') comparing date character vector ('--DTC') Datetime vector ('--DTM').","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_tmf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive the Time Imputation Flag — compute_tmf","text":"","code":"compute_tmf(dtc, dtm, ignore_seconds_flag = FALSE)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_tmf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive the Time Imputation Flag — compute_tmf","text":"dtc date character vector ('--DTC'). character date expected format like yyyy-mm-ddThh:mm:ss (partial complete). dtm Date vector compare ('--DTM'). datetime object expected. ignore_seconds_flag ADaM IG states given SDTM ('--DTC') variable, hours minutes ever collected, seconds imputed ('--DTM') 00, necessary set ('--TMF') 'S'. user can set TRUE 'S' Flag dropped ('--TMF'). Permitted Values: logical value","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_tmf.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive the Time Imputation Flag — compute_tmf","text":"time imputation flag ('--TMF') (character value 'H', 'M' , 'S' NA)","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_tmf.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Derive the Time Imputation Flag — compute_tmf","text":"Usually computation function can used %>%.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/compute_tmf.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive the Time Imputation Flag — compute_tmf","text":"","code":"library(lubridate)  compute_tmf(dtc = \"2019-07-18T15:25\", dtm = ymd_hms(\"2019-07-18T15:25:00\")) #> [1] \"S\" compute_tmf(dtc = \"2019-07-18T15\", dtm = ymd_hms(\"2019-07-18T15:25:00\")) #> [1] \"M\" compute_tmf(dtc = \"2019-07-18\", dtm = ymd(\"2019-07-18\")) #> [1] \"H\" compute_tmf(dtc = \"2022-05--T00:00\", dtm = ymd_hms(\"2022-05-15T23:59:59\")) #> [1] \"H\" compute_tmf(dtc = \"2022-05--T23:00\", dtm = ymd_hms(\"2022-05-15T23:59:59\")) #> [1] \"M\" compute_tmf(dtc = \"2022-05--T23:59:00\", dtm = ymd_hms(\"2022-05-15T23:59:59\")) #> [1] \"S\""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/consolidate_metadata.html","id":null,"dir":"Reference","previous_headings":"","what":"Consolidate Multiple Meta Datasets Into a Single One — consolidate_metadata","title":"Consolidate Multiple Meta Datasets Into a Single One — consolidate_metadata","text":"purpose function consolidate multiple meta datasets single one. example, global project specific parameter mappings single lookup table can created.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/consolidate_metadata.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Consolidate Multiple Meta Datasets Into a Single One — consolidate_metadata","text":"","code":"consolidate_metadata(   datasets,   key_vars,   source_var = SOURCE,   check_vars = \"warning\",   check_type = \"error\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/consolidate_metadata.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Consolidate Multiple Meta Datasets Into a Single One — consolidate_metadata","text":"datasets List datasets consolidate Permitted Values: named list datasets key_vars Key variables specified variables must unique input datasets. Permitted Values: list variables created exprs() source_var Source variable specified variable added output dataset. set name dataset observation originating . Permitted Values: symbol check_vars Check variables? \"message\", \"warning\", \"error\" specified, message issued variable names differ across input datasets (datasets). Permitted Values: \"none\", \"message\", \"warning\", \"error\" check_type Check uniqueness? \"warning\" \"error\" specified, message issued key variables (key_vars) unique key input datasets (datasets). Permitted Values: \"none\", \"warning\", \"error\"","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/consolidate_metadata.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Consolidate Multiple Meta Datasets Into a Single One — consolidate_metadata","text":"dataset contains one row group occurring input datasets.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/consolidate_metadata.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Consolidate Multiple Meta Datasets Into a Single One — consolidate_metadata","text":"observations input datasets put together single dataset. group (defined key_vars) exists one input datasets, observation last dataset selected.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/consolidate_metadata.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Consolidate Multiple Meta Datasets Into a Single One — consolidate_metadata","text":"","code":"library(tibble) glob_ranges <- tribble(   ~PARAMCD, ~ANRLO, ~ANRHI,   \"PULSE\",      60,    100,   \"SYSBP\",      90,    130,   \"DIABP\",      60,     80 ) proj_ranges <- tribble(   ~PARAMCD, ~ANRLO, ~ANRHI,   \"SYSBP\",     100,    140,   \"DIABP\",      70,     90 ) stud_ranges <- tribble(   ~PARAMCD, ~ANRLO, ~ANRHI,   \"BMI\",        18,     25 )  consolidate_metadata(   datasets = list(     global = glob_ranges,     project = proj_ranges,     study = stud_ranges   ),   key_vars = exprs(PARAMCD) ) #> # A tibble: 4 × 4 #>   SOURCE  PARAMCD ANRLO ANRHI #>   <chr>   <chr>   <dbl> <dbl> #> 1 study   BMI        18    25 #> 2 project DIABP      70    90 #> 3 global  PULSE      60   100 #> 4 project SYSBP     100   140"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/convert_blanks_to_na.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert Blank Strings Into NAs — convert_blanks_to_na","title":"Convert Blank Strings Into NAs — convert_blanks_to_na","text":"Turn SAS blank strings proper R NAs.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/convert_blanks_to_na.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert Blank Strings Into NAs — convert_blanks_to_na","text":"","code":"convert_blanks_to_na(x)  # Default S3 method convert_blanks_to_na(x)  # S3 method for class 'character' convert_blanks_to_na(x)  # S3 method for class 'list' convert_blanks_to_na(x)  # S3 method for class 'data.frame' convert_blanks_to_na(x)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/convert_blanks_to_na.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert Blank Strings Into NAs — convert_blanks_to_na","text":"x R object","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/convert_blanks_to_na.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert Blank Strings Into NAs — convert_blanks_to_na","text":"object class input","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/convert_blanks_to_na.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Convert Blank Strings Into NAs — convert_blanks_to_na","text":"default methods simply returns input unchanged. character method turns every instance \"\" NA_character_ preserving attributes. given data frame input function keeps non-character columns applies just described logic character columns. attributes labels preserved.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/convert_blanks_to_na.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert Blank Strings Into NAs — convert_blanks_to_na","text":"","code":"library(tibble)  convert_blanks_to_na(c(\"a\", \"b\", \"\", \"d\", \"\")) #> [1] \"a\" \"b\" NA  \"d\" NA   df <- tribble(   ~USUBJID,   ~RFICDTC,   \"1001\", \"2000-01-01\",   \"1002\", \"2001-01-01\",   \"1003\",           \"\" ) print(df) #> # A tibble: 3 × 2 #>   USUBJID RFICDTC      #>   <chr>   <chr>        #> 1 1001    \"2000-01-01\" #> 2 1002    \"2001-01-01\" #> 3 1003    \"\"           convert_blanks_to_na(df) #> # A tibble: 3 × 2 #>   USUBJID RFICDTC    #>   <chr>   <chr>      #> 1 1001    2000-01-01 #> 2 1002    2001-01-01 #> 3 1003    NA"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/convert_date_to_dtm.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert a Date into a Datetime Object — convert_date_to_dtm","title":"Convert a Date into a Datetime Object — convert_date_to_dtm","text":"Convert date (datetime, date, date character) Date vector (usually '--DTM'). Note: wrapper function function convert_dtc_to_dtm().","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/convert_date_to_dtm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert a Date into a Datetime Object — convert_date_to_dtm","text":"","code":"convert_date_to_dtm(   dt,   highest_imputation = \"h\",   date_imputation = \"first\",   time_imputation = \"first\",   min_dates = NULL,   max_dates = NULL,   preserve = FALSE )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/convert_date_to_dtm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert a Date into a Datetime Object — convert_date_to_dtm","text":"dt date convert. date character date expected format like yyyy-mm-ddThh:mm:ss. highest_imputation Highest imputation level highest_imputation argument controls components DTC value imputed missing. components specified level imputed. component higher level highest imputation level missing, NA_character_ returned. example, highest_imputation = \"D\" \"2020\" results NA_character_ month missing. \"n\" specified, imputation performed, .e., component missing, NA_character_ returned. \"Y\" specified, date_imputation \"first\" \"last\" min_dates max_dates specified respectively. Otherwise, NA_character_ returned year component missing. Permitted Values: \"Y\" (year, highest level), \"M\" (month), \"D\" (day), \"h\" (hour), \"m\" (minute), \"s\" (second), \"n\" (none, lowest level) date_imputation value impute day/month datepart missing. character value expected, either format month day specified \"mm-dd\": e.g. \"06-15\" 15th June (year can specified; imputing year \"first\" \"last\" together min_dates max_dates argument can used (see examples).), keyword: \"first\", \"mid\", \"last\" impute first/mid/last day/month. \"mid\" specified, missing components imputed middle possible range: month day missing, imputed \"06-30\" (middle year). day missing, imputed \"15\" (middle month). argument ignored highest_imputation less \"D\". time_imputation value impute time timepart missing. character value expected, either format hour, min sec specified \"hh:mm:ss\": e.g. \"00:00:00\" start day, keyword: \"first\",\"last\" impute start/end day. argument ignored highest_imputation = \"n\". min_dates Minimum dates list dates expected. ensured imputed date specified dates, e.g., imputed adverse event start date first treatment date. dates range possible dates dtc value considered. possible dates defined missing parts dtc date (see example ). ensures non-missing parts dtc date changed. date date-time object expected. example   returns \"2020-11-11T11:11:11\" possible dates \"2020-11\" range \"2020-11-01T00:00:00\" \"2020-11-30T23:59:59\". Therefore \"2020-12-06T12:12:12\" ignored. Returning \"2020-12-06T12:12:12\" changed month although missing (dtc date). date variables (datetime) list time imputed \"00:00:00\". Specifying date variables makes sense date imputed. time imputed, date variables affect result. max_dates Maximum dates list dates expected. ensured imputed date specified dates, e.g., imputed date data cut date. dates range possible dates considered. date date-time object expected. date variables (datetime) list time imputed \"23:59:59\". Specifying date variables makes sense date imputed. time imputed, date variables affect result. preserve Preserve lower level date/time part higher order part missing, e.g. preserve day month missing preserve minute hour missing. example \"2019---07\" return \"2019-06-07 preserve = TRUE (date_imputation = \"mid\"). Permitted Values: TRUE, FALSE","code":"impute_dtc_dtm(   \"2020-11\",   min_dates = list(    ymd_hms(\"2020-12-06T12:12:12\"),    ymd_hms(\"2020-11-11T11:11:11\")   ),   highest_imputation = \"M\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/convert_date_to_dtm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert a Date into a Datetime Object — convert_date_to_dtm","text":"datetime object","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/convert_date_to_dtm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Convert a Date into a Datetime Object — convert_date_to_dtm","text":"Usually computation function can used %>%.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/convert_date_to_dtm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert a Date into a Datetime Object — convert_date_to_dtm","text":"","code":"convert_date_to_dtm(\"2019-07-18T15:25:00\") #> [1] \"2019-07-18 15:25:00 UTC\" convert_date_to_dtm(Sys.time()) #> [1] \"2024-12-22 21:46:00 UTC\" convert_date_to_dtm(as.Date(\"2019-07-18\"), time_imputation = \"23:59:59\") #> [1] \"2019-07-18 23:59:59 UTC\" convert_date_to_dtm(\"2019-07-18\", time_imputation = \"23:59:59\") #> [1] \"2019-07-18 23:59:59 UTC\" convert_date_to_dtm(\"2019-07-18\") #> [1] \"2019-07-18 UTC\""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/convert_dtc_to_dt.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert a Date Character Vector into a Date Object — convert_dtc_to_dt","title":"Convert a Date Character Vector into a Date Object — convert_dtc_to_dt","text":"Convert date character vector (usually '–DTC') Date vector (usually '–DT').","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/convert_dtc_to_dt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert a Date Character Vector into a Date Object — convert_dtc_to_dt","text":"","code":"convert_dtc_to_dt(   dtc,   highest_imputation = \"n\",   date_imputation = \"first\",   min_dates = NULL,   max_dates = NULL,   preserve = FALSE )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/convert_dtc_to_dt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert a Date Character Vector into a Date Object — convert_dtc_to_dt","text":"dtc –DTC date convert. highest_imputation Highest imputation level highest_imputation argument controls components DTC value imputed missing. components specified level imputed. component higher level highest imputation level missing, NA_character_ returned. example, highest_imputation = \"D\" \"2020\" results NA_character_ month missing. \"n\" specified imputation performed, .e., component missing, NA_character_ returned. \"Y\" specified, date_imputation \"first\" \"last\" min_dates max_dates specified respectively. Otherwise, NA_character_ returned year component missing. Permitted Values: \"Y\" (year, highest level), \"M\" (month), \"D\" (day), \"n\" (none, lowest level) date_imputation value impute day/month datepart missing. character value expected, either format month day specified \"mm-dd\": e.g. \"06-15\" 15th June (year can specified; imputing year \"first\" \"last\" together min_dates max_dates argument can used (see examples).), keyword: \"first\", \"mid\", \"last\" impute first/mid/last day/month. \"mid\" specified, missing components imputed middle possible range: month day missing, imputed \"06-30\" (middle year). day missing, imputed \"15\" (middle month). argument ignored highest_imputation less \"D\". min_dates Minimum dates list dates expected. ensured imputed date specified dates, e.g., imputed adverse event start date first treatment date. dates range possible dates dtc value considered. possible dates defined missing parts dtc date (see example ). ensures non-missing parts dtc date changed. date date-time object expected. example   returns \"2020-11-11T11:11:11\" possible dates \"2020-11\" range \"2020-11-01T00:00:00\" \"2020-11-30T23:59:59\". Therefore \"2020-12-06T12:12:12\" ignored. Returning \"2020-12-06T12:12:12\" changed month although missing (dtc date). max_dates Maximum dates list dates expected. ensured imputed date specified dates, e.g., imputed date data cut date. dates range possible dates considered. date date-time object expected. preserve Preserve day month missing day present example \"2019---07\" return \"2019-06-07 preserve = TRUE (date_imputation = \"MID\"). Permitted Values: TRUE, FALSE","code":"impute_dtc_dtm(   \"2020-11\",   min_dates = list(    ymd_hms(\"2020-12-06T12:12:12\"),    ymd_hms(\"2020-11-11T11:11:11\")   ),   highest_imputation = \"M\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/convert_dtc_to_dt.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert a Date Character Vector into a Date Object — convert_dtc_to_dt","text":"date object","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/convert_dtc_to_dt.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Convert a Date Character Vector into a Date Object — convert_dtc_to_dt","text":"Usually computation function can used %>%.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/convert_dtc_to_dt.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert a Date Character Vector into a Date Object — convert_dtc_to_dt","text":"","code":"convert_dtc_to_dt(\"2019-07-18\") #> [1] \"2019-07-18\" convert_dtc_to_dt(\"2019-07\") #> [1] NA"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/convert_dtc_to_dtm.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert a Date Character Vector into a Datetime Object — convert_dtc_to_dtm","title":"Convert a Date Character Vector into a Datetime Object — convert_dtc_to_dtm","text":"Convert date character vector (usually '--DTC') Date vector (usually '--DTM').","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/convert_dtc_to_dtm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert a Date Character Vector into a Datetime Object — convert_dtc_to_dtm","text":"","code":"convert_dtc_to_dtm(   dtc,   highest_imputation = \"h\",   date_imputation = \"first\",   time_imputation = \"first\",   min_dates = NULL,   max_dates = NULL,   preserve = FALSE )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/convert_dtc_to_dtm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert a Date Character Vector into a Datetime Object — convert_dtc_to_dtm","text":"dtc '--DTC' date convert. highest_imputation Highest imputation level highest_imputation argument controls components DTC value imputed missing. components specified level imputed. component higher level highest imputation level missing, NA_character_ returned. example, highest_imputation = \"D\" \"2020\" results NA_character_ month missing. \"n\" specified, imputation performed, .e., component missing, NA_character_ returned. \"Y\" specified, date_imputation \"first\" \"last\" min_dates max_dates specified respectively. Otherwise, NA_character_ returned year component missing. Permitted Values: \"Y\" (year, highest level), \"M\" (month), \"D\" (day), \"h\" (hour), \"m\" (minute), \"s\" (second), \"n\" (none, lowest level) date_imputation value impute day/month datepart missing. character value expected, either format month day specified \"mm-dd\": e.g. \"06-15\" 15th June (year can specified; imputing year \"first\" \"last\" together min_dates max_dates argument can used (see examples).), keyword: \"first\", \"mid\", \"last\" impute first/mid/last day/month. \"mid\" specified, missing components imputed middle possible range: month day missing, imputed \"06-30\" (middle year). day missing, imputed \"15\" (middle month). argument ignored highest_imputation less \"D\". time_imputation value impute time timepart missing. character value expected, either format hour, min sec specified \"hh:mm:ss\": e.g. \"00:00:00\" start day, keyword: \"first\",\"last\" impute start/end day. argument ignored highest_imputation = \"n\". min_dates Minimum dates list dates expected. ensured imputed date specified dates, e.g., imputed adverse event start date first treatment date. dates range possible dates dtc value considered. possible dates defined missing parts dtc date (see example ). ensures non-missing parts dtc date changed. date date-time object expected. example   returns \"2020-11-11T11:11:11\" possible dates \"2020-11\" range \"2020-11-01T00:00:00\" \"2020-11-30T23:59:59\". Therefore \"2020-12-06T12:12:12\" ignored. Returning \"2020-12-06T12:12:12\" changed month although missing (dtc date). date variables (datetime) list time imputed \"00:00:00\". Specifying date variables makes sense date imputed. time imputed, date variables affect result. max_dates Maximum dates list dates expected. ensured imputed date specified dates, e.g., imputed date data cut date. dates range possible dates considered. date date-time object expected. date variables (datetime) list time imputed \"23:59:59\". Specifying date variables makes sense date imputed. time imputed, date variables affect result. preserve Preserve lower level date/time part higher order part missing, e.g. preserve day month missing preserve minute hour missing. example \"2019---07\" return \"2019-06-07 preserve = TRUE (date_imputation = \"mid\"). Permitted Values: TRUE, FALSE","code":"impute_dtc_dtm(   \"2020-11\",   min_dates = list(    ymd_hms(\"2020-12-06T12:12:12\"),    ymd_hms(\"2020-11-11T11:11:11\")   ),   highest_imputation = \"M\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/convert_dtc_to_dtm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert a Date Character Vector into a Datetime Object — convert_dtc_to_dtm","text":"datetime object","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/convert_dtc_to_dtm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Convert a Date Character Vector into a Datetime Object — convert_dtc_to_dtm","text":"Usually computation function can used %>%.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/convert_dtc_to_dtm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert a Date Character Vector into a Datetime Object — convert_dtc_to_dtm","text":"","code":"convert_dtc_to_dtm(\"2019-07-18T15:25:00\") #> [1] \"2019-07-18 15:25:00 UTC\" convert_dtc_to_dtm(\"2019-07-18T00:00:00\") # note Time = 00:00:00 is not printed #> [1] \"2019-07-18 UTC\" convert_dtc_to_dtm(\"2019-07-18\") #> [1] \"2019-07-18 UTC\""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/convert_na_to_blanks.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert NAs Into Blank Strings — convert_na_to_blanks","title":"Convert NAs Into Blank Strings — convert_na_to_blanks","text":"Turn NAs blank strings .","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/convert_na_to_blanks.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert NAs Into Blank Strings — convert_na_to_blanks","text":"","code":"convert_na_to_blanks(x)  # Default S3 method convert_na_to_blanks(x)  # S3 method for class 'character' convert_na_to_blanks(x)  # S3 method for class 'list' convert_na_to_blanks(x)  # S3 method for class 'data.frame' convert_na_to_blanks(x)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/convert_na_to_blanks.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert NAs Into Blank Strings — convert_na_to_blanks","text":"x R object","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/convert_na_to_blanks.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert NAs Into Blank Strings — convert_na_to_blanks","text":"object class input","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/convert_na_to_blanks.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Convert NAs Into Blank Strings — convert_na_to_blanks","text":"default methods simply returns input unchanged. character method turns every instance NA_character_ NA \"\" preserving attributes. given data frame input function keeps non-character columns applies just described logic character attributes labels preserved.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/convert_na_to_blanks.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert NAs Into Blank Strings — convert_na_to_blanks","text":"","code":"library(tibble)  convert_na_to_blanks(c(\"a\", \"b\", NA, \"d\", NA)) #> [1] \"a\" \"b\" \"\"  \"d\" \"\"   df <- tribble(   ~USUBJID,   ~RFICDTC,   \"1001\", \"2000-01-01\",   \"1002\", \"2001-01-01\",   \"1003\",           NA ) print(df) #> # A tibble: 3 × 2 #>   USUBJID RFICDTC    #>   <chr>   <chr>      #> 1 1001    2000-01-01 #> 2 1002    2001-01-01 #> 3 1003    NA         convert_na_to_blanks(df) #> # A tibble: 3 × 2 #>   USUBJID RFICDTC      #>   <chr>   <chr>        #> 1 1001    \"2000-01-01\" #> 2 1002    \"2001-01-01\" #> 3 1003    \"\""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/count_vals.html","id":null,"dir":"Reference","previous_headings":"","what":"Count Number of Observations Where a Variable Equals a Value — count_vals","title":"Count Number of Observations Where a Variable Equals a Value — count_vals","text":"Count number observations variable equals value.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/count_vals.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count Number of Observations Where a Variable Equals a Value — count_vals","text":"","code":"count_vals(var, val)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/count_vals.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count Number of Observations Where a Variable Equals a Value — count_vals","text":"var vector val value","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/count_vals.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Count Number of Observations Where a Variable Equals a Value — count_vals","text":"","code":"library(tibble) library(dplyr, warn.conflicts = FALSE) library(admiral) data <- tribble(   ~USUBJID, ~AVISITN, ~AVALC,   \"1\",      1,        \"PR\",   \"1\",      2,        \"CR\",   \"1\",      3,        \"NE\",   \"1\",      4,        \"CR\",   \"1\",      5,        \"NE\",   \"2\",      1,        \"CR\",   \"2\",      2,        \"PR\",   \"2\",      3,        \"CR\",   \"3\",      1,        \"CR\",   \"4\",      1,        \"CR\",   \"4\",      2,        \"NE\",   \"4\",      3,        \"NE\",   \"4\",      4,        \"CR\",   \"4\",      5,        \"PR\" )  # add variable providing the number of NEs for each subject group_by(data, USUBJID) %>%   mutate(nr_nes = count_vals(var = AVALC, val = \"NE\")) #> # A tibble: 14 × 4 #> # Groups:   USUBJID [4] #>    USUBJID AVISITN AVALC nr_nes #>    <chr>     <dbl> <chr>  <int> #>  1 1             1 PR         2 #>  2 1             2 CR         2 #>  3 1             3 NE         2 #>  4 1             4 CR         2 #>  5 1             5 NE         2 #>  6 2             1 CR         0 #>  7 2             2 PR         0 #>  8 2             3 CR         0 #>  9 3             1 CR         0 #> 10 4             1 CR         2 #> 11 4             2 NE         2 #> 12 4             3 NE         2 #> 13 4             4 CR         2 #> 14 4             5 PR         2"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/country_code_lookup.html","id":null,"dir":"Reference","previous_headings":"","what":"Country Code Lookup — country_code_lookup","title":"Country Code Lookup — country_code_lookup","text":"pre-defined country codes sourced ISO 3166 Standards. See also Wikipedia.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/country_code_lookup.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Country Code Lookup — country_code_lookup","text":"","code":"country_code_lookup"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/country_code_lookup.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Country Code Lookup — country_code_lookup","text":"object class tbl_df (inherits tbl, data.frame) 249 rows 3 columns.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/country_code_lookup.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Country Code Lookup — country_code_lookup","text":"country_code 3-letter ISO 3166-1 county code commonly found ADSL COUNTRY variable. country_name country long name corresponding 3-letter code. country_number numeric code corresponding alphabetic sorting 3-letter codes. see entire table console, run print(country_code_lookup).","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/country_code_lookup.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Country Code Lookup — country_code_lookup","text":"","code":"library(tibble) library(dplyr, warn.conflicts = FALSE) library(lubridate)  # Create reference dataset for periods adsl <- tribble(   ~USUBJID, ~SEX, ~COUNTRY,   \"ST01-01\", \"F\", \"AUT\",   \"ST01-02\", \"M\", \"MWI\",   \"ST01-03\", \"F\", \"GBR\",   \"ST01-04\", \"M\", \"CHE\",   \"ST01-05\", \"M\", \"NOR\",   \"ST01-06\", \"F\", \"JPN\",   \"ST01-07\", \"F\", \"USA\" )  covar <- adsl %>%   derive_vars_merged(     dataset_add = country_code_lookup,     new_vars = exprs(COUNTRYN = country_number, COUNTRYL = country_name),     by_vars = exprs(COUNTRY = country_code)   ) covar #> # A tibble: 7 × 5 #>   USUBJID SEX   COUNTRY COUNTRYN COUNTRYL                                        #>   <chr>   <chr> <chr>      <dbl> <chr>                                           #> 1 ST01-01 F     AUT           16 Austria                                         #> 2 ST01-02 M     MWI          157 Malawi                                          #> 3 ST01-03 F     GBR           80 United Kingdom of Great Britain and Northern I… #> 4 ST01-04 M     CHE           42 Switzerland                                     #> 5 ST01-05 M     NOR          168 Norway                                          #> 6 ST01-06 F     JPN          116 Japan                                           #> 7 ST01-07 F     USA          235 United States of America"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/create_period_dataset.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a Reference Dataset for Subperiods, Periods, or Phases — create_period_dataset","title":"Create a Reference Dataset for Subperiods, Periods, or Phases — create_period_dataset","text":"function creates reference dataset subperiods, periods, phases ADSL dataset. reference dataset can used derive subperiod, period, phase variables like ASPER, ASPRSDT, ASPREDT, APERIOD, APERSDT, APEREDT, TRTA, APHASEN, PHSDTM, PHEDTM, ... OCCDS BDS datasets.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/create_period_dataset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a Reference Dataset for Subperiods, Periods, or Phases — create_period_dataset","text":"","code":"create_period_dataset(   dataset,   new_vars,   subject_keys = get_admiral_option(\"subject_keys\") )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/create_period_dataset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a Reference Dataset for Subperiods, Periods, or Phases — create_period_dataset","text":"dataset Input dataset variables specified new_vars subject_keys arguments expected dataset. element new_vars least one variable form right hand side value must available dataset. new_vars New variables named list variables like exprs(PHSDT = PHwSDT, PHEDT = PHwEDT, APHASE = APHASEw) expected. left hand side elements defines variable output dataset, right hand side defines source variables ADSL dataset CDISC notation. lower case letter \"w\"  used refers phase variable, lower case letters \"xx\" used refers period variable, \"xx\" \"w\" used refers subperiod variable. one type must used, e.g., right hand side values must refer period variables. allowed mix example period subperiod variables. period subperiod variables required, separate reference datasets must created. subject_keys Variables uniquely identify subject list expressions expressions symbols returned exprs() expected.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/create_period_dataset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a Reference Dataset for Subperiods, Periods, or Phases — create_period_dataset","text":"period reference dataset (see \"Details\" section)","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/create_period_dataset.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create a Reference Dataset for Subperiods, Periods, or Phases — create_period_dataset","text":"subject subperiod/period/phase least one source variable NA observation added output dataset. Depending type source variable (subperiod, period, phase) variable ASPER, APERIOD, APHASEN added set number subperiod, period, phase. variables specified new_vars (left hand side) added output dataset set value source variable (right hand side).","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/create_period_dataset.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a Reference Dataset for Subperiods, Periods, or Phases — create_period_dataset","text":"","code":"library(tibble) library(dplyr, warn.conflicts = FALSE) library(lubridate)  # Create reference dataset for periods adsl <- tribble(   ~USUBJID, ~AP01SDT,     ~AP01EDT,     ~AP02SDT,     ~AP02EDT,     ~TRT01A, ~TRT02A,   \"1\",      \"2021-01-04\", \"2021-02-06\", \"2021-02-07\", \"2021-03-07\", \"A\",     \"B\",   \"2\",      \"2021-02-02\", \"2021-03-02\", \"2021-03-03\", \"2021-04-01\", \"B\",     \"A\", ) %>%   mutate(     across(matches(\"AP\\\\d\\\\d[ES]DT\"), ymd)   ) %>%   mutate(     STUDYID = \"xyz\"   )  create_period_dataset(   adsl,   new_vars = exprs(APERSDT = APxxSDT, APEREDT = APxxEDT, TRTA = TRTxxA) ) #> # A tibble: 4 × 6 #>   STUDYID USUBJID APERIOD APERSDT    APEREDT    TRTA  #>   <chr>   <chr>     <int> <date>     <date>     <chr> #> 1 xyz     1             1 2021-01-04 2021-02-06 A     #> 2 xyz     1             2 2021-02-07 2021-03-07 B     #> 3 xyz     2             1 2021-02-02 2021-03-02 B     #> 4 xyz     2             2 2021-03-03 2021-04-01 A      # Create reference dataset for phases adsl <- tribble(   ~USUBJID, ~PH1SDT,      ~PH1EDT,      ~PH2SDT,      ~PH2EDT,      ~APHASE1,    ~APHASE2,   \"1\",      \"2021-01-04\", \"2021-02-06\", \"2021-02-07\", \"2021-03-07\", \"TREATMENT\", \"FUP\",   \"2\",      \"2021-02-02\", \"2021-03-02\", NA,           NA,           \"TREATMENT\", NA ) %>%   mutate(     across(matches(\"PH\\\\d[ES]DT\"), ymd)   ) %>%   mutate(     STUDYID = \"xyz\"   )  create_period_dataset(   adsl,   new_vars = exprs(PHSDT = PHwSDT, PHEDT = PHwEDT, APHASE = APHASEw) ) #> # A tibble: 3 × 6 #>   STUDYID USUBJID APHASEN PHSDT      PHEDT      APHASE    #>   <chr>   <chr>     <int> <date>     <date>     <chr>     #> 1 xyz     1             1 2021-01-04 2021-02-06 TREATMENT #> 2 xyz     1             2 2021-02-07 2021-03-07 FUP       #> 3 xyz     2             1 2021-02-02 2021-03-02 TREATMENT  # Create reference datasets for subperiods adsl <- tribble(   ~USUBJID, ~P01S1SDT,    ~P01S1EDT,    ~P01S2SDT,    ~P01S2EDT,    ~P02S1SDT,    ~P02S1EDT,   \"1\",      \"2021-01-04\", \"2021-01-19\", \"2021-01-20\", \"2021-02-06\", \"2021-02-07\", \"2021-03-07\",   \"2\",      \"2021-02-02\", \"2021-03-02\", NA,           NA,           \"2021-03-03\", \"2021-04-01\" ) %>%   mutate(     across(matches(\"P\\\\d\\\\dS\\\\d[ES]DT\"), ymd)   ) %>%   mutate(     STUDYID = \"xyz\"   )  create_period_dataset(   adsl,   new_vars = exprs(ASPRSDT = PxxSwSDT, ASPREDT = PxxSwEDT) ) #> # A tibble: 5 × 6 #>   STUDYID USUBJID APERIOD ASPER ASPRSDT    ASPREDT    #>   <chr>   <chr>     <int> <int> <date>     <date>     #> 1 xyz     1             1     1 2021-01-04 2021-01-19 #> 2 xyz     1             1     2 2021-01-20 2021-02-06 #> 3 xyz     1             2     1 2021-02-07 2021-03-07 #> 4 xyz     2             1     1 2021-02-02 2021-03-02 #> 5 xyz     2             2     1 2021-03-03 2021-04-01"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/create_query_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a queries dataset as input dataset to the dataset_queries argument in derive_vars_query() — create_query_data","title":"Creates a queries dataset as input dataset to the dataset_queries argument in derive_vars_query() — create_query_data","text":"Creates queries dataset input dataset dataset_queries argument derive_vars_query() function defined Queries Dataset Documentation.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/create_query_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a queries dataset as input dataset to the dataset_queries argument in derive_vars_query() — create_query_data","text":"","code":"create_query_data(queries, version = NULL, get_terms_fun = NULL)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/create_query_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a queries dataset as input dataset to the dataset_queries argument in derive_vars_query() — create_query_data","text":"queries List queries list query() objects expected. version Dictionary version dictionary version used coding terms specified. queries basket (SMQ, SDG, ....) customized query including basket, parameter needs specified. Permitted Values: character string (expected format company-specific) get_terms_fun Function returns terms query specified queries parameter referring basket (.e., definition field set basket_select() object list contains least one basket_select() object) specified function called retrieve terms defining query. function provided admiral company specific, .e., implemented company level. function must return dataset terms defining basket. output dataset must contain following variables. SRCVAR: variable used defining term basket, e.g., AEDECOD TERMCHAR: name term variable SRCVAR referring character TERMNUM numeric id term variable SRCVAR referring numeric GRPNAME: name basket. values must observations. function must provide following parameters basket_select: basket_select() object. version: dictionary version. value specified version create_query_data() call passed parameter. keep_id: set TRUE, output dataset must contain GRPID variable. variable must set numeric id basket. temp_env: temporary environment passed parameter. can used store data used baskets create_query_data() call. example SMQs need read database SMQs can read stored environment first SMQ handled. SMQs terms can retrieved environment instead accessing database .","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/create_query_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Creates a queries dataset as input dataset to the dataset_queries argument in derive_vars_query() — create_query_data","text":"dataset used input dataset dataset_queries argument derive_vars_query()","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/create_query_data.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Creates a queries dataset as input dataset to the dataset_queries argument in derive_vars_query() — create_query_data","text":"query() object listed queries argument, terms belonging query (SRCVAR, TERMCHAR, TERMNUM) determined respect definition field query: definition field query() object basket_select() object, terms read basket database calling function specified get_terms_fun parameter. data frame, terms stored data frame used. list data frames basket_select() objects, terms data frames terms read basket database referenced basket_select() objects collated. following variables (described Queries Dataset Documentation) created: PREFIX: Prefix variables created derive_vars_query() specified prefix element. GRPNAME: Name query specified name element. GRPID: Id query specified id element. id element specified query, variable set NA. id element specified query, variable created. SCOPE: scope query specified scope element basket_select() object. queries defined basket_select() object, variable set NA. none queries defined basket_select() object, variable created. SCOPEN: numeric scope query. set 1 scope broad. Otherwise set 2. add_scope_num element equals FALSE, variable set NA. add_scope_num element equals FALSE baskets none queries basket , variable created. SRCVAR: Name variable used identify terms. TERMCHAR: Value term variable character variable. TERMNUM: Value term variable numeric variable. VERSION: Set value version argument. specified, variable created.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/create_query_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Creates a queries dataset as input dataset to the dataset_queries argument in derive_vars_query() — create_query_data","text":"","code":"library(tibble) library(dplyr, warn.conflicts = FALSE) library(pharmaversesdtm) library(admiral)  # creating a query dataset for a customized query cqterms <- tribble(   ~TERMCHAR, ~TERMNUM,   \"APPLICATION SITE ERYTHEMA\", 10003041L,   \"APPLICATION SITE PRURITUS\", 10003053L ) %>%   mutate(SRCVAR = \"AEDECOD\")  cq <- query(   prefix = \"CQ01\",   name = \"Application Site Issues\",   definition = cqterms )  create_query_data(queries = list(cq)) #> # A tibble: 2 × 5 #>   TERMCHAR                   TERMNUM SRCVAR  PREFIX GRPNAME                 #>   <chr>                        <int> <chr>   <chr>  <chr>                   #> 1 APPLICATION SITE ERYTHEMA 10003041 AEDECOD CQ01   Application Site Issues #> 2 APPLICATION SITE PRURITUS 10003053 AEDECOD CQ01   Application Site Issues  # create a query dataset for SMQs pregsmq <- query(   prefix = \"SMQ02\",   id = auto,   definition = basket_select(     name = \"Pregnancy and neonatal topics (SMQ)\",     scope = \"NARROW\",     type = \"smq\"   ) )  bilismq <- query(   prefix = \"SMQ04\",   definition = basket_select(     id = 20000121L,     scope = \"BROAD\",     type = \"smq\"   ) )  # The get_terms function from pharmaversesdtm is used for this example. # In a real application a company-specific function must be used. create_query_data(   queries = list(pregsmq, bilismq),   get_terms_fun = pharmaversesdtm:::get_terms,   version = \"20.1\" ) #> # A tibble: 43 × 7 #>    TERMCHAR                           SRCVAR GRPNAME  GRPID SCOPE PREFIX VERSION #>    <chr>                              <chr>  <chr>    <int> <chr> <chr>  <chr>   #>  1 Achromotrichia congenital          AEDEC… Pregna… 2.00e7 NARR… SMQ02  20.1    #>  2 Craniosynostosis                   AEDEC… Pregna… 2.00e7 NARR… SMQ02  20.1    #>  3 Hypophosphatasia                   AEDEC… Pregna… 2.00e7 NARR… SMQ02  20.1    #>  4 Congenital pyelocaliectasis        AEDEC… Pregna… 2.00e7 NARR… SMQ02  20.1    #>  5 Uterine contractions during pregn… AEDEC… Pregna… 2.00e7 NARR… SMQ02  20.1    #>  6 Ductus arteriosus premature closu… AEDEC… Pregna… 2.00e7 NARR… SMQ02  20.1    #>  7 Pseudotruncus arteriosus           AEDEC… Pregna… 2.00e7 NARR… SMQ02  20.1    #>  8 Lipomeningocele                    AEDEC… Pregna… 2.00e7 NARR… SMQ02  20.1    #>  9 Macrocephaly                       AEDEC… Pregna… 2.00e7 NARR… SMQ02  20.1    #> 10 Carnitine palmitoyltransferase de… AEDEC… Pregna… 2.00e7 NARR… SMQ02  20.1    #> # ℹ 33 more rows  # create a query dataset for SDGs sdg <- query(   prefix = \"SDG01\",   id = auto,   definition = basket_select(     name = \"5-aminosalicylates for ulcerative colitis\",     scope = NA_character_,     type = \"sdg\"   ) )  # The get_terms function from pharmaversesdtm is used for this example. # In a real application a company-specific function must be used. create_query_data(   queries = list(sdg),   get_terms_fun = pharmaversesdtm:::get_terms,   version = \"2019-09\" ) #> # A tibble: 16 × 7 #>    TERMCHAR                            SRCVAR GRPNAME GRPID SCOPE PREFIX VERSION #>    <chr>                               <chr>  <chr>   <int> <chr> <chr>  <chr>   #>  1 AMINOSALICYLIC ACID                 CMDEC… 5-amin…   220 NA    SDG01  2019-09 #>  2 AMINOSALICYLATE CALCIUM             CMDEC… 5-amin…   220 NA    SDG01  2019-09 #>  3 AMINOSALICYLATE CALCIUM ALUMINIUM   CMDEC… 5-amin…   220 NA    SDG01  2019-09 #>  4 AMINOSALICYLATE SODIUM              CMDEC… 5-amin…   220 NA    SDG01  2019-09 #>  5 SODIUM AMINOSALICYLATE DIHYDRATE    CMDEC… 5-amin…   220 NA    SDG01  2019-09 #>  6 AMINOSALICYLATE SODIUM;AMINOSALICY… CMDEC… 5-amin…   220 NA    SDG01  2019-09 #>  7 SULFASALAZINE                       CMDEC… 5-amin…   220 NA    SDG01  2019-09 #>  8 CALCIUM BENZAMIDOSALICYLATE         CMDEC… 5-amin…   220 NA    SDG01  2019-09 #>  9 OLSALAZINE                          CMDEC… 5-amin…   220 NA    SDG01  2019-09 #> 10 OLSALAZINE SODIUM                   CMDEC… 5-amin…   220 NA    SDG01  2019-09 #> 11 MESALAZINE                          CMDEC… 5-amin…   220 NA    SDG01  2019-09 #> 12 BALSALAZIDE                         CMDEC… 5-amin…   220 NA    SDG01  2019-09 #> 13 BALSALAZIDE SODIUM                  CMDEC… 5-amin…   220 NA    SDG01  2019-09 #> 14 BALSALAZIDE DISODIUM DIHYDRATE      CMDEC… 5-amin…   220 NA    SDG01  2019-09 #> 15 DERSALAZINE                         CMDEC… 5-amin…   220 NA    SDG01  2019-09 #> 16 DERSALAZINE SODIUM                  CMDEC… 5-amin…   220 NA    SDG01  2019-09  # creating a query dataset for a customized query including SMQs # The get_terms function from pharmaversesdtm is used for this example. # In a real application a company-specific function must be used. create_query_data(   queries = list(     query(       prefix = \"CQ03\",       name = \"Special issues of interest\",       definition = list(         basket_select(           name = \"Pregnancy and neonatal topics (SMQ)\",           scope = \"NARROW\",           type = \"smq\"         ),         cqterms       )     )   ),   get_terms_fun = pharmaversesdtm:::get_terms,   version = \"20.1\" ) #> # A tibble: 23 × 6 #>    TERMCHAR                                SRCVAR GRPNAME TERMNUM PREFIX VERSION #>    <chr>                                   <chr>  <chr>     <int> <chr>  <chr>   #>  1 Achromotrichia congenital               AEDEC… Specia…      NA CQ03   20.1    #>  2 Craniosynostosis                        AEDEC… Specia…      NA CQ03   20.1    #>  3 Hypophosphatasia                        AEDEC… Specia…      NA CQ03   20.1    #>  4 Congenital pyelocaliectasis             AEDEC… Specia…      NA CQ03   20.1    #>  5 Uterine contractions during pregnancy   AEDEC… Specia…      NA CQ03   20.1    #>  6 Ductus arteriosus premature closure     AEDEC… Specia…      NA CQ03   20.1    #>  7 Pseudotruncus arteriosus                AEDEC… Specia…      NA CQ03   20.1    #>  8 Lipomeningocele                         AEDEC… Specia…      NA CQ03   20.1    #>  9 Macrocephaly                            AEDEC… Specia…      NA CQ03   20.1    #> 10 Carnitine palmitoyltransferase deficie… AEDEC… Specia…      NA CQ03   20.1    #> # ℹ 13 more rows"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/create_single_dose_dataset.html","id":null,"dir":"Reference","previous_headings":"","what":"Create dataset of single doses — create_single_dose_dataset","title":"Create dataset of single doses — create_single_dose_dataset","text":"Derives dataset single dose aggregate dose information. may necessary e.g. calculating last dose adverse event ADAE deriving total dose parameter ADEX EXDOSFRQ != .","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/create_single_dose_dataset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create dataset of single doses — create_single_dose_dataset","text":"","code":"create_single_dose_dataset(   dataset,   dose_freq = EXDOSFRQ,   start_date = ASTDT,   start_datetime = NULL,   end_date = AENDT,   end_datetime = NULL,   lookup_table = dose_freq_lookup,   lookup_column = CDISC_VALUE,   nominal_time = NULL,   keep_source_vars = expr_c(get_admiral_option(\"subject_keys\"), dose_freq, start_date,     start_datetime, end_date, end_datetime) )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/create_single_dose_dataset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create dataset of single doses — create_single_dose_dataset","text":"dataset Input dataset variables specified dose_freq, start_date, end_date arguments expected dataset. dose_freq dose frequency aggregate dosing frequency used multiple doses row. Permitted Values: defined lookup table. start_date start date date object expected. object contain NA values. Refer derive_vars_dt() impute derive date date character vector date object. start_datetime start date-time date-time object expected. object contain NA values. Refer derive_vars_dtm() impute derive date-time date character vector date object. input dataset contains frequencies refer DOSE_WINDOW equals \"HOUR\" \"MINUTE\", parameter must specified. end_date end date date date-time object expected. object contain NA values. Refer derive_vars_dt() impute derive date date character vector date object. end_datetime end date-time date-time object expected. object contain NA values. Refer derive_vars_dtm() impute derive date-time date character vector date object. input dataset contains frequencies refer DOSE_WINDOW equals \"HOUR\" \"MINUTE\", parameter must specified. lookup_table dose frequency value lookup table table used look dose_freq values determine appropriate multiplier used row generation. lookup table default used, must columns DOSE_WINDOW, DOSE_COUNT, CONVERSION_FACTOR. default table dose_freq_lookup described detail . Permitted Values DOSE_WINDOW: \"MINUTE\", \"HOUR\", \"DAY\", \"WEEK\", \"MONTH\", \"YEAR\" lookup_column dose frequency value column lookup table column lookup_table. nominal_time nominal relative time first dose (NFRLT) Used PK analysis, hours 0 first dose.  can derived (VISITDY - 1) * 24 example. expanded single dose dataset created.  example EXDOFRQ \"QD\" result nominal_time incremented 24 hours expanded record. value can NULL needed. keep_source_vars List variables retained source dataset parameter can specified additional information required output dataset. example EXTRT studies one drug.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/create_single_dose_dataset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create dataset of single doses — create_single_dose_dataset","text":"input dataset single dose per row.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/create_single_dose_dataset.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create dataset of single doses — create_single_dose_dataset","text":"aggregate dose row split multiple rows represent single dose.number completed dose periods start_date start_datetime end_date end_datetime calculated compute_duration multiplied DOSE_COUNT. DOSE_WINDOW values \"WEEK\", \"MONTH\", \"YEAR\", CONVERSION_FACTOR used convert days time object added start_date. Observations dose frequency \"\" copied output dataset unchanged.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/create_single_dose_dataset.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create dataset of single doses — create_single_dose_dataset","text":"","code":"# Example with default lookup  library(lubridate) library(stringr) library(tibble) library(dplyr)  data <- tribble(   ~STUDYID, ~USUBJID, ~EXDOSFRQ, ~ASTDT, ~ASTDTM, ~AENDT, ~AENDTM,   \"STUDY01\", \"P01\", \"Q2D\", ymd(\"2021-01-01\"), ymd_hms(\"2021-01-01 10:30:00\"),   ymd(\"2021-01-07\"), ymd_hms(\"2021-01-07 11:30:00\"),   \"STUDY01\", \"P01\", \"Q3D\", ymd(\"2021-01-08\"), ymd_hms(\"2021-01-08 12:00:00\"),   ymd(\"2021-01-14\"), ymd_hms(\"2021-01-14 14:00:00\"),   \"STUDY01\", \"P01\", \"EVERY 2 WEEKS\", ymd(\"2021-01-15\"), ymd_hms(\"2021-01-15 09:57:00\"),   ymd(\"2021-01-29\"), ymd_hms(\"2021-01-29 10:57:00\") )  create_single_dose_dataset(data) #> # A tibble: 9 × 5 #>   STUDYID USUBJID EXDOSFRQ ASTDT      AENDT      #>   <chr>   <chr>   <chr>    <date>     <date>     #> 1 STUDY01 P01     ONCE     2021-01-01 2021-01-01 #> 2 STUDY01 P01     ONCE     2021-01-03 2021-01-03 #> 3 STUDY01 P01     ONCE     2021-01-05 2021-01-05 #> 4 STUDY01 P01     ONCE     2021-01-07 2021-01-07 #> 5 STUDY01 P01     ONCE     2021-01-08 2021-01-08 #> 6 STUDY01 P01     ONCE     2021-01-11 2021-01-11 #> 7 STUDY01 P01     ONCE     2021-01-14 2021-01-14 #> 8 STUDY01 P01     ONCE     2021-01-15 2021-01-15 #> 9 STUDY01 P01     ONCE     2021-01-29 2021-01-29  # Example with custom lookup  custom_lookup <- tribble(   ~Value,   ~DOSE_COUNT, ~DOSE_WINDOW, ~CONVERSION_FACTOR,   \"Q30MIN\", (1 / 30),    \"MINUTE\",                      1,   \"Q90MIN\", (1 / 90),    \"MINUTE\",                      1 )  data <- tribble(   ~STUDYID, ~USUBJID, ~EXDOSFRQ, ~ASTDT, ~ASTDTM, ~AENDT, ~AENDTM,   \"STUDY01\", \"P01\", \"Q30MIN\", ymd(\"2021-01-01\"), ymd_hms(\"2021-01-01T06:00:00\"),   ymd(\"2021-01-01\"), ymd_hms(\"2021-01-01T07:00:00\"),   \"STUDY02\", \"P02\", \"Q90MIN\", ymd(\"2021-01-01\"), ymd_hms(\"2021-01-01T06:00:00\"),   ymd(\"2021-01-01\"), ymd_hms(\"2021-01-01T09:00:00\") )  create_single_dose_dataset(data,   lookup_table = custom_lookup,   lookup_column = Value,   start_datetime = ASTDTM,   end_datetime = AENDTM ) #> # A tibble: 6 × 7 #>   STUDYID USUBJID EXDOSFRQ ASTDT      ASTDTM              AENDT      #>   <chr>   <chr>   <chr>    <date>     <dttm>              <date>     #> 1 STUDY01 P01     ONCE     2021-01-01 2021-01-01 06:00:00 2021-01-01 #> 2 STUDY01 P01     ONCE     2021-01-01 2021-01-01 06:30:00 2021-01-01 #> 3 STUDY01 P01     ONCE     2021-01-01 2021-01-01 07:00:00 2021-01-01 #> 4 STUDY02 P02     ONCE     2021-01-01 2021-01-01 06:00:00 2021-01-01 #> 5 STUDY02 P02     ONCE     2021-01-01 2021-01-01 07:30:00 2021-01-01 #> 6 STUDY02 P02     ONCE     2021-01-01 2021-01-01 09:00:00 2021-01-01 #> # ℹ 1 more variable: AENDTM <dttm> # Example with nominal time  data <- tribble(   ~STUDYID, ~USUBJID, ~EXDOSFRQ, ~NFRLT, ~ASTDT, ~ASTDTM, ~AENDT, ~AENDTM,   \"STUDY01\", \"P01\", \"BID\", 0, ymd(\"2021-01-01\"), ymd_hms(\"2021-01-01 08:00:00\"),   ymd(\"2021-01-07\"), ymd_hms(\"2021-01-07 20:00:00\"),   \"STUDY01\", \"P01\", \"BID\", 168, ymd(\"2021-01-08\"), ymd_hms(\"2021-01-08 08:00:00\"),   ymd(\"2021-01-14\"), ymd_hms(\"2021-01-14 20:00:00\"),   \"STUDY01\", \"P01\", \"BID\", 336, ymd(\"2021-01-15\"), ymd_hms(\"2021-01-15 08:00:00\"),   ymd(\"2021-01-29\"), ymd_hms(\"2021-01-29 20:00:00\") )  create_single_dose_dataset(data,   dose_freq = EXDOSFRQ,   start_date = ASTDT,   start_datetime = ASTDTM,   end_date = AENDT,   end_datetime = AENDTM,   lookup_table = dose_freq_lookup,   lookup_column = CDISC_VALUE,   nominal_time = NFRLT,   keep_source_vars = exprs(     USUBJID, EXDOSFRQ, ASTDT, ASTDTM, AENDT, AENDTM, NFRLT   ) ) #> # A tibble: 58 × 7 #>    USUBJID EXDOSFRQ ASTDT      ASTDTM              AENDT      #>    <chr>   <chr>    <date>     <dttm>              <date>     #>  1 P01     ONCE     2021-01-01 2021-01-01 08:00:00 2021-01-01 #>  2 P01     ONCE     2021-01-01 2021-01-01 20:00:00 2021-01-01 #>  3 P01     ONCE     2021-01-02 2021-01-02 08:00:00 2021-01-02 #>  4 P01     ONCE     2021-01-02 2021-01-02 20:00:00 2021-01-02 #>  5 P01     ONCE     2021-01-03 2021-01-03 08:00:00 2021-01-03 #>  6 P01     ONCE     2021-01-03 2021-01-03 20:00:00 2021-01-03 #>  7 P01     ONCE     2021-01-04 2021-01-04 08:00:00 2021-01-04 #>  8 P01     ONCE     2021-01-04 2021-01-04 20:00:00 2021-01-04 #>  9 P01     ONCE     2021-01-05 2021-01-05 08:00:00 2021-01-05 #> 10 P01     ONCE     2021-01-05 2021-01-05 20:00:00 2021-01-05 #> # ℹ 48 more rows #> # ℹ 2 more variables: AENDTM <dttm>, NFRLT <dbl>  # Example - derive a single dose dataset with imputations  # For either single drug administration records, or multiple drug administration # records covering a range of dates, fill-in of missing treatment end datetime # `EXENDTC` by substitution with an acceptable alternate, for example date of # death, date of datacut may be required. This example shows the # maximum possible number of single dose records to be derived. The example # requires the date of datacut `DCUTDT` to be specified correctly, or # if not appropriate to use `DCUTDT` as missing treatment end data and missing # treatment end datetime could set equal to treatment start date and treatment # start datetime. ADSL variables `DTHDT` and `DCUTDT` are preferred for # imputation use. # # All available trial treatments are included, allowing multiple different # last dose variables to be created in for example `use_ad_template(\"ADAE\")` # if required.  adsl <- tribble(   ~STUDYID, ~USUBJID, ~DTHDT,   \"01\", \"1211\", ymd(\"2013-01-14\"),   \"01\", \"1083\", ymd(\"2013-08-02\"),   \"01\", \"1445\", ymd(\"2014-11-01\"),   \"01\", \"1015\", NA,   \"01\", \"1023\", NA )  ex <- tribble(   ~STUDYID, ~USUBJID, ~EXSEQ, ~EXTRT, ~EXDOSE, ~EXDOSU, ~EXDOSFRQ, ~EXSTDTC, ~EXENDTC,   \"01\", \"1015\", 1, \"PLAC\", 0, \"mg\", \"QD\", \"2014-01-02\", \"2014-01-16\",   \"01\", \"1015\", 2, \"PLAC\", 0, \"mg\", \"QD\", \"2014-06-17\", \"2014-06-18\",   \"01\", \"1015\", 3, \"PLAC\", 0, \"mg\", \"QD\", \"2014-06-19\", NA_character_,   \"01\", \"1023\", 1, \"PLAC\", 0, \"mg\", \"QD\", \"2012-08-05\", \"2012-08-27\",   \"01\", \"1023\", 2, \"PLAC\", 0, \"mg\", \"QD\", \"2012-08-28\", \"2012-09-01\",   \"01\", \"1211\", 1, \"XANO\", 54, \"mg\", \"QD\", \"2012-11-15\", \"2012-11-28\",   \"01\", \"1211\", 2, \"XANO\", 54, \"mg\", \"QD\", \"2012-11-29\", NA_character_,   \"01\", \"1445\", 1, \"PLAC\", 0, \"mg\", \"QD\", \"2014-05-11\", \"2014-05-25\",   \"01\", \"1445\", 2, \"PLAC\", 0, \"mg\", \"QD\", \"2014-05-26\", \"2014-11-01\",   \"01\", \"1083\", 1, \"PLAC\", 0, \"mg\", \"QD\", \"2013-07-22\", \"2013-08-01\" )  adsl_death <- adsl %>%   mutate(     DTHDTM = convert_date_to_dtm(DTHDT),     # Remove `DCUT` setup line below if ADSL `DCUTDT` is populated.     DCUTDT = convert_dtc_to_dt(\"2015-03-06\"), # Example only, enter date.     DCUTDTM = convert_date_to_dtm(DCUTDT)   )  # Select valid dose records, non-missing `EXSTDTC` and `EXDOSE`. ex_mod <- ex %>%   filter(!is.na(EXSTDTC) & !is.na(EXDOSE)) %>%   derive_vars_merged(adsl_death, by_vars = get_admiral_option(\"subject_keys\")) %>%   # Example, set up missing `EXDOSFRQ` as QD daily dosing regime.   # Replace with study dosing regime per trial treatment.   mutate(EXDOSFRQ = if_else(is.na(EXDOSFRQ), \"QD\", EXDOSFRQ)) %>%   # Create EXxxDTM variables and replace missing `EXENDTM`.   derive_vars_dtm(     dtc = EXSTDTC,     new_vars_prefix = \"EXST\",     date_imputation = \"first\",     time_imputation = \"first\",     flag_imputation = \"none\",   ) %>%   derive_vars_dtm_to_dt(exprs(EXSTDTM)) %>%   derive_vars_dtm(     dtc = EXENDTC,     new_vars_prefix = \"EXEN\",     # Maximum imputed treatment end date must not be not greater than     # date of death or after the datacut date.     max_dates = exprs(DTHDTM, DCUTDTM),     date_imputation = \"last\",     time_imputation = \"last\",     flag_imputation = \"none\",     highest_imputation = \"Y\",   ) %>%   derive_vars_dtm_to_dt(exprs(EXENDTM)) %>%   # Select only unique values.   # Removes duplicated records before final step.   distinct(     STUDYID, USUBJID, EXTRT, EXDOSE, EXDOSFRQ, DCUTDT, DTHDT, EXSTDT,     EXSTDTM, EXENDT, EXENDTM, EXSTDTC, EXENDTC   )  create_single_dose_dataset(   ex_mod,   start_date = EXSTDT,   start_datetime = EXSTDTM,   end_date = EXENDT,   end_datetime = EXENDTM,   keep_source_vars = exprs(     STUDYID, USUBJID, EXTRT, EXDOSE, EXDOSFRQ,     DCUTDT, EXSTDT, EXSTDTM, EXENDT, EXENDTM, EXSTDTC, EXENDTC   ) ) #> # A tibble: 553 × 12 #>    STUDYID USUBJID EXTRT EXDOSE EXDOSFRQ DCUTDT     EXSTDT     #>    <chr>   <chr>   <chr>  <dbl> <chr>    <date>     <date>     #>  1 01      1015    PLAC       0 ONCE     2015-03-06 2014-01-02 #>  2 01      1015    PLAC       0 ONCE     2015-03-06 2014-01-03 #>  3 01      1015    PLAC       0 ONCE     2015-03-06 2014-01-04 #>  4 01      1015    PLAC       0 ONCE     2015-03-06 2014-01-05 #>  5 01      1015    PLAC       0 ONCE     2015-03-06 2014-01-06 #>  6 01      1015    PLAC       0 ONCE     2015-03-06 2014-01-07 #>  7 01      1015    PLAC       0 ONCE     2015-03-06 2014-01-08 #>  8 01      1015    PLAC       0 ONCE     2015-03-06 2014-01-09 #>  9 01      1015    PLAC       0 ONCE     2015-03-06 2014-01-10 #> 10 01      1015    PLAC       0 ONCE     2015-03-06 2014-01-11 #> # ℹ 543 more rows #> # ℹ 5 more variables: EXSTDTM <dttm>, EXENDT <date>, EXENDTM <dttm>, #> #   EXSTDTC <chr>, EXENDTC <chr>"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/date_source.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a date_source object — date_source","title":"Create a date_source object — date_source","text":"date_source() function superseded favor derive_vars_extreme_event(). Create date_source object input derive_var_extreme_dt() derive_var_extreme_dtm().","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/date_source.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a date_source object — date_source","text":"","code":"date_source(dataset_name, filter = NULL, date, set_values_to = NULL)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/date_source.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a date_source object — date_source","text":"dataset_name name dataset, .e. string, used search date. filter unquoted condition filtering dataset. date variable expression providing date. date datetime can specified. unquoted symbol expression expected. set_values_to Variables set","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/date_source.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a date_source object — date_source","text":"object class date_source.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/date_source.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a date_source object — date_source","text":"","code":"# treatment end date from ADSL trt_end_date <- date_source(   dataset_name = \"adsl\",   date = TRTEDT )  # lab date from LB where assessment was taken, i.e. not \"NOT DONE\" lb_date <- date_source(   dataset_name = \"lb\",   filter = LBSTAT != \"NOT DONE\" | is.na(LBSTAT),   date = convert_dtc_to_dt(LBDTC) )  # death date from ADSL including traceability variables death_date <- date_source(   dataset_name = \"adsl\",   date = DTHDT,   set_values_to = exprs(     LALVDOM = \"ADSL\",     LALVVAR = \"DTHDT\"   ) )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/default_qtc_paramcd.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Default Parameter Code for Corrected QT — default_qtc_paramcd","title":"Get Default Parameter Code for Corrected QT — default_qtc_paramcd","text":"Get Default Parameter Code Corrected QT","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/default_qtc_paramcd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Default Parameter Code for Corrected QT — default_qtc_paramcd","text":"","code":"default_qtc_paramcd(method)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/default_qtc_paramcd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Default Parameter Code for Corrected QT — default_qtc_paramcd","text":"method Method used QT correction Permitted Values: \"Bazett\", \"Fridericia\", \"Sagie\"","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/default_qtc_paramcd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Default Parameter Code for Corrected QT — default_qtc_paramcd","text":"\"QTCBR\" method \"Bazett\", \"QTCFR\" \"Fridericia\" \"QTLCR\" \"Sagie\". error otherwise.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/default_qtc_paramcd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get Default Parameter Code for Corrected QT — default_qtc_paramcd","text":"","code":"default_qtc_paramcd(\"Sagie\") #> $PARAMCD #> [1] \"QTLCR\" #>"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derivation_slice.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a derivation_slice Object — derivation_slice","title":"Create a derivation_slice Object — derivation_slice","text":"Create derivation_slice object input slice_derivation().","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derivation_slice.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a derivation_slice Object — derivation_slice","text":"","code":"derivation_slice(filter, args = NULL)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derivation_slice.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a derivation_slice Object — derivation_slice","text":"filter unquoted condition defining observations slice args Arguments derivation used slice params() object expected.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derivation_slice.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a derivation_slice Object — derivation_slice","text":"object class derivation_slice","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_basetype_records.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive Basetype Variable — derive_basetype_records","title":"Derive Basetype Variable — derive_basetype_records","text":"Baseline Type BASETYPE needed one definition baseline given Analysis Parameter PARAM dataset.  given parameter, Baseline Value BASE BASEC derived one definition baseline, BASETYPE must non-null records type parameter. value BASETYPE refers definition baseline characterizes value BASE row.  Please see section 4.2.1.6 ADaM Implementation Guide, version 1.3 background.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_basetype_records.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive Basetype Variable — derive_basetype_records","text":"","code":"derive_basetype_records(dataset, basetypes)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_basetype_records.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive Basetype Variable — derive_basetype_records","text":"dataset Input dataset variables specified basetypes argument expected dataset. basetypes named list expressions created using rlang::exprs() function names corresponds values newly created BASETYPE variables expressions used subset input dataset.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_basetype_records.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive Basetype Variable — derive_basetype_records","text":"input dataset variable BASETYPE added","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_basetype_records.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Derive Basetype Variable — derive_basetype_records","text":"Adds BASETYPE variable dataset duplicates records based upon provided conditions. element basetypes input dataset subset based upon provided expression BASETYPE variable set name expression. , subsets stacked. Records match condition kept BASETYPE set NA.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_basetype_records.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive Basetype Variable — derive_basetype_records","text":"","code":"library(tibble) library(dplyr, warn.conflicts = FALSE)  bds <- tribble(   ~USUBJID, ~EPOCH,         ~PARAMCD,  ~ASEQ, ~AVAL,   \"P01\",    \"RUN-IN\",       \"PARAM01\",     1,  10.0,   \"P01\",    \"RUN-IN\",       \"PARAM01\",     2,   9.8,   \"P01\",    \"DOUBLE-BLIND\", \"PARAM01\",     3,   9.2,   \"P01\",    \"DOUBLE-BLIND\", \"PARAM01\",     4,  10.1,   \"P01\",    \"OPEN-LABEL\",   \"PARAM01\",     5,  10.4,   \"P01\",    \"OPEN-LABEL\",   \"PARAM01\",     6,   9.9,   \"P02\",    \"RUN-IN\",       \"PARAM01\",     1,  12.1,   \"P02\",    \"DOUBLE-BLIND\", \"PARAM01\",     2,  10.2,   \"P02\",    \"DOUBLE-BLIND\", \"PARAM01\",     3,  10.8,   \"P02\",    \"OPEN-LABEL\",   \"PARAM01\",     4,  11.4,   \"P02\",    \"OPEN-LABEL\",   \"PARAM01\",     5,  10.8 )  bds_with_basetype <- derive_basetype_records(   dataset = bds,   basetypes = exprs(     \"RUN-IN\" = EPOCH %in% c(\"RUN-IN\", \"STABILIZATION\", \"DOUBLE-BLIND\", \"OPEN-LABEL\"),     \"DOUBLE-BLIND\" = EPOCH %in% c(\"DOUBLE-BLIND\", \"OPEN-LABEL\"),     \"OPEN-LABEL\" = EPOCH == \"OPEN-LABEL\"   ) )   # Below print statement will print all 23 records in the data frame # bds_with_basetype print(bds_with_basetype, n = Inf) #> # A tibble: 23 × 6 #>    USUBJID EPOCH        PARAMCD  ASEQ  AVAL BASETYPE     #>    <chr>   <chr>        <chr>   <dbl> <dbl> <chr>        #>  1 P01     RUN-IN       PARAM01     1  10   RUN-IN       #>  2 P01     RUN-IN       PARAM01     2   9.8 RUN-IN       #>  3 P01     DOUBLE-BLIND PARAM01     3   9.2 RUN-IN       #>  4 P01     DOUBLE-BLIND PARAM01     4  10.1 RUN-IN       #>  5 P01     OPEN-LABEL   PARAM01     5  10.4 RUN-IN       #>  6 P01     OPEN-LABEL   PARAM01     6   9.9 RUN-IN       #>  7 P02     RUN-IN       PARAM01     1  12.1 RUN-IN       #>  8 P02     DOUBLE-BLIND PARAM01     2  10.2 RUN-IN       #>  9 P02     DOUBLE-BLIND PARAM01     3  10.8 RUN-IN       #> 10 P02     OPEN-LABEL   PARAM01     4  11.4 RUN-IN       #> 11 P02     OPEN-LABEL   PARAM01     5  10.8 RUN-IN       #> 12 P01     DOUBLE-BLIND PARAM01     3   9.2 DOUBLE-BLIND #> 13 P01     DOUBLE-BLIND PARAM01     4  10.1 DOUBLE-BLIND #> 14 P01     OPEN-LABEL   PARAM01     5  10.4 DOUBLE-BLIND #> 15 P01     OPEN-LABEL   PARAM01     6   9.9 DOUBLE-BLIND #> 16 P02     DOUBLE-BLIND PARAM01     2  10.2 DOUBLE-BLIND #> 17 P02     DOUBLE-BLIND PARAM01     3  10.8 DOUBLE-BLIND #> 18 P02     OPEN-LABEL   PARAM01     4  11.4 DOUBLE-BLIND #> 19 P02     OPEN-LABEL   PARAM01     5  10.8 DOUBLE-BLIND #> 20 P01     OPEN-LABEL   PARAM01     5  10.4 OPEN-LABEL   #> 21 P01     OPEN-LABEL   PARAM01     6   9.9 OPEN-LABEL   #> 22 P02     OPEN-LABEL   PARAM01     4  11.4 OPEN-LABEL   #> 23 P02     OPEN-LABEL   PARAM01     5  10.8 OPEN-LABEL    count(bds_with_basetype, BASETYPE, name = \"Number of Records\") #> # A tibble: 3 × 2 #>   BASETYPE     `Number of Records` #>   <chr>                      <int> #> 1 DOUBLE-BLIND                   8 #> 2 OPEN-LABEL                     4 #> 3 RUN-IN                        11  # An example where all parameter records need to be included for 2 different # baseline type derivations (such as LAST and WORST) bds <- tribble(   ~USUBJID, ~EPOCH,         ~PARAMCD,  ~ASEQ, ~AVAL,   \"P01\",    \"RUN-IN\",       \"PARAM01\",     1,  10.0,   \"P01\",    \"RUN-IN\",       \"PARAM01\",     2,   9.8,   \"P01\",    \"DOUBLE-BLIND\", \"PARAM01\",     3,   9.2,   \"P01\",    \"DOUBLE-BLIND\", \"PARAM01\",     4,  10.1 )  bds_with_basetype <- derive_basetype_records(   dataset = bds,   basetypes = exprs(     \"LAST\" = TRUE,     \"WORST\" = TRUE   ) )  print(bds_with_basetype, n = Inf) #> # A tibble: 8 × 6 #>   USUBJID EPOCH        PARAMCD  ASEQ  AVAL BASETYPE #>   <chr>   <chr>        <chr>   <dbl> <dbl> <chr>    #> 1 P01     RUN-IN       PARAM01     1  10   LAST     #> 2 P01     RUN-IN       PARAM01     2   9.8 LAST     #> 3 P01     DOUBLE-BLIND PARAM01     3   9.2 LAST     #> 4 P01     DOUBLE-BLIND PARAM01     4  10.1 LAST     #> 5 P01     RUN-IN       PARAM01     1  10   WORST    #> 6 P01     RUN-IN       PARAM01     2   9.8 WORST    #> 7 P01     DOUBLE-BLIND PARAM01     3   9.2 WORST    #> 8 P01     DOUBLE-BLIND PARAM01     4  10.1 WORST     count(bds_with_basetype, BASETYPE, name = \"Number of Records\") #> # A tibble: 2 × 2 #>   BASETYPE `Number of Records` #>   <chr>                  <int> #> 1 LAST                       4 #> 2 WORST                      4"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_expected_records.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive Expected Records — derive_expected_records","title":"Derive Expected Records — derive_expected_records","text":"Add expected records new observations 'group' dataset contains missing observations.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_expected_records.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive Expected Records — derive_expected_records","text":"","code":"derive_expected_records(   dataset,   dataset_ref,   by_vars = NULL,   set_values_to = NULL )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_expected_records.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive Expected Records — derive_expected_records","text":"dataset Input dataset variables specified dataset_ref by_vars arguments expected dataset. dataset_ref Expected observations dataset Data frame expected observations, e.g., expected combinations PARAMCD, PARAM, AVISIT, AVISITN, ... by_vars Grouping variables group defined by_vars observations dataset_ref added output dataset corresponding observation input dataset. Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) set_values_to Variables set specified variables set specified values new observations. list variable name-value pairs expected. LHS refers variable. RHS refers values set variable. can string, symbol, numeric value, NA, expressions, e.g., exprs(PARAMCD =   \"TDOSE\", PARCAT1 = \"OVERALL\").","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_expected_records.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive Expected Records — derive_expected_records","text":"input dataset missed expected observations added by_vars. Note, variable populated new parameter rows specified by_vars set_values_to.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_expected_records.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Derive Expected Records — derive_expected_records","text":"group (variables specified by_vars parameter), records dataset_ref missing input dataset added output dataset.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_expected_records.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive Expected Records — derive_expected_records","text":"","code":"library(tibble)  adqs <- tribble(   ~USUBJID, ~PARAMCD, ~AVISITN, ~AVISIT, ~AVAL,   \"1\",      \"a\",             1, \"WEEK 1\",   10,   \"1\",      \"b\",             1, \"WEEK 1\",   11,   \"2\",      \"a\",             2, \"WEEK 2\",   12,   \"2\",      \"b\",             2, \"WEEK 2\",   14 )  # Example 1. visit variables are parameter independent parm_visit_ref <- tribble(   ~AVISITN, ~AVISIT,   1,        \"WEEK 1\",   2,        \"WEEK 2\" )  derive_expected_records(   dataset = adqs,   dataset_ref = parm_visit_ref,   by_vars = exprs(USUBJID, PARAMCD),   set_values_to = exprs(DTYPE = \"DERIVED\") ) #> # A tibble: 8 × 6 #>   USUBJID PARAMCD AVISITN AVISIT  AVAL DTYPE   #>   <chr>   <chr>     <dbl> <chr>  <dbl> <chr>   #> 1 1       a             1 WEEK 1    10 NA      #> 2 1       a             2 WEEK 2    NA DERIVED #> 3 1       b             1 WEEK 1    11 NA      #> 4 1       b             2 WEEK 2    NA DERIVED #> 5 2       a             1 WEEK 1    NA DERIVED #> 6 2       a             2 WEEK 2    12 NA      #> 7 2       b             1 WEEK 1    NA DERIVED #> 8 2       b             2 WEEK 2    14 NA       # Example 2. visit variables are parameter dependent parm_visit_ref <- tribble(   ~PARAMCD, ~AVISITN, ~AVISIT,   \"a\",             1, \"WEEK 1\",   \"a\",             2, \"WEEK 2\",   \"b\",             1, \"WEEK 1\" )  derive_expected_records(   dataset = adqs,   dataset_ref = parm_visit_ref,   by_vars = exprs(USUBJID, PARAMCD),   set_values_to = exprs(DTYPE = \"DERIVED\") ) #> # A tibble: 7 × 6 #>   USUBJID PARAMCD AVISITN AVISIT  AVAL DTYPE   #>   <chr>   <chr>     <dbl> <chr>  <dbl> <chr>   #> 1 1       a             1 WEEK 1    10 NA      #> 2 1       a             2 WEEK 2    NA DERIVED #> 3 1       b             1 WEEK 1    11 NA      #> 4 2       a             1 WEEK 1    NA DERIVED #> 5 2       a             2 WEEK 2    12 NA      #> 6 2       b             1 WEEK 1    NA DERIVED #> 7 2       b             2 WEEK 2    14 NA"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_extreme_event.html","id":null,"dir":"Reference","previous_headings":"","what":"Add the Worst or Best Observation for Each By Group as New Records — derive_extreme_event","title":"Add the Worst or Best Observation for Each By Group as New Records — derive_extreme_event","text":"Add first available record events group new records, variables selected observation kept. can used selecting extreme observation series user-defined events. distinguishes derive_extreme_event() derive_extreme_records(), extreme records derived based certain order existing variables.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_extreme_event.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add the Worst or Best Observation for Each By Group as New Records — derive_extreme_event","text":"","code":"derive_extreme_event(   dataset = NULL,   by_vars,   events,   tmp_event_nr_var = NULL,   order,   mode,   source_datasets = NULL,   check_type = \"warning\",   set_values_to = NULL,   keep_source_vars = exprs(everything()) )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_extreme_event.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add the Worst or Best Observation for Each By Group as New Records — derive_extreme_event","text":"dataset Input dataset variables specified by_vars order arguments expected dataset. by_vars Grouping variables Default: NULL Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) events Conditions new values defining events list event() event_joined() objects expected. observations listed events considered deriving extreme event. multiple records meet filter condition, take first record sorted order. data grouped by_vars, .e., summary functions like () () can used condition. event_joined() events observations selected calling filter_joined(). condition field passed filter_join argument. tmp_event_nr_var Temporary event number variable specified variable added source datasets set number event selecting records event. can used order determine record used records one event selected. variable included output dataset. order Sort order particular event events one observation, within event group, records ordered specified order. handling NAs sorting variables see Sort Order. Permitted Values: list expressions created exprs(), e.g., exprs(ADT, desc(AVAL)) mode Selection mode (first last) particular event events one observation, \"first\"/\"last\" used select first/last record type event sorting order. Permitted Values: \"first\", \"last\" source_datasets Source datasets named list datasets expected. dataset_name field event() event_joined() refers dataset provided list. check_type Check uniqueness? \"warning\" \"error\" specified, specified message issued observations input dataset unique respect variables order. Default: \"warning\" Permitted Values: \"none\", \"warning\", \"error\" set_values_to Variables set specified variables set specified values new observations. Set list variables specified value new records LHS refer variable. RHS refers values set variable. can string, symbol, numeric value, expression NA. summary functions used, values summarized variables specified by_vars. example:   keep_source_vars Variables keep source dataset event specified variables kept selected observations. variables specified by_vars created set_values_to always kept. keep_source_vars field event take precedence value keep_source_vars argument. Permitted Values: list expressions element symbol tidyselect expression, e.g., exprs(VISIT, VISITNUM, starts_with(\"RS\")).","code":"set_values_to = exprs(     AVAL = sum(AVAL),     DTYPE = \"AVERAGE\",   )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_extreme_event.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add the Worst or Best Observation for Each By Group as New Records — derive_extreme_event","text":"input dataset best worst observation group added new observations.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_extreme_event.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Add the Worst or Best Observation for Each By Group as New Records — derive_extreme_event","text":"event select observations consider: event class event, observations source dataset restricted condition first last (mode) observation per group (by_vars) selected. event class event_joined, filter_joined() called select observations. variables specified set_values_to field event added selected observations. variable specified tmp_event_nr_var added set number event. variables specified keep_source_vars field event, variables (by_vars) variables created set_values_to kept. keep_source_vars = NULL used event derive_extreme_event() value keep_source_vars argument derive_extreme_event() used. selected observations bound together. group (respect variables specified by_vars parameter) first last observation (respect order specified order parameter mode specified mode parameter) selected. variables specified set_values_to parameter added selected observations. observations added input dataset.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_extreme_event.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add the Worst or Best Observation for Each By Group as New Records — derive_extreme_event","text":"","code":"library(tibble) library(dplyr) library(lubridate)  adqs <- tribble(   ~USUBJID, ~PARAMCD,       ~AVALC,        ~ADY,   \"1\",      \"NO SLEEP\",     \"N\",              1,   \"1\",      \"WAKE UP\",      \"N\",              2,   \"1\",      \"FALL ASLEEP\",  \"N\",              3,   \"2\",      \"NO SLEEP\",     \"N\",              1,   \"2\",      \"WAKE UP\",      \"Y\",              2,   \"2\",      \"WAKE UP\",      \"Y\",              3,   \"2\",      \"FALL ASLEEP\",  \"N\",              4,   \"3\",      \"NO SLEEP\",     NA_character_,    1 )  # Add a new record for each USUBJID storing the the worst sleeping problem. derive_extreme_event(   adqs,   by_vars = exprs(USUBJID),   events = list(     event(       condition = PARAMCD == \"NO SLEEP\" & AVALC == \"Y\",       set_values_to = exprs(AVALC = \"No sleep\", AVAL = 1)     ),     event(       condition = PARAMCD == \"WAKE UP\" & AVALC == \"Y\",       set_values_to = exprs(AVALC = \"Waking up more than three times\", AVAL = 2)     ),     event(       condition = PARAMCD == \"FALL ASLEEP\" & AVALC == \"Y\",       set_values_to = exprs(AVALC = \"More than 30 mins to fall asleep\", AVAL = 3)     ),     event(       condition = all(AVALC == \"N\"),       set_values_to = exprs(         AVALC = \"No sleeping problems\", AVAL = 4       )     ),     event(       condition = TRUE,       set_values_to = exprs(AVALC = \"Missing\", AVAL = 99)     )   ),   tmp_event_nr_var = event_nr,   order = exprs(event_nr, desc(ADY)),   mode = \"first\",   set_values_to = exprs(     PARAMCD = \"WSP\",     PARAM = \"Worst Sleeping Problems\"   ) ) #> # A tibble: 11 × 6 #>    USUBJID PARAMCD     AVALC                             ADY  AVAL PARAM         #>    <chr>   <chr>       <chr>                           <dbl> <dbl> <chr>         #>  1 1       NO SLEEP    N                                   1    NA NA            #>  2 1       WAKE UP     N                                   2    NA NA            #>  3 1       FALL ASLEEP N                                   3    NA NA            #>  4 2       NO SLEEP    N                                   1    NA NA            #>  5 2       WAKE UP     Y                                   2    NA NA            #>  6 2       WAKE UP     Y                                   3    NA NA            #>  7 2       FALL ASLEEP N                                   4    NA NA            #>  8 3       NO SLEEP    NA                                  1    NA NA            #>  9 1       WSP         No sleeping problems                3     4 Worst Sleepi… #> 10 2       WSP         Waking up more than three times     3     2 Worst Sleepi… #> 11 3       WSP         Missing                             1    99 Worst Sleepi…  # Use different mode by event adhy <- tribble(   ~USUBJID, ~AVISITN, ~CRIT1FL,   \"1\",             1, \"Y\",   \"1\",             2, \"Y\",   \"2\",             1, \"Y\",   \"2\",             2, NA_character_,   \"2\",             3, \"Y\",   \"2\",             4, NA_character_ ) %>%   mutate(     PARAMCD = \"ALKPH\",     PARAM = \"Alkaline Phosphatase (U/L)\"   )  derive_extreme_event(   adhy,   by_vars = exprs(USUBJID),   events = list(     event(       condition = is.na(CRIT1FL),       set_values_to = exprs(AVALC = \"N\")     ),     event(       condition = CRIT1FL == \"Y\",       mode = \"last\",       set_values_to = exprs(AVALC = \"Y\")     )   ),   tmp_event_nr_var = event_nr,   order = exprs(event_nr, AVISITN),   mode = \"first\",   keep_source_vars = exprs(AVISITN),   set_values_to = exprs(     PARAMCD = \"ALK2\",     PARAM = \"ALKPH <= 2 times ULN\"   ) ) #> # A tibble: 8 × 6 #>   USUBJID AVISITN CRIT1FL PARAMCD PARAM                      AVALC #>   <chr>     <dbl> <chr>   <chr>   <chr>                      <chr> #> 1 1             1 Y       ALKPH   Alkaline Phosphatase (U/L) NA    #> 2 1             2 Y       ALKPH   Alkaline Phosphatase (U/L) NA    #> 3 2             1 Y       ALKPH   Alkaline Phosphatase (U/L) NA    #> 4 2             2 NA      ALKPH   Alkaline Phosphatase (U/L) NA    #> 5 2             3 Y       ALKPH   Alkaline Phosphatase (U/L) NA    #> 6 2             4 NA      ALKPH   Alkaline Phosphatase (U/L) NA    #> 7 1             2 NA      ALK2    ALKPH <= 2 times ULN       Y     #> 8 2             2 NA      ALK2    ALKPH <= 2 times ULN       N      # Derive confirmed best overall response (using event_joined()) # CR - complete response, PR - partial response, SD - stable disease # NE - not evaluable, PD - progressive disease adsl <- tribble(   ~USUBJID, ~TRTSDTC,   \"1\",      \"2020-01-01\",   \"2\",      \"2019-12-12\",   \"3\",      \"2019-11-11\",   \"4\",      \"2019-12-30\",   \"5\",      \"2020-01-01\",   \"6\",      \"2020-02-02\",   \"7\",      \"2020-02-02\",   \"8\",      \"2020-02-01\" ) %>%   mutate(TRTSDT = ymd(TRTSDTC))  adrs <- tribble(   ~USUBJID, ~ADTC,        ~AVALC,   \"1\",      \"2020-01-01\", \"PR\",   \"1\",      \"2020-02-01\", \"CR\",   \"1\",      \"2020-02-16\", \"NE\",   \"1\",      \"2020-03-01\", \"CR\",   \"1\",      \"2020-04-01\", \"SD\",   \"2\",      \"2020-01-01\", \"SD\",   \"2\",      \"2020-02-01\", \"PR\",   \"2\",      \"2020-03-01\", \"SD\",   \"2\",      \"2020-03-13\", \"CR\",   \"4\",      \"2020-01-01\", \"PR\",   \"4\",      \"2020-03-01\", \"NE\",   \"4\",      \"2020-04-01\", \"NE\",   \"4\",      \"2020-05-01\", \"PR\",   \"5\",      \"2020-01-01\", \"PR\",   \"5\",      \"2020-01-10\", \"PR\",   \"5\",      \"2020-01-20\", \"PR\",   \"6\",      \"2020-02-06\", \"PR\",   \"6\",      \"2020-02-16\", \"CR\",   \"6\",      \"2020-03-30\", \"PR\",   \"7\",      \"2020-02-06\", \"PR\",   \"7\",      \"2020-02-16\", \"CR\",   \"7\",      \"2020-04-01\", \"NE\",   \"8\",      \"2020-02-16\", \"PD\" ) %>%   mutate(     ADT = ymd(ADTC),     PARAMCD = \"OVR\",     PARAM = \"Overall Response by Investigator\"   ) %>%   derive_vars_merged(     dataset_add = adsl,     by_vars = exprs(USUBJID),     new_vars = exprs(TRTSDT)   )  derive_extreme_event(   adrs,   by_vars = exprs(USUBJID),   tmp_event_nr_var = event_nr,   order = exprs(event_nr, ADT),   mode = \"first\",   source_datasets = list(adsl = adsl),   events = list(     event_joined(       description = paste(         \"CR needs to be confirmed by a second CR at least 28 days later\",         \"at most one NE is acceptable between the two assessments\"       ),       join_vars = exprs(AVALC, ADT),       join_type = \"after\",       first_cond_upper = AVALC.join == \"CR\" &         ADT.join >= ADT + 28,       condition = AVALC == \"CR\" &         all(AVALC.join %in% c(\"CR\", \"NE\")) &         count_vals(var = AVALC.join, val = \"NE\") <= 1,       set_values_to = exprs(         AVALC = \"CR\"       )     ),     event_joined(       description = paste(         \"PR needs to be confirmed by a second CR or PR at least 28 days later,\",         \"at most one NE is acceptable between the two assessments\"       ),       join_vars = exprs(AVALC, ADT),       join_type = \"after\",       first_cond_upper = AVALC.join %in% c(\"CR\", \"PR\") &         ADT.join >= ADT + 28,       condition = AVALC == \"PR\" &         all(AVALC.join %in% c(\"CR\", \"PR\", \"NE\")) &         count_vals(var = AVALC.join, val = \"NE\") <= 1,       set_values_to = exprs(         AVALC = \"PR\"       )     ),     event(       description = paste(         \"CR, PR, or SD are considered as SD if occurring at least 28\",         \"after treatment start\"       ),       condition = AVALC %in% c(\"CR\", \"PR\", \"SD\") & ADT >= TRTSDT + 28,       set_values_to = exprs(         AVALC = \"SD\"       )     ),     event(       condition = AVALC == \"PD\",       set_values_to = exprs(         AVALC = \"PD\"       )     ),     event(       condition = AVALC %in% c(\"CR\", \"PR\", \"SD\", \"NE\"),       set_values_to = exprs(         AVALC = \"NE\"       )     ),     event(       description = \"set response to MISSING for patients without records in ADRS\",       dataset_name = \"adsl\",       condition = TRUE,       set_values_to = exprs(         AVALC = \"MISSING\"       ),       keep_source_vars = exprs(TRTSDT)     )   ),   set_values_to = exprs(     PARAMCD = \"CBOR\",     PARAM = \"Best Confirmed Overall Response by Investigator\"   ) ) %>%   filter(PARAMCD == \"CBOR\") #> # A tibble: 8 × 7 #>   USUBJID ADTC       AVALC   ADT        PARAMCD PARAM                 TRTSDT     #>   <chr>   <chr>      <chr>   <date>     <chr>   <chr>                 <date>     #> 1 1       2020-02-01 CR      2020-02-01 CBOR    Best Confirmed Overa… 2020-01-01 #> 2 2       2020-02-01 SD      2020-02-01 CBOR    Best Confirmed Overa… 2019-12-12 #> 3 3       NA         MISSING NA         CBOR    Best Confirmed Overa… 2019-11-11 #> 4 4       2020-05-01 SD      2020-05-01 CBOR    Best Confirmed Overa… 2019-12-30 #> 5 5       2020-01-01 NE      2020-01-01 CBOR    Best Confirmed Overa… 2020-01-01 #> 6 6       2020-02-06 PR      2020-02-06 CBOR    Best Confirmed Overa… 2020-02-02 #> 7 7       2020-02-06 NE      2020-02-06 CBOR    Best Confirmed Overa… 2020-02-02 #> 8 8       2020-02-16 PD      2020-02-16 CBOR    Best Confirmed Overa… 2020-02-01"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_extreme_records.html","id":null,"dir":"Reference","previous_headings":"","what":"Add the First or Last Observation for Each By Group as New Records — derive_extreme_records","title":"Add the First or Last Observation for Each By Group as New Records — derive_extreme_records","text":"Add first last observation group new observations. new observations can selected additional dataset. function can used adding maximum minimum value separate visit. variables selected observation kept. distinguishes derive_extreme_records() derive_summary_records(), variables populated new records.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_extreme_records.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add the First or Last Observation for Each By Group as New Records — derive_extreme_records","text":"","code":"derive_extreme_records(   dataset = NULL,   dataset_add,   dataset_ref = NULL,   by_vars = NULL,   order = NULL,   mode = NULL,   filter_add = NULL,   check_type = \"warning\",   exist_flag = NULL,   true_value = \"Y\",   false_value = NA_character_,   keep_source_vars = exprs(everything()),   set_values_to )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_extreme_records.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add the First or Last Observation for Each By Group as New Records — derive_extreme_records","text":"dataset Input dataset dataset_add Additional dataset additional dataset, determines groups returned input dataset, based groups exist dataset subset filter_add. variables specified by_vars filter_add parameters expected dataset. mode order specified, first last observation within group, defined by_vars, selected. dataset_ref Reference dataset variables specified by_vars expected. observation specified dataset new observation added input dataset. by_vars Grouping variables dataset_ref specified, argument must specified. Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) order Sort order Within group observations ordered specified order. Permitted Values: list expressions created exprs(), e.g., exprs(ADT, desc(AVAL)) mode Selection mode (first last) \"first\" specified, first observation group added input dataset. \"last\" specified, last observation group added input dataset. Permitted Values: \"first\", \"last\" filter_add Filter additional dataset (dataset_add) observations dataset_add fulfilling specified condition considered. check_type Check uniqueness? \"warning\" \"error\" specified, specified message issued observations (restricted) additional dataset unique respect variables order. Permitted Values: \"none\", \"warning\", \"error\" exist_flag Existence flag specified variable added output dataset. groups least one observation additional dataset (dataset_add) exist_flag set value specified true_value argument. groups exist_flag set value specified false_value argument. Permitted Values: Variable name true_value True value new observations selected additional dataset (dataset_add), exist_flag set specified value. false_value False value new observations selected additional dataset (dataset_add), exist_flag set specified value. keep_source_vars Variables kept new records named list tidyselect expressions created exprs() defining variables kept new records. variables specified by_vars set_values_to need specified kept automatically. set_values_to Variables set specified variables set specified values new observations. Set list variables specified value new records LHS refer variable. RHS refers values set variable. can string, symbol, numeric value, expression NA. summary functions used, values summarized variables specified by_vars. example:","code":"set_values_to = exprs(     AVAL = sum(AVAL),     DTYPE = \"AVERAGE\",   )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_extreme_records.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add the First or Last Observation for Each By Group as New Records — derive_extreme_records","text":"input dataset first last observation group added new observations.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_extreme_records.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Add the First or Last Observation for Each By Group as New Records — derive_extreme_records","text":"additional dataset (dataset_add) restricted specified filter_add argument. group (respect variables specified by_vars argument) first last observation (respect order specified order argument mode specified mode argument) selected. dataset_ref specified, observations dataset_ref selected records added. variables specified set_values_to argument added selected observations. variables specified keep_source_vars argument selected along variables specified by_vars set_values_to arguments. observations added input dataset.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_extreme_records.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add the First or Last Observation for Each By Group as New Records — derive_extreme_records","text":"","code":"library(tibble) library(dplyr, warn.conflicts = FALSE) library(lubridate)  adlb <- tribble(   ~USUBJID, ~AVISITN, ~AVAL, ~LBSEQ,   \"1\",      1,          113,      1,   \"1\",      2,          113,      2,   \"1\",      3,          117,      3,   \"2\",      1,          101,      1,   \"2\",      2,          101,      2,   \"2\",      3,           95,      3 )  # Add a new record for each USUBJID storing the minimum value (first AVAL). # If multiple records meet the minimum criterion, take the first value by # AVISITN. Set AVISITN = 97 and DTYPE = MINIMUM for these new records. # Specify the variables that need to be kept in the new records. derive_extreme_records(   adlb,   dataset_add = adlb,   by_vars = exprs(USUBJID),   order = exprs(AVAL, AVISITN),   mode = \"first\",   filter_add = !is.na(AVAL),   keep_source_vars = exprs(AVAL),   set_values_to = exprs(     AVISITN = 97,     DTYPE = \"MINIMUM\"   ) ) #> # A tibble: 8 × 5 #>   USUBJID AVISITN  AVAL LBSEQ DTYPE   #>   <chr>     <dbl> <dbl> <dbl> <chr>   #> 1 1             1   113     1 NA      #> 2 1             2   113     2 NA      #> 3 1             3   117     3 NA      #> 4 2             1   101     1 NA      #> 5 2             2   101     2 NA      #> 6 2             3    95     3 NA      #> 7 1            97   113    NA MINIMUM #> 8 2            97    95    NA MINIMUM  # Add a new record for each USUBJID storing the maximum value (last AVAL). # If multiple records meet the maximum criterion, take the first value by # AVISITN. Set AVISITN = 98 and DTYPE = MAXIMUM for these new records. derive_extreme_records(   adlb,   dataset_add = adlb,   by_vars = exprs(USUBJID),   order = exprs(desc(AVAL), AVISITN),   mode = \"first\",   filter_add = !is.na(AVAL),   set_values_to = exprs(     AVISITN = 98,     DTYPE = \"MAXIMUM\"   ) ) #> # A tibble: 8 × 5 #>   USUBJID AVISITN  AVAL LBSEQ DTYPE   #>   <chr>     <dbl> <dbl> <dbl> <chr>   #> 1 1             1   113     1 NA      #> 2 1             2   113     2 NA      #> 3 1             3   117     3 NA      #> 4 2             1   101     1 NA      #> 5 2             2   101     2 NA      #> 6 2             3    95     3 NA      #> 7 1            98   117     3 MAXIMUM #> 8 2            98   101     1 MAXIMUM  # Add a new record for each USUBJID storing for the last value. # Set AVISITN = 99 and DTYPE = LOV for these new records. derive_extreme_records(   adlb,   dataset_add = adlb,   by_vars = exprs(USUBJID),   order = exprs(AVISITN),   mode = \"last\",   set_values_to = exprs(     AVISITN = 99,     DTYPE = \"LOV\"   ) ) #> # A tibble: 8 × 5 #>   USUBJID AVISITN  AVAL LBSEQ DTYPE #>   <chr>     <dbl> <dbl> <dbl> <chr> #> 1 1             1   113     1 NA    #> 2 1             2   113     2 NA    #> 3 1             3   117     3 NA    #> 4 2             1   101     1 NA    #> 5 2             2   101     2 NA    #> 6 2             3    95     3 NA    #> 7 1            99   117     3 LOV   #> 8 2            99    95     3 LOV    # Derive a new parameter for the first disease progression (PD) adsl <- tribble(   ~USUBJID, ~DTHDT,   \"1\",      ymd(\"2022-05-13\"),   \"2\",      ymd(\"\"),   \"3\",      ymd(\"\") ) %>%   mutate(STUDYID = \"XX1234\")  adrs <- tribble(   ~USUBJID, ~ADTC,        ~AVALC,   \"1\",      \"2020-01-02\", \"PR\",   \"1\",      \"2020-02-01\", \"CR\",   \"1\",      \"2020-03-01\", \"CR\",   \"1\",      \"2020-04-01\", \"SD\",   \"2\",      \"2021-06-15\", \"SD\",   \"2\",      \"2021-07-16\", \"PD\",   \"2\",      \"2021-09-14\", \"PD\" ) %>%   mutate(     STUDYID = \"XX1234\",     ADT = ymd(ADTC),     PARAMCD = \"OVR\",     PARAM = \"Overall Response\",     ANL01FL = \"Y\"   ) %>%   select(-ADTC)  derive_extreme_records(   adrs,   dataset_ref = adsl,   dataset_add = adrs,   by_vars = exprs(STUDYID, USUBJID),   filter_add = PARAMCD == \"OVR\" & AVALC == \"PD\",   order = exprs(ADT),   exist_flag = AVALC,   true_value = \"Y\",   false_value = \"N\",   mode = \"first\",   set_values_to = exprs(     PARAMCD = \"PD\",     PARAM = \"Disease Progression\",     AVAL = yn_to_numeric(AVALC),     ANL01FL = \"Y\",     ADT = ADT   ) ) #> # A tibble: 10 × 8 #>    USUBJID AVALC STUDYID ADT        PARAMCD PARAM               ANL01FL  AVAL #>    <chr>   <chr> <chr>   <date>     <chr>   <chr>               <chr>   <dbl> #>  1 1       PR    XX1234  2020-01-02 OVR     Overall Response    Y          NA #>  2 1       CR    XX1234  2020-02-01 OVR     Overall Response    Y          NA #>  3 1       CR    XX1234  2020-03-01 OVR     Overall Response    Y          NA #>  4 1       SD    XX1234  2020-04-01 OVR     Overall Response    Y          NA #>  5 2       SD    XX1234  2021-06-15 OVR     Overall Response    Y          NA #>  6 2       PD    XX1234  2021-07-16 OVR     Overall Response    Y          NA #>  7 2       PD    XX1234  2021-09-14 OVR     Overall Response    Y          NA #>  8 2       Y     XX1234  2021-07-16 PD      Disease Progression Y           1 #>  9 1       N     XX1234  NA         PD      Disease Progression Y           0 #> 10 3       N     XX1234  NA         PD      Disease Progression Y           0  # derive parameter indicating death derive_extreme_records(   dataset_ref = adsl,   dataset_add = adsl,   by_vars = exprs(STUDYID, USUBJID),   filter_add = !is.na(DTHDT),   exist_flag = AVALC,   true_value = \"Y\",   false_value = \"N\",   mode = \"first\",   set_values_to = exprs(     PARAMCD = \"DEATH\",     PARAM = \"Death\",     ANL01FL = \"Y\",     ADT = DTHDT   ) ) #> # A tibble: 3 × 8 #>   STUDYID USUBJID PARAMCD PARAM ANL01FL ADT        DTHDT      AVALC #>   <chr>   <chr>   <chr>   <chr> <chr>   <date>     <date>     <chr> #> 1 XX1234  1       DEATH   Death Y       2022-05-13 2022-05-13 Y     #> 2 XX1234  2       DEATH   Death Y       NA         NA         N     #> 3 XX1234  3       DEATH   Death Y       NA         NA         N"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_locf_records.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive LOCF (Last Observation Carried Forward) Records — derive_locf_records","title":"Derive LOCF (Last Observation Carried Forward) Records — derive_locf_records","text":"Adds LOCF records new observations 'group' dataset contain observations missed visits/time points.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_locf_records.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive LOCF (Last Observation Carried Forward) Records — derive_locf_records","text":"","code":"derive_locf_records(   dataset,   dataset_ref,   by_vars,   analysis_var = AVAL,   order,   keep_vars = NULL )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_locf_records.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive LOCF (Last Observation Carried Forward) Records — derive_locf_records","text":"dataset Input dataset variables specified by_vars, analysis_var, order, keep_vars arguments expected dataset. dataset_ref Expected observations dataset Data frame combinations PARAMCD, PARAM, AVISIT, AVISITN, ... expected dataset expected. by_vars Grouping variables group defined by_vars observations dataset_ref added output dataset corresponding observation input dataset analysis_var NA corresponding observation input dataset. Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) analysis_var Analysis variable. Default: AVAL Permitted Values: variable order Sort order dataset sorted order carrying last observation forward (e.g. AVAL) within by_vars. handling NAs sorting variables see Sort Order. keep_vars Variables need carrying last observation forward Keep variables need carrying last observation forward analysis_var (e.g., PARAMN, VISITNUM). default NULL, variables specified by_vars analysis_var populated newly created records.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_locf_records.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive LOCF (Last Observation Carried Forward) Records — derive_locf_records","text":"input dataset new \"LOCF\" observations added by_vars. Note, variable populated new parameter rows specified by_vars.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_locf_records.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Derive LOCF (Last Observation Carried Forward) Records — derive_locf_records","text":"group (respect variables specified by_vars parameter) observations dataset_ref added output dataset corresponding observation input dataset analysis_var NA corresponding observation input dataset. new observations, analysis_var set non-missing analysis_var previous observation input dataset (sorted order) DTYPE set \"LOCF\".","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_locf_records.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Derive LOCF (Last Observation Carried Forward) Records — derive_locf_records","text":"G Gayatri","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_locf_records.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive LOCF (Last Observation Carried Forward) Records — derive_locf_records","text":"","code":"library(dplyr) library(tibble)  advs <- tribble(   ~STUDYID,  ~USUBJID,      ~PARAMCD, ~PARAMN, ~AVAL, ~AVISITN, ~AVISIT,   \"CDISC01\", \"01-701-1015\", \"PULSE\",        1,    65,        0, \"BASELINE\",   \"CDISC01\", \"01-701-1015\", \"DIABP\",        2,    79,        0, \"BASELINE\",   \"CDISC01\", \"01-701-1015\", \"DIABP\",        2,    80,        2, \"WEEK 2\",   \"CDISC01\", \"01-701-1015\", \"DIABP\",        2,    NA,        4, \"WEEK 4\",   \"CDISC01\", \"01-701-1015\", \"DIABP\",        2,    NA,        6, \"WEEK 6\",   \"CDISC01\", \"01-701-1015\", \"SYSBP\",        3,   130,        0, \"BASELINE\",   \"CDISC01\", \"01-701-1015\", \"SYSBP\",        3,   132,        2, \"WEEK 2\",   \"CDISC01\", \"01-701-1028\", \"PULSE\",        1,    61,        0, \"BASELINE\",   \"CDISC01\", \"01-701-1028\", \"PULSE\",        1,    60,        6, \"WEEK 6\",   \"CDISC01\", \"01-701-1028\", \"DIABP\",        2,    51,        0, \"BASELINE\",   \"CDISC01\", \"01-701-1028\", \"DIABP\",        2,    50,        2, \"WEEK 2\",   \"CDISC01\", \"01-701-1028\", \"DIABP\",        2,    51,        4, \"WEEK 4\",   \"CDISC01\", \"01-701-1028\", \"DIABP\",        2,    50,        6, \"WEEK 6\",   \"CDISC01\", \"01-701-1028\", \"SYSBP\",        3,   121,        0, \"BASELINE\",   \"CDISC01\", \"01-701-1028\", \"SYSBP\",        3,   121,        2, \"WEEK 2\",   \"CDISC01\", \"01-701-1028\", \"SYSBP\",        3,   121,        4, \"WEEK 4\",   \"CDISC01\", \"01-701-1028\", \"SYSBP\",        3,   121,        6, \"WEEK 6\" )  # A dataset with all the combinations of PARAMCD, PARAM, AVISIT, AVISITN, ... which are expected. advs_expected_obsv <- tribble(   ~PARAMCD, ~AVISITN, ~AVISIT,   \"PULSE\",         0, \"BASELINE\",   \"PULSE\",         6, \"WEEK 6\",   \"DIABP\",         0, \"BASELINE\",   \"DIABP\",         2, \"WEEK 2\",   \"DIABP\",         4, \"WEEK 4\",   \"DIABP\",         6, \"WEEK 6\",   \"SYSBP\",         0, \"BASELINE\",   \"SYSBP\",         2, \"WEEK 2\",   \"SYSBP\",         4, \"WEEK 4\",   \"SYSBP\",         6, \"WEEK 6\" )  derive_locf_records(   dataset = advs,   dataset_ref = advs_expected_obsv,   by_vars = exprs(STUDYID, USUBJID, PARAMCD),   order = exprs(AVISITN, AVISIT),   keep_vars = exprs(PARAMN) ) |>   arrange(USUBJID, PARAMCD, AVISIT) #> # A tibble: 22 × 8 #>    STUDYID USUBJID     PARAMCD PARAMN  AVAL AVISITN AVISIT   DTYPE #>    <chr>   <chr>       <chr>    <dbl> <dbl>   <dbl> <chr>    <chr> #>  1 CDISC01 01-701-1015 DIABP        2    79       0 BASELINE NA    #>  2 CDISC01 01-701-1015 DIABP        2    80       2 WEEK 2   NA    #>  3 CDISC01 01-701-1015 DIABP        2    80       4 WEEK 4   LOCF  #>  4 CDISC01 01-701-1015 DIABP        2    NA       4 WEEK 4   NA    #>  5 CDISC01 01-701-1015 DIABP        2    80       6 WEEK 6   LOCF  #>  6 CDISC01 01-701-1015 DIABP        2    NA       6 WEEK 6   NA    #>  7 CDISC01 01-701-1015 PULSE        1    65       0 BASELINE NA    #>  8 CDISC01 01-701-1015 PULSE        1    65       6 WEEK 6   LOCF  #>  9 CDISC01 01-701-1015 SYSBP        3   130       0 BASELINE NA    #> 10 CDISC01 01-701-1015 SYSBP        3   132       2 WEEK 2   NA    #> # ℹ 12 more rows"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_bmi.html","id":null,"dir":"Reference","previous_headings":"","what":"Adds a Parameter for BMI — derive_param_bmi","title":"Adds a Parameter for BMI — derive_param_bmi","text":"Adds record BMI/Body Mass Index using Weight Height group (e.g., subject visit) source parameters available. Note: wrapper function generic derive_param_computed().","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_bmi.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adds a Parameter for BMI — derive_param_bmi","text":"","code":"derive_param_bmi(   dataset,   by_vars,   set_values_to = exprs(PARAMCD = \"BMI\"),   weight_code = \"WEIGHT\",   height_code = \"HEIGHT\",   get_unit_expr,   filter = NULL,   constant_by_vars = NULL )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_bmi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adds a Parameter for BMI — derive_param_bmi","text":"dataset Input dataset variables specified by_vars argument expected dataset. PARAMCD, AVAL expected well. variable specified by_vars PARAMCD must unique key input dataset restricting filter condition (filter parameter) parameters specified weight_code height_code. by_vars Grouping variables group defined by_vars observation added output dataset. variables specified by_vars populated newly created records. Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) set_values_to Variables set specified variables set specified values new observations. example exprs(PARAMCD = \"MAP\") defines parameter code new parameter. Permitted Values: List variable-value pairs weight_code WEIGHT parameter code observations PARAMCD equals specified value considered WEIGHT. expected WEIGHT measured kg Permitted Values: character value height_code HEIGHT parameter code observations PARAMCD equals specified value considered HEIGHT. expected HEIGHT measured cm Permitted Values: character value Permitted Values: logical scalar get_unit_expr expression providing unit parameter result used check units input parameters. Permitted Values: expression evaluable input dataset results character value filter Filter condition specified condition applied input dataset deriving new parameter, .e., observations fulfilling condition taken account. Permitted Values: condition constant_by_vars variables HEIGHT constant HEIGHT constant, HEIGHT parameters (measured ) merged parameters using specified variables. height constant (e.g. measured screening baseline) use constant_by_vars select subject-level variable merge (e.g. USUBJID). produce BMI visits weight measured.  Otherwise calculated visits height weight collected. Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT)","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_bmi.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adds a Parameter for BMI — derive_param_bmi","text":"input dataset new parameter added. Note, variable populated new parameter rows specified by_vars.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_bmi.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Adds a Parameter for BMI — derive_param_bmi","text":"analysis value new parameter derived $$BMI = \\frac{WEIGHT}{HEIGHT^2}$$","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_bmi.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Adds a Parameter for BMI — derive_param_bmi","text":"","code":"# Example 1: Derive BMI where height is measured only once using constant_by_vars advs <- tibble::tribble(   ~USUBJID, ~PARAMCD, ~PARAM, ~AVAL, ~AVISIT,   \"01-701-1015\", \"HEIGHT\", \"Height (cm)\", 147, \"SCREENING\",   \"01-701-1015\", \"WEIGHT\", \"Weight (kg)\", 54.0, \"SCREENING\",   \"01-701-1015\", \"WEIGHT\", \"Weight (kg)\", 54.4, \"BASELINE\",   \"01-701-1015\", \"WEIGHT\", \"Weight (kg)\", 53.1, \"WEEK 2\",   \"01-701-1028\", \"HEIGHT\", \"Height (cm)\", 163, \"SCREENING\",   \"01-701-1028\", \"WEIGHT\", \"Weight (kg)\", 78.5, \"SCREENING\",   \"01-701-1028\", \"WEIGHT\", \"Weight (kg)\", 80.3, \"BASELINE\",   \"01-701-1028\", \"WEIGHT\", \"Weight (kg)\", 80.7, \"WEEK 2\" )  derive_param_bmi(   advs,   by_vars = exprs(USUBJID, AVISIT),   weight_code = \"WEIGHT\",   height_code = \"HEIGHT\",   set_values_to = exprs(     PARAMCD = \"BMI\",     PARAM = \"Body Mass Index (kg/m^2)\"   ),   get_unit_expr = extract_unit(PARAM),   constant_by_vars = exprs(USUBJID) ) #> # A tibble: 14 × 5 #>    USUBJID     PARAMCD PARAM                     AVAL AVISIT    #>    <chr>       <chr>   <chr>                    <dbl> <chr>     #>  1 01-701-1015 HEIGHT  Height (cm)              147   SCREENING #>  2 01-701-1015 WEIGHT  Weight (kg)               54   SCREENING #>  3 01-701-1015 WEIGHT  Weight (kg)               54.4 BASELINE  #>  4 01-701-1015 WEIGHT  Weight (kg)               53.1 WEEK 2    #>  5 01-701-1028 HEIGHT  Height (cm)              163   SCREENING #>  6 01-701-1028 WEIGHT  Weight (kg)               78.5 SCREENING #>  7 01-701-1028 WEIGHT  Weight (kg)               80.3 BASELINE  #>  8 01-701-1028 WEIGHT  Weight (kg)               80.7 WEEK 2    #>  9 01-701-1015 BMI     Body Mass Index (kg/m^2)  25.0 SCREENING #> 10 01-701-1015 BMI     Body Mass Index (kg/m^2)  25.2 BASELINE  #> 11 01-701-1015 BMI     Body Mass Index (kg/m^2)  24.6 WEEK 2    #> 12 01-701-1028 BMI     Body Mass Index (kg/m^2)  29.5 SCREENING #> 13 01-701-1028 BMI     Body Mass Index (kg/m^2)  30.2 BASELINE  #> 14 01-701-1028 BMI     Body Mass Index (kg/m^2)  30.4 WEEK 2     # Example 2: Derive BMI where height is measured only once and keep only one record # where both height and weight are measured. derive_param_bmi(   advs,   by_vars = exprs(USUBJID, AVISIT),   weight_code = \"WEIGHT\",   height_code = \"HEIGHT\",   set_values_to = exprs(     PARAMCD = \"BMI\",     PARAM = \"Body Mass Index (kg/m^2)\"   ),   get_unit_expr = extract_unit(PARAM) ) #> # A tibble: 10 × 5 #>    USUBJID     PARAMCD PARAM                     AVAL AVISIT    #>    <chr>       <chr>   <chr>                    <dbl> <chr>     #>  1 01-701-1015 HEIGHT  Height (cm)              147   SCREENING #>  2 01-701-1015 WEIGHT  Weight (kg)               54   SCREENING #>  3 01-701-1015 WEIGHT  Weight (kg)               54.4 BASELINE  #>  4 01-701-1015 WEIGHT  Weight (kg)               53.1 WEEK 2    #>  5 01-701-1028 HEIGHT  Height (cm)              163   SCREENING #>  6 01-701-1028 WEIGHT  Weight (kg)               78.5 SCREENING #>  7 01-701-1028 WEIGHT  Weight (kg)               80.3 BASELINE  #>  8 01-701-1028 WEIGHT  Weight (kg)               80.7 WEEK 2    #>  9 01-701-1015 BMI     Body Mass Index (kg/m^2)  25.0 SCREENING #> 10 01-701-1028 BMI     Body Mass Index (kg/m^2)  29.5 SCREENING  # Example 3: Pediatric study where height and weight are measured multiple times advs <- tibble::tribble(   ~USUBJID, ~PARAMCD, ~PARAM, ~AVAL, ~VISIT,   \"01-101-1001\", \"HEIGHT\", \"Height (cm)\", 47.1, \"BASELINE\",   \"01-101-1001\", \"HEIGHT\", \"Height (cm)\", 59.1, \"WEEK 12\",   \"01-101-1001\", \"HEIGHT\", \"Height (cm)\", 64.7, \"WEEK 24\",   \"01-101-1001\", \"HEIGHT\", \"Height (cm)\", 68.2, \"WEEK 48\",   \"01-101-1001\", \"WEIGHT\", \"Weight (kg)\", 2.6, \"BASELINE\",   \"01-101-1001\", \"WEIGHT\", \"Weight (kg)\", 5.3, \"WEEK 12\",   \"01-101-1001\", \"WEIGHT\", \"Weight (kg)\", 6.7, \"WEEK 24\",   \"01-101-1001\", \"WEIGHT\", \"Weight (kg)\", 7.4, \"WEEK 48\", )  derive_param_bmi(   advs,   by_vars = exprs(USUBJID, VISIT),   weight_code = \"WEIGHT\",   height_code = \"HEIGHT\",   set_values_to = exprs(     PARAMCD = \"BMI\",     PARAM = \"Body Mass Index (kg/m^2)\"   ),   get_unit_expr = extract_unit(PARAM) ) #> # A tibble: 12 × 5 #>    USUBJID     PARAMCD PARAM                     AVAL VISIT    #>    <chr>       <chr>   <chr>                    <dbl> <chr>    #>  1 01-101-1001 HEIGHT  Height (cm)               47.1 BASELINE #>  2 01-101-1001 HEIGHT  Height (cm)               59.1 WEEK 12  #>  3 01-101-1001 HEIGHT  Height (cm)               64.7 WEEK 24  #>  4 01-101-1001 HEIGHT  Height (cm)               68.2 WEEK 48  #>  5 01-101-1001 WEIGHT  Weight (kg)                2.6 BASELINE #>  6 01-101-1001 WEIGHT  Weight (kg)                5.3 WEEK 12  #>  7 01-101-1001 WEIGHT  Weight (kg)                6.7 WEEK 24  #>  8 01-101-1001 WEIGHT  Weight (kg)                7.4 WEEK 48  #>  9 01-101-1001 BMI     Body Mass Index (kg/m^2)  11.7 BASELINE #> 10 01-101-1001 BMI     Body Mass Index (kg/m^2)  15.2 WEEK 12  #> 11 01-101-1001 BMI     Body Mass Index (kg/m^2)  16.0 WEEK 24  #> 12 01-101-1001 BMI     Body Mass Index (kg/m^2)  15.9 WEEK 48"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_bsa.html","id":null,"dir":"Reference","previous_headings":"","what":"Adds a Parameter for BSA (Body Surface Area) Using the Specified Method — derive_param_bsa","title":"Adds a Parameter for BSA (Body Surface Area) Using the Specified Method — derive_param_bsa","text":"Adds record BSA (Body Surface Area) using specified derivation method group (e.g., subject visit) source parameters available. Note: wrapper function generic derive_param_computed().","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_bsa.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adds a Parameter for BSA (Body Surface Area) Using the Specified Method — derive_param_bsa","text":"","code":"derive_param_bsa(   dataset,   by_vars,   method,   set_values_to = exprs(PARAMCD = \"BSA\"),   height_code = \"HEIGHT\",   weight_code = \"WEIGHT\",   get_unit_expr,   filter = NULL,   constant_by_vars = NULL )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_bsa.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adds a Parameter for BSA (Body Surface Area) Using the Specified Method — derive_param_bsa","text":"dataset Input dataset variables specified by_vars argument expected dataset. PARAMCD, AVAL expected well. variable specified by_vars PARAMCD must unique key input dataset restricting filter condition (filter parameter) parameters specified HEIGHT WEIGHT. by_vars Grouping variables group defined by_vars observation added output dataset. variables specified by_vars populated newly created records. Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) method Derivation method use. Note HEIGHT expected cm WEIGHT expected kg: Mosteller: sqrt(height * weight / 3600) DuBois-DuBois: 0.20247 * (height/100) ^ 0.725 * weight ^ 0.425 Haycock: 0.024265 * height ^ 0.3964 * weight ^ 0.5378 Gehan-George: 0.0235 * height ^ 0.42246 * weight ^ 0.51456 Boyd: 0.0003207 * (height ^ 0.3) * (1000 * weight) ^ (0.7285 - (0.0188 * log10(1000 * weight))) Fujimoto: 0.008883 * height ^ 0.663 * weight ^ 0.444 Takahira: 0.007241 * height ^ 0.725 * weight ^ 0.425 Permitted Values: character value set_values_to Variables set specified variables set specified values new observations. example exprs(PARAMCD = \"MAP\") defines parameter code new parameter. Permitted Values: List variable-value pairs height_code HEIGHT parameter code observations PARAMCD equals specified value considered HEIGHT assessments. expected HEIGHT measured cm. Permitted Values: character value weight_code WEIGHT parameter code observations PARAMCD equals specified value considered WEIGHT assessments. expected WEIGHT measured kg. Permitted Values: character value get_unit_expr expression providing unit parameter result used check units input parameters. Permitted Values: expression evaluable input dataset results character value filter Filter condition specified condition applied input dataset deriving new parameter, .e., observations fulfilling condition taken account. Permitted Values: condition constant_by_vars variables HEIGHT constant HEIGHT constant, HEIGHT parameters (measured ) merged parameters using specified variables. height constant (e.g. measured screening baseline) use constant_by_vars select subject-level variable merge (e.g. USUBJID). produce BSA visits weight measured.  Otherwise calculated visits height weight collected. Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT)","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_bsa.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adds a Parameter for BSA (Body Surface Area) Using the Specified Method — derive_param_bsa","text":"input dataset new parameter added. Note, variable populated new parameter rows specified by_vars.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_bsa.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Adds a Parameter for BSA (Body Surface Area) Using the Specified Method — derive_param_bsa","text":"","code":"library(tibble)  # Example 1: Derive BSA where height is measured only once using constant_by_vars advs <- tibble::tribble(   ~USUBJID, ~PARAMCD, ~PARAM, ~AVAL, ~VISIT,   \"01-701-1015\", \"HEIGHT\", \"Height (cm)\", 170, \"BASELINE\",   \"01-701-1015\", \"WEIGHT\", \"Weight (kg)\", 75, \"BASELINE\",   \"01-701-1015\", \"WEIGHT\", \"Weight (kg)\", 78, \"MONTH 1\",   \"01-701-1015\", \"WEIGHT\", \"Weight (kg)\", 80, \"MONTH 2\",   \"01-701-1028\", \"HEIGHT\", \"Height (cm)\", 185, \"BASELINE\",   \"01-701-1028\", \"WEIGHT\", \"Weight (kg)\", 90, \"BASELINE\",   \"01-701-1028\", \"WEIGHT\", \"Weight (kg)\", 88, \"MONTH 1\",   \"01-701-1028\", \"WEIGHT\", \"Weight (kg)\", 85, \"MONTH 2\", )  derive_param_bsa(   advs,   by_vars = exprs(USUBJID, VISIT),   method = \"Mosteller\",   set_values_to = exprs(     PARAMCD = \"BSA\",     PARAM = \"Body Surface Area (m^2)\"   ),   get_unit_expr = extract_unit(PARAM),   constant_by_vars = exprs(USUBJID) ) #> # A tibble: 14 × 5 #>    USUBJID     PARAMCD PARAM                     AVAL VISIT    #>    <chr>       <chr>   <chr>                    <dbl> <chr>    #>  1 01-701-1015 HEIGHT  Height (cm)             170    BASELINE #>  2 01-701-1015 WEIGHT  Weight (kg)              75    BASELINE #>  3 01-701-1015 WEIGHT  Weight (kg)              78    MONTH 1  #>  4 01-701-1015 WEIGHT  Weight (kg)              80    MONTH 2  #>  5 01-701-1028 HEIGHT  Height (cm)             185    BASELINE #>  6 01-701-1028 WEIGHT  Weight (kg)              90    BASELINE #>  7 01-701-1028 WEIGHT  Weight (kg)              88    MONTH 1  #>  8 01-701-1028 WEIGHT  Weight (kg)              85    MONTH 2  #>  9 01-701-1015 BSA     Body Surface Area (m^2)   1.88 BASELINE #> 10 01-701-1015 BSA     Body Surface Area (m^2)   1.92 MONTH 1  #> 11 01-701-1015 BSA     Body Surface Area (m^2)   1.94 MONTH 2  #> 12 01-701-1028 BSA     Body Surface Area (m^2)   2.15 BASELINE #> 13 01-701-1028 BSA     Body Surface Area (m^2)   2.13 MONTH 1  #> 14 01-701-1028 BSA     Body Surface Area (m^2)   2.09 MONTH 2   derive_param_bsa(   advs,   by_vars = exprs(USUBJID, VISIT),   method = \"Fujimoto\",   set_values_to = exprs(     PARAMCD = \"BSA\",     PARAM = \"Body Surface Area (m^2)\"   ),   get_unit_expr = extract_unit(PARAM),   constant_by_vars = exprs(USUBJID) ) #> # A tibble: 14 × 5 #>    USUBJID     PARAMCD PARAM                     AVAL VISIT    #>    <chr>       <chr>   <chr>                    <dbl> <chr>    #>  1 01-701-1015 HEIGHT  Height (cm)             170    BASELINE #>  2 01-701-1015 WEIGHT  Weight (kg)              75    BASELINE #>  3 01-701-1015 WEIGHT  Weight (kg)              78    MONTH 1  #>  4 01-701-1015 WEIGHT  Weight (kg)              80    MONTH 2  #>  5 01-701-1028 HEIGHT  Height (cm)             185    BASELINE #>  6 01-701-1028 WEIGHT  Weight (kg)              90    BASELINE #>  7 01-701-1028 WEIGHT  Weight (kg)              88    MONTH 1  #>  8 01-701-1028 WEIGHT  Weight (kg)              85    MONTH 2  #>  9 01-701-1015 BSA     Body Surface Area (m^2)   1.82 BASELINE #> 10 01-701-1015 BSA     Body Surface Area (m^2)   1.85 MONTH 1  #> 11 01-701-1015 BSA     Body Surface Area (m^2)   1.87 MONTH 2  #> 12 01-701-1028 BSA     Body Surface Area (m^2)   2.09 BASELINE #> 13 01-701-1028 BSA     Body Surface Area (m^2)   2.07 MONTH 1  #> 14 01-701-1028 BSA     Body Surface Area (m^2)   2.03 MONTH 2   # Example 2: Derive BSA where height is measured only once and keep only one record # where both height and weight are measured.  derive_param_bsa(   advs,   by_vars = exprs(USUBJID, VISIT),   method = \"Mosteller\",   set_values_to = exprs(     PARAMCD = \"BSA\",     PARAM = \"Body Surface Area (m^2)\"   ),   get_unit_expr = extract_unit(PARAM) ) #> # A tibble: 10 × 5 #>    USUBJID     PARAMCD PARAM                     AVAL VISIT    #>    <chr>       <chr>   <chr>                    <dbl> <chr>    #>  1 01-701-1015 HEIGHT  Height (cm)             170    BASELINE #>  2 01-701-1015 WEIGHT  Weight (kg)              75    BASELINE #>  3 01-701-1015 WEIGHT  Weight (kg)              78    MONTH 1  #>  4 01-701-1015 WEIGHT  Weight (kg)              80    MONTH 2  #>  5 01-701-1028 HEIGHT  Height (cm)             185    BASELINE #>  6 01-701-1028 WEIGHT  Weight (kg)              90    BASELINE #>  7 01-701-1028 WEIGHT  Weight (kg)              88    MONTH 1  #>  8 01-701-1028 WEIGHT  Weight (kg)              85    MONTH 2  #>  9 01-701-1015 BSA     Body Surface Area (m^2)   1.88 BASELINE #> 10 01-701-1028 BSA     Body Surface Area (m^2)   2.15 BASELINE  # Example 3: Pediatric study where height and weight are measured multiple times advs <- tibble::tribble(   ~USUBJID, ~PARAMCD, ~PARAM, ~AVAL, ~VISIT,   \"01-101-1001\", \"HEIGHT\", \"Height (cm)\", 47.1, \"BASELINE\",   \"01-101-1001\", \"HEIGHT\", \"Height (cm)\", 59.1, \"WEEK 12\",   \"01-101-1001\", \"HEIGHT\", \"Height (cm)\", 64.7, \"WEEK 24\",   \"01-101-1001\", \"HEIGHT\", \"Height (cm)\", 68.2, \"WEEK 48\",   \"01-101-1001\", \"WEIGHT\", \"Weight (kg)\", 2.6, \"BASELINE\",   \"01-101-1001\", \"WEIGHT\", \"Weight (kg)\", 5.3, \"WEEK 12\",   \"01-101-1001\", \"WEIGHT\", \"Weight (kg)\", 6.7, \"WEEK 24\",   \"01-101-1001\", \"WEIGHT\", \"Weight (kg)\", 7.4, \"WEEK 48\", ) derive_param_bsa(   advs,   by_vars = exprs(USUBJID, VISIT),   method = \"Mosteller\",   set_values_to = exprs(     PARAMCD = \"BSA\",     PARAM = \"Body Surface Area (m^2)\"   ),   get_unit_expr = extract_unit(PARAM) ) #> # A tibble: 12 × 5 #>    USUBJID     PARAMCD PARAM                     AVAL VISIT    #>    <chr>       <chr>   <chr>                    <dbl> <chr>    #>  1 01-101-1001 HEIGHT  Height (cm)             47.1   BASELINE #>  2 01-101-1001 HEIGHT  Height (cm)             59.1   WEEK 12  #>  3 01-101-1001 HEIGHT  Height (cm)             64.7   WEEK 24  #>  4 01-101-1001 HEIGHT  Height (cm)             68.2   WEEK 48  #>  5 01-101-1001 WEIGHT  Weight (kg)              2.6   BASELINE #>  6 01-101-1001 WEIGHT  Weight (kg)              5.3   WEEK 12  #>  7 01-101-1001 WEIGHT  Weight (kg)              6.7   WEEK 24  #>  8 01-101-1001 WEIGHT  Weight (kg)              7.4   WEEK 48  #>  9 01-101-1001 BSA     Body Surface Area (m^2)  0.184 BASELINE #> 10 01-101-1001 BSA     Body Surface Area (m^2)  0.295 WEEK 12  #> 11 01-101-1001 BSA     Body Surface Area (m^2)  0.347 WEEK 24  #> 12 01-101-1001 BSA     Body Surface Area (m^2)  0.374 WEEK 48"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_computed.html","id":null,"dir":"Reference","previous_headings":"","what":"Adds a Parameter Computed from the Analysis Value of Other Parameters — derive_param_computed","title":"Adds a Parameter Computed from the Analysis Value of Other Parameters — derive_param_computed","text":"Adds parameter computed analysis value parameters. expected analysis value new parameter defined expression using analysis values parameters, addition/sum, subtraction/difference, multiplication/product, division/ratio, exponentiation/logarithm, formula.  example mean arterial pressure (MAP) can derived systolic (SYSBP) diastolic blood pressure (DIABP) formula $$MAP = \\frac{SYSBP + 2 DIABP}{3}$$","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_computed.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adds a Parameter Computed from the Analysis Value of Other Parameters — derive_param_computed","text":"","code":"derive_param_computed(   dataset = NULL,   dataset_add = NULL,   by_vars,   parameters,   set_values_to,   filter = NULL,   constant_by_vars = NULL,   constant_parameters = NULL,   keep_nas = FALSE )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_computed.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adds a Parameter Computed from the Analysis Value of Other Parameters — derive_param_computed","text":"dataset Input dataset variables specified by_vars argument expected dataset. PARAMCD expected well. variable specified by_vars PARAMCD must unique key input dataset restricting filter condition (filter parameter) parameters specified parameters. dataset_add Additional dataset variables specified by_vars parameter expected. variable specified by_vars PARAMCD must unique key additional dataset restricting parameters specified parameters. argument specified, observations additional dataset considered addition observations input dataset (dataset restricted filter). by_vars Grouping variables group defined by_vars observation added output dataset. variables specified by_vars populated newly created records. Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) parameters Required parameter codes expected parameter codes (PARAMCD) required derive new parameter specified parameter constant_parameters parameter. observations considered parameter code, e.g., SDTM dataset used, temporary parameter codes can derived specifying list expressions. name element defines temporary parameter code expression condition selecting records. example parameters = exprs(HGHT = VSTESTCD == \"HEIGHT\") selects observations VSTESTCD == \"HEIGHT\" input data (dataset dataset_add), sets PARAMCD = \"HGHT\" observations, adds observations consider. Unnamed elements list expressions considered parameter codes. example, parameters = exprs(WEIGHT, HGHT = VSTESTCD == \"HEIGHT\") uses parameter code \"WEIGHT\" creates temporary parameter code \"HGHT\". Permitted Values: character vector PARAMCD values list expressions set_values_to Variables set specified variables set specified values new observations. values variables parameters specified parameters can accessed using <variable name>.<parameter code>. example   defines analysis value parameter code new parameter. Variable names expression must contain one dot. Permitted Values: List variable-value pairs filter Filter condition specified condition applied input dataset deriving new parameter, .e., observations fulfilling condition taken account. Permitted Values: condition constant_by_vars variables constant parameters constant parameters (parameters measured ) merged parameters using specified variables. (Refer Example 2) Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) constant_parameters Required constant parameter codes expected parameter codes (PARAMCD) required derive new parameter measured specified . example BMI derived height measured weight measured visit. Height specified constant_parameters parameter. (Refer Example 2) observations considered parameter code, e.g., SDTM dataset used, temporary parameter codes can derived specifying list expressions. name element defines temporary parameter code expression condition selecting records. example constant_parameters = exprs(HGHT = VSTESTCD == \"HEIGHT\") selects observations VSTESTCD == \"HEIGHT\" input data (dataset dataset_add), sets PARAMCD = \"HGHT\" observations, adds observations consider. Unnamed elements list expressions considered parameter codes. example, constant_parameters = exprs(WEIGHT, HGHT = VSTESTCD == \"HEIGHT\") uses parameter code \"WEIGHT\" creates temporary parameter code \"HGHT\". Permitted Values: character vector PARAMCD values list expressions keep_nas Keep observations NAs argument set TRUE, observations added even values contributing computed value NA (see Example 1b). argument set list variables, observations added even specified variables NA (see Example 1c). Permitted Values: TRUE, FALSE, list variables created exprs() e.g. exprs(ADTF, ATMF)","code":"exprs(   AVAL = (AVAL.SYSBP + 2 * AVAL.DIABP) / 3,   PARAMCD = \"MAP\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_computed.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adds a Parameter Computed from the Analysis Value of Other Parameters — derive_param_computed","text":"input dataset new parameter added. Note, variable populated new parameter rows specified by_vars.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_computed.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Adds a Parameter Computed from the Analysis Value of Other Parameters — derive_param_computed","text":"group (respect variables specified by_vars parameter) observation added output dataset filtered input dataset (dataset) additional dataset (dataset_add) contains exactly one observation parameter code specified parameters contributing values like AVAL.SYSBP NA. keep_nas can used specify variables NAs acceptable. See also Example 1b 1c. new observations variables specified set_values_to set provided values. values variables input dataset set NA.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_computed.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Adds a Parameter Computed from the Analysis Value of Other Parameters — derive_param_computed","text":"","code":"library(tibble) library(dplyr) library(lubridate)  # Example 1a: Derive MAP advs <- tribble(   ~USUBJID,      ~PARAMCD, ~PARAM,                            ~AVAL, ~VISIT,   \"01-701-1015\", \"DIABP\",  \"Diastolic Blood Pressure (mmHg)\",    51, \"BASELINE\",   \"01-701-1015\", \"DIABP\",  \"Diastolic Blood Pressure (mmHg)\",    50, \"WEEK 2\",   \"01-701-1015\", \"SYSBP\",  \"Systolic Blood Pressure (mmHg)\",    121, \"BASELINE\",   \"01-701-1015\", \"SYSBP\",  \"Systolic Blood Pressure (mmHg)\",    121, \"WEEK 2\",   \"01-701-1028\", \"DIABP\",  \"Diastolic Blood Pressure (mmHg)\",    79, \"BASELINE\",   \"01-701-1028\", \"DIABP\",  \"Diastolic Blood Pressure (mmHg)\",    80, \"WEEK 2\",   \"01-701-1028\", \"SYSBP\",  \"Systolic Blood Pressure (mmHg)\",    130, \"BASELINE\",   \"01-701-1028\", \"SYSBP\",  \"Systolic Blood Pressure (mmHg)\",     NA, \"WEEK 2\" ) %>%   mutate(     AVALU = \"mmHg\",     ADT = case_when(       VISIT == \"BASELINE\" ~ as.Date(\"2024-01-10\"),       VISIT == \"WEEK 2\" ~ as.Date(\"2024-01-24\")     ),     ADTF = NA_character_   )  derive_param_computed(   advs,   by_vars = exprs(USUBJID, VISIT),   parameters = c(\"SYSBP\", \"DIABP\"),   set_values_to = exprs(     AVAL = (AVAL.SYSBP + 2 * AVAL.DIABP) / 3,     PARAMCD = \"MAP\",     PARAM = \"Mean Arterial Pressure (mmHg)\",     AVALU = \"mmHg\",     ADT = ADT.SYSBP   ) ) #> # A tibble: 11 × 8 #>    USUBJID     PARAMCD PARAM                   AVAL VISIT AVALU ADT        ADTF  #>    <chr>       <chr>   <chr>                  <dbl> <chr> <chr> <date>     <chr> #>  1 01-701-1015 DIABP   Diastolic Blood Press…  51   BASE… mmHg  2024-01-10 NA    #>  2 01-701-1015 DIABP   Diastolic Blood Press…  50   WEEK… mmHg  2024-01-24 NA    #>  3 01-701-1015 SYSBP   Systolic Blood Pressu… 121   BASE… mmHg  2024-01-10 NA    #>  4 01-701-1015 SYSBP   Systolic Blood Pressu… 121   WEEK… mmHg  2024-01-24 NA    #>  5 01-701-1028 DIABP   Diastolic Blood Press…  79   BASE… mmHg  2024-01-10 NA    #>  6 01-701-1028 DIABP   Diastolic Blood Press…  80   WEEK… mmHg  2024-01-24 NA    #>  7 01-701-1028 SYSBP   Systolic Blood Pressu… 130   BASE… mmHg  2024-01-10 NA    #>  8 01-701-1028 SYSBP   Systolic Blood Pressu…  NA   WEEK… mmHg  2024-01-24 NA    #>  9 01-701-1015 MAP     Mean Arterial Pressur…  74.3 BASE… mmHg  2024-01-10 NA    #> 10 01-701-1015 MAP     Mean Arterial Pressur…  73.7 WEEK… mmHg  2024-01-24 NA    #> 11 01-701-1028 MAP     Mean Arterial Pressur…  96   BASE… mmHg  2024-01-10 NA     # Example 1b: Using option `keep_nas = TRUE` to derive MAP in the case where some/all # values of a variable used in the computation are missing  derive_param_computed(   advs,   by_vars = exprs(USUBJID, VISIT),   parameters = c(\"SYSBP\", \"DIABP\"),   set_values_to = exprs(     AVAL = (AVAL.SYSBP + 2 * AVAL.DIABP) / 3,     PARAMCD = \"MAP\",     PARAM = \"Mean Arterial Pressure (mmHg)\",     AVALU = \"mmHg\",     ADT = ADT.SYSBP,     ADTF = ADTF.SYSBP   ),   keep_nas = TRUE ) #> # A tibble: 12 × 8 #>    USUBJID     PARAMCD PARAM                   AVAL VISIT AVALU ADT        ADTF  #>    <chr>       <chr>   <chr>                  <dbl> <chr> <chr> <date>     <chr> #>  1 01-701-1015 DIABP   Diastolic Blood Press…  51   BASE… mmHg  2024-01-10 NA    #>  2 01-701-1015 DIABP   Diastolic Blood Press…  50   WEEK… mmHg  2024-01-24 NA    #>  3 01-701-1015 SYSBP   Systolic Blood Pressu… 121   BASE… mmHg  2024-01-10 NA    #>  4 01-701-1015 SYSBP   Systolic Blood Pressu… 121   WEEK… mmHg  2024-01-24 NA    #>  5 01-701-1028 DIABP   Diastolic Blood Press…  79   BASE… mmHg  2024-01-10 NA    #>  6 01-701-1028 DIABP   Diastolic Blood Press…  80   WEEK… mmHg  2024-01-24 NA    #>  7 01-701-1028 SYSBP   Systolic Blood Pressu… 130   BASE… mmHg  2024-01-10 NA    #>  8 01-701-1028 SYSBP   Systolic Blood Pressu…  NA   WEEK… mmHg  2024-01-24 NA    #>  9 01-701-1015 MAP     Mean Arterial Pressur…  74.3 BASE… mmHg  2024-01-10 NA    #> 10 01-701-1015 MAP     Mean Arterial Pressur…  73.7 WEEK… mmHg  2024-01-24 NA    #> 11 01-701-1028 MAP     Mean Arterial Pressur…  96   BASE… mmHg  2024-01-10 NA    #> 12 01-701-1028 MAP     Mean Arterial Pressur…  NA   WEEK… mmHg  2024-01-24 NA     # Example 1c: Using option `keep_nas = exprs(ADTF)` to derive MAP in the case where # some/all values of a variable used in the computation are missing but ignoring ADTF  derive_param_computed(   advs,   by_vars = exprs(USUBJID, VISIT),   parameters = c(\"SYSBP\", \"DIABP\"),   set_values_to = exprs(     AVAL = (AVAL.SYSBP + 2 * AVAL.DIABP) / 3,     PARAMCD = \"MAP\",     PARAM = \"Mean Arterial Pressure (mmHg)\",     AVALU = \"mmHg\",     ADT = ADT.SYSBP,     ADTF = ADTF.SYSBP   ),   keep_nas = exprs(ADTF) ) #> # A tibble: 11 × 8 #>    USUBJID     PARAMCD PARAM                   AVAL VISIT AVALU ADT        ADTF  #>    <chr>       <chr>   <chr>                  <dbl> <chr> <chr> <date>     <chr> #>  1 01-701-1015 DIABP   Diastolic Blood Press…  51   BASE… mmHg  2024-01-10 NA    #>  2 01-701-1015 DIABP   Diastolic Blood Press…  50   WEEK… mmHg  2024-01-24 NA    #>  3 01-701-1015 SYSBP   Systolic Blood Pressu… 121   BASE… mmHg  2024-01-10 NA    #>  4 01-701-1015 SYSBP   Systolic Blood Pressu… 121   WEEK… mmHg  2024-01-24 NA    #>  5 01-701-1028 DIABP   Diastolic Blood Press…  79   BASE… mmHg  2024-01-10 NA    #>  6 01-701-1028 DIABP   Diastolic Blood Press…  80   WEEK… mmHg  2024-01-24 NA    #>  7 01-701-1028 SYSBP   Systolic Blood Pressu… 130   BASE… mmHg  2024-01-10 NA    #>  8 01-701-1028 SYSBP   Systolic Blood Pressu…  NA   WEEK… mmHg  2024-01-24 NA    #>  9 01-701-1015 MAP     Mean Arterial Pressur…  74.3 BASE… mmHg  2024-01-10 NA    #> 10 01-701-1015 MAP     Mean Arterial Pressur…  73.7 WEEK… mmHg  2024-01-24 NA    #> 11 01-701-1028 MAP     Mean Arterial Pressur…  96   BASE… mmHg  2024-01-10 NA     # Example 2: Derive BMI where height is measured only once advs <- tribble(   ~USUBJID,      ~PARAMCD, ~PARAM,        ~AVAL, ~AVALU, ~VISIT,   \"01-701-1015\", \"HEIGHT\", \"Height (cm)\", 147.0, \"cm\",   \"SCREENING\",   \"01-701-1015\", \"WEIGHT\", \"Weight (kg)\",  54.0, \"kg\",   \"SCREENING\",   \"01-701-1015\", \"WEIGHT\", \"Weight (kg)\",  54.4, \"kg\",   \"BASELINE\",   \"01-701-1015\", \"WEIGHT\", \"Weight (kg)\",  53.1, \"kg\",   \"WEEK 2\",   \"01-701-1028\", \"HEIGHT\", \"Height (cm)\", 163.0, \"cm\",   \"SCREENING\",   \"01-701-1028\", \"WEIGHT\", \"Weight (kg)\",  78.5, \"kg\",   \"SCREENING\",   \"01-701-1028\", \"WEIGHT\", \"Weight (kg)\",  80.3, \"kg\",   \"BASELINE\",   \"01-701-1028\", \"WEIGHT\", \"Weight (kg)\",  80.7, \"kg\",   \"WEEK 2\" )  derive_param_computed(   advs,   by_vars = exprs(USUBJID, VISIT),   parameters = \"WEIGHT\",   set_values_to = exprs(     AVAL = AVAL.WEIGHT / (AVAL.HEIGHT / 100)^2,     PARAMCD = \"BMI\",     PARAM = \"Body Mass Index (kg/m^2)\",     AVALU = \"kg/m^2\"   ),   constant_parameters = c(\"HEIGHT\"),   constant_by_vars = exprs(USUBJID) ) #> # A tibble: 14 × 6 #>    USUBJID     PARAMCD PARAM                     AVAL AVALU  VISIT     #>    <chr>       <chr>   <chr>                    <dbl> <chr>  <chr>     #>  1 01-701-1015 HEIGHT  Height (cm)              147   cm     SCREENING #>  2 01-701-1015 WEIGHT  Weight (kg)               54   kg     SCREENING #>  3 01-701-1015 WEIGHT  Weight (kg)               54.4 kg     BASELINE  #>  4 01-701-1015 WEIGHT  Weight (kg)               53.1 kg     WEEK 2    #>  5 01-701-1028 HEIGHT  Height (cm)              163   cm     SCREENING #>  6 01-701-1028 WEIGHT  Weight (kg)               78.5 kg     SCREENING #>  7 01-701-1028 WEIGHT  Weight (kg)               80.3 kg     BASELINE  #>  8 01-701-1028 WEIGHT  Weight (kg)               80.7 kg     WEEK 2    #>  9 01-701-1015 BMI     Body Mass Index (kg/m^2)  25.0 kg/m^2 SCREENING #> 10 01-701-1015 BMI     Body Mass Index (kg/m^2)  25.2 kg/m^2 BASELINE  #> 11 01-701-1015 BMI     Body Mass Index (kg/m^2)  24.6 kg/m^2 WEEK 2    #> 12 01-701-1028 BMI     Body Mass Index (kg/m^2)  29.5 kg/m^2 SCREENING #> 13 01-701-1028 BMI     Body Mass Index (kg/m^2)  30.2 kg/m^2 BASELINE  #> 14 01-701-1028 BMI     Body Mass Index (kg/m^2)  30.4 kg/m^2 WEEK 2     # Example 3: Using data from an additional dataset and other variables than AVAL qs <- tribble(   ~USUBJID, ~AVISIT,   ~QSTESTCD, ~QSORRES, ~QSSTRESN,   \"1\",      \"WEEK 2\",  \"CHSF112\", NA,               1,   \"1\",      \"WEEK 2\",  \"CHSF113\", \"Yes\",           NA,   \"1\",      \"WEEK 2\",  \"CHSF114\", NA,               1,   \"1\",      \"WEEK 4\",  \"CHSF112\", NA,               2,   \"1\",      \"WEEK 4\",  \"CHSF113\", \"No\",            NA,   \"1\",      \"WEEK 4\",  \"CHSF114\", NA,               1 )  adchsf <- tribble(   ~USUBJID, ~AVISIT,  ~PARAMCD, ~QSSTRESN, ~AVAL,   \"1\",      \"WEEK 2\", \"CHSF12\", 1,             6,   \"1\",      \"WEEK 2\", \"CHSF14\", 1,             6,   \"1\",      \"WEEK 4\", \"CHSF12\", 2,            12,   \"1\",      \"WEEK 4\", \"CHSF14\", 1,             6 ) %>%   mutate(QSORRES = NA_character_)  derive_param_computed(   adchsf,   dataset_add = qs,   by_vars = exprs(USUBJID, AVISIT),   parameters = exprs(CHSF12, CHSF13 = QSTESTCD %in% c(\"CHSF113\", \"CHSF213\"), CHSF14),   set_values_to = exprs(     AVAL = case_when(       QSORRES.CHSF13 == \"Not applicable\" ~ 0,       QSORRES.CHSF13 == \"Yes\" ~ 38,       QSORRES.CHSF13 == \"No\" ~ if_else(         QSSTRESN.CHSF12 > QSSTRESN.CHSF14,         25,         0       )     ),     PARAMCD = \"CHSF13\"   ) ) #> # A tibble: 6 × 6 #>   USUBJID AVISIT PARAMCD QSSTRESN  AVAL QSORRES #>   <chr>   <chr>  <chr>      <dbl> <dbl> <chr>   #> 1 1       WEEK 2 CHSF12         1     6 NA      #> 2 1       WEEK 2 CHSF14         1     6 NA      #> 3 1       WEEK 4 CHSF12         2    12 NA      #> 4 1       WEEK 4 CHSF14         1     6 NA      #> 5 1       WEEK 2 CHSF13        NA    38 NA      #> 6 1       WEEK 4 CHSF13        NA    25 NA       # Example 4: Computing more than one variable adlb_tbilialk <- tribble(   ~USUBJID, ~PARAMCD, ~AVALC, ~ADTM,        ~ADTF,   \"1\",      \"ALK2\",   \"Y\",    \"2021-05-13\", NA_character_,   \"1\",      \"TBILI2\", \"Y\",    \"2021-06-30\", \"D\",   \"2\",      \"ALK2\",   \"Y\",    \"2021-12-31\", \"M\",   \"2\",      \"TBILI2\", \"N\",    \"2021-11-11\", NA_character_,   \"3\",      \"ALK2\",   \"N\",    \"2021-04-03\", NA_character_,   \"3\",      \"TBILI2\", \"N\",    \"2021-04-04\", NA_character_ ) %>%   mutate(ADTM = ymd(ADTM))  derive_param_computed(   dataset_add = adlb_tbilialk,   by_vars = exprs(USUBJID),   parameters = c(\"ALK2\", \"TBILI2\"),   set_values_to = exprs(     AVALC = if_else(AVALC.TBILI2 == \"Y\" & AVALC.ALK2 == \"Y\", \"Y\", \"N\"),     ADTM = pmax(ADTM.TBILI2, ADTM.ALK2),     ADTF = if_else(ADTM == ADTM.TBILI2, ADTF.TBILI2, ADTF.ALK2),     PARAMCD = \"TB2AK2\",     PARAM = \"TBILI > 2 times ULN and ALKPH <= 2 times ULN\"   ),   keep_nas = TRUE ) #> # A tibble: 3 × 6 #>   USUBJID AVALC ADTM       ADTF  PARAMCD PARAM                                   #>   <chr>   <chr> <date>     <chr> <chr>   <chr>                                   #> 1 1       Y     2021-06-30 D     TB2AK2  TBILI > 2 times ULN and ALKPH <= 2 tim… #> 2 2       N     2021-12-31 M     TB2AK2  TBILI > 2 times ULN and ALKPH <= 2 tim… #> 3 3       N     2021-04-04 NA    TB2AK2  TBILI > 2 times ULN and ALKPH <= 2 tim…"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_doseint.html","id":null,"dir":"Reference","previous_headings":"","what":"Adds a Parameter for Dose Intensity — derive_param_doseint","title":"Adds a Parameter for Dose Intensity — derive_param_doseint","text":"Adds record dose intensity group (e.g., subject visit) source parameters available. Note: wrapper function generic derive_param_computed(). analysis value new parameter derived Total Dose / Planned Dose * 100","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_doseint.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adds a Parameter for Dose Intensity — derive_param_doseint","text":"","code":"derive_param_doseint(   dataset,   by_vars,   set_values_to = exprs(PARAMCD = \"TNDOSINT\"),   tadm_code = \"TNDOSE\",   tpadm_code = \"TSNDOSE\",   zero_doses = \"Inf\",   filter = NULL )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_doseint.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adds a Parameter for Dose Intensity — derive_param_doseint","text":"dataset Input dataset variables specified by_vars argument expected dataset. PARAMCD, AVAL expected well. variable specified by_vars PARAMCD must unique key input dataset restricting filter condition (filter parameter) parameters specified tadm_code padm_code. by_vars Grouping variables variables specified by_vars populated newly created records. Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) set_values_to Variables set specified variables set specified values new observations. example exprs(PARAMCD = \"MAP\") defines parameter code new parameter. Permitted Values: List variable-value pairs tadm_code Total Doses Administered parameter code observations PARAMCD equals specified value considered total dose administered.  AVAL associated PARAMCD numerator dose intensity calculation. Permitted Values: character value tpadm_code Total Doses Planned parameter code observations PARAMCD equals specified value considered total planned dose.  AVAL associated PARAMCD denominator dose intensity calculation. Permitted Values: character value zero_doses Flag indicating logic handling 0 planned administered doses by_vars group Default: Inf Permitted Values: Inf, 100 record returned either planned (tpadm_code) administered (tadm_code) AVAL NA.  record returned record exist tadm_code tpadm_code specified by_var. zero_doses = Inf: planned dose (tpadm_code) 0 administered dose (tadm_code) 0, NaN returned. planned dose (tpadm_code) 0 administered dose (tadm_code) > 0, Inf returned. zero_doses = 100 : planned dose (tpadm_code) 0 administered dose (tadm_code) 0, 0 returned. planned dose (tpadm_code) 0 administered dose (tadm_code) > 0, 100 returned. filter Filter condition specified condition applied input dataset deriving new parameter, .e., observations fulfilling condition taken account. Permitted Values: condition","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_doseint.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adds a Parameter for Dose Intensity — derive_param_doseint","text":"input dataset new parameter rows added. Note, variable populated new parameter rows specified by_vars.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_doseint.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Adds a Parameter for Dose Intensity — derive_param_doseint","text":"","code":"library(tibble) library(lubridate, warn.conflicts = FALSE)  adex <- tribble(   ~USUBJID, ~PARAMCD, ~VISIT, ~ANL01FL, ~ASTDT, ~AENDT, ~AVAL,   \"P001\", \"TNDOSE\", \"V1\", \"Y\", ymd(\"2020-01-01\"), ymd(\"2020-01-30\"), 59,   \"P001\", \"TSNDOSE\", \"V1\", \"Y\", ymd(\"2020-01-01\"), ymd(\"2020-02-01\"), 96,   \"P001\", \"TNDOSE\", \"V2\", \"Y\", ymd(\"2020-02-01\"), ymd(\"2020-03-15\"), 88,   \"P001\", \"TSNDOSE\", \"V2\", \"Y\", ymd(\"2020-02-05\"), ymd(\"2020-03-01\"), 88,   \"P002\", \"TNDOSE\", \"V1\", \"Y\", ymd(\"2021-01-01\"), ymd(\"2021-01-30\"), 0,   \"P002\", \"TSNDOSE\", \"V1\", \"Y\", ymd(\"2021-01-01\"), ymd(\"2021-02-01\"), 0,   \"P002\", \"TNDOSE\", \"V2\", \"Y\", ymd(\"2021-02-01\"), ymd(\"2021-03-15\"), 52,   \"P002\", \"TSNDOSE\", \"V2\", \"Y\", ymd(\"2021-02-05\"), ymd(\"2021-03-01\"), 0 )  derive_param_doseint(   adex,   by_vars = exprs(USUBJID, VISIT),   set_values_to = exprs(PARAMCD = \"TNDOSINT\"),   tadm_code = \"TNDOSE\",   tpadm_code = \"TSNDOSE\" ) #> # A tibble: 12 × 7 #>    USUBJID PARAMCD  VISIT ANL01FL ASTDT      AENDT       AVAL #>    <chr>   <chr>    <chr> <chr>   <date>     <date>     <dbl> #>  1 P001    TNDOSE   V1    Y       2020-01-01 2020-01-30  59   #>  2 P001    TSNDOSE  V1    Y       2020-01-01 2020-02-01  96   #>  3 P001    TNDOSE   V2    Y       2020-02-01 2020-03-15  88   #>  4 P001    TSNDOSE  V2    Y       2020-02-05 2020-03-01  88   #>  5 P002    TNDOSE   V1    Y       2021-01-01 2021-01-30   0   #>  6 P002    TSNDOSE  V1    Y       2021-01-01 2021-02-01   0   #>  7 P002    TNDOSE   V2    Y       2021-02-01 2021-03-15  52   #>  8 P002    TSNDOSE  V2    Y       2021-02-05 2021-03-01   0   #>  9 P001    TNDOSINT V1    NA      NA         NA          61.5 #> 10 P001    TNDOSINT V2    NA      NA         NA         100   #> 11 P002    TNDOSINT V1    NA      NA         NA         NaN   #> 12 P002    TNDOSINT V2    NA      NA         NA         Inf    derive_param_doseint(   adex,   by_vars = exprs(USUBJID, VISIT),   set_values_to = exprs(PARAMCD = \"TDOSINT2\"),   tadm_code = \"TNDOSE\",   tpadm_code = \"TSNDOSE\",   zero_doses = \"100\" ) #> # A tibble: 12 × 7 #>    USUBJID PARAMCD  VISIT ANL01FL ASTDT      AENDT       AVAL #>    <chr>   <chr>    <chr> <chr>   <date>     <date>     <dbl> #>  1 P001    TNDOSE   V1    Y       2020-01-01 2020-01-30  59   #>  2 P001    TSNDOSE  V1    Y       2020-01-01 2020-02-01  96   #>  3 P001    TNDOSE   V2    Y       2020-02-01 2020-03-15  88   #>  4 P001    TSNDOSE  V2    Y       2020-02-05 2020-03-01  88   #>  5 P002    TNDOSE   V1    Y       2021-01-01 2021-01-30   0   #>  6 P002    TSNDOSE  V1    Y       2021-01-01 2021-02-01   0   #>  7 P002    TNDOSE   V2    Y       2021-02-01 2021-03-15  52   #>  8 P002    TSNDOSE  V2    Y       2021-02-05 2021-03-01   0   #>  9 P001    TDOSINT2 V1    NA      NA         NA          61.5 #> 10 P001    TDOSINT2 V2    NA      NA         NA         100   #> 11 P002    TDOSINT2 V1    NA      NA         NA           0   #> 12 P002    TDOSINT2 V2    NA      NA         NA         100"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_exist_flag.html","id":null,"dir":"Reference","previous_headings":"","what":"Add an Existence Flag Parameter — derive_param_exist_flag","title":"Add an Existence Flag Parameter — derive_param_exist_flag","text":"Add new parameter indicating certain event exists dataset. AVALC AVAL indicate event occurred . example, function can derive parameter indicating measurable disease baseline.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_exist_flag.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add an Existence Flag Parameter — derive_param_exist_flag","text":"","code":"derive_param_exist_flag(   dataset = NULL,   dataset_ref,   dataset_add,   condition,   true_value = \"Y\",   false_value = NA_character_,   missing_value = NA_character_,   filter_add = NULL,   by_vars = get_admiral_option(\"subject_keys\"),   set_values_to )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_exist_flag.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add an Existence Flag Parameter — derive_param_exist_flag","text":"dataset Input dataset variables specified by_vars argument expected dataset. PARAMCD expected well. dataset_ref Reference dataset, e.g., ADSL variables specified by_vars expected. group (defined by_vars) specified dataset (dataset_ref), existence flag calculated added new observation input datasets (dataset). dataset_add Additional dataset variables specified by_vars parameter expected. dataset used check event occurred . observation dataset fulfilling event condition (condition) considered event. condition Event condition condition evaluated additional dataset (dataset_add). groups evaluates TRUE least AVALC set true value (true_value) new observations. groups evaluates FALSE NA observations AVALC set false value (false_value). groups present additional dataset AVALC set missing value (missing_value). true_value True value groups least one observations additional dataset (dataset_add) fulfilling event condition (condition), AVALC set specified value (true_value). Default: \"Y\" Permitted Value: character scalar false_value False value groups least one observations additional dataset (dataset_add) none fulfilling event condition (condition), AVALC set specified value (false_value). Default: NA_character_ Permitted Value: character scalar missing_value Values used missing information groups without observation additional dataset (dataset_add), AVALC set specified value (missing_value). Default: NA_character_ Permitted Value: character scalar filter_add Filter additional data observations fulfilling specified condition taken account flagging. parameter specified, observations considered. Permitted Values: condition by_vars Grouping variables Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) set_values_to Variables set named list returned exprs() defining variables set new parameter, e.g. exprs(PARAMCD = \"MDIS\", PARAM = \"Measurable Disease Baseline\") expected. values must symbols, character strings, numeric values, NA, expressions.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_exist_flag.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add an Existence Flag Parameter — derive_param_exist_flag","text":"input dataset new parameter indicating event occurred (AVALC variables specified by_vars set_value_to populated new parameter).","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_exist_flag.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Add an Existence Flag Parameter — derive_param_exist_flag","text":"additional dataset (dataset_add) restricted observations matching filter_add condition. group dataset_ref new observation created. AVALC variable added set true value (true_value) group least one observation exists (restricted) additional dataset condition evaluates TRUE. set false value (false_value) group least one observation exists observations condition evaluates FALSE NA. Otherwise, set missing value (missing_value), .e., groups dataset_add. variables specified set_values_to parameter added new observations. new observations added input dataset.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_exist_flag.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add an Existence Flag Parameter — derive_param_exist_flag","text":"","code":"library(tibble) library(dplyr, warn.conflicts = FALSE) library(lubridate)  # Derive a new parameter for measurable disease at baseline adsl <- tribble(   ~USUBJID,   \"1\",   \"2\",   \"3\" ) %>%   mutate(STUDYID = \"XX1234\")  tu <- tribble(   ~USUBJID, ~VISIT,      ~TUSTRESC,   \"1\",      \"SCREENING\", \"TARGET\",   \"1\",      \"WEEK 1\",    \"TARGET\",   \"1\",      \"WEEK 5\",    \"TARGET\",   \"1\",      \"WEEK 9\",    \"NON-TARGET\",   \"2\",      \"SCREENING\", \"NON-TARGET\",   \"2\",      \"SCREENING\", \"NON-TARGET\" ) %>%   mutate(     STUDYID = \"XX1234\",     TUTESTCD = \"TUMIDENT\"   )  derive_param_exist_flag(   dataset_ref = adsl,   dataset_add = tu,   filter_add = TUTESTCD == \"TUMIDENT\" & VISIT == \"SCREENING\",   condition = TUSTRESC == \"TARGET\",   false_value = \"N\",   missing_value = \"N\",   set_values_to = exprs(     AVAL = yn_to_numeric(AVALC),     PARAMCD = \"MDIS\",     PARAM = \"Measurable Disease at Baseline\"   ) ) #> # A tibble: 3 × 6 #>   USUBJID STUDYID AVALC  AVAL PARAMCD PARAM                          #>   <chr>   <chr>   <chr> <dbl> <chr>   <chr>                          #> 1 1       XX1234  Y         1 MDIS    Measurable Disease at Baseline #> 2 2       XX1234  N         0 MDIS    Measurable Disease at Baseline #> 3 3       XX1234  N         0 MDIS    Measurable Disease at Baseline"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_exposure.html","id":null,"dir":"Reference","previous_headings":"","what":"Add an Aggregated Parameter and Derive the Associated Start and End Dates — derive_param_exposure","title":"Add an Aggregated Parameter and Derive the Associated Start and End Dates — derive_param_exposure","text":"Add record computed aggregated analysis value another parameter compute start (ASTDT(M))end date (AENDT(M)) minimum maximum date by_vars.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_exposure.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add an Aggregated Parameter and Derive the Associated Start and End Dates — derive_param_exposure","text":"","code":"derive_param_exposure(   dataset = NULL,   dataset_add,   by_vars,   input_code,   filter_add = NULL,   set_values_to = NULL )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_exposure.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add an Aggregated Parameter and Derive the Associated Start and End Dates — derive_param_exposure","text":"dataset Input dataset variables specified by_vars argument expected dataset. dataset_add Additional dataset variables specified by_vars, analysis_var, PARAMCD, alongside either ASTDTM AENDTM ASTDT AENDT also expected. Observations specified dataset going used calculate added new records input dataset (dataset). by_vars Grouping variables group defined by_vars observation added output dataset. variables specified by_vars populated newly created records. Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) input_code Required parameter code observations PARAMCD equals specified value considered compute summary record. Permitted Values: character PARAMCD value filter_add Filter condition logical expression apply summary calculation. default, filtering expressions computed within by_vars help aggregating, lagging, ranking function involved. example, filter_add = (AVAL > mean(AVAL, na.rm = TRUE)) filter AVAL values greater mean AVAL by_vars. filter_add = (dplyr::n() > 2) filter n count by_vars greater 2. set_values_to Variable-value pairs Set list variables specified value new observation(s) LHS refer variable. expected least PARAMCD defined. RHS refers values set variable. can string, symbol, numeric value, NA, expression. (e.g.  exprs(PARAMCD = \"TDOSE\",PARCAT1 = \"OVERALL\")). Permitted Values: List variable-value pairs","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_exposure.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add an Aggregated Parameter and Derive the Associated Start and End Dates — derive_param_exposure","text":"input dataset new record added group (defined by_vars parameter). , variable populated new record specified by_vars. new record, set_values_to lists specified variable computes value, variable(s) specified LHS set_values_to set paired value (RHS). addition, start end date computed minimum/maximum dates by_vars. input datasets contains AxxDTM AxxDT ASTDTM,AENDTM, ASTDT, AENDT computed AxxDTM ASTDTM,AENDTM computed AxxDT ASTDT,AENDT computed.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_exposure.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Add an Aggregated Parameter and Derive the Associated Start and End Dates — derive_param_exposure","text":"group (respect variables specified by_vars parameter), observation added output dataset defined values set defined variables","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_exposure.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add an Aggregated Parameter and Derive the Associated Start and End Dates — derive_param_exposure","text":"","code":"library(tibble) library(dplyr, warn.conflicts = FALSE) library(lubridate, warn.conflicts = FALSE) library(stringr, warn.conflicts = FALSE) adex <- tribble(   ~USUBJID, ~PARAMCD, ~AVAL, ~AVALC, ~VISIT, ~ASTDT, ~AENDT,   \"1015\", \"DOSE\", 80, NA_character_, \"BASELINE\", ymd(\"2014-01-02\"), ymd(\"2014-01-16\"),   \"1015\", \"DOSE\", 85, NA_character_, \"WEEK 2\", ymd(\"2014-01-17\"), ymd(\"2014-06-18\"),   \"1015\", \"DOSE\", 82, NA_character_, \"WEEK 24\", ymd(\"2014-06-19\"), ymd(\"2014-07-02\"),   \"1015\", \"ADJ\", NA, NA_character_, \"BASELINE\", ymd(\"2014-01-02\"), ymd(\"2014-01-16\"),   \"1015\", \"ADJ\", NA, NA_character_, \"WEEK 2\", ymd(\"2014-01-17\"), ymd(\"2014-06-18\"),   \"1015\", \"ADJ\", NA, NA_character_, \"WEEK 24\", ymd(\"2014-06-19\"), ymd(\"2014-07-02\"),   \"1017\", \"DOSE\", 80, NA_character_, \"BASELINE\", ymd(\"2014-01-05\"), ymd(\"2014-01-19\"),   \"1017\", \"DOSE\", 50, NA_character_, \"WEEK 2\", ymd(\"2014-01-20\"), ymd(\"2014-05-10\"),   \"1017\", \"DOSE\", 65, NA_character_, \"WEEK 24\", ymd(\"2014-05-10\"), ymd(\"2014-07-02\"),   \"1017\", \"ADJ\", NA, NA_character_, \"BASELINE\", ymd(\"2014-01-05\"), ymd(\"2014-01-19\"),   \"1017\", \"ADJ\", NA, \"ADVERSE EVENT\", \"WEEK 2\", ymd(\"2014-01-20\"), ymd(\"2014-05-10\"),   \"1017\", \"ADJ\", NA, NA_character_, \"WEEK 24\", ymd(\"2014-05-10\"), ymd(\"2014-07-02\") ) %>%   mutate(ASTDTM = ymd_hms(paste(ASTDT, \"00:00:00\")), AENDTM = ymd_hms(paste(AENDT, \"00:00:00\")))  # Cumulative dose adex %>%   derive_param_exposure(     dataset_add = adex,     by_vars = exprs(USUBJID),     set_values_to = exprs(       PARAMCD = \"TDOSE\",       PARCAT1 = \"OVERALL\",       AVAL = sum(AVAL, na.rm = TRUE)     ),     input_code = \"DOSE\"   ) %>%   select(-ASTDTM, -AENDTM) #> # A tibble: 14 × 8 #>    USUBJID PARAMCD  AVAL AVALC         VISIT    ASTDT      AENDT      PARCAT1 #>    <chr>   <chr>   <dbl> <chr>         <chr>    <date>     <date>     <chr>   #>  1 1015    DOSE       80 NA            BASELINE 2014-01-02 2014-01-16 NA      #>  2 1015    DOSE       85 NA            WEEK 2   2014-01-17 2014-06-18 NA      #>  3 1015    DOSE       82 NA            WEEK 24  2014-06-19 2014-07-02 NA      #>  4 1015    ADJ        NA NA            BASELINE 2014-01-02 2014-01-16 NA      #>  5 1015    ADJ        NA NA            WEEK 2   2014-01-17 2014-06-18 NA      #>  6 1015    ADJ        NA NA            WEEK 24  2014-06-19 2014-07-02 NA      #>  7 1017    DOSE       80 NA            BASELINE 2014-01-05 2014-01-19 NA      #>  8 1017    DOSE       50 NA            WEEK 2   2014-01-20 2014-05-10 NA      #>  9 1017    DOSE       65 NA            WEEK 24  2014-05-10 2014-07-02 NA      #> 10 1017    ADJ        NA NA            BASELINE 2014-01-05 2014-01-19 NA      #> 11 1017    ADJ        NA ADVERSE EVENT WEEK 2   2014-01-20 2014-05-10 NA      #> 12 1017    ADJ        NA NA            WEEK 24  2014-05-10 2014-07-02 NA      #> 13 1015    TDOSE     247 NA            NA       2014-01-02 2014-07-02 OVERALL #> 14 1017    TDOSE     195 NA            NA       2014-01-05 2014-07-02 OVERALL  # average dose in w2-24 adex %>%   derive_param_exposure(     dataset_add = adex,     by_vars = exprs(USUBJID),     filter_add = VISIT %in% c(\"WEEK 2\", \"WEEK 24\"),     set_values_to = exprs(       PARAMCD = \"AVDW224\",       PARCAT1 = \"WEEK2-24\",       AVAL = mean(AVAL, na.rm = TRUE)     ),     input_code = \"DOSE\"   ) %>%   select(-ASTDTM, -AENDTM) #> # A tibble: 14 × 8 #>    USUBJID PARAMCD  AVAL AVALC         VISIT    ASTDT      AENDT      PARCAT1  #>    <chr>   <chr>   <dbl> <chr>         <chr>    <date>     <date>     <chr>    #>  1 1015    DOSE     80   NA            BASELINE 2014-01-02 2014-01-16 NA       #>  2 1015    DOSE     85   NA            WEEK 2   2014-01-17 2014-06-18 NA       #>  3 1015    DOSE     82   NA            WEEK 24  2014-06-19 2014-07-02 NA       #>  4 1015    ADJ      NA   NA            BASELINE 2014-01-02 2014-01-16 NA       #>  5 1015    ADJ      NA   NA            WEEK 2   2014-01-17 2014-06-18 NA       #>  6 1015    ADJ      NA   NA            WEEK 24  2014-06-19 2014-07-02 NA       #>  7 1017    DOSE     80   NA            BASELINE 2014-01-05 2014-01-19 NA       #>  8 1017    DOSE     50   NA            WEEK 2   2014-01-20 2014-05-10 NA       #>  9 1017    DOSE     65   NA            WEEK 24  2014-05-10 2014-07-02 NA       #> 10 1017    ADJ      NA   NA            BASELINE 2014-01-05 2014-01-19 NA       #> 11 1017    ADJ      NA   ADVERSE EVENT WEEK 2   2014-01-20 2014-05-10 NA       #> 12 1017    ADJ      NA   NA            WEEK 24  2014-05-10 2014-07-02 NA       #> 13 1015    AVDW224  83.5 NA            NA       2014-01-17 2014-07-02 WEEK2-24 #> 14 1017    AVDW224  57.5 NA            NA       2014-01-20 2014-07-02 WEEK2-24  # Any dose adjustment? adex %>%   derive_param_exposure(     dataset_add = adex,     by_vars = exprs(USUBJID),     set_values_to = exprs(       PARAMCD = \"TADJ\",       PARCAT1 = \"OVERALL\",       AVALC = if_else(sum(!is.na(AVALC)) > 0, \"Y\", NA_character_)     ),     input_code = \"ADJ\"   ) %>%   select(-ASTDTM, -AENDTM) #> # A tibble: 14 × 8 #>    USUBJID PARAMCD  AVAL AVALC         VISIT    ASTDT      AENDT      PARCAT1 #>    <chr>   <chr>   <dbl> <chr>         <chr>    <date>     <date>     <chr>   #>  1 1015    DOSE       80 NA            BASELINE 2014-01-02 2014-01-16 NA      #>  2 1015    DOSE       85 NA            WEEK 2   2014-01-17 2014-06-18 NA      #>  3 1015    DOSE       82 NA            WEEK 24  2014-06-19 2014-07-02 NA      #>  4 1015    ADJ        NA NA            BASELINE 2014-01-02 2014-01-16 NA      #>  5 1015    ADJ        NA NA            WEEK 2   2014-01-17 2014-06-18 NA      #>  6 1015    ADJ        NA NA            WEEK 24  2014-06-19 2014-07-02 NA      #>  7 1017    DOSE       80 NA            BASELINE 2014-01-05 2014-01-19 NA      #>  8 1017    DOSE       50 NA            WEEK 2   2014-01-20 2014-05-10 NA      #>  9 1017    DOSE       65 NA            WEEK 24  2014-05-10 2014-07-02 NA      #> 10 1017    ADJ        NA NA            BASELINE 2014-01-05 2014-01-19 NA      #> 11 1017    ADJ        NA ADVERSE EVENT WEEK 2   2014-01-20 2014-05-10 NA      #> 12 1017    ADJ        NA NA            WEEK 24  2014-05-10 2014-07-02 NA      #> 13 1015    TADJ       NA NA            NA       2014-01-02 2014-07-02 OVERALL #> 14 1017    TADJ       NA Y             NA       2014-01-05 2014-07-02 OVERALL"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_extreme_record.html","id":null,"dir":"Reference","previous_headings":"","what":"Adds a Parameter Based on First or Last Record from Multiple Sources — derive_param_extreme_record","title":"Adds a Parameter Based on First or Last Record from Multiple Sources — derive_param_extreme_record","text":"derive_param_extreme_record() function superseded favor derive_extreme_event(). Generates parameter based first last observation multiple source datasets, based user-defined filter, order group criteria. variables selected observation kept.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_extreme_record.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adds a Parameter Based on First or Last Record from Multiple Sources — derive_param_extreme_record","text":"","code":"derive_param_extreme_record(   dataset = NULL,   sources,   source_datasets,   by_vars = NULL,   order,   mode,   set_values_to )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_extreme_record.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adds a Parameter Based on First or Last Record from Multiple Sources — derive_param_extreme_record","text":"dataset Input dataset sources Sources list records_source() objects expected. source_datasets Source datasets named list datasets expected. dataset_name field records_source() refers dataset provided list. variables specified order by_vars arguments expected applying new_vars. by_vars Grouping variables argument specified, group observations selected separately. Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) order Sort order argument set non-null value, group first last observation source datasets selected respect specified order. Variables created via new_vars e.g., imputed date variables, can specified well (see examples ). Please note NA considered last value. .e., order variable NA mode = \"last\", observation chosen mode = \"first\" observation chosen observations variable NA. Permitted Values: list expressions created exprs(), e.g., exprs(ADT, desc(AVAL)) mode Selection mode (first last) \"first\" specified, group first observation respect order included output dataset. \"last\" specified, last observation included output dataset. Permitted Values:  \"first\", \"last\" set_values_to Variables set specified variables set specified values new observations. list variable name-value pairs expected. LHS refers variable. RHS refers values set variable. can string, symbol, numeric value NA, e.g., exprs(PARAMCD = \"PD\", PARAM =   \"First Progressive Disease\").","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_extreme_record.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adds a Parameter Based on First or Last Record from Multiple Sources — derive_param_extreme_record","text":"input dataset first last observation group added new observations.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_extreme_record.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Adds a Parameter Based on First or Last Record from Multiple Sources — derive_param_extreme_record","text":"following steps performed create output dataset: source dataset observations specified filter element selected. Variables specified new_vars created source dataset. first last observation (respect order variable) group (specified by_vars) multiple sources selected added input dataset.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_extreme_record.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Adds a Parameter Based on First or Last Record from Multiple Sources — derive_param_extreme_record","text":"","code":"aevent_samp <- tibble::tribble(   ~USUBJID, ~PARAMCD,                       ~PARAM,     ~RSSTDTC,   \"1\",          \"PD\",  \"First Progressive Disease\", \"2022-04-01\",   \"2\",          \"PD\",  \"First Progressive Disease\", \"2021-04-01\",   \"3\",          \"PD\",  \"First Progressive Disease\", \"2023-04-01\" )  cm <- tibble::tribble(   ~STUDYID, ~USUBJID, ~CMDECOD,     ~CMSTDTC,   \"1001\",        \"1\",    \"ACT\", \"2021-12-25\" )  pr <- tibble::tribble(   ~STUDYID, ~USUBJID, ~PRDECOD,     ~PRSTDTC,   \"1001\",        \"1\",    \"ACS\", \"2021-12-27\",   \"1001\",        \"2\",    \"ACS\", \"2020-12-25\",   \"1001\",        \"3\",    \"ACS\", \"2022-12-25\", ) derive_param_extreme_record(   dataset = aevent_samp,   sources = list(     records_source(       dataset_name = \"cm\",       filter = CMDECOD == \"ACT\",       new_vars = exprs(         ADT = convert_dtc_to_dt(CMSTDTC),         AVALC = CMDECOD       )     ),     records_source(       dataset_name = \"pr\",       filter = PRDECOD == \"ACS\",       new_vars = exprs(         ADT = convert_dtc_to_dt(PRSTDTC),         AVALC = PRDECOD       )     )   ),   source_datasets = list(cm = cm, pr = pr),   by_vars = exprs(USUBJID),   order = exprs(ADT),   mode = \"first\",   set_values_to = exprs(     PARAMCD = \"FIRSTACT\",     PARAM = \"First Anti-Cancer Therapy\"   ) ) #> # A tibble: 6 × 6 #>   USUBJID PARAMCD  PARAM                     RSSTDTC    ADT        AVALC #>   <chr>   <chr>    <chr>                     <chr>      <date>     <chr> #> 1 1       PD       First Progressive Disease 2022-04-01 NA         NA    #> 2 2       PD       First Progressive Disease 2021-04-01 NA         NA    #> 3 3       PD       First Progressive Disease 2023-04-01 NA         NA    #> 4 1       FIRSTACT First Anti-Cancer Therapy NA         2021-12-25 ACT   #> 5 2       FIRSTACT First Anti-Cancer Therapy NA         2020-12-25 ACS   #> 6 3       FIRSTACT First Anti-Cancer Therapy NA         2022-12-25 ACS"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_framingham.html","id":null,"dir":"Reference","previous_headings":"","what":"Adds a Parameter for Framingham Heart Study Cardiovascular Disease 10-Year Risk Score — derive_param_framingham","title":"Adds a Parameter for Framingham Heart Study Cardiovascular Disease 10-Year Risk Score — derive_param_framingham","text":"Adds record framingham score (FCVD101) group (e.g., subject visit) source parameters available.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_framingham.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adds a Parameter for Framingham Heart Study Cardiovascular Disease 10-Year Risk Score — derive_param_framingham","text":"","code":"derive_param_framingham(   dataset,   by_vars,   set_values_to = exprs(PARAMCD = \"FCVD101\"),   sysbp_code = \"SYSBP\",   chol_code = \"CHOL\",   cholhdl_code = \"CHOLHDL\",   age = AGE,   sex = SEX,   smokefl = SMOKEFL,   diabetfl = DIABETFL,   trthypfl = TRTHYPFL,   get_unit_expr,   filter = NULL )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_framingham.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adds a Parameter for Framingham Heart Study Cardiovascular Disease 10-Year Risk Score — derive_param_framingham","text":"dataset Input dataset variables specified by_vars argument expected dataset. PARAMCD, AVAL expected well. variable specified by_vars PARAMCD must unique key input dataset restricting filter condition (filter parameter) parameters specified sysbp_code, chol_code hdl_code. by_vars Grouping variables variables specified by_vars populated newly created records. Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) set_values_to Variables set specified variables set specified values new observations. example exprs(PARAMCD = \"MAP\") defines parameter code new parameter. Permitted Values: List variable-value pairs sysbp_code Systolic blood pressure parameter code observations PARAMCD equals specified value considered systolic blood pressure assessments. Permitted Values: character value chol_code Total serum cholesterol code observations PARAMCD equals specified value considered total cholesterol assessments. must measured mg/dL. Permitted Values: character value cholhdl_code HDL serum cholesterol code observations PARAMCD equals specified value considered HDL cholesterol assessments. must measured mg/dL. Permitted Values: character value age Subject age variable containing subject's age. Permitted Values: numeric variable name refers subject age column input dataset sex Subject sex variable containing subject's sex. Permitted Values: character variable name refers subject sex column input dataset smokefl Smoking status flag flag indicating smoking status. Permitted Values: character variable name refers smoking status column input dataset. diabetfl Diabetic flag flag indicating diabetic status. Permitted Values: character variable name refers diabetic status column input dataset trthypfl Treated hypertension medication flag flag indicating subject treated hypertension medication. Permitted Values: character variable name refers column indicates whether subject treated high blood pressure get_unit_expr expression providing unit parameter result used check units input parameters. Permitted Values: expression evaluable input dataset results character value filter Filter condition specified condition applied input dataset deriving new parameter, .e., observations fulfilling condition taken account. Permitted Values: condition","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_framingham.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adds a Parameter for Framingham Heart Study Cardiovascular Disease 10-Year Risk Score — derive_param_framingham","text":"input dataset new parameter added","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_framingham.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Adds a Parameter for Framingham Heart Study Cardiovascular Disease 10-Year Risk Score — derive_param_framingham","text":"values age, sex, smokefl, diabetfl trthypfl added by_vars list. predicted probability cardiovascular disease (CVD) within 10-years according Framingham formula. See AHA Journal article General Cardiovascular Risk Profile Use Primary Care reference. Women: Men: equation calculating risk: $$RiskFactors = (log(Age) * AgeFactor) + (log(TotalChol) * TotalCholFactor) + (log(CholHDL) * CholHDLFactor) \\\\ + (log(SysBP) * SysBPFactor) + Smoker + Diabetes Present - AvgRisk$$ $$Risk = 100 * (1 - RiskPeriodFactor^{RiskFactors})$$","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_framingham.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Adds a Parameter for Framingham Heart Study Cardiovascular Disease 10-Year Risk Score — derive_param_framingham","text":"","code":"library(tibble)  adcvrisk <- tribble(   ~USUBJID, ~PARAMCD, ~PARAM, ~AVAL, ~AVALU,   ~VISIT, ~AGE, ~SEX, ~SMOKEFL, ~DIABETFL, ~TRTHYPFL,   \"01-701-1015\", \"SYSBP\", \"Systolic Blood Pressure (mmHg)\", 121,   \"mmHg\", \"BASELINE\", 44, \"F\", \"N\", \"N\", \"N\",   \"01-701-1015\", \"SYSBP\", \"Systolic Blood Pressure (mmHg)\", 115,   \"mmHg\", \"WEEK 2\", 44, \"F\", \"N\", \"N\", \"Y\",   \"01-701-1015\", \"CHOL\", \"Total Cholesterol (mg/dL)\", 216.16,   \"mg/dL\", \"BASELINE\", 44, \"F\", \"N\", \"N\", \"N\",   \"01-701-1015\", \"CHOL\", \"Total Cholesterol (mg/dL)\", 210.78,   \"mg/dL\", \"WEEK 2\", 44, \"F\", \"N\", \"N\", \"Y\",   \"01-701-1015\", \"CHOLHDL\", \"Cholesterol/HDL-Cholesterol (mg/dL)\", 54.91,   \"mg/dL\", \"BASELINE\", 44, \"F\", \"N\", \"N\", \"N\",   \"01-701-1015\", \"CHOLHDL\", \"Cholesterol/HDL-Cholesterol (mg/dL)\", 26.72,   \"mg/dL\", \"WEEK 2\", 44, \"F\", \"N\", \"N\", \"Y\",   \"01-701-1028\", \"SYSBP\", \"Systolic Blood Pressure (mmHg)\", 119,   \"mmHg\", \"BASELINE\", 55, \"M\", \"Y\", \"Y\", \"Y\",   \"01-701-1028\", \"SYSBP\", \"Systolic Blood Pressure (mmHg)\", 101,   \"mmHg\", \"WEEK 2\", 55, \"M\", \"Y\", \"Y\", \"Y\",   \"01-701-1028\", \"CHOL\", \"Total Cholesterol (mg/dL)\", 292.01,   \"mg/dL\", \"BASELINE\", 55, \"M\", \"Y\", \"Y\", \"Y\",   \"01-701-1028\", \"CHOL\", \"Total Cholesterol (mg/dL)\", 246.73,   \"mg/dL\", \"WEEK 2\", 55, \"M\", \"Y\", \"Y\", \"Y\",   \"01-701-1028\", \"CHOLHDL\", \"Cholesterol/HDL-Cholesterol (mg/dL)\", 65.55,   \"mg/dL\", \"BASELINE\", 55, \"M\", \"Y\", \"Y\", \"Y\",   \"01-701-1028\", \"CHOLHDL\", \"Cholesterol/HDL-Cholesterol (mg/dL)\", 44.62,   \"mg/dL\", \"WEEK 2\", 55, \"M\", \"Y\", \"Y\", \"Y\" )   adcvrisk %>%   derive_param_framingham(     by_vars = exprs(USUBJID, VISIT),     set_values_to = exprs(       PARAMCD = \"FCVD101\",       PARAM = \"FCVD1-Framingham CVD 10-Year Risk Score (%)\"     ),     get_unit_expr = AVALU   ) #> # A tibble: 16 × 11 #>    USUBJID     PARAMCD PARAM       AVAL AVALU VISIT   AGE SEX   SMOKEFL DIABETFL #>    <chr>       <chr>   <chr>      <dbl> <chr> <chr> <dbl> <chr> <chr>   <chr>    #>  1 01-701-1015 SYSBP   Systolic… 121    mmHg  BASE…    44 F     N       N        #>  2 01-701-1015 SYSBP   Systolic… 115    mmHg  WEEK…    44 F     N       N        #>  3 01-701-1015 CHOL    Total Ch… 216.   mg/dL BASE…    44 F     N       N        #>  4 01-701-1015 CHOL    Total Ch… 211.   mg/dL WEEK…    44 F     N       N        #>  5 01-701-1015 CHOLHDL Choleste…  54.9  mg/dL BASE…    44 F     N       N        #>  6 01-701-1015 CHOLHDL Choleste…  26.7  mg/dL WEEK…    44 F     N       N        #>  7 01-701-1028 SYSBP   Systolic… 119    mmHg  BASE…    55 M     Y       Y        #>  8 01-701-1028 SYSBP   Systolic… 101    mmHg  WEEK…    55 M     Y       Y        #>  9 01-701-1028 CHOL    Total Ch… 292.   mg/dL BASE…    55 M     Y       Y        #> 10 01-701-1028 CHOL    Total Ch… 247.   mg/dL WEEK…    55 M     Y       Y        #> 11 01-701-1028 CHOLHDL Choleste…  65.6  mg/dL BASE…    55 M     Y       Y        #> 12 01-701-1028 CHOLHDL Choleste…  44.6  mg/dL WEEK…    55 M     Y       Y        #> 13 01-701-1015 FCVD101 FCVD1-Fr…   3.14 NA    BASE…    44 F     N       N        #> 14 01-701-1015 FCVD101 FCVD1-Fr…   5.80 NA    WEEK…    44 F     N       N        #> 15 01-701-1028 FCVD101 FCVD1-Fr…  42.3  NA    BASE…    55 M     Y       Y        #> 16 01-701-1028 FCVD101 FCVD1-Fr…  37.5  NA    WEEK…    55 M     Y       Y        #> # ℹ 1 more variable: TRTHYPFL <chr>  derive_param_framingham(   adcvrisk,   by_vars = exprs(USUBJID, VISIT),   set_values_to = exprs(     PARAMCD = \"FCVD101\",     PARAM = \"FCVD1-Framingham CVD 10-Year Risk Score (%)\"   ),   get_unit_expr = extract_unit(PARAM) ) #> # A tibble: 16 × 11 #>    USUBJID     PARAMCD PARAM       AVAL AVALU VISIT   AGE SEX   SMOKEFL DIABETFL #>    <chr>       <chr>   <chr>      <dbl> <chr> <chr> <dbl> <chr> <chr>   <chr>    #>  1 01-701-1015 SYSBP   Systolic… 121    mmHg  BASE…    44 F     N       N        #>  2 01-701-1015 SYSBP   Systolic… 115    mmHg  WEEK…    44 F     N       N        #>  3 01-701-1015 CHOL    Total Ch… 216.   mg/dL BASE…    44 F     N       N        #>  4 01-701-1015 CHOL    Total Ch… 211.   mg/dL WEEK…    44 F     N       N        #>  5 01-701-1015 CHOLHDL Choleste…  54.9  mg/dL BASE…    44 F     N       N        #>  6 01-701-1015 CHOLHDL Choleste…  26.7  mg/dL WEEK…    44 F     N       N        #>  7 01-701-1028 SYSBP   Systolic… 119    mmHg  BASE…    55 M     Y       Y        #>  8 01-701-1028 SYSBP   Systolic… 101    mmHg  WEEK…    55 M     Y       Y        #>  9 01-701-1028 CHOL    Total Ch… 292.   mg/dL BASE…    55 M     Y       Y        #> 10 01-701-1028 CHOL    Total Ch… 247.   mg/dL WEEK…    55 M     Y       Y        #> 11 01-701-1028 CHOLHDL Choleste…  65.6  mg/dL BASE…    55 M     Y       Y        #> 12 01-701-1028 CHOLHDL Choleste…  44.6  mg/dL WEEK…    55 M     Y       Y        #> 13 01-701-1015 FCVD101 FCVD1-Fr…   3.14 NA    BASE…    44 F     N       N        #> 14 01-701-1015 FCVD101 FCVD1-Fr…   5.80 NA    WEEK…    44 F     N       N        #> 15 01-701-1028 FCVD101 FCVD1-Fr…  42.3  NA    BASE…    55 M     Y       Y        #> 16 01-701-1028 FCVD101 FCVD1-Fr…  37.5  NA    WEEK…    55 M     Y       Y        #> # ℹ 1 more variable: TRTHYPFL <chr>"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_map.html","id":null,"dir":"Reference","previous_headings":"","what":"Adds a Parameter for Mean Arterial Pressure — derive_param_map","title":"Adds a Parameter for Mean Arterial Pressure — derive_param_map","text":"Adds record mean arterial pressure (MAP) group (e.g., subject visit) source parameters available. Note: wrapper function generic derive_param_computed().","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_map.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adds a Parameter for Mean Arterial Pressure — derive_param_map","text":"","code":"derive_param_map(   dataset,   by_vars,   set_values_to = exprs(PARAMCD = \"MAP\"),   sysbp_code = \"SYSBP\",   diabp_code = \"DIABP\",   hr_code = NULL,   get_unit_expr,   filter = NULL )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_map.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adds a Parameter for Mean Arterial Pressure — derive_param_map","text":"dataset Input dataset variables specified by_vars argument expected dataset. PARAMCD, AVAL expected well. variable specified by_vars PARAMCD must unique key input dataset restricting filter condition (filter parameter) parameters specified sysbp_code, diabp_code hr_code. by_vars Grouping variables group defined by_vars observation added output dataset. variables specified by_vars populated newly created records. Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) set_values_to Variables set specified variables set specified values new observations. example exprs(PARAMCD = \"MAP\") defines parameter code new parameter. Permitted Values: List variable-value pairs sysbp_code Systolic blood pressure parameter code observations PARAMCD equals specified value considered systolic blood pressure assessments. Permitted Values: character value diabp_code Diastolic blood pressure parameter code observations PARAMCD equals specified value considered diastolic blood pressure assessments. Permitted Values: character value hr_code Heart rate parameter code observations PARAMCD equals specified value considered heart rate assessments. Permitted Values: character value get_unit_expr expression providing unit parameter result used check units input parameters. Permitted Values: expression evaluable input dataset results character value filter Filter condition specified condition applied input dataset deriving new parameter, .e., observations fulfilling condition taken account. Permitted Values: condition","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_map.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adds a Parameter for Mean Arterial Pressure — derive_param_map","text":"input dataset new parameter added. Note, variable populated new parameter rows specified by_vars.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_map.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Adds a Parameter for Mean Arterial Pressure — derive_param_map","text":"analysis value new parameter derived $$\\frac{2DIABP + SYSBP}{3}$$ based diastolic systolic blood pressure $$DIABP + 0.01 e^{4.14 - \\frac{40.74}{HR}} (SYSBP - DIABP)$$ based diastolic, systolic blood pressure, heart rate.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_map.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Adds a Parameter for Mean Arterial Pressure — derive_param_map","text":"","code":"library(tibble) library(dplyr, warn.conflicts = FALSE)  advs <- tibble::tribble(   ~USUBJID, ~PARAMCD, ~PARAM, ~AVAL, ~VISIT,   \"01-701-1015\", \"PULSE\", \"Pulse (beats/min)\", 59, \"BASELINE\",   \"01-701-1015\", \"PULSE\", \"Pulse (beats/min)\", 61, \"WEEK 2\",   \"01-701-1015\", \"DIABP\", \"Diastolic Blood Pressure (mmHg)\", 51, \"BASELINE\",   \"01-701-1015\", \"DIABP\", \"Diastolic Blood Pressure (mmHg)\", 50, \"WEEK 2\",   \"01-701-1015\", \"SYSBP\", \"Systolic Blood Pressure (mmHg)\", 121, \"BASELINE\",   \"01-701-1015\", \"SYSBP\", \"Systolic Blood Pressure (mmHg)\", 121, \"WEEK 2\",   \"01-701-1028\", \"PULSE\", \"Pulse (beats/min)\", 62, \"BASELINE\",   \"01-701-1028\", \"PULSE\", \"Pulse (beats/min)\", 77, \"WEEK 2\",   \"01-701-1028\", \"DIABP\", \"Diastolic Blood Pressure (mmHg)\", 79, \"BASELINE\",   \"01-701-1028\", \"DIABP\", \"Diastolic Blood Pressure (mmHg)\", 80, \"WEEK 2\",   \"01-701-1028\", \"SYSBP\", \"Systolic Blood Pressure (mmHg)\", 130, \"BASELINE\",   \"01-701-1028\", \"SYSBP\", \"Systolic Blood Pressure (mmHg)\", 132, \"WEEK 2\" )  # Derive MAP based on diastolic and systolic blood pressure advs %>%   derive_param_map(     by_vars = exprs(USUBJID, VISIT),     set_values_to = exprs(       PARAMCD = \"MAP\",       PARAM = \"Mean Arterial Pressure (mmHg)\"     ),     get_unit_expr = extract_unit(PARAM)   ) %>%   filter(PARAMCD != \"PULSE\") #> # A tibble: 12 × 5 #>    USUBJID     PARAMCD PARAM                            AVAL VISIT    #>    <chr>       <chr>   <chr>                           <dbl> <chr>    #>  1 01-701-1015 DIABP   Diastolic Blood Pressure (mmHg)  51   BASELINE #>  2 01-701-1015 DIABP   Diastolic Blood Pressure (mmHg)  50   WEEK 2   #>  3 01-701-1015 SYSBP   Systolic Blood Pressure (mmHg)  121   BASELINE #>  4 01-701-1015 SYSBP   Systolic Blood Pressure (mmHg)  121   WEEK 2   #>  5 01-701-1028 DIABP   Diastolic Blood Pressure (mmHg)  79   BASELINE #>  6 01-701-1028 DIABP   Diastolic Blood Pressure (mmHg)  80   WEEK 2   #>  7 01-701-1028 SYSBP   Systolic Blood Pressure (mmHg)  130   BASELINE #>  8 01-701-1028 SYSBP   Systolic Blood Pressure (mmHg)  132   WEEK 2   #>  9 01-701-1015 MAP     Mean Arterial Pressure (mmHg)    74.3 BASELINE #> 10 01-701-1015 MAP     Mean Arterial Pressure (mmHg)    73.7 WEEK 2   #> 11 01-701-1028 MAP     Mean Arterial Pressure (mmHg)    96   BASELINE #> 12 01-701-1028 MAP     Mean Arterial Pressure (mmHg)    97.3 WEEK 2    # Derive MAP based on diastolic and systolic blood pressure and heart rate derive_param_map(   advs,   by_vars = exprs(USUBJID, VISIT),   hr_code = \"PULSE\",   set_values_to = exprs(     PARAMCD = \"MAP\",     PARAM = \"Mean Arterial Pressure (mmHg)\"   ),   get_unit_expr = extract_unit(PARAM) ) #> # A tibble: 16 × 5 #>    USUBJID     PARAMCD PARAM                            AVAL VISIT    #>    <chr>       <chr>   <chr>                           <dbl> <chr>    #>  1 01-701-1015 PULSE   Pulse (beats/min)                59   BASELINE #>  2 01-701-1015 PULSE   Pulse (beats/min)                61   WEEK 2   #>  3 01-701-1015 DIABP   Diastolic Blood Pressure (mmHg)  51   BASELINE #>  4 01-701-1015 DIABP   Diastolic Blood Pressure (mmHg)  50   WEEK 2   #>  5 01-701-1015 SYSBP   Systolic Blood Pressure (mmHg)  121   BASELINE #>  6 01-701-1015 SYSBP   Systolic Blood Pressure (mmHg)  121   WEEK 2   #>  7 01-701-1028 PULSE   Pulse (beats/min)                62   BASELINE #>  8 01-701-1028 PULSE   Pulse (beats/min)                77   WEEK 2   #>  9 01-701-1028 DIABP   Diastolic Blood Pressure (mmHg)  79   BASELINE #> 10 01-701-1028 DIABP   Diastolic Blood Pressure (mmHg)  80   WEEK 2   #> 11 01-701-1028 SYSBP   Systolic Blood Pressure (mmHg)  130   BASELINE #> 12 01-701-1028 SYSBP   Systolic Blood Pressure (mmHg)  132   WEEK 2   #> 13 01-701-1015 MAP     Mean Arterial Pressure (mmHg)    73.0 BASELINE #> 14 01-701-1015 MAP     Mean Arterial Pressure (mmHg)    72.9 WEEK 2   #> 15 01-701-1028 MAP     Mean Arterial Pressure (mmHg)    95.6 BASELINE #> 16 01-701-1028 MAP     Mean Arterial Pressure (mmHg)    99.2 WEEK 2"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_qtc.html","id":null,"dir":"Reference","previous_headings":"","what":"Adds a Parameter for Corrected QT (an ECG measurement) — derive_param_qtc","title":"Adds a Parameter for Corrected QT (an ECG measurement) — derive_param_qtc","text":"Adds record corrected QT using either Bazett's, Fridericia's Sagie's formula group (e.g., subject visit) source parameters available. Note: wrapper function generic derive_param_computed().","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_qtc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adds a Parameter for Corrected QT (an ECG measurement) — derive_param_qtc","text":"","code":"derive_param_qtc(   dataset,   by_vars,   method,   set_values_to = default_qtc_paramcd(method),   qt_code = \"QT\",   rr_code = \"RR\",   get_unit_expr,   filter = NULL )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_qtc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adds a Parameter for Corrected QT (an ECG measurement) — derive_param_qtc","text":"dataset Input dataset variables specified by_vars get_unit_expr arguments expected dataset. PARAMCD, AVAL expected well. variable specified by_vars PARAMCD must unique key input dataset restricting filter condition (filter argument) parameters specified qt_code rr_code. by_vars Grouping variables variables specified by_vars populated newly created records. Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) method Method used QT correction See compute_qtc() details. Permitted Values: \"Bazett\", \"Fridericia\", \"Sagie\" set_values_to Variables set specified variables set specified values new observations. example exprs(PARAMCD = \"MAP\") defines parameter code new parameter. Permitted Values: List variable-value pairs qt_code QT parameter code observations PARAMCD equals specified value considered QT interval assessments. expected QT measured ms msec. Permitted Values: character value rr_code RR parameter code observations PARAMCD equals specified value considered RR interval assessments. expected RR measured ms msec. Permitted Values: character value get_unit_expr expression providing unit parameter result used check units input parameters. Permitted Values: expression evaluable input dataset results character value filter Filter condition specified condition applied input dataset deriving new parameter, .e., observations fulfilling condition taken account. Permitted Values: condition","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_qtc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adds a Parameter for Corrected QT (an ECG measurement) — derive_param_qtc","text":"input dataset new parameter added. Note, variable populated new parameter rows specified by_vars.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_qtc.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Adds a Parameter for Corrected QT (an ECG measurement) — derive_param_qtc","text":"","code":"library(tibble)  adeg <- tribble(   ~USUBJID,      ~PARAMCD, ~PARAM,                   ~AVAL, ~AVALU,      ~VISIT,   \"01-701-1015\", \"HR\",     \"Heart Rate (beats/min)\", 70.14, \"beats/min\", \"BASELINE\",   \"01-701-1015\", \"QT\",     \"QT Duration (ms)\",         370, \"ms\",        \"WEEK 2\",   \"01-701-1015\", \"HR\",     \"Heart Rate (beats/min)\", 62.66, \"beats/min\", \"WEEK 1\",   \"01-701-1015\", \"RR\",     \"RR Duration (ms)\",         710, \"ms\",        \"WEEK 2\",   \"01-701-1028\", \"HR\",     \"Heart Rate (beats/min)\", 85.45, \"beats/min\", \"BASELINE\",   \"01-701-1028\", \"QT\",     \"QT Duration (ms)\",         480, \"ms\",        \"WEEK 2\",   \"01-701-1028\", \"QT\",     \"QT Duration (ms)\",         350, \"ms\",        \"WEEK 3\",   \"01-701-1028\", \"HR\",     \"Heart Rate (beats/min)\", 56.54, \"beats/min\", \"WEEK 3\",   \"01-701-1028\", \"RR\",     \"RR Duration (ms)\",         842, \"ms\",        \"WEEK 2\" )  derive_param_qtc(   adeg,   by_vars = exprs(USUBJID, VISIT),   method = \"Bazett\",   set_values_to = exprs(     PARAMCD = \"QTCBR\",     PARAM = \"QTcB - Bazett's Correction Formula Rederived (ms)\",     AVALU = \"ms\"   ),   get_unit_expr = AVALU ) #> # A tibble: 11 × 6 #>    USUBJID     PARAMCD PARAM                                    AVAL AVALU VISIT #>    <chr>       <chr>   <chr>                                   <dbl> <chr> <chr> #>  1 01-701-1015 HR      Heart Rate (beats/min)                   70.1 beat… BASE… #>  2 01-701-1015 QT      QT Duration (ms)                        370   ms    WEEK… #>  3 01-701-1015 HR      Heart Rate (beats/min)                   62.7 beat… WEEK… #>  4 01-701-1015 RR      RR Duration (ms)                        710   ms    WEEK… #>  5 01-701-1028 HR      Heart Rate (beats/min)                   85.4 beat… BASE… #>  6 01-701-1028 QT      QT Duration (ms)                        480   ms    WEEK… #>  7 01-701-1028 QT      QT Duration (ms)                        350   ms    WEEK… #>  8 01-701-1028 HR      Heart Rate (beats/min)                   56.5 beat… WEEK… #>  9 01-701-1028 RR      RR Duration (ms)                        842   ms    WEEK… #> 10 01-701-1015 QTCBR   QTcB - Bazett's Correction Formula Red… 439.  ms    WEEK… #> 11 01-701-1028 QTCBR   QTcB - Bazett's Correction Formula Red… 523.  ms    WEEK…  derive_param_qtc(   adeg,   by_vars = exprs(USUBJID, VISIT),   method = \"Fridericia\",   set_values_to = exprs(     PARAMCD = \"QTCFR\",     PARAM = \"QTcF - Fridericia's Correction Formula Rederived (ms)\",     AVALU = \"ms\"   ),   get_unit_expr = extract_unit(PARAM) ) #> # A tibble: 11 × 6 #>    USUBJID     PARAMCD PARAM                                    AVAL AVALU VISIT #>    <chr>       <chr>   <chr>                                   <dbl> <chr> <chr> #>  1 01-701-1015 HR      Heart Rate (beats/min)                   70.1 beat… BASE… #>  2 01-701-1015 QT      QT Duration (ms)                        370   ms    WEEK… #>  3 01-701-1015 HR      Heart Rate (beats/min)                   62.7 beat… WEEK… #>  4 01-701-1015 RR      RR Duration (ms)                        710   ms    WEEK… #>  5 01-701-1028 HR      Heart Rate (beats/min)                   85.4 beat… BASE… #>  6 01-701-1028 QT      QT Duration (ms)                        480   ms    WEEK… #>  7 01-701-1028 QT      QT Duration (ms)                        350   ms    WEEK… #>  8 01-701-1028 HR      Heart Rate (beats/min)                   56.5 beat… WEEK… #>  9 01-701-1028 RR      RR Duration (ms)                        842   ms    WEEK… #> 10 01-701-1015 QTCFR   QTcF - Fridericia's Correction Formula… 415.  ms    WEEK… #> 11 01-701-1028 QTCFR   QTcF - Fridericia's Correction Formula… 508.  ms    WEEK…  derive_param_qtc(   adeg,   by_vars = exprs(USUBJID, VISIT),   method = \"Sagie\",   set_values_to = exprs(     PARAMCD = \"QTLCR\",     PARAM = \"QTlc - Sagie's Correction Formula Rederived (ms)\",     AVALU = \"ms\"   ),   get_unit_expr = extract_unit(PARAM) ) #> # A tibble: 11 × 6 #>    USUBJID     PARAMCD PARAM                                    AVAL AVALU VISIT #>    <chr>       <chr>   <chr>                                   <dbl> <chr> <chr> #>  1 01-701-1015 HR      Heart Rate (beats/min)                   70.1 beat… BASE… #>  2 01-701-1015 QT      QT Duration (ms)                        370   ms    WEEK… #>  3 01-701-1015 HR      Heart Rate (beats/min)                   62.7 beat… WEEK… #>  4 01-701-1015 RR      RR Duration (ms)                        710   ms    WEEK… #>  5 01-701-1028 HR      Heart Rate (beats/min)                   85.4 beat… BASE… #>  6 01-701-1028 QT      QT Duration (ms)                        480   ms    WEEK… #>  7 01-701-1028 QT      QT Duration (ms)                        350   ms    WEEK… #>  8 01-701-1028 HR      Heart Rate (beats/min)                   56.5 beat… WEEK… #>  9 01-701-1028 RR      RR Duration (ms)                        842   ms    WEEK… #> 10 01-701-1015 QTLCR   QTlc - Sagie's Correction Formula Rede… 415.  ms    WEEK… #> 11 01-701-1028 QTLCR   QTlc - Sagie's Correction Formula Rede… 504.  ms    WEEK…"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_rr.html","id":null,"dir":"Reference","previous_headings":"","what":"Adds a Parameter for Derived RR (an ECG measurement) — derive_param_rr","title":"Adds a Parameter for Derived RR (an ECG measurement) — derive_param_rr","text":"Adds record derived RR based heart rate group (e.g., subject visit) source parameters available. Note: wrapper function generic derive_param_computed(). analysis value new parameter derived $$\\frac{60000}{HR}$$","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_rr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adds a Parameter for Derived RR (an ECG measurement) — derive_param_rr","text":"","code":"derive_param_rr(   dataset,   by_vars,   set_values_to = exprs(PARAMCD = \"RRR\"),   hr_code = \"HR\",   get_unit_expr,   filter = NULL )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_rr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adds a Parameter for Derived RR (an ECG measurement) — derive_param_rr","text":"dataset Input dataset variables specified by_vars argument expected dataset. PARAMCD, AVAL expected well. variable specified by_vars PARAMCD must unique key input dataset restricting filter condition (filter argument) parameters specified hr_code. by_vars Grouping variables group defined by_vars observation added output dataset. variables specified by_vars populated newly created records. Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) set_values_to Variables set specified variables set specified values new observations. example exprs(PARAMCD = \"MAP\") defines parameter code new parameter. Permitted Values: List variable-value pairs hr_code HR parameter code observations PARAMCD equals specified value considered heart rate assessments. Permitted Values: character value get_unit_expr expression providing unit parameter result used check units input parameters. Permitted Values: expression evaluable input dataset results character value filter Filter condition specified condition applied input dataset deriving new parameter, .e., observations fulfilling condition taken account. Permitted Values: condition","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_rr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adds a Parameter for Derived RR (an ECG measurement) — derive_param_rr","text":"input dataset new parameter added. Note, variable populated new parameter rows specified by_vars.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_rr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Adds a Parameter for Derived RR (an ECG measurement) — derive_param_rr","text":"","code":"library(tibble)  adeg <- tribble(   ~USUBJID, ~PARAMCD, ~PARAM, ~AVAL, ~AVALU, ~VISIT,   \"01-701-1015\", \"HR\", \"Heart Rate\", 70.14, \"beats/min\", \"BASELINE\",   \"01-701-1015\", \"QT\", \"QT Duration\", 370, \"ms\", \"WEEK 2\",   \"01-701-1015\", \"HR\", \"Heart Rate\", 62.66, \"beats/min\", \"WEEK 1\",   \"01-701-1015\", \"RR\", \"RR Duration\", 710, \"ms\", \"WEEK 2\",   \"01-701-1028\", \"HR\", \"Heart Rate\", 85.45, \"beats/min\", \"BASELINE\",   \"01-701-1028\", \"QT\", \"QT Duration\", 480, \"ms\", \"WEEK 2\",   \"01-701-1028\", \"QT\", \"QT Duration\", 350, \"ms\", \"WEEK 3\",   \"01-701-1028\", \"HR\", \"Heart Rate\", 56.54, \"beats/min\", \"WEEK 3\",   \"01-701-1028\", \"RR\", \"RR Duration\", 842, \"ms\", \"WEEK 2\" )  derive_param_rr(   adeg,   by_vars = exprs(USUBJID, VISIT),   set_values_to = exprs(     PARAMCD = \"RRR\",     PARAM = \"RR Duration Rederived (ms)\",     AVALU = \"ms\"   ),   get_unit_expr = AVALU ) #> # A tibble: 13 × 6 #>    USUBJID     PARAMCD PARAM                        AVAL AVALU     VISIT    #>    <chr>       <chr>   <chr>                       <dbl> <chr>     <chr>    #>  1 01-701-1015 HR      Heart Rate                   70.1 beats/min BASELINE #>  2 01-701-1015 QT      QT Duration                 370   ms        WEEK 2   #>  3 01-701-1015 HR      Heart Rate                   62.7 beats/min WEEK 1   #>  4 01-701-1015 RR      RR Duration                 710   ms        WEEK 2   #>  5 01-701-1028 HR      Heart Rate                   85.4 beats/min BASELINE #>  6 01-701-1028 QT      QT Duration                 480   ms        WEEK 2   #>  7 01-701-1028 QT      QT Duration                 350   ms        WEEK 3   #>  8 01-701-1028 HR      Heart Rate                   56.5 beats/min WEEK 3   #>  9 01-701-1028 RR      RR Duration                 842   ms        WEEK 2   #> 10 01-701-1015 RRR     RR Duration Rederived (ms)  855.  ms        BASELINE #> 11 01-701-1015 RRR     RR Duration Rederived (ms)  958.  ms        WEEK 1   #> 12 01-701-1028 RRR     RR Duration Rederived (ms)  702.  ms        BASELINE #> 13 01-701-1028 RRR     RR Duration Rederived (ms) 1061.  ms        WEEK 3"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_tte.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive a Time-to-Event Parameter — derive_param_tte","title":"Derive a Time-to-Event Parameter — derive_param_tte","text":"Add time--event parameter input dataset.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_tte.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive a Time-to-Event Parameter — derive_param_tte","text":"","code":"derive_param_tte(   dataset = NULL,   dataset_adsl,   source_datasets,   by_vars = NULL,   start_date = TRTSDT,   event_conditions,   censor_conditions,   create_datetime = FALSE,   set_values_to,   subject_keys = get_admiral_option(\"subject_keys\") )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_tte.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive a Time-to-Event Parameter — derive_param_tte","text":"dataset Input dataset PARAMCD expected. dataset_adsl ADSL input dataset variables specified start_date, subject_keys expected. source_datasets Source datasets named list datasets expected. dataset_name field tte_source() refers dataset provided list. by_vars variables parameter specified, separate time event parameters derived group. variables must least one source datasets. source dataset must contain either variables none variables. variables included output dataset. Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) start_date Time event origin date variable STARTDT set specified date. value taken ADSL dataset. event censoring date origin date, ADT set origin date. event_conditions Sources conditions defining events list event_source() objects expected. censor_conditions Sources conditions defining censorings list censor_source() objects expected. create_datetime Create datetime variables? set TRUE, variables ADTM STARTDTM created. Otherwise, variables ADT STARTDT created. set_values_to Variables set named list returned exprs() defining variables set new parameter, e.g. exprs(PARAMCD = \"OS\", PARAM = \"Overall Survival\") expected. values must symbols, character strings, numeric values, expressions, NA. subject_keys Variables uniquely identify subject list symbols created using exprs() expected.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_tte.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive a Time-to-Event Parameter — derive_param_tte","text":"input dataset new parameter added","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_tte.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Derive a Time-to-Event Parameter — derive_param_tte","text":"following steps performed create observations new parameter: Deriving events: event source dataset observations specified filter element selected. patient first observation (respect date) selected. ADT variable set variable specified date element. date variable datetime variable, datepart copied. CNSR variable added set censor element. variables specified set_values_to element added. selected observations event source datasets combined single dataset. patient first observation (respect ADT variable) single dataset selected. Deriving censoring observations: censoring source dataset observations specified filter element selected. patient last observation (respect date) selected. ADT variable set variable specified date element. date variable datetime variable, datepart copied. CNSR variable added set censor element. variables specified set_values_to element added. selected observations censoring source datasets combined single dataset. patient last observation (respect ADT variable) single dataset selected. subject (defined subject_keys parameter) observation selected. event available, event observation selected. Otherwise censoring observation selected. Finally: variable specified start_date joined ADSL dataset. subjects datasets kept, .e., subjects event censoring observation dataset_adsl. variables defined set_values_to parameter added. ADT/ADTM variable set maximum ADT/ADTM STARTDT/STARTDTM (depending create_datetime parameter). new observations added output dataset.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_tte.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive a Time-to-Event Parameter — derive_param_tte","text":"","code":"library(tibble) library(dplyr, warn.conflicts = FALSE) library(lubridate) data(\"admiral_adsl\")  adsl <- admiral_adsl  # derive overall survival parameter death <- event_source(   dataset_name = \"adsl\",   filter = DTHFL == \"Y\",   date = DTHDT,   set_values_to = exprs(     EVNTDESC = \"DEATH\",     SRCDOM = \"ADSL\",     SRCVAR = \"DTHDT\"   ) )  last_alive_dt <- censor_source(   dataset_name = \"adsl\",   date = LSTALVDT,   set_values_to = exprs(     EVNTDESC = \"LAST DATE KNOWN ALIVE\",     SRCDOM = \"ADSL\",     SRCVAR = \"LSTALVDT\"   ) )  derive_param_tte(   dataset_adsl = adsl,   event_conditions = list(death),   censor_conditions = list(last_alive_dt),   source_datasets = list(adsl = adsl),   set_values_to = exprs(     PARAMCD = \"OS\",     PARAM = \"Overall Survival\"   ) ) %>%   select(-STUDYID) %>%   filter(row_number() %in% 20:30) #> # A tibble: 11 × 9 #>    USUBJID     EVNTDESC  SRCDOM SRCVAR  CNSR ADT        STARTDT    PARAMCD PARAM #>    <chr>       <chr>     <chr>  <chr>  <int> <date>     <date>     <chr>   <chr> #>  1 01-701-1203 LAST DAT… ADSL   LSTAL…     1 2013-08-03 2013-02-02 OS      Over… #>  2 01-701-1211 DEATH     ADSL   DTHDT      0 2013-01-14 2012-11-15 OS      Over… #>  3 01-701-1234 LAST DAT… ADSL   LSTAL…     1 2013-09-22 2013-03-30 OS      Over… #>  4 01-701-1239 LAST DAT… ADSL   LSTAL…     1 2014-07-11 2014-01-11 OS      Over… #>  5 01-701-1275 LAST DAT… ADSL   LSTAL…     1 2014-06-14 2014-02-07 OS      Over… #>  6 01-701-1287 LAST DAT… ADSL   LSTAL…     1 2014-07-26 2014-01-25 OS      Over… #>  7 01-701-1294 LAST DAT… ADSL   LSTAL…     1 2013-06-14 2013-03-24 OS      Over… #>  8 01-701-1302 LAST DAT… ADSL   LSTAL…     1 2013-11-05 2013-08-29 OS      Over… #>  9 01-701-1317 LAST DAT… ADSL   LSTAL…     1 2014-11-20 2014-05-22 OS      Over… #> 10 01-701-1324 LAST DAT… ADSL   LSTAL…     1 2013-04-02 2012-10-02 OS      Over… #> 11 01-701-1341 LAST DAT… ADSL   LSTAL…     1 2013-02-07 2013-01-05 OS      Over…  # derive duration of response # only observations for subjects in dataset_adsl are created adsl <- tribble(   ~USUBJID, ~DTHFL, ~DTHDT,            ~RSPDT,   \"01\",     \"Y\",    ymd(\"2021-06-12\"), ymd(\"2021-03-04\"),   \"02\",     \"N\",    NA,                NA,   \"03\",     \"Y\",    ymd(\"2021-08-21\"), NA,   \"04\",     \"N\",    NA,                ymd(\"2021-04-14\") ) %>%   mutate(STUDYID = \"AB42\")  adrs <- tribble(   ~USUBJID, ~AVALC, ~ADT,              ~ASEQ,   \"01\",     \"SD\",   ymd(\"2021-01-03\"), 1,   \"01\",     \"PR\",   ymd(\"2021-03-04\"), 2,   \"01\",     \"PD\",   ymd(\"2021-05-05\"), 3,   \"02\",     \"PD\",   ymd(\"2021-02-03\"), 1,   \"04\",     \"SD\",   ymd(\"2021-02-13\"), 1,   \"04\",     \"PR\",   ymd(\"2021-04-14\"), 2,   \"04\",     \"CR\",   ymd(\"2021-05-15\"), 3 ) %>%   mutate(STUDYID = \"AB42\", PARAMCD = \"OVR\")  pd <- event_source(   dataset_name = \"adrs\",   filter = AVALC == \"PD\",   date = ADT,   set_values_to = exprs(     EVENTDESC = \"PD\",     SRCDOM = \"ADRS\",     SRCVAR = \"ADTM\",     SRCSEQ = ASEQ   ) )  death <- event_source(   dataset_name = \"adsl\",   filter = DTHFL == \"Y\",   date = DTHDT,   set_values_to = exprs(     EVENTDESC = \"DEATH\",     SRCDOM = \"ADSL\",     SRCVAR = \"DTHDT\"   ) )  lastvisit <- censor_source(   dataset_name = \"adrs\",   date = ADT,   censor = 1,   set_values_to = exprs(     EVENTDESC = \"LAST TUMOR ASSESSMENT\",     SRCDOM = \"ADRS\",     SRCVAR = \"ADTM\",     SRCSEQ = ASEQ   ) )  first_response <- censor_source(   dataset_name = \"adsl\",   date = RSPDT,   censor = 1,   set_values_to = exprs(     EVENTDESC = \"FIRST RESPONSE\",     SRCDOM = \"ADSL\",     SRCVAR = \"RSPDT\"   ) )  derive_param_tte(   dataset_adsl = filter(adsl, !is.na(RSPDT)),   start_date = RSPDT,   event_conditions = list(pd, death),   censor_conditions = list(lastvisit, first_response),   source_datasets = list(adsl = adsl, adrs = adrs),   set_values_to = exprs(     PARAMCD = \"DURRSP\",     PARAM = \"Duration of Response\"   ) ) #> # A tibble: 2 × 11 #>   USUBJID ADT        STUDYID EVENTDESC     SRCDOM SRCVAR SRCSEQ  CNSR STARTDT    #>   <chr>   <date>     <chr>   <chr>         <chr>  <chr>   <dbl> <int> <date>     #> 1 01      2021-05-05 AB42    PD            ADRS   ADTM        3     0 2021-03-04 #> 2 04      2021-05-15 AB42    LAST TUMOR A… ADRS   ADTM        3     1 2021-04-14 #> # ℹ 2 more variables: PARAMCD <chr>, PARAM <chr>  # derive time to adverse event for each preferred term adsl <- tribble(   ~USUBJID, ~TRTSDT,           ~EOSDT,   \"01\",     ymd(\"2020-12-06\"), ymd(\"2021-03-06\"),   \"02\",     ymd(\"2021-01-16\"), ymd(\"2021-02-03\") ) %>%   mutate(STUDYID = \"AB42\")  ae <- tribble(   ~USUBJID, ~AESTDTC,           ~AESEQ, ~AEDECOD,   \"01\",     \"2021-01-03T10:56\", 1,      \"Flu\",   \"01\",     \"2021-03-04\",       2,      \"Cough\",   \"01\",     \"2021\",             3,      \"Flu\" ) %>%   mutate(STUDYID = \"AB42\")  ae_ext <- derive_vars_dt(   ae,   dtc = AESTDTC,   new_vars_prefix = \"AEST\",   highest_imputation = \"M\",   flag_imputation = \"none\" )  ttae <- event_source(   dataset_name = \"ae\",   date = AESTDT,   set_values_to = exprs(     EVNTDESC = \"AE\",     SRCDOM = \"AE\",     SRCVAR = \"AESTDTC\",     SRCSEQ = AESEQ   ) )  eos <- censor_source(   dataset_name = \"adsl\",   date = EOSDT,   set_values_to = exprs(     EVNTDESC = \"END OF STUDY\",     SRCDOM = \"ADSL\",     SRCVAR = \"EOSDT\"   ) )  derive_param_tte(   dataset_adsl = adsl,   by_vars = exprs(AEDECOD),   start_date = TRTSDT,   event_conditions = list(ttae),   censor_conditions = list(eos),   source_datasets = list(adsl = adsl, ae = ae_ext),   set_values_to = exprs(     PARAMCD = paste0(\"TTAE\", as.numeric(as.factor(AEDECOD))),     PARAM = paste(\"Time to First\", AEDECOD, \"Adverse Event\"),     PARCAT1 = \"TTAE\",     PARCAT2 = AEDECOD   ) ) %>%   select(USUBJID, STARTDT, PARAMCD, PARAM, ADT, CNSR, SRCSEQ) #> # A tibble: 4 × 7 #>   USUBJID STARTDT    PARAMCD PARAM                       ADT         CNSR SRCSEQ #>   <chr>   <date>     <chr>   <chr>                       <date>     <int>  <dbl> #> 1 01      2020-12-06 TTAE1   Time to First Cough Advers… 2021-03-04     0      2 #> 2 01      2020-12-06 TTAE2   Time to First Flu Adverse … 2021-01-01     0      3 #> 3 02      2021-01-16 TTAE1   Time to First Cough Advers… 2021-02-03     1     NA #> 4 02      2021-01-16 TTAE2   Time to First Flu Adverse … 2021-02-03     1     NA"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_wbc_abs.html","id":null,"dir":"Reference","previous_headings":"","what":"Add a parameter for lab differentials converted to absolute values — derive_param_wbc_abs","title":"Add a parameter for lab differentials converted to absolute values — derive_param_wbc_abs","text":"Add parameter converting lab differentials fraction percentage absolute values","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_wbc_abs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add a parameter for lab differentials converted to absolute values — derive_param_wbc_abs","text":"","code":"derive_param_wbc_abs(   dataset,   by_vars,   set_values_to,   get_unit_expr,   wbc_unit = \"10^9/L\",   wbc_code = \"WBC\",   diff_code,   diff_type = \"fraction\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_wbc_abs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add a parameter for lab differentials converted to absolute values — derive_param_wbc_abs","text":"dataset Input dataset variables specified by_vars argument expected dataset. PARAMCD, AVAL expected well. variable specified by_vars PARAMCD must unique key input dataset, parameters specified wbc_code diff_code. by_vars Grouping variables Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) set_values_to Variables set named list returned exprs() defining variables set new parameter, e.g. exprs(PARAMCD = \"LYMPH\", PARAM = \"Lymphocytes Abs (10^9/L)\") expected. get_unit_expr expression providing unit parameter result used check units input parameters. Permitted Values: variable containing unit input dataset, function call, example, get_unit_expr = extract_unit(PARAM). wbc_unit string containing required unit WBC parameter Default: \"10^9/L\" wbc_code White Blood Cell (WBC) parameter observations PARAMCD equals specified value considered WBC absolute results use converting differentials. Default: \"WBC\" Permitted Values: character value diff_code white blood differential parameter observations PARAMCD equals specified value considered white blood differential lab results fraction percentage value converted absolute value. diff_type string specifying type differential Permitted Values: \"percent\", \"fraction\" Default: fraction","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_wbc_abs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add a parameter for lab differentials converted to absolute values — derive_param_wbc_abs","text":"input dataset new parameter added","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_wbc_abs.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Add a parameter for lab differentials converted to absolute values — derive_param_wbc_abs","text":"diff_type \"percent\", analysis value new parameter derived $$\\frac{White Blood Cell Count  * Percentage Value}{100}$$ diff_type \"fraction\", analysis value new parameter derived $$White Blood Cell Count  * Fraction Value$$ New records created group records (grouped by_vars) 1) white blood cell component absolute value already available input dataset, 2) white blood cell absolute value (identified wbc_code) white blood cell differential (identified diff_code) present.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_param_wbc_abs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add a parameter for lab differentials converted to absolute values — derive_param_wbc_abs","text":"","code":"library(tibble)  test_lb <- tribble(   ~USUBJID, ~PARAMCD, ~AVAL, ~PARAM, ~VISIT,   \"P01\", \"WBC\", 33, \"Leukocyte Count (10^9/L)\", \"CYCLE 1 DAY 1\",   \"P01\", \"WBC\", 38, \"Leukocyte Count (10^9/L)\", \"CYCLE 2 DAY 1\",   \"P01\", \"LYMLE\", 0.90, \"Lymphocytes (fraction of 1)\", \"CYCLE 1 DAY 1\",   \"P01\", \"LYMLE\", 0.70, \"Lymphocytes (fraction of 1)\", \"CYCLE 2 DAY 1\",   \"P01\", \"ALB\", 36, \"Albumin (g/dL)\", \"CYCLE 2 DAY 1\",   \"P02\", \"WBC\", 33, \"Leukocyte Count (10^9/L)\", \"CYCLE 1 DAY 1\",   \"P02\", \"LYMPH\", 29, \"Lymphocytes Abs (10^9/L)\", \"CYCLE 1 DAY 1\",   \"P02\", \"LYMLE\", 0.87, \"Lymphocytes (fraction of 1)\", \"CYCLE 1 DAY 1\",   \"P03\", \"LYMLE\", 0.89, \"Lymphocytes (fraction of 1)\", \"CYCLE 1 DAY 1\" )  derive_param_wbc_abs(   dataset = test_lb,   by_vars = exprs(USUBJID, VISIT),   set_values_to = exprs(     PARAMCD = \"LYMPH\",     PARAM = \"Lymphocytes Abs (10^9/L)\",     DTYPE = \"CALCULATION\"   ),   get_unit_expr = extract_unit(PARAM),   wbc_code = \"WBC\",   diff_code = \"LYMLE\",   diff_type = \"fraction\" ) #> # A tibble: 11 × 6 #>    USUBJID PARAMCD  AVAL PARAM                       VISIT         DTYPE       #>    <chr>   <chr>   <dbl> <chr>                       <chr>         <chr>       #>  1 P01     WBC     33    Leukocyte Count (10^9/L)    CYCLE 1 DAY 1 NA          #>  2 P01     WBC     38    Leukocyte Count (10^9/L)    CYCLE 2 DAY 1 NA          #>  3 P01     LYMLE    0.9  Lymphocytes (fraction of 1) CYCLE 1 DAY 1 NA          #>  4 P01     LYMLE    0.7  Lymphocytes (fraction of 1) CYCLE 2 DAY 1 NA          #>  5 P01     ALB     36    Albumin (g/dL)              CYCLE 2 DAY 1 NA          #>  6 P02     WBC     33    Leukocyte Count (10^9/L)    CYCLE 1 DAY 1 NA          #>  7 P02     LYMPH   29    Lymphocytes Abs (10^9/L)    CYCLE 1 DAY 1 NA          #>  8 P02     LYMLE    0.87 Lymphocytes (fraction of 1) CYCLE 1 DAY 1 NA          #>  9 P03     LYMLE    0.89 Lymphocytes (fraction of 1) CYCLE 1 DAY 1 NA          #> 10 P01     LYMPH   29.7  Lymphocytes Abs (10^9/L)    CYCLE 1 DAY 1 CALCULATION #> 11 P01     LYMPH   26.6  Lymphocytes Abs (10^9/L)    CYCLE 2 DAY 1 CALCULATION"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_summary_records.html","id":null,"dir":"Reference","previous_headings":"","what":"Add New Records Within By Groups Using Aggregation Functions — derive_summary_records","title":"Add New Records Within By Groups Using Aggregation Functions — derive_summary_records","text":"uncommon analysis need whereby one needs derive analysis value (AVAL) multiple records. ADaM basic dataset structure variable DTYPE available indicate new derived records added dataset.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_summary_records.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add New Records Within By Groups Using Aggregation Functions — derive_summary_records","text":"","code":"derive_summary_records(   dataset = NULL,   dataset_add,   dataset_ref = NULL,   by_vars,   filter_add = NULL,   set_values_to,   missing_values = NULL )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_summary_records.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add New Records Within By Groups Using Aggregation Functions — derive_summary_records","text":"dataset Input dataset variables specified by_vars argument expected dataset. dataset_add Additional dataset variables specified by_vars expected. Observations specified dataset going used calculate added new records input dataset (dataset). dataset_ref Reference dataset variables specified by_vars expected. observation specified dataset new observation added input dataset. by_vars Grouping variables Variables consider generation groupwise summary records. Providing names variables exprs() create groupwise summary generate summary records specified groups. Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) filter_add Filter condition logical expression apply summary calculation. default, filtering expressions computed within by_vars help aggregating, lagging, ranking function involved. example, filter_add = (AVAL > mean(AVAL, na.rm = TRUE)) filter AVAL values greater mean AVAL by_vars. filter_add = (dplyr::n() > 2) filter n count by_vars greater 2. set_values_to Variables set specified variables set specified values new observations. Set list variables specified value new records LHS refer variable. RHS refers values set variable. can string, symbol, numeric value, expression NA. summary functions used, values summarized variables specified by_vars. example:   missing_values Values missing summary values observations reference dataset (dataset_ref) complete mapping defined summarization defined set_values_to.  variables specified set_values_to can specified missing_values. Permitted Values: named list expressions, e.g., exprs(AVAL = -9999)","code":"set_values_to = exprs(     AVAL = sum(AVAL),     DTYPE = \"AVERAGE\",   )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_summary_records.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add New Records Within By Groups Using Aggregation Functions — derive_summary_records","text":"data frame derived records appended original dataset.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_summary_records.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Add New Records Within By Groups Using Aggregation Functions — derive_summary_records","text":"records values within by_vars function retain common values newly derived records. Otherwise new value set NA.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_summary_records.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add New Records Within By Groups Using Aggregation Functions — derive_summary_records","text":"","code":"library(tibble) library(dplyr)  adeg <- tribble(   ~USUBJID,   ~EGSEQ, ~PARAM,             ~AVISIT,    ~EGDTC,             ~AVAL, ~TRTA,   \"XYZ-1001\", 1,      \"QTcF Int. (msec)\", \"Baseline\", \"2016-02-24T07:50\", 385,   NA_character_,   \"XYZ-1001\", 2,      \"QTcF Int. (msec)\", \"Baseline\", \"2016-02-24T07:52\", 399,   NA_character_,   \"XYZ-1001\", 3,      \"QTcF Int. (msec)\", \"Baseline\", \"2016-02-24T07:56\", 396,   NA_character_,   \"XYZ-1001\", 4,      \"QTcF Int. (msec)\", \"Visit 2\",  \"2016-03-08T09:45\", 384,   \"Placebo\",   \"XYZ-1001\", 5,      \"QTcF Int. (msec)\", \"Visit 2\",  \"2016-03-08T09:48\", 393,   \"Placebo\",   \"XYZ-1001\", 6,      \"QTcF Int. (msec)\", \"Visit 2\",  \"2016-03-08T09:51\", 388,   \"Placebo\",   \"XYZ-1001\", 7,      \"QTcF Int. (msec)\", \"Visit 3\",  \"2016-03-22T10:45\", 385,   \"Placebo\",   \"XYZ-1001\", 8,      \"QTcF Int. (msec)\", \"Visit 3\",  \"2016-03-22T10:48\", 394,   \"Placebo\",   \"XYZ-1001\", 9,      \"QTcF Int. (msec)\", \"Visit 3\",  \"2016-03-22T10:51\", 402,   \"Placebo\",   \"XYZ-1002\", 1,      \"QTcF Int. (msec)\", \"Baseline\", \"2016-02-22T07:58\", 399,   NA_character_,   \"XYZ-1002\", 2,      \"QTcF Int. (msec)\", \"Baseline\", \"2016-02-22T07:58\", 410,   NA_character_,   \"XYZ-1002\", 3,      \"QTcF Int. (msec)\", \"Baseline\", \"2016-02-22T08:01\", 392,   NA_character_,   \"XYZ-1002\", 4,      \"QTcF Int. (msec)\", \"Visit 2\",  \"2016-03-06T09:50\", 401,   \"Active 20mg\",   \"XYZ-1002\", 5,      \"QTcF Int. (msec)\", \"Visit 2\",  \"2016-03-06T09:53\", 407,   \"Active 20mg\",   \"XYZ-1002\", 6,      \"QTcF Int. (msec)\", \"Visit 2\",  \"2016-03-06T09:56\", 400,   \"Active 20mg\",   \"XYZ-1002\", 7,      \"QTcF Int. (msec)\", \"Visit 3\",  \"2016-03-24T10:50\", 412,   \"Active 20mg\",   \"XYZ-1002\", 8,      \"QTcF Int. (msec)\", \"Visit 3\",  \"2016-03-24T10:53\", 414,   \"Active 20mg\",   \"XYZ-1002\", 9,      \"QTcF Int. (msec)\", \"Visit 3\",  \"2016-03-24T10:56\", 402,   \"Active 20mg\" ) %>%   mutate(     ADTM = convert_dtc_to_dtm(EGDTC)   )  # Summarize the average of the triplicate ECG interval values (AVAL) derive_summary_records(   adeg,   dataset_add = adeg,   by_vars = exprs(USUBJID, PARAM, AVISIT),   set_values_to = exprs(     AVAL = mean(AVAL, na.rm = TRUE),     DTYPE = \"AVERAGE\"   ) ) %>%   arrange(USUBJID, AVISIT) #> # A tibble: 24 × 9 #>    USUBJID  EGSEQ PARAM       AVISIT EGDTC  AVAL TRTA  ADTM                DTYPE #>    <chr>    <dbl> <chr>       <chr>  <chr> <dbl> <chr> <dttm>              <chr> #>  1 XYZ-1001     1 QTcF Int. … Basel… 2016…  385  NA    2016-02-24 07:50:00 NA    #>  2 XYZ-1001     2 QTcF Int. … Basel… 2016…  399  NA    2016-02-24 07:52:00 NA    #>  3 XYZ-1001     3 QTcF Int. … Basel… 2016…  396  NA    2016-02-24 07:56:00 NA    #>  4 XYZ-1001    NA QTcF Int. … Basel… NA     393. NA    NA                  AVER… #>  5 XYZ-1001     4 QTcF Int. … Visit… 2016…  384  Plac… 2016-03-08 09:45:00 NA    #>  6 XYZ-1001     5 QTcF Int. … Visit… 2016…  393  Plac… 2016-03-08 09:48:00 NA    #>  7 XYZ-1001     6 QTcF Int. … Visit… 2016…  388  Plac… 2016-03-08 09:51:00 NA    #>  8 XYZ-1001    NA QTcF Int. … Visit… NA     388. NA    NA                  AVER… #>  9 XYZ-1001     7 QTcF Int. … Visit… 2016…  385  Plac… 2016-03-22 10:45:00 NA    #> 10 XYZ-1001     8 QTcF Int. … Visit… 2016…  394  Plac… 2016-03-22 10:48:00 NA    #> # ℹ 14 more rows  # Derive more than one summary variable derive_summary_records(   adeg,   dataset_add = adeg,   by_vars = exprs(USUBJID, PARAM, AVISIT),   set_values_to = exprs(     AVAL = mean(AVAL),     ADTM = max(ADTM),     DTYPE = \"AVERAGE\"   ) ) %>%   arrange(USUBJID, AVISIT) %>%   select(-EGSEQ, -TRTA) #> # A tibble: 24 × 7 #>    USUBJID  PARAM            AVISIT   EGDTC       AVAL ADTM                DTYPE #>    <chr>    <chr>            <chr>    <chr>      <dbl> <dttm>              <chr> #>  1 XYZ-1001 QTcF Int. (msec) Baseline 2016-02-2…  385  2016-02-24 07:50:00 NA    #>  2 XYZ-1001 QTcF Int. (msec) Baseline 2016-02-2…  399  2016-02-24 07:52:00 NA    #>  3 XYZ-1001 QTcF Int. (msec) Baseline 2016-02-2…  396  2016-02-24 07:56:00 NA    #>  4 XYZ-1001 QTcF Int. (msec) Baseline NA          393. 2016-02-24 07:56:00 AVER… #>  5 XYZ-1001 QTcF Int. (msec) Visit 2  2016-03-0…  384  2016-03-08 09:45:00 NA    #>  6 XYZ-1001 QTcF Int. (msec) Visit 2  2016-03-0…  393  2016-03-08 09:48:00 NA    #>  7 XYZ-1001 QTcF Int. (msec) Visit 2  2016-03-0…  388  2016-03-08 09:51:00 NA    #>  8 XYZ-1001 QTcF Int. (msec) Visit 2  NA          388. 2016-03-08 09:51:00 AVER… #>  9 XYZ-1001 QTcF Int. (msec) Visit 3  2016-03-2…  385  2016-03-22 10:45:00 NA    #> 10 XYZ-1001 QTcF Int. (msec) Visit 3  2016-03-2…  394  2016-03-22 10:48:00 NA    #> # ℹ 14 more rows  # Sample ADEG dataset with triplicate record for only AVISIT = 'Baseline' adeg <- tribble(   ~USUBJID,   ~EGSEQ, ~PARAM,             ~AVISIT,    ~EGDTC,             ~AVAL, ~TRTA,   \"XYZ-1001\", 1,      \"QTcF Int. (msec)\", \"Baseline\", \"2016-02-24T07:50\", 385,   NA_character_,   \"XYZ-1001\", 2,      \"QTcF Int. (msec)\", \"Baseline\", \"2016-02-24T07:52\", 399,   NA_character_,   \"XYZ-1001\", 3,      \"QTcF Int. (msec)\", \"Baseline\", \"2016-02-24T07:56\", 396,   NA_character_,   \"XYZ-1001\", 4,      \"QTcF Int. (msec)\", \"Visit 2\",  \"2016-03-08T09:48\", 393,   \"Placebo\",   \"XYZ-1001\", 5,      \"QTcF Int. (msec)\", \"Visit 2\",  \"2016-03-08T09:51\", 388,   \"Placebo\",   \"XYZ-1001\", 6,      \"QTcF Int. (msec)\", \"Visit 3\",  \"2016-03-22T10:48\", 394,   \"Placebo\",   \"XYZ-1001\", 7,      \"QTcF Int. (msec)\", \"Visit 3\",  \"2016-03-22T10:51\", 402,   \"Placebo\",   \"XYZ-1002\", 1,      \"QTcF Int. (msec)\", \"Baseline\", \"2016-02-22T07:58\", 399,   NA_character_,   \"XYZ-1002\", 2,      \"QTcF Int. (msec)\", \"Baseline\", \"2016-02-22T07:58\", 410,   NA_character_,   \"XYZ-1002\", 3,      \"QTcF Int. (msec)\", \"Baseline\", \"2016-02-22T08:01\", 392,   NA_character_,   \"XYZ-1002\", 4,      \"QTcF Int. (msec)\", \"Visit 2\",  \"2016-03-06T09:53\", 407,   \"Active 20mg\",   \"XYZ-1002\", 5,      \"QTcF Int. (msec)\", \"Visit 2\",  \"2016-03-06T09:56\", 400,   \"Active 20mg\",   \"XYZ-1002\", 6,      \"QTcF Int. (msec)\", \"Visit 3\",  \"2016-03-24T10:53\", 414,   \"Active 20mg\",   \"XYZ-1002\", 7,      \"QTcF Int. (msec)\", \"Visit 3\",  \"2016-03-24T10:56\", 402,   \"Active 20mg\" )  # Compute the average of AVAL only if there are more than 2 records within the # by group derive_summary_records(   adeg,   dataset_add = adeg,   by_vars = exprs(USUBJID, PARAM, AVISIT),   filter_add = n() > 2,   set_values_to = exprs(     AVAL = mean(AVAL, na.rm = TRUE),     DTYPE = \"AVERAGE\"   ) ) %>%   arrange(USUBJID, AVISIT) #> # A tibble: 16 × 8 #>    USUBJID  EGSEQ PARAM            AVISIT   EGDTC             AVAL TRTA    DTYPE #>    <chr>    <dbl> <chr>            <chr>    <chr>            <dbl> <chr>   <chr> #>  1 XYZ-1001     1 QTcF Int. (msec) Baseline 2016-02-24T07:50  385  NA      NA    #>  2 XYZ-1001     2 QTcF Int. (msec) Baseline 2016-02-24T07:52  399  NA      NA    #>  3 XYZ-1001     3 QTcF Int. (msec) Baseline 2016-02-24T07:56  396  NA      NA    #>  4 XYZ-1001    NA QTcF Int. (msec) Baseline NA                393. NA      AVER… #>  5 XYZ-1001     4 QTcF Int. (msec) Visit 2  2016-03-08T09:48  393  Placebo NA    #>  6 XYZ-1001     5 QTcF Int. (msec) Visit 2  2016-03-08T09:51  388  Placebo NA    #>  7 XYZ-1001     6 QTcF Int. (msec) Visit 3  2016-03-22T10:48  394  Placebo NA    #>  8 XYZ-1001     7 QTcF Int. (msec) Visit 3  2016-03-22T10:51  402  Placebo NA    #>  9 XYZ-1002     1 QTcF Int. (msec) Baseline 2016-02-22T07:58  399  NA      NA    #> 10 XYZ-1002     2 QTcF Int. (msec) Baseline 2016-02-22T07:58  410  NA      NA    #> 11 XYZ-1002     3 QTcF Int. (msec) Baseline 2016-02-22T08:01  392  NA      NA    #> 12 XYZ-1002    NA QTcF Int. (msec) Baseline NA                400. NA      AVER… #> 13 XYZ-1002     4 QTcF Int. (msec) Visit 2  2016-03-06T09:53  407  Active… NA    #> 14 XYZ-1002     5 QTcF Int. (msec) Visit 2  2016-03-06T09:56  400  Active… NA    #> 15 XYZ-1002     6 QTcF Int. (msec) Visit 3  2016-03-24T10:53  414  Active… NA    #> 16 XYZ-1002     7 QTcF Int. (msec) Visit 3  2016-03-24T10:56  402  Active… NA"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_age_years.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive Age in Years — derive_var_age_years","title":"Derive Age in Years — derive_var_age_years","text":"Converts given age variable (age_var) unit 'years' current units given age_var+U variable age_unit argument stores new variable (new_var).","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_age_years.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive Age in Years — derive_var_age_years","text":"","code":"derive_var_age_years(dataset, age_var, age_unit = NULL, new_var)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_age_years.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive Age in Years — derive_var_age_years","text":"dataset Input dataset variables specified age_var argument expected dataset. age_var Age variable. numeric object expected. age_unit Age unit. age_unit argument expected variable age_var+U dataset. gives unit age_var variable used convert AGE 'years' grouping can occur. Default: NULL Permitted Values: 'years', 'months', 'weeks', 'days', 'hours', 'minutes', 'seconds' new_var New age variable created years. returned values doubles integers. '","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_age_years.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive Age in Years — derive_var_age_years","text":"input dataset (dataset) new_var variable added years.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_age_years.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Derive Age in Years — derive_var_age_years","text":"function used convert age variable unit 'years' can used create age groups. resulting column contains equivalent years double. Note, underlying computations assume equal number days year (365.25).","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_age_years.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive Age in Years — derive_var_age_years","text":"","code":"library(tibble)  # Derive age with age units specified data <- tribble(   ~AGE, ~AGEU,   27, \"days\",   24, \"months\",   3, \"years\",   4, \"weeks\",   1, \"years\" )  derive_var_age_years(data, AGE, new_var = AAGE) #> # A tibble: 5 × 3 #>     AGE AGEU     AAGE #>   <dbl> <chr>   <dbl> #> 1    27 days   0.0739 #> 2    24 months 2      #> 3     3 years  3      #> 4     4 weeks  0.0767 #> 5     1 years  1       # Derive age without age units variable specified data <- tribble(   ~AGE,   12,   24,   36,   48 ) derive_var_age_years(data, AGE, age_unit = \"months\", new_var = AAGE) #> # A tibble: 4 × 2 #>     AGE  AAGE #>   <dbl> <dbl> #> 1    12     1 #> 2    24     2 #> 3    36     3 #> 4    48     4"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_analysis_ratio.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive Ratio Variable — derive_var_analysis_ratio","title":"Derive Ratio Variable — derive_var_analysis_ratio","text":"Derives ratio variable BDS dataset based user specified variables.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_analysis_ratio.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive Ratio Variable — derive_var_analysis_ratio","text":"","code":"derive_var_analysis_ratio(dataset, numer_var, denom_var, new_var = NULL)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_analysis_ratio.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive Ratio Variable — derive_var_analysis_ratio","text":"dataset Input dataset variables specified numer_var denom_var arguments expected dataset. numer_var Variable containing numeric values used numerator ratio calculation. denom_var Variable containing numeric values used denominator ratio calculation. new_var user-defined variable appended dataset. default behavior take denominator variable prefix R2 append dataset. Using argument override default behavior. Default NULL.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_analysis_ratio.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive Ratio Variable — derive_var_analysis_ratio","text":"input dataset ratio variable appended","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_analysis_ratio.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Derive Ratio Variable — derive_var_analysis_ratio","text":"user wishing calculate Ratio Baseline, AVAL / BASE returned new variable R2BASE appended input dataset. Ratio Analysis Range Lower Limit AVAL / ANRLO return new variable R2ANRLO, Ratio Analysis Range  Upper Limit AVAL / ANRHI return new variable R2ANRLO. Please note denominator variable prefix R2----. user can override default returned variables using new_var argument. Also, values 0 denominator return NA derivation. Reference CDISC ADaM Implementation Guide Version 1.1 Section 3.3.4 Analysis Parameter Variables BDS Datasets","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_analysis_ratio.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive Ratio Variable — derive_var_analysis_ratio","text":"","code":"library(tibble)  data <- tribble(   ~USUBJID, ~PARAMCD, ~SEQ, ~AVAL, ~BASE, ~ANRLO, ~ANRHI,   \"P01\", \"ALT\", 1, 27, 27, 6, 34,   \"P01\", \"ALT\", 2, 41, 27, 6, 34,   \"P01\", \"ALT\", 3, 17, 27, 6, 34,   \"P02\", \"ALB\", 1, 38, 38, 33, 49,   \"P02\", \"ALB\", 2, 39, 38, 33, 49,   \"P02\", \"ALB\", 3, 37, 38, 33, 49 )  # Returns \"R2\" prefixed variables data %>%   derive_var_analysis_ratio(numer_var = AVAL, denom_var = BASE) %>%   derive_var_analysis_ratio(numer_var = AVAL, denom_var = ANRLO) %>%   derive_var_analysis_ratio(numer_var = AVAL, denom_var = ANRHI) #> # A tibble: 6 × 10 #>   USUBJID PARAMCD   SEQ  AVAL  BASE ANRLO ANRHI R2BASE R2ANRLO R2ANRHI #>   <chr>   <chr>   <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl>   <dbl>   <dbl> #> 1 P01     ALT         1    27    27     6    34  1        4.5    0.794 #> 2 P01     ALT         2    41    27     6    34  1.52     6.83   1.21  #> 3 P01     ALT         3    17    27     6    34  0.630    2.83   0.5   #> 4 P02     ALB         1    38    38    33    49  1        1.15   0.776 #> 5 P02     ALB         2    39    38    33    49  1.03     1.18   0.796 #> 6 P02     ALB         3    37    38    33    49  0.974    1.12   0.755  # Returns user-defined variables data %>%   derive_var_analysis_ratio(numer_var = AVAL, denom_var = BASE, new_var = R01BASE) %>%   derive_var_analysis_ratio(numer_var = AVAL, denom_var = ANRLO, new_var = R01ANRLO) %>%   derive_var_analysis_ratio(numer_var = AVAL, denom_var = ANRHI, new_var = R01ANRHI) #> # A tibble: 6 × 10 #>   USUBJID PARAMCD   SEQ  AVAL  BASE ANRLO ANRHI R01BASE R01ANRLO R01ANRHI #>   <chr>   <chr>   <dbl> <dbl> <dbl> <dbl> <dbl>   <dbl>    <dbl>    <dbl> #> 1 P01     ALT         1    27    27     6    34   1         4.5     0.794 #> 2 P01     ALT         2    41    27     6    34   1.52      6.83    1.21  #> 3 P01     ALT         3    17    27     6    34   0.630     2.83    0.5   #> 4 P02     ALB         1    38    38    33    49   1         1.15    0.776 #> 5 P02     ALB         2    39    38    33    49   1.03      1.18    0.796 #> 6 P02     ALB         3    37    38    33    49   0.974     1.12    0.755"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_anrind.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive Reference Range Indicator — derive_var_anrind","title":"Derive Reference Range Indicator — derive_var_anrind","text":"Derive Reference Range Indicator","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_anrind.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive Reference Range Indicator — derive_var_anrind","text":"","code":"derive_var_anrind(   dataset,   signif_dig = get_admiral_option(\"signif_digits\"),   use_a1hia1lo = FALSE )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_anrind.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive Reference Range Indicator — derive_var_anrind","text":"dataset Input dataset ANRLO, ANRHI, AVAL expected use_a1hia1lo set TRUE, A1LO A1H1 expected well. signif_dig Number significant digits use comparing values. Significant digits used avoid floating point discrepancies comparing numeric values. See blog: admiral handles floating points use_a1hia1lo logical value indicating whether use A1H1 A1LO derivation ANRIND.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_anrind.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive Reference Range Indicator — derive_var_anrind","text":"input dataset additional column ANRIND","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_anrind.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Derive Reference Range Indicator — derive_var_anrind","text":"case A1H1 A1LO used, ANRIND set : \"NORMAL\" AVAL greater equal ANRLO less equal ANRHI; AVAL greater equal ANRLO ANRHI missing; AVAL less equal ANRHI ANRLO missing \"LOW\" AVAL less ANRLO either A1LO missing AVAL greater equal A1LO \"HIGH\" AVAL greater ANRHI either A1HI missing AVAL less equal A1HI \"LOW LOW\" AVAL less A1LO \"HIGH HIGH\" AVAL greater A1HI case A1H1 A1LO used, ANRIND set : \"NORMAL\" AVAL greater equal ANRLO less equal ANRHI; AVAL greater equal ANRLO ANRHI missing; AVAL less equal ANRHI ANRLO missing \"LOW\" AVAL less ANRLO \"HIGH\" AVAL greater ANRHI","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_anrind.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive Reference Range Indicator — derive_var_anrind","text":"","code":"library(tibble) library(dplyr, warn.conflicts = FALSE)  vs <- tibble::tribble(   ~USUBJID, ~PARAMCD, ~AVAL, ~ANRLO, ~ANRHI, ~A1LO, ~A1HI,   \"P01\",       \"PUL\",    70,     60,    100,    40,   110,   \"P01\",       \"PUL\",    57,     60,    100,    40,   110,   \"P01\",       \"PUL\",    60,     60,    100,    40,   110,   \"P01\",     \"DIABP\",   102,     60,     80,    40,    90,   \"P02\",       \"PUL\",   109,     60,    100,    40,   110,   \"P02\",       \"PUL\",   100,     60,    100,    40,   110,   \"P02\",     \"DIABP\",    80,     60,     80,    40,    90,   \"P03\",       \"PUL\",    39,     60,    100,    40,   110,   \"P03\",       \"PUL\",    40,     60,    100,    40,   110 )  vs %>% derive_var_anrind(use_a1hia1lo = TRUE) #> # A tibble: 9 × 8 #>   USUBJID PARAMCD  AVAL ANRLO ANRHI  A1LO  A1HI ANRIND    #>   <chr>   <chr>   <dbl> <dbl> <dbl> <dbl> <dbl> <chr>     #> 1 P01     PUL        70    60   100    40   110 NORMAL    #> 2 P01     PUL        57    60   100    40   110 LOW       #> 3 P01     PUL        60    60   100    40   110 NORMAL    #> 4 P01     DIABP     102    60    80    40    90 HIGH HIGH #> 5 P02     PUL       109    60   100    40   110 HIGH      #> 6 P02     PUL       100    60   100    40   110 NORMAL    #> 7 P02     DIABP      80    60    80    40    90 NORMAL    #> 8 P03     PUL        39    60   100    40   110 LOW LOW   #> 9 P03     PUL        40    60   100    40   110 LOW       vs %>% derive_var_anrind(use_a1hia1lo = FALSE) #> # A tibble: 9 × 8 #>   USUBJID PARAMCD  AVAL ANRLO ANRHI  A1LO  A1HI ANRIND #>   <chr>   <chr>   <dbl> <dbl> <dbl> <dbl> <dbl> <chr>  #> 1 P01     PUL        70    60   100    40   110 NORMAL #> 2 P01     PUL        57    60   100    40   110 LOW    #> 3 P01     PUL        60    60   100    40   110 NORMAL #> 4 P01     DIABP     102    60    80    40    90 HIGH   #> 5 P02     PUL       109    60   100    40   110 HIGH   #> 6 P02     PUL       100    60   100    40   110 NORMAL #> 7 P02     DIABP      80    60    80    40    90 NORMAL #> 8 P03     PUL        39    60   100    40   110 LOW    #> 9 P03     PUL        40    60   100    40   110 LOW"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_atoxgr.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive Lab High toxicity Grade 0 - 4 and Low Toxicity Grades 0 - (-4) — derive_var_atoxgr","title":"Derive Lab High toxicity Grade 0 - 4 and Low Toxicity Grades 0 - (-4) — derive_var_atoxgr","text":"Derives character lab grade based high low severity/toxicity grade(s).","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_atoxgr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive Lab High toxicity Grade 0 - 4 and Low Toxicity Grades 0 - (-4) — derive_var_atoxgr","text":"","code":"derive_var_atoxgr(   dataset,   lotox_description_var = ATOXDSCL,   hitox_description_var = ATOXDSCH )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_atoxgr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive Lab High toxicity Grade 0 - 4 and Low Toxicity Grades 0 - (-4) — derive_var_atoxgr","text":"dataset Input dataset variables specified lotox_description_var hitox_description_var arguments expected dataset. ATOXGRL, ATOXGRH expected well. lotox_description_var Variable containing toxicity grade description low values, eg. \"Anemia\" hitox_description_var Variable containing toxicity grade description high values, eg. \"Hemoglobin Increased\".","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_atoxgr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive Lab High toxicity Grade 0 - 4 and Low Toxicity Grades 0 - (-4) — derive_var_atoxgr","text":"input data set character variable added","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_atoxgr.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Derive Lab High toxicity Grade 0 - 4 and Low Toxicity Grades 0 - (-4) — derive_var_atoxgr","text":"Created variable ATOXGR contain values \"-4\", \"-3\", \"-2\", \"-1\" low values \"1\", \"2\", \"3\", \"4\" high values, contain \"0\" value gradable satisfy criteria high low values. ATOXGR set missing information available give grade. Function applies following rules: High low missing - overall missing Low grade missing > 0 - overall holds low grade High grade missing > 0 - overall holds high grade (high direction low direction NORMAL) high grade normal - overall NORMAL (low direction high direction NORMAL) low grade normal - overall NORMAL otherwise set missing","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_atoxgr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive Lab High toxicity Grade 0 - 4 and Low Toxicity Grades 0 - (-4) — derive_var_atoxgr","text":"","code":"library(tibble)  adlb <- tribble(   ~ATOXDSCL,          ~ATOXDSCH,        ~ATOXGRL,      ~ATOXGRH,   \"Hypoglycemia\",     \"Hyperglycemia\",  NA_character_, \"0\",   \"Hypoglycemia\",     \"Hyperglycemia\",  \"0\",           \"1\",   \"Hypoglycemia\",     \"Hyperglycemia\",  \"0\",           \"0\",   NA_character_,      \"INR Increased\",  NA_character_, \"0\",   \"Hypophosphatemia\", NA_character_,    \"1\",           NA_character_ )  derive_var_atoxgr(adlb) #> # A tibble: 5 × 5 #>   ATOXDSCL         ATOXDSCH      ATOXGRL ATOXGRH ATOXGR #>   <chr>            <chr>         <chr>   <chr>   <chr>  #> 1 Hypoglycemia     Hyperglycemia NA      0       NA     #> 2 Hypoglycemia     Hyperglycemia 0       1       1      #> 3 Hypoglycemia     Hyperglycemia 0       0       0      #> 4 NA               INR Increased NA      0       0      #> 5 Hypophosphatemia NA            1       NA      -1"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_atoxgr_dir.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive Lab Toxicity Grade 0 - 4 — derive_var_atoxgr_dir","title":"Derive Lab Toxicity Grade 0 - 4 — derive_var_atoxgr_dir","text":"Derives character lab grade based severity/toxicity criteria.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_atoxgr_dir.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive Lab Toxicity Grade 0 - 4 — derive_var_atoxgr_dir","text":"","code":"derive_var_atoxgr_dir(   dataset,   new_var,   tox_description_var,   meta_criteria,   criteria_direction,   abnormal_indicator = NULL,   get_unit_expr,   signif_dig = get_admiral_option(\"signif_digits\") )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_atoxgr_dir.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive Lab Toxicity Grade 0 - 4 — derive_var_atoxgr_dir","text":"dataset Input dataset variables specified tox_description_var argument expected dataset. new_var Name character grade variable create, example, ATOXGRH ATOXGRL. tox_description_var Variable containing description grading criteria. example: \"Anemia\" \"INR Increased\". meta_criteria Metadata data set holding criteria (normally case statement) Permitted Values: atoxgr_criteria_ctcv4, atoxgr_criteria_ctcv5, atoxgr_criteria_daids atoxgr_criteria_ctcv4 implements Common Terminology Criteria Adverse Events (CTCAE) v4.0 atoxgr_criteria_ctcv5 implements Common Terminology Criteria Adverse Events (CTCAE) v5.0 atoxgr_criteria_daids implements Division AIDS (DAIDS) Table Grading Severity Adult Pediatric Adverse Events metadata following variables: TERM: variable hold term describing criteria applied particular lab test, eg. \"Anemia\" \"INR Increased\". Note: variable case insensitive. DIRECTION: variable hold direction abnormality particular lab test value. \"L\" LOW values, \"H\" HIGH values. Note: variable case insensitive. SI_UNIT_CHECK: variable hold unit particular lab test. Used check input data criteria based absolute values. VAR_CHECK: variable hold comma separated list variables used criteria. Used check input data variables exist. GRADE_CRITERIA_CODE: variable hold code creates grade based defined criteria. FILTER: Required DAIDS grading, specifies admiral code filter lab data based subset subjects (e.g. AGE > 18 YEARS) criteria_direction Direction (L= Low, H = High) toxicity grade. Permitted Values: \"L\", \"H\" abnormal_indicator Value BNRIND derivation indicate abnormal value. Usually \"HIGH\" criteria_direction = \"H\" \"LOW\" criteria_direction = \"L\". required meta_criteria = atoxgr_criteria_ctcv5 BNRIND required variable. Currently terms \"Alanine aminotransferase increased\", \"Alkaline phosphatase increased\", \"Aspartate aminotransferase increased\", \"Blood bilirubin increased\" \"GGT increased\" get_unit_expr expression providing unit parameter result used check units input parameters. Compared SI_UNIT_CHECK metadata (see meta_criteria parameter). Permitted Values: variable containing unit input dataset, function call, example, get_unit_expr = extract_unit(PARAM). signif_dig Number significant digits use comparing lab value another value. Significant digits used avoid floating point discrepancies comparing numeric values. See blog: admiral handles floating points","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_atoxgr_dir.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive Lab Toxicity Grade 0 - 4 — derive_var_atoxgr_dir","text":"input dataset character variable added","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_atoxgr_dir.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Derive Lab Toxicity Grade 0 - 4 — derive_var_atoxgr_dir","text":"new_var derived values NA, \"0\", \"1\", \"2\", \"3\", \"4\", \"4\" severe grade \"4\" lab value satisfies criteria grade 4. \"3\" lab value satisfies criteria grade 3. \"2\" lab value satisfies criteria grade 2. \"1\" lab value satisfies criteria grade 1. \"0\" grade can derived grade \"1\", \"2\", \"3\" \"4\". NA grade derived.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_atoxgr_dir.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive Lab Toxicity Grade 0 - 4 — derive_var_atoxgr_dir","text":"","code":"library(tibble)  data <- tribble(   ~ATOXDSCL,                    ~AVAL, ~ANRLO, ~ANRHI, ~PARAM,   \"Hypoglycemia\",               119,   4,      7,      \"Glucose (mmol/L)\",   \"Lymphocyte count decreased\", 0.7,   1,      4,      \"Lymphocytes Abs (10^9/L)\",   \"Anemia\",                     129,   120,    180,    \"Hemoglobin (g/L)\",   \"White blood cell decreased\", 10,    5,      20,     \"White blood cell (10^9/L)\",   \"White blood cell decreased\", 15,    5,      20,     \"White blood cell (10^9/L)\",   \"Anemia\",                     140,   120,    180,    \"Hemoglobin (g/L)\" )  derive_var_atoxgr_dir(data,   new_var = ATOXGRL,   tox_description_var = ATOXDSCL,   meta_criteria = atoxgr_criteria_ctcv5,   criteria_direction = \"L\",   get_unit_expr = extract_unit(PARAM) ) #> # A tibble: 6 × 6 #>   ATOXDSCL                    AVAL ANRLO ANRHI PARAM                     ATOXGRL #>   <chr>                      <dbl> <dbl> <dbl> <chr>                     <chr>   #> 1 Anemia                     129     120   180 Hemoglobin (g/L)          0       #> 2 Anemia                     140     120   180 Hemoglobin (g/L)          0       #> 3 Hypoglycemia               119       4     7 Glucose (mmol/L)          0       #> 4 Lymphocyte count decreased   0.7     1     4 Lymphocytes Abs (10^9/L)  2       #> 5 White blood cell decreased  10       5    20 White blood cell (10^9/L) 0       #> 6 White blood cell decreased  15       5    20 White blood cell (10^9/L) 0        data <- tribble(   ~ATOXDSCH,                     ~AVAL,  ~ANRLO,   ~ANRHI, ~PARAM,   \"CPK increased\",               129,    0,        30,     \"Creatine Kinase (U/L)\",   \"Lymphocyte count increased\",  4,      1,        4,      \"Lymphocytes Abs (10^9/L)\",   \"Lymphocyte count increased\",  2,      1,        4,      \"Lymphocytes Abs (10^9/L)\",   \"CPK increased\",               140,    120,      180,    \"Creatine Kinase (U/L)\" )  derive_var_atoxgr_dir(data,   new_var = ATOXGRH,   tox_description_var = ATOXDSCH,   meta_criteria = atoxgr_criteria_ctcv5,   criteria_direction = \"H\",   get_unit_expr = extract_unit(PARAM) ) #> # A tibble: 4 × 6 #>   ATOXDSCH                    AVAL ANRLO ANRHI PARAM                    ATOXGRH #>   <chr>                      <dbl> <dbl> <dbl> <chr>                    <chr>   #> 1 CPK increased                129     0    30 Creatine Kinase (U/L)    2       #> 2 CPK increased                140   120   180 Creatine Kinase (U/L)    0       #> 3 Lymphocyte count increased     4     1     4 Lymphocytes Abs (10^9/L) 0       #> 4 Lymphocyte count increased     2     1     4 Lymphocytes Abs (10^9/L) 0"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_base.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive Baseline Variables — derive_var_base","title":"Derive Baseline Variables — derive_var_base","text":"Derive baseline variables, e.g. BASE BNRIND, BDS dataset. Note: wrapper function generic derive_vars_merged().","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_base.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive Baseline Variables — derive_var_base","text":"","code":"derive_var_base(   dataset,   by_vars,   source_var = AVAL,   new_var = BASE,   filter = ABLFL == \"Y\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_base.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive Baseline Variables — derive_var_base","text":"dataset Input dataset variables specified by_vars source_var arguments expected dataset. by_vars Grouping variables Grouping variables uniquely identifying set records calculate new_var. Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) source_var column extract baseline value, e.g. AVAL new_var name newly created baseline column, e.g. BASE filter condition used filter dataset baseline records. default ABLFL == \"Y\"","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_base.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive Baseline Variables — derive_var_base","text":"new data.frame containing records variables input dataset plus new_var variable","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_base.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Derive Baseline Variables — derive_var_base","text":"by_vars group, baseline record identified condition specified filter defaults ABLFL == \"Y\". Subsequently, every value new_var variable by_vars group set value source_var variable baseline record. case multiple baseline records within by_vars error issued.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_base.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive Baseline Variables — derive_var_base","text":"","code":"library(tibble)  dataset <- tribble(   ~STUDYID, ~USUBJID, ~PARAMCD, ~AVAL, ~AVALC,   ~AVISIT,    ~ABLFL, ~ANRIND,   \"TEST01\", \"PAT01\", \"PARAM01\", 10.12, NA,       \"Baseline\", \"Y\",    \"NORMAL\",   \"TEST01\", \"PAT01\", \"PARAM01\", 9.700, NA,       \"Day 7\",    NA,     \"LOW\",   \"TEST01\", \"PAT01\", \"PARAM01\", 15.01, NA,       \"Day 14\",   NA,     \"HIGH\",   \"TEST01\", \"PAT01\", \"PARAM02\", 8.350, NA,       \"Baseline\", \"Y\",    \"LOW\",   \"TEST01\", \"PAT01\", \"PARAM02\",    NA, NA,       \"Day 7\",    NA,     NA,   \"TEST01\", \"PAT01\", \"PARAM02\", 8.350, NA,       \"Day 14\",   NA,     \"LOW\",   \"TEST01\", \"PAT01\", \"PARAM03\",    NA, \"LOW\",    \"Baseline\", \"Y\",    NA,   \"TEST01\", \"PAT01\", \"PARAM03\",    NA, \"LOW\",    \"Day 7\",    NA,     NA,   \"TEST01\", \"PAT01\", \"PARAM03\",    NA, \"MEDIUM\", \"Day 14\",   NA,     NA,   \"TEST01\", \"PAT01\", \"PARAM04\",    NA, \"HIGH\",   \"Baseline\", \"Y\",    NA,   \"TEST01\", \"PAT01\", \"PARAM04\",    NA, \"HIGH\",   \"Day 7\",    NA,     NA,   \"TEST01\", \"PAT01\", \"PARAM04\",    NA, \"MEDIUM\", \"Day 14\",   NA,     NA )  ## Derive `BASE` variable from `AVAL` derive_var_base(   dataset,   by_vars = exprs(USUBJID, PARAMCD),   source_var = AVAL,   new_var = BASE ) #> # A tibble: 12 × 9 #>    STUDYID USUBJID PARAMCD  AVAL AVALC  AVISIT   ABLFL ANRIND  BASE #>    <chr>   <chr>   <chr>   <dbl> <chr>  <chr>    <chr> <chr>  <dbl> #>  1 TEST01  PAT01   PARAM01 10.1  NA     Baseline Y     NORMAL 10.1  #>  2 TEST01  PAT01   PARAM01  9.7  NA     Day 7    NA    LOW    10.1  #>  3 TEST01  PAT01   PARAM01 15.0  NA     Day 14   NA    HIGH   10.1  #>  4 TEST01  PAT01   PARAM02  8.35 NA     Baseline Y     LOW     8.35 #>  5 TEST01  PAT01   PARAM02 NA    NA     Day 7    NA    NA      8.35 #>  6 TEST01  PAT01   PARAM02  8.35 NA     Day 14   NA    LOW     8.35 #>  7 TEST01  PAT01   PARAM03 NA    LOW    Baseline Y     NA     NA    #>  8 TEST01  PAT01   PARAM03 NA    LOW    Day 7    NA    NA     NA    #>  9 TEST01  PAT01   PARAM03 NA    MEDIUM Day 14   NA    NA     NA    #> 10 TEST01  PAT01   PARAM04 NA    HIGH   Baseline Y     NA     NA    #> 11 TEST01  PAT01   PARAM04 NA    HIGH   Day 7    NA    NA     NA    #> 12 TEST01  PAT01   PARAM04 NA    MEDIUM Day 14   NA    NA     NA     ## Derive `BASEC` variable from `AVALC` derive_var_base(   dataset,   by_vars = exprs(USUBJID, PARAMCD),   source_var = AVALC,   new_var = BASEC ) #> # A tibble: 12 × 9 #>    STUDYID USUBJID PARAMCD  AVAL AVALC  AVISIT   ABLFL ANRIND BASEC #>    <chr>   <chr>   <chr>   <dbl> <chr>  <chr>    <chr> <chr>  <chr> #>  1 TEST01  PAT01   PARAM01 10.1  NA     Baseline Y     NORMAL NA    #>  2 TEST01  PAT01   PARAM01  9.7  NA     Day 7    NA    LOW    NA    #>  3 TEST01  PAT01   PARAM01 15.0  NA     Day 14   NA    HIGH   NA    #>  4 TEST01  PAT01   PARAM02  8.35 NA     Baseline Y     LOW    NA    #>  5 TEST01  PAT01   PARAM02 NA    NA     Day 7    NA    NA     NA    #>  6 TEST01  PAT01   PARAM02  8.35 NA     Day 14   NA    LOW    NA    #>  7 TEST01  PAT01   PARAM03 NA    LOW    Baseline Y     NA     LOW   #>  8 TEST01  PAT01   PARAM03 NA    LOW    Day 7    NA    NA     LOW   #>  9 TEST01  PAT01   PARAM03 NA    MEDIUM Day 14   NA    NA     LOW   #> 10 TEST01  PAT01   PARAM04 NA    HIGH   Baseline Y     NA     HIGH  #> 11 TEST01  PAT01   PARAM04 NA    HIGH   Day 7    NA    NA     HIGH  #> 12 TEST01  PAT01   PARAM04 NA    MEDIUM Day 14   NA    NA     HIGH   ## Derive `BNRIND` variable from `ANRIND` derive_var_base(   dataset,   by_vars = exprs(USUBJID, PARAMCD),   source_var = ANRIND,   new_var = BNRIND ) #> # A tibble: 12 × 9 #>    STUDYID USUBJID PARAMCD  AVAL AVALC  AVISIT   ABLFL ANRIND BNRIND #>    <chr>   <chr>   <chr>   <dbl> <chr>  <chr>    <chr> <chr>  <chr>  #>  1 TEST01  PAT01   PARAM01 10.1  NA     Baseline Y     NORMAL NORMAL #>  2 TEST01  PAT01   PARAM01  9.7  NA     Day 7    NA    LOW    NORMAL #>  3 TEST01  PAT01   PARAM01 15.0  NA     Day 14   NA    HIGH   NORMAL #>  4 TEST01  PAT01   PARAM02  8.35 NA     Baseline Y     LOW    LOW    #>  5 TEST01  PAT01   PARAM02 NA    NA     Day 7    NA    NA     LOW    #>  6 TEST01  PAT01   PARAM02  8.35 NA     Day 14   NA    LOW    LOW    #>  7 TEST01  PAT01   PARAM03 NA    LOW    Baseline Y     NA     NA     #>  8 TEST01  PAT01   PARAM03 NA    LOW    Day 7    NA    NA     NA     #>  9 TEST01  PAT01   PARAM03 NA    MEDIUM Day 14   NA    NA     NA     #> 10 TEST01  PAT01   PARAM04 NA    HIGH   Baseline Y     NA     NA     #> 11 TEST01  PAT01   PARAM04 NA    HIGH   Day 7    NA    NA     NA     #> 12 TEST01  PAT01   PARAM04 NA    MEDIUM Day 14   NA    NA     NA"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_chg.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive Change from Baseline — derive_var_chg","title":"Derive Change from Baseline — derive_var_chg","text":"Derive change baseline (CHG) BDS dataset","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_chg.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive Change from Baseline — derive_var_chg","text":"","code":"derive_var_chg(dataset)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_chg.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive Change from Baseline — derive_var_chg","text":"dataset Input dataset AVAL BASE expected.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_chg.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive Change from Baseline — derive_var_chg","text":"input dataset additional column named CHG","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_chg.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Derive Change from Baseline — derive_var_chg","text":"Change baseline calculated subtracting baseline value analysis value.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_chg.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive Change from Baseline — derive_var_chg","text":"","code":"library(tibble)  advs <- tribble(   ~USUBJID, ~PARAMCD, ~AVAL, ~ABLFL, ~BASE,   \"P01\",    \"WEIGHT\", 80,    \"Y\",    80,   \"P01\",    \"WEIGHT\", 80.8,  NA,     80,   \"P01\",    \"WEIGHT\", 81.4,  NA,     80,   \"P02\",    \"WEIGHT\", 75.3,  \"Y\",    75.3,   \"P02\",    \"WEIGHT\", 76,    NA,     75.3 ) derive_var_chg(advs) #> # A tibble: 5 × 6 #>   USUBJID PARAMCD  AVAL ABLFL  BASE   CHG #>   <chr>   <chr>   <dbl> <chr> <dbl> <dbl> #> 1 P01     WEIGHT   80   Y      80   0     #> 2 P01     WEIGHT   80.8 NA     80   0.800 #> 3 P01     WEIGHT   81.4 NA     80   1.40  #> 4 P02     WEIGHT   75.3 Y      75.3 0     #> 5 P02     WEIGHT   76   NA     75.3 0.700"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_dthcaus.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive Death Cause — derive_var_dthcaus","title":"Derive Death Cause — derive_var_dthcaus","text":"derive_var_dthcaus() function superseded favor derive_vars_extreme_event(). Derive death cause (DTHCAUS) add traceability variables required.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_dthcaus.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive Death Cause — derive_var_dthcaus","text":"","code":"derive_var_dthcaus(   dataset,   ...,   source_datasets,   subject_keys = get_admiral_option(\"subject_keys\") )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_dthcaus.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive Death Cause — derive_var_dthcaus","text":"dataset Input dataset variables specified subject_keys argument expected dataset. ... Objects class \"dthcaus_source\" created dthcaus_source(). source_datasets named list containing datasets search death cause subject_keys Variables uniquely identify subject list expressions expressions symbols returned exprs() expected.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_dthcaus.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive Death Cause — derive_var_dthcaus","text":"input dataset DTHCAUS variable added.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_dthcaus.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Derive Death Cause — derive_var_dthcaus","text":"function derives DTHCAUS along user-defined traceability variables, required. subject death info multiple sources, one source earliest death date used. dates equivalent, first source kept, user provide inputs preferred order.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_dthcaus.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive Death Cause — derive_var_dthcaus","text":"","code":"library(tibble) library(dplyr, warn.conflicts = FALSE)  adsl <- tribble(   ~STUDYID,  ~USUBJID,   \"STUDY01\", \"PAT01\",   \"STUDY01\", \"PAT02\",   \"STUDY01\", \"PAT03\" ) ae <- tribble(   ~STUDYID,  ~USUBJID, ~AESEQ, ~AEDECOD,       ~AEOUT,  ~AEDTHDTC,   \"STUDY01\", \"PAT01\",  12,     \"SUDDEN DEATH\", \"FATAL\", \"2021-04-04\" )  ds <- tribble(   ~STUDYID, ~USUBJID, ~DSSEQ, ~DSDECOD, ~DSTERM, ~DSSTDTC,   \"STUDY01\", \"PAT02\", 1, \"INFORMED CONSENT OBTAINED\", \"INFORMED CONSENT OBTAINED\", \"2021-04-03\",   \"STUDY01\", \"PAT02\", 2, \"RANDOMIZATION\", \"RANDOMIZATION\", \"2021-04-11\",   \"STUDY01\", \"PAT02\", 3, \"DEATH\", \"DEATH DUE TO PROGRESSION OF DISEASE\", \"2022-02-01\",   \"STUDY01\", \"PAT03\", 1, \"DEATH\", \"POST STUDY REPORTING OF DEATH\", \"2022-03-03\" )  # Derive `DTHCAUS` only - for on-study deaths only src_ae <- dthcaus_source(   dataset_name = \"ae\",   filter = AEOUT == \"FATAL\",   date = convert_dtc_to_dt(AEDTHDTC),   mode = \"first\",   dthcaus = AEDECOD )  src_ds <- dthcaus_source(   dataset_name = \"ds\",   filter = DSDECOD == \"DEATH\" & grepl(\"DEATH DUE TO\", DSTERM),   date = convert_dtc_to_dt(DSSTDTC),   mode = \"first\",   dthcaus = DSTERM )  derive_var_dthcaus(adsl, src_ae, src_ds, source_datasets = list(ae = ae, ds = ds)) #> # A tibble: 3 × 3 #>   STUDYID USUBJID DTHCAUS                             #>   <chr>   <chr>   <chr>                               #> 1 STUDY01 PAT01   SUDDEN DEATH                        #> 2 STUDY01 PAT02   DEATH DUE TO PROGRESSION OF DISEASE #> 3 STUDY01 PAT03   NA                                   # Derive `DTHCAUS` and add traceability variables - for on-study deaths only src_ae <- dthcaus_source(   dataset_name = \"ae\",   filter = AEOUT == \"FATAL\",   date = convert_dtc_to_dt(AEDTHDTC),   mode = \"first\",   dthcaus = AEDECOD,   set_values_to = exprs(DTHDOM = \"AE\", DTHSEQ = AESEQ) )  src_ds <- dthcaus_source(   dataset_name = \"ds\",   filter = DSDECOD == \"DEATH\" & grepl(\"DEATH DUE TO\", DSTERM),   date = convert_dtc_to_dt(DSSTDTC),   mode = \"first\",   dthcaus = DSTERM,   set_values_to = exprs(DTHDOM = \"DS\", DTHSEQ = DSSEQ) )  derive_var_dthcaus(adsl, src_ae, src_ds, source_datasets = list(ae = ae, ds = ds)) #> # A tibble: 3 × 5 #>   STUDYID USUBJID DTHCAUS                             DTHDOM DTHSEQ #>   <chr>   <chr>   <chr>                               <chr>   <dbl> #> 1 STUDY01 PAT01   SUDDEN DEATH                        AE         12 #> 2 STUDY01 PAT02   DEATH DUE TO PROGRESSION OF DISEASE DS          3 #> 3 STUDY01 PAT03   NA                                  NA         NA  # Derive `DTHCAUS` as above - now including post-study deaths with different `DTHCAUS` value src_ae <- dthcaus_source(   dataset_name = \"ae\",   filter = AEOUT == \"FATAL\",   date = convert_dtc_to_dt(AEDTHDTC),   mode = \"first\",   dthcaus = AEDECOD,   set_values_to = exprs(DTHDOM = \"AE\", DTHSEQ = AESEQ) )  ds <- mutate(   ds,   DSSTDT = convert_dtc_to_dt(DSSTDTC) )  src_ds <- dthcaus_source(   dataset_name = \"ds\",   filter = DSDECOD == \"DEATH\" & grepl(\"DEATH DUE TO\", DSTERM),   date = DSSTDT,   mode = \"first\",   dthcaus = DSTERM,   set_values_to = exprs(DTHDOM = \"DS\", DTHSEQ = DSSEQ) )  src_ds_post <- dthcaus_source(   dataset_name = \"ds\",   filter = DSDECOD == \"DEATH\" & DSTERM == \"POST STUDY REPORTING OF DEATH\",   date = DSSTDT,   mode = \"first\",   dthcaus = \"POST STUDY: UNKNOWN CAUSE\",   set_values_to = exprs(DTHDOM = \"DS\", DTHSEQ = DSSEQ) )  derive_var_dthcaus(   adsl,   src_ae, src_ds, src_ds_post,   source_datasets = list(ae = ae, ds = ds) ) #> # A tibble: 3 × 5 #>   STUDYID USUBJID DTHCAUS                             DTHDOM DTHSEQ #>   <chr>   <chr>   <chr>                               <chr>   <dbl> #> 1 STUDY01 PAT01   SUDDEN DEATH                        AE         12 #> 2 STUDY01 PAT02   DEATH DUE TO PROGRESSION OF DISEASE DS          3 #> 3 STUDY01 PAT03   POST STUDY: UNKNOWN CAUSE           DS          1"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_extreme_dt.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive First or Last Date from Multiple Sources — derive_var_extreme_dt","title":"Derive First or Last Date from Multiple Sources — derive_var_extreme_dt","text":"derive_var_extreme_dt() function superseded favor derive_vars_extreme_event(). Add first last date multiple sources dataset, e.g., last known alive date (LSTALVDT). Note: wrapper function function derive_var_extreme_dtm().","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_extreme_dt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive First or Last Date from Multiple Sources — derive_var_extreme_dt","text":"","code":"derive_var_extreme_dt(   dataset,   new_var,   ...,   source_datasets,   mode,   subject_keys = get_admiral_option(\"subject_keys\") )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_extreme_dt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive First or Last Date from Multiple Sources — derive_var_extreme_dt","text":"dataset Input dataset variables specified subject_keys argument expected dataset. new_var Name variable create ... Source(s) dates. One date_source() objects expected. source_datasets named list containing datasets search first last date mode Selection mode (first last) \"first\" specified, first date subject selected. \"last\" specified, last date subject selected. Permitted Values:  \"first\", \"last\" subject_keys Variables uniquely identify subject list expressions expressions symbols returned exprs() expected.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_extreme_dt.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive First or Last Date from Multiple Sources — derive_var_extreme_dt","text":"input dataset new variable added.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_extreme_dt.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Derive First or Last Date from Multiple Sources — derive_var_extreme_dt","text":"following steps performed create output dataset: source dataset observations specified filter element selected observations date NA removed. patient first last observation (respect date mode) selected. new variable set variable expression specified date element. variables specified set_values_to element added. selected observations source datasets combined single dataset. patient first last observation (respect new variable mode) single dataset selected new variable merged input dataset. time part removed new variable.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_extreme_dt.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive First or Last Date from Multiple Sources — derive_var_extreme_dt","text":"","code":"library(dplyr, warn.conflicts = FALSE) ae <- tribble(   ~STUDYID,  ~DOMAIN,  ~USUBJID, ~AESEQ,     ~AESTDTC,     ~AEENDTC,   \"PILOT01\",    \"AE\", \"01-1130\",      5, \"2014-05-09\", \"2014-05-09\",   \"PILOT01\",    \"AE\", \"01-1130\",      6, \"2014-05-22\",           NA,   \"PILOT01\",    \"AE\", \"01-1130\",      4, \"2014-05-09\", \"2014-05-09\",   \"PILOT01\",    \"AE\", \"01-1130\",      8, \"2014-05-22\",           NA,   \"PILOT01\",    \"AE\", \"01-1130\",      7, \"2014-05-22\",           NA,   \"PILOT01\",    \"AE\", \"01-1130\",      2, \"2014-03-09\", \"2014-03-09\",   \"PILOT01\",    \"AE\", \"01-1130\",      1, \"2014-03-09\", \"2014-03-16\",   \"PILOT01\",    \"AE\", \"01-1130\",      3, \"2014-03-09\", \"2014-03-16\",   \"PILOT01\",    \"AE\", \"01-1133\",      1, \"2012-12-27\",           NA,   \"PILOT01\",    \"AE\", \"01-1133\",      3, \"2012-12-27\",           NA,   \"PILOT01\",    \"AE\", \"01-1133\",      2, \"2012-12-27\",           NA,   \"PILOT01\",    \"AE\", \"01-1133\",      4, \"2012-12-27\",           NA,   \"PILOT01\",    \"AE\", \"01-1211\",      5, \"2012-11-29\",           NA,   \"PILOT01\",    \"AE\", \"01-1211\",      1, \"2012-11-16\",           NA,   \"PILOT01\",    \"AE\", \"01-1211\",      7, \"2013-01-11\",           NA,   \"PILOT01\",    \"AE\", \"01-1211\",      8, \"2013-01-11\",           NA,   \"PILOT01\",    \"AE\", \"01-1211\",      4, \"2012-11-22\",           NA,   \"PILOT01\",    \"AE\", \"01-1211\",      2, \"2012-11-21\", \"2012-11-21\",   \"PILOT01\",    \"AE\", \"01-1211\",      3, \"2012-11-21\",           NA,   \"PILOT01\",    \"AE\", \"01-1211\",      6, \"2012-12-09\",           NA,   \"PILOT01\",    \"AE\", \"01-1211\",      9, \"2013-01-14\", \"2013-01-14\",   \"PILOT01\",    \"AE\", \"09-1081\",      2, \"2014-05-01\",           NA,   \"PILOT01\",    \"AE\", \"09-1081\",      1, \"2014-04-07\",           NA,   \"PILOT01\",    \"AE\", \"09-1088\",      1, \"2014-05-08\",           NA,   \"PILOT01\",    \"AE\", \"09-1088\",      2, \"2014-08-02\",           NA )  adsl <- tribble(   ~STUDYID,   ~USUBJID,              ~TRTEDTM,      ~TRTEDT,   \"PILOT01\", \"01-1130\", \"2014-08-16 23:59:59\", \"2014-08-16\",   \"PILOT01\", \"01-1133\", \"2013-04-28 23:59:59\", \"2013-04-28\",   \"PILOT01\", \"01-1211\", \"2013-01-12 23:59:59\", \"2013-01-12\",   \"PILOT01\", \"09-1081\", \"2014-04-27 23:59:59\", \"2014-04-27\",   \"PILOT01\", \"09-1088\", \"2014-10-09 23:59:59\", \"2014-10-09\" ) %>%   mutate(     across(TRTEDTM:TRTEDT, as.Date)   )   lb <- tribble(   ~STUDYID,  ~DOMAIN,  ~USUBJID, ~LBSEQ,             ~LBDTC,   \"PILOT01\",    \"LB\", \"01-1130\",    219, \"2014-06-07T13:20\",   \"PILOT01\",    \"LB\", \"01-1130\",    322, \"2014-08-16T13:10\",   \"PILOT01\",    \"LB\", \"01-1133\",    268, \"2013-04-18T15:30\",   \"PILOT01\",    \"LB\", \"01-1133\",    304, \"2013-04-29T10:13\",   \"PILOT01\",    \"LB\", \"01-1211\",      8, \"2012-10-30T14:26\",   \"PILOT01\",    \"LB\", \"01-1211\",    162, \"2013-01-08T12:13\",   \"PILOT01\",    \"LB\", \"09-1081\",     47, \"2014-02-01T10:55\",   \"PILOT01\",    \"LB\", \"09-1081\",    219, \"2014-05-10T11:15\",   \"PILOT01\",    \"LB\", \"09-1088\",    283, \"2014-09-27T12:13\",   \"PILOT01\",    \"LB\", \"09-1088\",    322, \"2014-10-09T13:25\" )  dm <- tribble(   ~STUDYID,  ~DOMAIN,  ~USUBJID, ~AGE,   ~AGEU,   \"PILOT01\",    \"DM\", \"01-1130\",   84, \"YEARS\",   \"PILOT01\",    \"DM\", \"01-1133\",   81, \"YEARS\",   \"PILOT01\",    \"DM\", \"01-1211\",   76, \"YEARS\",   \"PILOT01\",    \"DM\", \"09-1081\",   86, \"YEARS\",   \"PILOT01\",    \"DM\", \"09-1088\",   69, \"YEARS\" )  ae_start <- date_source(   dataset_name = \"ae\",   date = convert_dtc_to_dt(AESTDTC, highest_imputation = \"M\") ) ae_end <- date_source(   dataset_name = \"ae\",   date = convert_dtc_to_dt(AEENDTC, highest_imputation = \"M\") )  ae_ext <- ae %>%   derive_vars_dt(     dtc = AESTDTC,     new_vars_prefix = \"AEST\",     highest_imputation = \"M\"   ) %>%   derive_vars_dt(     dtc = AEENDTC,     new_vars_prefix = \"AEEN\",     highest_imputation = \"M\"   )  lb_date <- date_source(   dataset_name = \"lb\",   date = convert_dtc_to_dt(LBDTC) )  lb_ext <- derive_vars_dt(   lb,   dtc = LBDTC,   new_vars_prefix = \"LB\" )  adsl_date <- date_source(dataset_name = \"adsl\", date = TRTEDT)  dm %>%   derive_var_extreme_dt(     new_var = LSTALVDT,     ae_start, ae_end, lb_date, adsl_date,     source_datasets = list(       adsl = adsl,       ae = ae_ext,       lb = lb_ext     ),     mode = \"last\"   ) %>%   select(USUBJID, LSTALVDT) #> # A tibble: 5 × 2 #>   USUBJID LSTALVDT   #>   <chr>   <date>     #> 1 01-1130 2014-08-16 #> 2 01-1133 2013-04-29 #> 3 01-1211 2013-01-14 #> 4 09-1081 2014-05-10 #> 5 09-1088 2014-10-09  # derive last alive date and traceability variables ae_start <- date_source(   dataset_name = \"ae\",   date = convert_dtc_to_dt(AESTDTC, highest_imputation = \"M\"),   set_values_to = exprs(     LALVDOM = \"AE\",     LALVSEQ = AESEQ,     LALVVAR = \"AESTDTC\"   ) )  ae_end <- date_source(   dataset_name = \"ae\",   date = convert_dtc_to_dt(AEENDTC, highest_imputation = \"M\"),   set_values_to = exprs(     LALVDOM = \"AE\",     LALVSEQ = AESEQ,     LALVVAR = \"AEENDTC\"   ) )  lb_date <- date_source(   dataset_name = \"lb\",   date = convert_dtc_to_dt(LBDTC),   set_values_to = exprs(     LALVDOM = \"LB\",     LALVSEQ = LBSEQ,     LALVVAR = \"LBDTC\"   ) )  adsl_date <- date_source(   dataset_name = \"adsl\",   date = TRTEDT,   set_values_to = exprs(     LALVDOM = \"ADSL\",     LALVSEQ = NA_integer_,     LALVVAR = \"TRTEDT\"   ) )  dm %>%   derive_var_extreme_dt(     new_var = LSTALVDT,     ae_start, ae_end, lb_date, adsl_date,     source_datasets = list(       adsl = adsl,       ae = ae_ext,       lb = lb_ext     ),     mode = \"last\"   ) %>%   select(USUBJID, LSTALVDT, LALVDOM, LALVSEQ, LALVVAR) #> # A tibble: 5 × 5 #>   USUBJID LSTALVDT   LALVDOM LALVSEQ LALVVAR #>   <chr>   <date>     <chr>     <dbl> <chr>   #> 1 01-1130 2014-08-16 ADSL         NA TRTEDT  #> 2 01-1133 2013-04-29 LB          304 LBDTC   #> 3 01-1211 2013-01-14 AE            9 AEENDTC #> 4 09-1081 2014-05-10 LB          219 LBDTC   #> 5 09-1088 2014-10-09 ADSL         NA TRTEDT"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_extreme_dtm.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive First or Last Datetime from Multiple Sources — derive_var_extreme_dtm","title":"Derive First or Last Datetime from Multiple Sources — derive_var_extreme_dtm","text":"derive_var_extreme_dtm() function superseded favor derive_vars_extreme_event(). Add first last datetime multiple sources dataset, e.g., last known alive datetime (LSTALVDTM).","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_extreme_dtm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive First or Last Datetime from Multiple Sources — derive_var_extreme_dtm","text":"","code":"derive_var_extreme_dtm(   dataset,   new_var,   ...,   source_datasets,   mode,   subject_keys = get_admiral_option(\"subject_keys\") )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_extreme_dtm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive First or Last Datetime from Multiple Sources — derive_var_extreme_dtm","text":"dataset Input dataset variables specified subject_keys argument expected dataset. new_var Name variable create ... Source(s) dates. One date_source() objects expected. source_datasets named list containing datasets search first last date mode Selection mode (first last) \"first\" specified, first date subject selected. \"last\" specified, last date subject selected. Permitted Values:  \"first\", \"last\" subject_keys Variables uniquely identify subject list expressions expressions symbols returned exprs() expected.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_extreme_dtm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive First or Last Datetime from Multiple Sources — derive_var_extreme_dtm","text":"input dataset new variable added.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_extreme_dtm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Derive First or Last Datetime from Multiple Sources — derive_var_extreme_dtm","text":"following steps performed create output dataset: source dataset observations specified filter element selected observations date NA removed. patient first last observation (respect date mode) selected. new variable set variable expression specified date element. date variable (rather datetime), time imputed \"00:00:00\". variables specified set_values_to element added. selected observations source datasets combined single dataset. patient first last observation (respect new variable mode) single dataset selected new variable merged input dataset.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_extreme_dtm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive First or Last Datetime from Multiple Sources — derive_var_extreme_dtm","text":"","code":"library(dplyr, warn.conflicts = FALSE) library(lubridate) dm <- tribble(   ~STUDYID,  ~DOMAIN,  ~USUBJID, ~AGE,   ~AGEU,   \"PILOT01\",    \"DM\", \"01-1130\",   84, \"YEARS\",   \"PILOT01\",    \"DM\", \"01-1133\",   81, \"YEARS\",   \"PILOT01\",    \"DM\", \"01-1211\",   76, \"YEARS\",   \"PILOT01\",    \"DM\", \"09-1081\",   86, \"YEARS\",   \"PILOT01\",    \"DM\", \"09-1088\",   69, \"YEARS\" ) ae <- tribble(   ~STUDYID,  ~DOMAIN,  ~USUBJID, ~AESEQ,     ~AESTDTC,     ~AEENDTC,   \"PILOT01\",    \"AE\", \"01-1130\",      5, \"2014-05-09\", \"2014-05-09\",   \"PILOT01\",    \"AE\", \"01-1130\",      6, \"2014-05-22\",           NA,   \"PILOT01\",    \"AE\", \"01-1130\",      4, \"2014-05-09\", \"2014-05-09\",   \"PILOT01\",    \"AE\", \"01-1130\",      8, \"2014-05-22\",           NA,   \"PILOT01\",    \"AE\", \"01-1130\",      7, \"2014-05-22\",           NA,   \"PILOT01\",    \"AE\", \"01-1130\",      2, \"2014-03-09\", \"2014-03-09\",   \"PILOT01\",    \"AE\", \"01-1130\",      1, \"2014-03-09\", \"2014-03-16\",   \"PILOT01\",    \"AE\", \"01-1130\",      3, \"2014-03-09\", \"2014-03-16\",   \"PILOT01\",    \"AE\", \"01-1133\",      1, \"2012-12-27\",           NA,   \"PILOT01\",    \"AE\", \"01-1133\",      3, \"2012-12-27\",           NA,   \"PILOT01\",    \"AE\", \"01-1133\",      2, \"2012-12-27\",           NA,   \"PILOT01\",    \"AE\", \"01-1133\",      4, \"2012-12-27\",           NA,   \"PILOT01\",    \"AE\", \"01-1211\",      5, \"2012-11-29\",           NA,   \"PILOT01\",    \"AE\", \"01-1211\",      1, \"2012-11-16\",           NA,   \"PILOT01\",    \"AE\", \"01-1211\",      7, \"2013-01-11\",           NA,   \"PILOT01\",    \"AE\", \"01-1211\",      8, \"2013-01-11\",           NA,   \"PILOT01\",    \"AE\", \"01-1211\",      4, \"2012-11-22\",           NA,   \"PILOT01\",    \"AE\", \"01-1211\",      2, \"2012-11-21\", \"2012-11-21\",   \"PILOT01\",    \"AE\", \"01-1211\",      3, \"2012-11-21\",           NA,   \"PILOT01\",    \"AE\", \"01-1211\",      6, \"2012-12-09\",           NA,   \"PILOT01\",    \"AE\", \"01-1211\",      9, \"2013-01-14\", \"2013-01-14\",   \"PILOT01\",    \"AE\", \"09-1081\",      2, \"2014-05-01\",           NA,   \"PILOT01\",    \"AE\", \"09-1081\",      1, \"2014-04-07\",           NA,   \"PILOT01\",    \"AE\", \"09-1088\",      1, \"2014-05-08\",           NA,   \"PILOT01\",    \"AE\", \"09-1088\",      2, \"2014-08-02\",           NA ) lb <- tribble(   ~STUDYID,  ~DOMAIN,  ~USUBJID, ~LBSEQ,             ~LBDTC,   \"PILOT01\",    \"LB\", \"01-1130\",    219, \"2014-06-07T13:20\",   \"PILOT01\",    \"LB\", \"01-1130\",    322, \"2014-08-16T13:10\",   \"PILOT01\",    \"LB\", \"01-1133\",    268, \"2013-04-18T15:30\",   \"PILOT01\",    \"LB\", \"01-1133\",    304, \"2013-04-29T10:13\",   \"PILOT01\",    \"LB\", \"01-1211\",      8, \"2012-10-30T14:26\",   \"PILOT01\",    \"LB\", \"01-1211\",    162, \"2013-01-08T12:13\",   \"PILOT01\",    \"LB\", \"09-1081\",     47, \"2014-02-01T10:55\",   \"PILOT01\",    \"LB\", \"09-1081\",    219, \"2014-05-10T11:15\",   \"PILOT01\",    \"LB\", \"09-1088\",    283, \"2014-09-27T12:13\",   \"PILOT01\",    \"LB\", \"09-1088\",    322, \"2014-10-09T13:25\" ) adsl <- tribble(   ~STUDYID,   ~USUBJID,              ~TRTEDTM,   \"PILOT01\", \"01-1130\", \"2014-08-16 23:59:59\",   \"PILOT01\", \"01-1133\", \"2013-04-28 23:59:59\",   \"PILOT01\", \"01-1211\", \"2013-01-12 23:59:59\",   \"PILOT01\", \"09-1081\", \"2014-04-27 23:59:59\",   \"PILOT01\", \"09-1088\", \"2014-10-09 23:59:59\" ) %>%   mutate(     TRTEDTM = as_datetime(TRTEDTM)   )  # derive last known alive datetime (LSTALVDTM) ae_start <- date_source(   dataset_name = \"ae\",   date = convert_dtc_to_dtm(AESTDTC, highest_imputation = \"M\"), ) ae_end <- date_source(   dataset_name = \"ae\",   date = convert_dtc_to_dtm(AEENDTC, highest_imputation = \"M\"), )  ae_ext <- ae %>%   derive_vars_dtm(     dtc = AESTDTC,     new_vars_prefix = \"AEST\",     highest_imputation = \"M\"   ) %>%   derive_vars_dtm(     dtc = AEENDTC,     new_vars_prefix = \"AEEN\",     highest_imputation = \"M\"   )  lb_date <- date_source(   dataset_name = \"lb\",   date = convert_dtc_to_dtm(LBDTC), )  lb_ext <- derive_vars_dtm(   lb,   dtc = LBDTC,   new_vars_prefix = \"LB\" )  adsl_date <- date_source(   dataset_name = \"adsl\",   date = TRTEDTM )  dm %>%   derive_var_extreme_dtm(     new_var = LSTALVDTM,     ae_start, ae_end, lb_date, adsl_date,     source_datasets = list(       adsl = adsl,       ae = ae_ext,       lb = lb_ext     ),     mode = \"last\"   ) %>%   select(USUBJID, LSTALVDTM) #> # A tibble: 5 × 2 #>   USUBJID LSTALVDTM           #>   <chr>   <dttm>              #> 1 01-1130 2014-08-16 23:59:59 #> 2 01-1133 2013-04-29 10:13:00 #> 3 01-1211 2013-01-14 00:00:00 #> 4 09-1081 2014-05-10 11:15:00 #> 5 09-1088 2014-10-09 23:59:59  # derive last alive datetime and traceability variables ae_start <- date_source(   dataset_name = \"ae\",   date = convert_dtc_to_dtm(AESTDTC, highest_imputation = \"M\"),   set_values_to = exprs(     LALVDOM = \"AE\",     LALVSEQ = AESEQ,     LALVVAR = \"AESTDTC\"   ) )  ae_end <- date_source(   dataset_name = \"ae\",   date = convert_dtc_to_dtm(AEENDTC, highest_imputation = \"M\"),   set_values_to = exprs(     LALVDOM = \"AE\",     LALVSEQ = AESEQ,     LALVVAR = \"AEENDTC\"   ) ) lb_date <- date_source(   dataset_name = \"lb\",   date = convert_dtc_to_dtm(LBDTC),   set_values_to = exprs(     LALVDOM = \"LB\",     LALVSEQ = LBSEQ,     LALVVAR = \"LBDTC\"   ) )  adsl_date <- date_source(   dataset_name = \"adsl\",   date = TRTEDTM,   set_values_to = exprs(     LALVDOM = \"ADSL\",     LALVSEQ = NA_integer_,     LALVVAR = \"TRTEDTM\"   ) )  dm %>%   derive_var_extreme_dtm(     new_var = LSTALVDTM,     ae_start, ae_end, lb_date, adsl_date,     source_datasets = list(       adsl = adsl,       ae = ae_ext,       lb = lb_ext     ),     mode = \"last\"   ) %>%   select(USUBJID, LSTALVDTM, LALVDOM, LALVSEQ, LALVVAR) #> # A tibble: 5 × 5 #>   USUBJID LSTALVDTM           LALVDOM LALVSEQ LALVVAR #>   <chr>   <dttm>              <chr>     <dbl> <chr>   #> 1 01-1130 2014-08-16 23:59:59 ADSL         NA TRTEDTM #> 2 01-1133 2013-04-29 10:13:00 LB          304 LBDTC   #> 3 01-1211 2013-01-14 00:00:00 AE            9 AEENDTC #> 4 09-1081 2014-05-10 11:15:00 LB          219 LBDTC   #> 5 09-1088 2014-10-09 23:59:59 ADSL         NA TRTEDTM"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_extreme_flag.html","id":null,"dir":"Reference","previous_headings":"","what":"Add a Variable Flagging the First or Last Observation Within Each By Group — derive_var_extreme_flag","title":"Add a Variable Flagging the First or Last Observation Within Each By Group — derive_var_extreme_flag","text":"Add variable flagging first last observation within group","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_extreme_flag.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add a Variable Flagging the First or Last Observation Within Each By Group — derive_var_extreme_flag","text":"","code":"derive_var_extreme_flag(   dataset,   by_vars,   order,   new_var,   mode,   true_value = \"Y\",   false_value = NA_character_,   flag_all = FALSE,   check_type = \"warning\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_extreme_flag.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add a Variable Flagging the First or Last Observation Within Each By Group — derive_var_extreme_flag","text":"dataset Input dataset variables specified by_vars argument expected dataset. by_vars Grouping variables Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) order Sort order first last observation determined respect specified order. handling NAs sorting variables see Sort Order. Permitted Values: list variables functions variables new_var Variable add specified variable added output dataset. set value set true_value first last observation (depending mode) group. Permitted Values: list name-value pairs mode Flag mode Determines first last observation flagged. Permitted Values: \"first\", \"last\" true_value True value value specified variable new_var, applicable first last observation (depending mode) group. Permitted Values: atomic scalar false_value False value value specified variable new_var, applicable first last observation (depending mode) group. Permitted Values: atomic scalar flag_all Flag setting logical value set TRUE, records flagged error warning issued first last record unique. check_type Check uniqueness? \"warning\" \"error\" specified, specified message issued observations input dataset unique respect variables order. Default: \"warning\" Permitted Values: \"none\", \"warning\", \"error\"","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_extreme_flag.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add a Variable Flagging the First or Last Observation Within Each By Group — derive_var_extreme_flag","text":"input dataset new flag variable added","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_extreme_flag.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Add a Variable Flagging the First or Last Observation Within Each By Group — derive_var_extreme_flag","text":"group (respect variables specified by_vars parameter), new_var set \"Y\" first last observation (respect order specified order parameter flag mode specified mode parameter). case user wants flag multiple records grouping, example records happen visit time, argument flag_all can set TRUE. Otherwise, new_var set NA. Thus, direction \"worst\" considered fixed parameters dataset depending order mode, .e. every parameter first last record flagged across whole dataset.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_extreme_flag.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add a Variable Flagging the First or Last Observation Within Each By Group — derive_var_extreme_flag","text":"","code":"library(tibble) library(dplyr, warn.conflicts = FALSE) example_vs <- tribble(   ~USUBJID, ~VSTESTCD,      ~VISIT, ~VISITNUM, ~VSTPTNUM, ~VSSTRESN,   \"1001\",     \"DIABP\", \"SCREENING\",         1,        10,        64,   \"1001\",     \"DIABP\", \"SCREENING\",         1,        11,        66,   \"1001\",     \"DIABP\",  \"BASELINE\",         2,       100,        68,   \"1001\",     \"DIABP\",  \"BASELINE\",         2,       101,        68,   \"1001\",     \"DIABP\",    \"WEEK 2\",         3,       200,        72,   \"1001\",     \"DIABP\",    \"WEEK 2\",         3,       201,        71,   \"1001\",     \"DIABP\",    \"WEEK 4\",         4,       300,        70,   \"1001\",     \"DIABP\",    \"WEEK 4\",         4,       301,        70 )  # Flag last value for each patient, test, and visit, baseline observations are ignored example_vs %>%   restrict_derivation(     derivation = derive_var_extreme_flag,     args = params(       by_vars = exprs(USUBJID, VSTESTCD, VISIT),       order = exprs(VSTPTNUM),       new_var = LASTFL,       mode = \"last\"     ),     filter = VISIT != \"BASELINE\"   ) %>%   arrange(USUBJID, VSTESTCD, VISITNUM, VSTPTNUM) %>%   select(USUBJID, VSTESTCD, VISIT, VSTPTNUM, VSSTRESN, LASTFL) #> # A tibble: 8 × 6 #>   USUBJID VSTESTCD VISIT     VSTPTNUM VSSTRESN LASTFL #>   <chr>   <chr>    <chr>        <dbl>    <dbl> <chr>  #> 1 1001    DIABP    SCREENING       10       64 NA     #> 2 1001    DIABP    SCREENING       11       66 Y      #> 3 1001    DIABP    BASELINE       100       68 NA     #> 4 1001    DIABP    BASELINE       101       68 NA     #> 5 1001    DIABP    WEEK 2         200       72 NA     #> 6 1001    DIABP    WEEK 2         201       71 Y      #> 7 1001    DIABP    WEEK 4         300       70 NA     #> 8 1001    DIABP    WEEK 4         301       70 Y       # Baseline (ABLFL) examples:  input <- tribble(   ~STUDYID, ~USUBJID,  ~PARAMCD,     ~AVISIT,                  ~ADT, ~AVAL,    ~DTYPE,   \"TEST01\",  \"PAT01\", \"PARAM01\",  \"BASELINE\", as.Date(\"2021-04-27\"),  15.0,        NA,   \"TEST01\",  \"PAT01\", \"PARAM01\",  \"BASELINE\", as.Date(\"2021-04-25\"),  14.0,        NA,   \"TEST01\",  \"PAT01\", \"PARAM01\",  \"BASELINE\", as.Date(\"2021-04-23\"),  15.0, \"AVERAGE\",   \"TEST01\",  \"PAT01\", \"PARAM01\",    \"WEEK 1\", as.Date(\"2021-04-27\"),  10.0, \"AVERAGE\",   \"TEST01\",  \"PAT01\", \"PARAM01\",    \"WEEK 2\", as.Date(\"2021-04-30\"),  12.0,        NA,   \"TEST01\",  \"PAT02\", \"PARAM01\", \"SCREENING\", as.Date(\"2021-04-27\"),  15.0, \"AVERAGE\",   \"TEST01\",  \"PAT02\", \"PARAM01\",  \"BASELINE\", as.Date(\"2021-04-25\"),  14.0, \"AVERAGE\",   \"TEST01\",  \"PAT02\", \"PARAM01\",  \"BASELINE\", as.Date(\"2021-04-23\"),  15.0, \"AVERAGE\",   \"TEST01\",  \"PAT02\", \"PARAM01\",    \"WEEK 1\", as.Date(\"2021-04-27\"),  10.0, \"AVERAGE\",   \"TEST01\",  \"PAT02\", \"PARAM01\",    \"WEEK 2\", as.Date(\"2021-04-30\"),  12.0, \"AVERAGE\",   \"TEST01\",  \"PAT01\", \"PARAM02\", \"SCREENING\", as.Date(\"2021-04-27\"),  15.0, \"AVERAGE\",   \"TEST01\",  \"PAT01\", \"PARAM02\", \"SCREENING\", as.Date(\"2021-04-25\"),  14.0, \"AVERAGE\",   \"TEST01\",  \"PAT01\", \"PARAM02\", \"SCREENING\", as.Date(\"2021-04-23\"),  15.0,        NA,   \"TEST01\",  \"PAT01\", \"PARAM02\",  \"BASELINE\", as.Date(\"2021-04-27\"),  10.0, \"AVERAGE\",   \"TEST01\",  \"PAT01\", \"PARAM02\",    \"WEEK 2\", as.Date(\"2021-04-30\"),  12.0,        NA,   \"TEST01\",  \"PAT02\", \"PARAM02\", \"SCREENING\", as.Date(\"2021-04-27\"),  15.0,        NA,   \"TEST01\",  \"PAT02\", \"PARAM02\",  \"BASELINE\", as.Date(\"2021-04-25\"),  14.0,        NA,   \"TEST01\",  \"PAT02\", \"PARAM02\",    \"WEEK 1\", as.Date(\"2021-04-23\"),  15.0,        NA,   \"TEST01\",  \"PAT02\", \"PARAM02\",    \"WEEK 1\", as.Date(\"2021-04-27\"),  10.0,        NA,   \"TEST01\",  \"PAT02\", \"PARAM02\",  \"BASELINE\", as.Date(\"2021-04-30\"),  12.0,        NA )  # Last observation restrict_derivation(   input,   derivation = derive_var_extreme_flag,   args = params(     by_vars = exprs(USUBJID, PARAMCD),     order = exprs(ADT),     new_var = ABLFL,     mode = \"last\"   ),   filter = AVISIT == \"BASELINE\" ) #> # A tibble: 20 × 8 #>    STUDYID USUBJID PARAMCD AVISIT    ADT         AVAL DTYPE   ABLFL #>    <chr>   <chr>   <chr>   <chr>     <date>     <dbl> <chr>   <chr> #>  1 TEST01  PAT01   PARAM01 BASELINE  2021-04-23    15 AVERAGE NA    #>  2 TEST01  PAT01   PARAM01 BASELINE  2021-04-25    14 NA      NA    #>  3 TEST01  PAT01   PARAM01 BASELINE  2021-04-27    15 NA      Y     #>  4 TEST01  PAT01   PARAM02 BASELINE  2021-04-27    10 AVERAGE Y     #>  5 TEST01  PAT02   PARAM01 BASELINE  2021-04-23    15 AVERAGE NA    #>  6 TEST01  PAT02   PARAM01 BASELINE  2021-04-25    14 AVERAGE Y     #>  7 TEST01  PAT02   PARAM02 BASELINE  2021-04-25    14 NA      NA    #>  8 TEST01  PAT02   PARAM02 BASELINE  2021-04-30    12 NA      Y     #>  9 TEST01  PAT01   PARAM01 WEEK 1    2021-04-27    10 AVERAGE NA    #> 10 TEST01  PAT01   PARAM01 WEEK 2    2021-04-30    12 NA      NA    #> 11 TEST01  PAT02   PARAM01 SCREENING 2021-04-27    15 AVERAGE NA    #> 12 TEST01  PAT02   PARAM01 WEEK 1    2021-04-27    10 AVERAGE NA    #> 13 TEST01  PAT02   PARAM01 WEEK 2    2021-04-30    12 AVERAGE NA    #> 14 TEST01  PAT01   PARAM02 SCREENING 2021-04-27    15 AVERAGE NA    #> 15 TEST01  PAT01   PARAM02 SCREENING 2021-04-25    14 AVERAGE NA    #> 16 TEST01  PAT01   PARAM02 SCREENING 2021-04-23    15 NA      NA    #> 17 TEST01  PAT01   PARAM02 WEEK 2    2021-04-30    12 NA      NA    #> 18 TEST01  PAT02   PARAM02 SCREENING 2021-04-27    15 NA      NA    #> 19 TEST01  PAT02   PARAM02 WEEK 1    2021-04-23    15 NA      NA    #> 20 TEST01  PAT02   PARAM02 WEEK 1    2021-04-27    10 NA      NA     # Worst observation - Direction = High restrict_derivation(   input,   derivation = derive_var_extreme_flag,   args = params(     by_vars = exprs(USUBJID, PARAMCD),     order = exprs(AVAL, ADT),     new_var = ABLFL,     mode = \"last\"   ),   filter = AVISIT == \"BASELINE\" ) #> # A tibble: 20 × 8 #>    STUDYID USUBJID PARAMCD AVISIT    ADT         AVAL DTYPE   ABLFL #>    <chr>   <chr>   <chr>   <chr>     <date>     <dbl> <chr>   <chr> #>  1 TEST01  PAT01   PARAM01 BASELINE  2021-04-25    14 NA      NA    #>  2 TEST01  PAT01   PARAM01 BASELINE  2021-04-23    15 AVERAGE NA    #>  3 TEST01  PAT01   PARAM01 BASELINE  2021-04-27    15 NA      Y     #>  4 TEST01  PAT01   PARAM02 BASELINE  2021-04-27    10 AVERAGE Y     #>  5 TEST01  PAT02   PARAM01 BASELINE  2021-04-25    14 AVERAGE NA    #>  6 TEST01  PAT02   PARAM01 BASELINE  2021-04-23    15 AVERAGE Y     #>  7 TEST01  PAT02   PARAM02 BASELINE  2021-04-30    12 NA      NA    #>  8 TEST01  PAT02   PARAM02 BASELINE  2021-04-25    14 NA      Y     #>  9 TEST01  PAT01   PARAM01 WEEK 1    2021-04-27    10 AVERAGE NA    #> 10 TEST01  PAT01   PARAM01 WEEK 2    2021-04-30    12 NA      NA    #> 11 TEST01  PAT02   PARAM01 SCREENING 2021-04-27    15 AVERAGE NA    #> 12 TEST01  PAT02   PARAM01 WEEK 1    2021-04-27    10 AVERAGE NA    #> 13 TEST01  PAT02   PARAM01 WEEK 2    2021-04-30    12 AVERAGE NA    #> 14 TEST01  PAT01   PARAM02 SCREENING 2021-04-27    15 AVERAGE NA    #> 15 TEST01  PAT01   PARAM02 SCREENING 2021-04-25    14 AVERAGE NA    #> 16 TEST01  PAT01   PARAM02 SCREENING 2021-04-23    15 NA      NA    #> 17 TEST01  PAT01   PARAM02 WEEK 2    2021-04-30    12 NA      NA    #> 18 TEST01  PAT02   PARAM02 SCREENING 2021-04-27    15 NA      NA    #> 19 TEST01  PAT02   PARAM02 WEEK 1    2021-04-23    15 NA      NA    #> 20 TEST01  PAT02   PARAM02 WEEK 1    2021-04-27    10 NA      NA     # Worst observation - Direction = Lo restrict_derivation(   input,   derivation = derive_var_extreme_flag,   args = params(     by_vars = exprs(USUBJID, PARAMCD),     order = exprs(desc(AVAL), ADT),     new_var = ABLFL,     mode = \"last\"   ),   filter = AVISIT == \"BASELINE\" ) #> # A tibble: 20 × 8 #>    STUDYID USUBJID PARAMCD AVISIT    ADT         AVAL DTYPE   ABLFL #>    <chr>   <chr>   <chr>   <chr>     <date>     <dbl> <chr>   <chr> #>  1 TEST01  PAT01   PARAM01 BASELINE  2021-04-23    15 AVERAGE NA    #>  2 TEST01  PAT01   PARAM01 BASELINE  2021-04-27    15 NA      NA    #>  3 TEST01  PAT01   PARAM01 BASELINE  2021-04-25    14 NA      Y     #>  4 TEST01  PAT01   PARAM02 BASELINE  2021-04-27    10 AVERAGE Y     #>  5 TEST01  PAT02   PARAM01 BASELINE  2021-04-23    15 AVERAGE NA    #>  6 TEST01  PAT02   PARAM01 BASELINE  2021-04-25    14 AVERAGE Y     #>  7 TEST01  PAT02   PARAM02 BASELINE  2021-04-25    14 NA      NA    #>  8 TEST01  PAT02   PARAM02 BASELINE  2021-04-30    12 NA      Y     #>  9 TEST01  PAT01   PARAM01 WEEK 1    2021-04-27    10 AVERAGE NA    #> 10 TEST01  PAT01   PARAM01 WEEK 2    2021-04-30    12 NA      NA    #> 11 TEST01  PAT02   PARAM01 SCREENING 2021-04-27    15 AVERAGE NA    #> 12 TEST01  PAT02   PARAM01 WEEK 1    2021-04-27    10 AVERAGE NA    #> 13 TEST01  PAT02   PARAM01 WEEK 2    2021-04-30    12 AVERAGE NA    #> 14 TEST01  PAT01   PARAM02 SCREENING 2021-04-27    15 AVERAGE NA    #> 15 TEST01  PAT01   PARAM02 SCREENING 2021-04-25    14 AVERAGE NA    #> 16 TEST01  PAT01   PARAM02 SCREENING 2021-04-23    15 NA      NA    #> 17 TEST01  PAT01   PARAM02 WEEK 2    2021-04-30    12 NA      NA    #> 18 TEST01  PAT02   PARAM02 SCREENING 2021-04-27    15 NA      NA    #> 19 TEST01  PAT02   PARAM02 WEEK 1    2021-04-23    15 NA      NA    #> 20 TEST01  PAT02   PARAM02 WEEK 1    2021-04-27    10 NA      NA     # Average observation restrict_derivation(   input,   derivation = derive_var_extreme_flag,   args = params(     by_vars = exprs(USUBJID, PARAMCD),     order = exprs(ADT, desc(AVAL)),     new_var = ABLFL,     mode = \"last\"   ),   filter = AVISIT == \"BASELINE\" & DTYPE == \"AVERAGE\" ) #> # A tibble: 20 × 8 #>    STUDYID USUBJID PARAMCD AVISIT    ADT         AVAL DTYPE   ABLFL #>    <chr>   <chr>   <chr>   <chr>     <date>     <dbl> <chr>   <chr> #>  1 TEST01  PAT01   PARAM01 BASELINE  2021-04-23    15 AVERAGE Y     #>  2 TEST01  PAT01   PARAM02 BASELINE  2021-04-27    10 AVERAGE Y     #>  3 TEST01  PAT02   PARAM01 BASELINE  2021-04-23    15 AVERAGE NA    #>  4 TEST01  PAT02   PARAM01 BASELINE  2021-04-25    14 AVERAGE Y     #>  5 TEST01  PAT01   PARAM01 BASELINE  2021-04-27    15 NA      NA    #>  6 TEST01  PAT01   PARAM01 BASELINE  2021-04-25    14 NA      NA    #>  7 TEST01  PAT01   PARAM01 WEEK 1    2021-04-27    10 AVERAGE NA    #>  8 TEST01  PAT01   PARAM01 WEEK 2    2021-04-30    12 NA      NA    #>  9 TEST01  PAT02   PARAM01 SCREENING 2021-04-27    15 AVERAGE NA    #> 10 TEST01  PAT02   PARAM01 WEEK 1    2021-04-27    10 AVERAGE NA    #> 11 TEST01  PAT02   PARAM01 WEEK 2    2021-04-30    12 AVERAGE NA    #> 12 TEST01  PAT01   PARAM02 SCREENING 2021-04-27    15 AVERAGE NA    #> 13 TEST01  PAT01   PARAM02 SCREENING 2021-04-25    14 AVERAGE NA    #> 14 TEST01  PAT01   PARAM02 SCREENING 2021-04-23    15 NA      NA    #> 15 TEST01  PAT01   PARAM02 WEEK 2    2021-04-30    12 NA      NA    #> 16 TEST01  PAT02   PARAM02 SCREENING 2021-04-27    15 NA      NA    #> 17 TEST01  PAT02   PARAM02 BASELINE  2021-04-25    14 NA      NA    #> 18 TEST01  PAT02   PARAM02 WEEK 1    2021-04-23    15 NA      NA    #> 19 TEST01  PAT02   PARAM02 WEEK 1    2021-04-27    10 NA      NA    #> 20 TEST01  PAT02   PARAM02 BASELINE  2021-04-30    12 NA      NA     # OCCURDS Examples example_ae <- tribble(   ~USUBJID,         ~AEBODSYS,    ~AEDECOD,   ~AESEV, ~AESTDY, ~AESEQ,   \"1015\", \"GENERAL DISORDERS\",  \"ERYTHEMA\",   \"MILD\",       2,      1,   \"1015\", \"GENERAL DISORDERS\",  \"PRURITUS\",   \"MILD\",       2,      2,   \"1015\",      \"GI DISORDERS\", \"DIARRHOEA\",   \"MILD\",       8,      3,   \"1023\", \"CARDIAC DISORDERS\",  \"AV BLOCK\",   \"MILD\",      22,      4,   \"1023\",    \"SKIN DISORDERS\",  \"ERYTHEMA\",   \"MILD\",       3,      1,   \"1023\",    \"SKIN DISORDERS\",  \"ERYTHEMA\", \"SEVERE\",       5,      2,   \"1023\",    \"SKIN DISORDERS\",  \"ERYTHEMA\",   \"MILD\",       8,      3 )  # Most severe AE first occurrence per patient example_ae %>%   mutate(     TEMP_AESEVN =       as.integer(factor(AESEV, levels = c(\"SEVERE\", \"MODERATE\", \"MILD\")))   ) %>%   derive_var_extreme_flag(     new_var = AOCCIFL,     by_vars = exprs(USUBJID),     order = exprs(TEMP_AESEVN, AESTDY, AESEQ),     mode = \"first\"   ) %>%   arrange(USUBJID, AESTDY, AESEQ) %>%   select(USUBJID, AEDECOD, AESEV, AESTDY, AESEQ, AOCCIFL) #> # A tibble: 7 × 6 #>   USUBJID AEDECOD   AESEV  AESTDY AESEQ AOCCIFL #>   <chr>   <chr>     <chr>   <dbl> <dbl> <chr>   #> 1 1015    ERYTHEMA  MILD        2     1 Y       #> 2 1015    PRURITUS  MILD        2     2 NA      #> 3 1015    DIARRHOEA MILD        8     3 NA      #> 4 1023    ERYTHEMA  MILD        3     1 NA      #> 5 1023    ERYTHEMA  SEVERE      5     2 Y       #> 6 1023    ERYTHEMA  MILD        8     3 NA      #> 7 1023    AV BLOCK  MILD       22     4 NA       # Most severe AE first occurrence per patient (flag all cases) example_ae %>%   mutate(     TEMP_AESEVN =       as.integer(factor(AESEV, levels = c(\"SEVERE\", \"MODERATE\", \"MILD\")))   ) %>%   derive_var_extreme_flag(     new_var = AOCCIFL,     by_vars = exprs(USUBJID),     order = exprs(TEMP_AESEVN, AESTDY),     mode = \"first\",     flag_all = TRUE   ) %>%   arrange(USUBJID, AESTDY) %>%   select(USUBJID, AEDECOD, AESEV, AESTDY, AOCCIFL) #> # A tibble: 7 × 5 #>   USUBJID AEDECOD   AESEV  AESTDY AOCCIFL #>   <chr>   <chr>     <chr>   <dbl> <chr>   #> 1 1015    ERYTHEMA  MILD        2 Y       #> 2 1015    PRURITUS  MILD        2 Y       #> 3 1015    DIARRHOEA MILD        8 NA      #> 4 1023    ERYTHEMA  MILD        3 NA      #> 5 1023    ERYTHEMA  SEVERE      5 Y       #> 6 1023    ERYTHEMA  MILD        8 NA      #> 7 1023    AV BLOCK  MILD       22 NA       # Most severe AE first occurrence per patient per body system example_ae %>%   mutate(     TEMP_AESEVN =       as.integer(factor(AESEV, levels = c(\"SEVERE\", \"MODERATE\", \"MILD\")))   ) %>%   derive_var_extreme_flag(     new_var = AOCCSIFL,     by_vars = exprs(USUBJID, AEBODSYS),     order = exprs(TEMP_AESEVN, AESTDY, AESEQ),     mode = \"first\"   ) %>%   arrange(USUBJID, AESTDY, AESEQ) %>%   select(USUBJID, AEBODSYS, AESEV, AESTDY, AOCCSIFL) #> # A tibble: 7 × 5 #>   USUBJID AEBODSYS          AESEV  AESTDY AOCCSIFL #>   <chr>   <chr>             <chr>   <dbl> <chr>    #> 1 1015    GENERAL DISORDERS MILD        2 Y        #> 2 1015    GENERAL DISORDERS MILD        2 NA       #> 3 1015    GI DISORDERS      MILD        8 Y        #> 4 1023    SKIN DISORDERS    MILD        3 NA       #> 5 1023    SKIN DISORDERS    SEVERE      5 Y        #> 6 1023    SKIN DISORDERS    MILD        8 NA       #> 7 1023    CARDIAC DISORDERS MILD       22 Y"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_joined_exist_flag.html","id":null,"dir":"Reference","previous_headings":"","what":"Derives a Flag Based on an Existing Flag — derive_var_joined_exist_flag","title":"Derives a Flag Based on an Existing Flag — derive_var_joined_exist_flag","text":"Derive flag depends observations dataset. example, flagging events need confirmed second event.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_joined_exist_flag.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derives a Flag Based on an Existing Flag — derive_var_joined_exist_flag","text":"","code":"derive_var_joined_exist_flag(   dataset,   dataset_add,   by_vars,   order,   new_var,   tmp_obs_nr_var = NULL,   join_vars,   join_type,   first_cond_lower = NULL,   first_cond_upper = NULL,   filter_add = NULL,   filter_join,   true_value = \"Y\",   false_value = NA_character_,   check_type = \"warning\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_joined_exist_flag.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derives a Flag Based on an Existing Flag — derive_var_joined_exist_flag","text":"dataset Input dataset variables specified by_vars join_vars arguments expected dataset. dataset_add Additional dataset variables specified by_vars, join_vars, order expected. by_vars Grouping variables specified variables used joining input dataset (dataset) additional dataset (dataset_add). Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) order Order observations ordered specified order. handling NAs sorting variables see Sort Order. new_var New variable specified variable added input dataset. tmp_obs_nr_var Temporary observation number specified variable added input dataset (dataset) additional dataset (dataset_add). set observation number respect order. group (by_vars) observation number starts 1. variable can used conditions (filter_join, first_cond_upper, first_cond_lower). included output dataset. can also used flag consecutive observations last observation (see last example ). join_vars Variables keep joined dataset variables needed observations specified parameter. specified variables added joined dataset suffix \".join\". example flag observations AVALC == \"Y\" AVALC == \"Y\" least one subsequent visit join_vars = exprs(AVALC, AVISITN) filter_join = AVALC == \"Y\" & AVALC.join == \"Y\" & AVISITN < AVISITN.join specified. *.join variables included output dataset. join_type Observations keep joining argument determines joined observations kept respect original observation. example, join_type = \"\" specified observations original observations kept. example confirmed response BOR oncology setting confirmed deterioration questionnaires confirmatory assessment must assessment. Thus join_type = \"\" used. Whereas, sometimes might allow confirmatory observations occur prior observation. example, identify AEs occurring seven days COVID AE. Thus join_type = \"\" used. Permitted Values: \"\", \"\", \"\" first_cond_lower Condition selecting range data () argument specified, observations restricted first observation current observation specified condition fulfilled current observation. condition fulfilled observations, observations considered, .e., observation flagged. parameter specified filter_join contains summary functions apply observations certain observation current observation current observation. example see last example . first_cond_upper Condition selecting range data () argument specified, observations restricted first observation specified condition fulfilled. condition fulfilled observations, observations considered, .e., observation flagged. parameter specified filter_join contains summary functions apply observations confirmation assessment. example see third example . filter_add Filter additional dataset (dataset_add) observations dataset_add fulfilling specified condition joined input dataset. argument specified, observations joined. Variables created order new_vars arguments can used condition. condition can include summary functions like () (). additional dataset grouped variables (by_vars). Permitted Values: condition filter_join Condition selecting observations filter applied joined dataset flagging confirmed observations. condition can include summary functions like () (). joined dataset grouped original observations. .e., summary function applied observations confirmation observation. example, filter_join = AVALC == \"CR\" & (AVALC.join %% c(\"CR\", \"NE\")) & count_vals(var = AVALC.join, val = \"NE\") <= 1 selects observations response \"CR\" observations confirmation observation response \"CR\" \"NE\" one \"NE\". true_value Value new_var flagged observations false_value Value new_var observations flagged check_type Check uniqueness? \"warning\" \"error\" specified, specified message issued observations input dataset unique respect variables order. Permitted Values: \"none\", \"warning\", \"error\"","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_joined_exist_flag.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derives a Flag Based on an Existing Flag — derive_var_joined_exist_flag","text":"input dataset variable specified new_var added.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_joined_exist_flag.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Derives a Flag Based on an Existing Flag — derive_var_joined_exist_flag","text":"example usage might flagging patient received two required medications within certain timeframe . oncology setting, example, function used flag response value can confirmed assessment. commonly used endpoints best overall response. following steps performed produce output dataset.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_joined_exist_flag.html","id":"step-","dir":"Reference","previous_headings":"","what":"Step 1","title":"Derives a Flag Based on an Existing Flag — derive_var_joined_exist_flag","text":"variables specified order added additional dataset (dataset_add). variables specified join_vars added additional dataset (dataset_add). records additional dataset (dataset_add) restricted matching filter_add condition. input dataset (dataset) joined restricted additional dataset variables specified by_vars. additional dataset variables specified join_vars kept. suffix \".join\" added variables also exist input dataset. example, by_vars = USUBJID, join_vars = exprs(AVISITN, AVALC) input dataset additional dataset   joined dataset ","code":"# A tibble: 2 x 4 USUBJID AVISITN AVALC  AVAL <chr>     <dbl> <chr> <dbl> 1             1 Y         1 1             2 N         0 A tibble: 4 x 6 USUBJID AVISITN AVALC  AVAL AVISITN.join AVALC.join <chr>     <dbl> <chr> <dbl>        <dbl> <chr> 1             1 Y         1            1 Y 1             1 Y         1            2 N 1             2 N         0            1 Y 1             2 N         0            2 N"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_joined_exist_flag.html","id":"step--1","dir":"Reference","previous_headings":"","what":"Step 2","title":"Derives a Flag Based on an Existing Flag — derive_var_joined_exist_flag","text":"joined dataset restricted observations respect join_type order. dataset example previous step join_type = \"\" order = exprs(AVISITN) restricted ","code":"A tibble: 4 x 6 USUBJID AVISITN AVALC  AVAL AVISITN.join AVALC.join <chr>     <dbl> <chr> <dbl>        <dbl> <chr> 1             1 Y         1            2 N"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_joined_exist_flag.html","id":"step--2","dir":"Reference","previous_headings":"","what":"Step 3","title":"Derives a Flag Based on an Existing Flag — derive_var_joined_exist_flag","text":"first_cond_lower specified, observation input dataset joined dataset restricted observations first observation first_cond_lower fulfilled (observation fulfilling condition included) observation input dataset. observation input dataset condition fulfilled, observation removed. first_cond_upper specified, observation input dataset joined dataset restricted observations first observation first_cond_upper fulfilled (observation fulfilling condition included). observation input dataset condition fulfilled, observation removed. example see last example \"Examples\" section.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_joined_exist_flag.html","id":"step--3","dir":"Reference","previous_headings":"","what":"Step 4","title":"Derives a Flag Based on an Existing Flag — derive_var_joined_exist_flag","text":"joined dataset grouped observations input dataset restricted observations fulfilling condition specified filter_join.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_joined_exist_flag.html","id":"step--4","dir":"Reference","previous_headings":"","what":"Step 5","title":"Derives a Flag Based on an Existing Flag — derive_var_joined_exist_flag","text":"first observation group selected","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_joined_exist_flag.html","id":"step--5","dir":"Reference","previous_headings":"","what":"Step 6","title":"Derives a Flag Based on an Existing Flag — derive_var_joined_exist_flag","text":"variable specified new_var added input dataset. set true_value observations selected previous step. observations set false_value.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_joined_exist_flag.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derives a Flag Based on an Existing Flag — derive_var_joined_exist_flag","text":"","code":"library(tibble)  # flag observations with a duration longer than 30 and # at, after, or up to 7 days before a COVID AE (ACOVFL == \"Y\") adae <- tribble(   ~USUBJID, ~ADY, ~ACOVFL, ~ADURN,   \"1\",        10, \"N\",          1,   \"1\",        21, \"N\",         50,   \"1\",        23, \"Y\",         14,   \"1\",        32, \"N\",         31,   \"1\",        42, \"N\",         20,   \"2\",        11, \"Y\",         13,   \"2\",        23, \"N\",          2,   \"3\",        13, \"Y\",         12,   \"4\",        14, \"N\",         32,   \"4\",        21, \"N\",         41 )  derive_var_joined_exist_flag(   adae,   dataset_add = adae,   new_var = ALCOVFL,   by_vars = exprs(USUBJID),   join_vars = exprs(ACOVFL, ADY),   join_type = \"all\",   order = exprs(ADY),   filter_join = ADURN > 30 & ACOVFL.join == \"Y\" & ADY >= ADY.join - 7 ) #> # A tibble: 10 × 5 #>    USUBJID   ADY ACOVFL ADURN ALCOVFL #>    <chr>   <dbl> <chr>  <dbl> <chr>   #>  1 1          10 N          1 NA      #>  2 1          21 N         50 Y       #>  3 1          23 Y         14 NA      #>  4 1          32 N         31 Y       #>  5 1          42 N         20 NA      #>  6 2          11 Y         13 NA      #>  7 2          23 N          2 NA      #>  8 3          13 Y         12 NA      #>  9 4          14 N         32 NA      #> 10 4          21 N         41 NA       # flag observations with AVALC == \"Y\" and AVALC == \"Y\" at one subsequent visit data <- tribble(   ~USUBJID, ~AVISITN, ~AVALC,   \"1\",      1,        \"Y\",   \"1\",      2,        \"N\",   \"1\",      3,        \"Y\",   \"1\",      4,        \"N\",   \"2\",      1,        \"Y\",   \"2\",      2,        \"N\",   \"3\",      1,        \"Y\",   \"4\",      1,        \"N\",   \"4\",      2,        \"N\", )  derive_var_joined_exist_flag(   data,   dataset_add = data,   by_vars = exprs(USUBJID),   new_var = CONFFL,   join_vars = exprs(AVALC, AVISITN),   join_type = \"after\",   order = exprs(AVISITN),   filter_join = AVALC == \"Y\" & AVALC.join == \"Y\" & AVISITN < AVISITN.join ) #> # A tibble: 9 × 4 #>   USUBJID AVISITN AVALC CONFFL #>   <chr>     <dbl> <chr> <chr>  #> 1 1             1 Y     Y      #> 2 1             2 N     NA     #> 3 1             3 Y     NA     #> 4 1             4 N     NA     #> 5 2             1 Y     NA     #> 6 2             2 N     NA     #> 7 3             1 Y     NA     #> 8 4             1 N     NA     #> 9 4             2 N     NA      # select observations with AVALC == \"CR\", AVALC == \"CR\" at a subsequent visit, # only \"CR\" or \"NE\" in between, and at most one \"NE\" in between data <- tribble(   ~USUBJID, ~AVISITN, ~AVALC,   \"1\",      1,        \"PR\",   \"1\",      2,        \"CR\",   \"1\",      3,        \"NE\",   \"1\",      4,        \"CR\",   \"1\",      5,        \"NE\",   \"2\",      1,        \"CR\",   \"2\",      2,        \"PR\",   \"2\",      3,        \"CR\",   \"3\",      1,        \"CR\",   \"4\",      1,        \"CR\",   \"4\",      2,        \"NE\",   \"4\",      3,        \"NE\",   \"4\",      4,        \"CR\",   \"4\",      5,        \"PR\" )  derive_var_joined_exist_flag(   data,   dataset_add = data,   by_vars = exprs(USUBJID),   join_vars = exprs(AVALC),   join_type = \"after\",   order = exprs(AVISITN),   new_var = CONFFL,   first_cond_upper = AVALC.join == \"CR\",   filter_join = AVALC == \"CR\" & all(AVALC.join %in% c(\"CR\", \"NE\")) &     count_vals(var = AVALC.join, val = \"NE\") <= 1 ) #> # A tibble: 14 × 4 #>    USUBJID AVISITN AVALC CONFFL #>    <chr>     <dbl> <chr> <chr>  #>  1 1             1 PR    NA     #>  2 1             2 CR    Y      #>  3 1             3 NE    NA     #>  4 1             4 CR    NA     #>  5 1             5 NE    NA     #>  6 2             1 CR    NA     #>  7 2             2 PR    NA     #>  8 2             3 CR    NA     #>  9 3             1 CR    NA     #> 10 4             1 CR    NA     #> 11 4             2 NE    NA     #> 12 4             3 NE    NA     #> 13 4             4 CR    NA     #> 14 4             5 PR    NA      # flag observations with AVALC == \"PR\", AVALC == \"CR\" or AVALC == \"PR\" # at a subsequent visit at least 20 days later, only \"CR\", \"PR\", or \"NE\" # in between, at most one \"NE\" in between, and \"CR\" is not followed by \"PR\" data <- tribble(   ~USUBJID, ~ADY, ~AVALC,   \"1\",         6, \"PR\",   \"1\",        12, \"CR\",   \"1\",        24, \"NE\",   \"1\",        32, \"CR\",   \"1\",        48, \"PR\",   \"2\",         3, \"PR\",   \"2\",        21, \"CR\",   \"2\",        33, \"PR\",   \"3\",        11, \"PR\",   \"4\",         7, \"PR\",   \"4\",        12, \"NE\",   \"4\",        24, \"NE\",   \"4\",        32, \"PR\",   \"4\",        55, \"PR\" )  derive_var_joined_exist_flag(   data,   dataset_add = data,   by_vars = exprs(USUBJID),   join_vars = exprs(AVALC, ADY),   join_type = \"after\",   order = exprs(ADY),   new_var = CONFFL,   first_cond_upper = AVALC.join %in% c(\"CR\", \"PR\") & ADY.join - ADY >= 20,   filter_join = AVALC == \"PR\" &     all(AVALC.join %in% c(\"CR\", \"PR\", \"NE\")) &     count_vals(var = AVALC.join, val = \"NE\") <= 1 &     (       min_cond(var = ADY.join, cond = AVALC.join == \"CR\") >         max_cond(var = ADY.join, cond = AVALC.join == \"PR\") |         count_vals(var = AVALC.join, val = \"CR\") == 0     ) ) #> # A tibble: 14 × 4 #>    USUBJID   ADY AVALC CONFFL #>    <chr>   <dbl> <chr> <chr>  #>  1 1           6 PR    NA     #>  2 1          12 CR    NA     #>  3 1          24 NE    NA     #>  4 1          32 CR    NA     #>  5 1          48 PR    NA     #>  6 2           3 PR    NA     #>  7 2          21 CR    NA     #>  8 2          33 PR    NA     #>  9 3          11 PR    NA     #> 10 4           7 PR    NA     #> 11 4          12 NE    NA     #> 12 4          24 NE    NA     #> 13 4          32 PR    Y      #> 14 4          55 PR    NA      # flag observations with CRIT1FL == \"Y\" at two consecutive visits or at the last visit data <- tribble(   ~USUBJID, ~AVISITN, ~CRIT1FL,   \"1\",      1,        \"Y\",   \"1\",      2,        \"N\",   \"1\",      3,        \"Y\",   \"1\",      5,        \"N\",   \"2\",      1,        \"Y\",   \"2\",      3,        \"Y\",   \"2\",      5,        \"N\",   \"3\",      1,        \"Y\",   \"4\",      1,        \"Y\",   \"4\",      2,        \"N\", )  derive_var_joined_exist_flag(   data,   dataset_add = data,   by_vars = exprs(USUBJID),   new_var = CONFFL,   tmp_obs_nr_var = tmp_obs_nr,   join_vars = exprs(CRIT1FL),   join_type = \"all\",   order = exprs(AVISITN),   filter_join = CRIT1FL == \"Y\" & CRIT1FL.join == \"Y\" &     (tmp_obs_nr + 1 == tmp_obs_nr.join | tmp_obs_nr == max(tmp_obs_nr.join)) ) #> # A tibble: 10 × 4 #>    USUBJID AVISITN CRIT1FL CONFFL #>    <chr>     <dbl> <chr>   <chr>  #>  1 1             1 Y       NA     #>  2 1             2 N       NA     #>  3 1             3 Y       NA     #>  4 1             5 N       NA     #>  5 2             1 Y       Y      #>  6 2             3 Y       NA     #>  7 2             5 N       NA     #>  8 3             1 Y       Y      #>  9 4             1 Y       NA     #> 10 4             2 N       NA      # first_cond_lower and first_cond_upper argument myd <- tribble(   ~subj, ~day, ~val,   \"1\",      1, \"++\",   \"1\",      2, \"-\",   \"1\",      3, \"0\",   \"1\",      4, \"+\",   \"1\",      5, \"++\",   \"1\",      6, \"-\",   \"2\",      1, \"-\",   \"2\",      2, \"++\",   \"2\",      3, \"+\",   \"2\",      4, \"0\",   \"2\",      5, \"-\",   \"2\",      6, \"++\" )  # flag \"0\" where all results from the first \"++\" before the \"0\" up to the \"0\" # (excluding the \"0\") are \"+\" or \"++\" derive_var_joined_exist_flag(   myd,   dataset_add = myd,   by_vars = exprs(subj),   order = exprs(day),   new_var = flag,   join_vars = exprs(val),   join_type = \"before\",   first_cond_lower = val.join == \"++\",   filter_join = val == \"0\" & all(val.join %in% c(\"+\", \"++\")) ) #> # A tibble: 12 × 4 #>    subj    day val   flag  #>    <chr> <dbl> <chr> <chr> #>  1 1         1 ++    NA    #>  2 1         2 -     NA    #>  3 1         3 0     NA    #>  4 1         4 +     NA    #>  5 1         5 ++    NA    #>  6 1         6 -     NA    #>  7 2         1 -     NA    #>  8 2         2 ++    NA    #>  9 2         3 +     NA    #> 10 2         4 0     Y     #> 11 2         5 -     NA    #> 12 2         6 ++    NA     # flag \"0\" where all results from the \"0\" (excluding the \"0\") up to the first # \"++\" after the \"0\" are \"+\" or \"++\" derive_var_joined_exist_flag(   myd,   dataset_add = myd,   by_vars = exprs(subj),   order = exprs(day),   new_var = flag,   join_vars = exprs(val),   join_type = \"after\",   first_cond_upper = val.join == \"++\",   filter_join = val == \"0\" & all(val.join %in% c(\"+\", \"++\")) ) #> # A tibble: 12 × 4 #>    subj    day val   flag  #>    <chr> <dbl> <chr> <chr> #>  1 1         1 ++    NA    #>  2 1         2 -     NA    #>  3 1         3 0     Y     #>  4 1         4 +     NA    #>  5 1         5 ++    NA    #>  6 1         6 -     NA    #>  7 2         1 -     NA    #>  8 2         2 ++    NA    #>  9 2         3 +     NA    #> 10 2         4 0     NA    #> 11 2         5 -     NA    #> 12 2         6 ++    NA     # flag each dose which is lower than the previous dose per subject ex <- tribble(   ~USUBJID, ~EXSTDTM,          ~EXDOSE,   \"1\",      \"2024-01-01T08:00\",      2,   \"1\",      \"2024-01-02T08:00\",      4,   \"2\",      \"2024-01-01T08:30\",      1,   \"2\",      \"2024-01-02T08:30\",      4,   \"2\",      \"2024-01-03T08:30\",      3,   \"2\",      \"2024-01-04T08:30\",      2,   \"2\",      \"2024-01-05T08:30\",      2 )  derive_var_joined_exist_flag(   ex,   dataset_add = ex,   by_vars = exprs(USUBJID),   order = exprs(EXSTDTM),   new_var = DOSREDFL,   tmp_obs_nr_var = tmp_dose_nr,   join_vars = exprs(EXDOSE),   join_type = \"before\",   filter_join = (     tmp_dose_nr == tmp_dose_nr.join + 1 # Look only at adjacent doses     & EXDOSE > 0 & EXDOSE.join > 0 # Both doses are valid     & EXDOSE < EXDOSE.join # Dose is lower than previous   ) ) #> # A tibble: 7 × 4 #>   USUBJID EXSTDTM          EXDOSE DOSREDFL #>   <chr>   <chr>             <dbl> <chr>    #> 1 1       2024-01-01T08:00      2 NA       #> 2 1       2024-01-02T08:00      4 NA       #> 3 2       2024-01-01T08:30      1 NA       #> 4 2       2024-01-02T08:30      4 NA       #> 5 2       2024-01-03T08:30      3 Y        #> 6 2       2024-01-04T08:30      2 Y        #> 7 2       2024-01-05T08:30      2 NA        # derive definitive deterioration flag as any deterioration (CHGCAT1 = \"Worsened\") # by parameter that is not followed by a non-deterioration adqs <- tribble(   ~USUBJID, ~PARAMCD, ~ADY, ~CHGCAT1,   \"1\",      \"QS1\",      10, \"Improved\",   \"1\",      \"QS1\",      21, \"Improved\",   \"1\",      \"QS1\",      23, \"Improved\",   \"1\",      \"QS2\",      32, \"Worsened\",   \"1\",      \"QS2\",      42, \"Improved\",   \"2\",      \"QS1\",      11, \"Worsened\",   \"2\",      \"QS1\",      24, \"Worsened\" )  derive_var_joined_exist_flag(   adqs,   dataset_add = adqs,   new_var = DDETERFL,   by_vars = exprs(USUBJID, PARAMCD),   join_vars = exprs(CHGCAT1),   join_type = \"all\",   order = exprs(ADY),   filter_join = all(CHGCAT1.join == \"Worsened\" | ADY > ADY.join) ) #> # A tibble: 7 × 5 #>   USUBJID PARAMCD   ADY CHGCAT1  DDETERFL #>   <chr>   <chr>   <dbl> <chr>    <chr>    #> 1 1       QS1        10 Improved NA       #> 2 1       QS1        21 Improved NA       #> 3 1       QS1        23 Improved NA       #> 4 1       QS2        32 Worsened NA       #> 5 1       QS2        42 Improved NA       #> 6 2       QS1        11 Worsened Y        #> 7 2       QS1        24 Worsened Y"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_merged_ef_msrc.html","id":null,"dir":"Reference","previous_headings":"","what":"Merge an Existence Flag From Multiple Sources — derive_var_merged_ef_msrc","title":"Merge an Existence Flag From Multiple Sources — derive_var_merged_ef_msrc","text":"Adds flag variable input dataset indicates exists least one observation one source datasets fulfilling certain condition. example, dose adjustment flag added ADEX dose adjustment information collected different datasets, e.g., EX, EC, FA.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_merged_ef_msrc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Merge an Existence Flag From Multiple Sources — derive_var_merged_ef_msrc","text":"","code":"derive_var_merged_ef_msrc(   dataset,   by_vars,   flag_events,   source_datasets,   new_var,   true_value = \"Y\",   false_value = NA_character_,   missing_value = NA_character_ )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_merged_ef_msrc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Merge an Existence Flag From Multiple Sources — derive_var_merged_ef_msrc","text":"dataset Input dataset variables specified by_vars argument expected dataset. by_vars Grouping variables Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) flag_events Flag events list flag_event() objects expected. event condition (condition field) evaluated source dataset referenced dataset_name field. evaluates TRUE least , new variable set true_value. source_datasets Source datasets named list datasets expected. dataset_name field flag_event() refers dataset provided list. new_var New variable specified variable added input dataset. true_value True value new variable (new_var) set specified value groups least one source object (sources) condition evaluate TRUE. values true_value, false_value, missing_value must type. false_value False value new variable (new_var) set specified value groups occur least one source (sources) condition never evaluates TRUE. values true_value, false_value, missing_value must type. missing_value Values used missing information new variable set specified value groups without observations sources (sources). values true_value, false_value, missing_value must type.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_merged_ef_msrc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Merge an Existence Flag From Multiple Sources — derive_var_merged_ef_msrc","text":"output dataset contains observations variables input dataset additionally variable specified new_var.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_merged_ef_msrc.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Merge an Existence Flag From Multiple Sources — derive_var_merged_ef_msrc","text":"flag_event() object specified flag_events: condition (condition) evaluated dataset referenced dataset_name. by_vars field specified dataset grouped specified variables evaluating condition. named elements used by_vars like by_vars = exprs(USUBJID, EXLNKID = ECLNKID), variables renamed evaluation. by_vars element specified, observations grouped variables specified by_vars argument. new variable (new_var) added input dataset set true value (true_value) group least one condition evaluates TRUE one sources. set false value (false_value) group least one observation exists observations condition evaluates FALSE NA. Otherwise, set missing value (missing_value).","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_merged_ef_msrc.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Merge an Existence Flag From Multiple Sources — derive_var_merged_ef_msrc","text":"","code":"library(dplyr)  # Derive a flag indicating anti-cancer treatment based on CM and PR adsl <- tribble(   ~USUBJID,   \"1\",   \"2\",   \"3\",   \"4\" )  cm <- tribble(   ~USUBJID, ~CMCAT,        ~CMSEQ,   \"1\",      \"ANTI-CANCER\",      1,   \"1\",      \"GENERAL\",          2,   \"2\",      \"GENERAL\",          1,   \"3\",      \"ANTI-CANCER\",      1 )  # Assuming all records in PR indicate cancer treatment pr <- tibble::tribble(   ~USUBJID, ~PRSEQ,   \"2\",      1,   \"3\",      1 )  derive_var_merged_ef_msrc(   adsl,   by_vars = exprs(USUBJID),   flag_events = list(     flag_event(       dataset_name = \"cm\",       condition = CMCAT == \"ANTI-CANCER\"     ),     flag_event(       dataset_name = \"pr\"     )   ),   source_datasets = list(cm = cm, pr = pr),   new_var = CANCTRFL ) #> # A tibble: 4 × 2 #>   USUBJID CANCTRFL #>   <chr>   <chr>    #> 1 1       Y        #> 2 2       Y        #> 3 3       Y        #> 4 4       NA        # Using different by variables depending on the source # Add a dose adjustment flag to ADEX based on ADEX, EC, and FA adex <- tribble(   ~USUBJID, ~EXLNKID, ~EXADJ,   \"1\",      \"1\",      \"AE\",   \"1\",      \"2\",      NA_character_,   \"1\",      \"3\",      NA_character_,   \"2\",      \"1\",      NA_character_,   \"3\",      \"1\",      NA_character_ )  ec <- tribble(   ~USUBJID, ~ECLNKID, ~ECADJ,   \"1\",      \"3\",      \"AE\",   \"3\",      \"1\",      NA_character_ )  fa <- tribble(   ~USUBJID, ~FALNKID, ~FATESTCD, ~FAOBJ,            ~FASTRESC,   \"3\",      \"1\",      \"OCCUR\",   \"DOSE ADJUSTMENT\", \"Y\" )  derive_var_merged_ef_msrc(   adex,   by_vars = exprs(USUBJID, EXLNKID),   flag_events = list(     flag_event(       dataset_name = \"ex\",       condition = !is.na(EXADJ)     ),     flag_event(       dataset_name = \"ec\",       condition = !is.na(ECADJ),       by_vars = exprs(USUBJID, EXLNKID = ECLNKID)     ),     flag_event(       dataset_name = \"fa\",       condition = FATESTCD == \"OCCUR\" & FAOBJ == \"DOSE ADJUSTMENT\" & FASTRESC == \"Y\",       by_vars = exprs(USUBJID, EXLNKID = FALNKID)     )   ),   source_datasets = list(ex = adex, ec = ec, fa = fa),   new_var = DOSADJFL ) #> # A tibble: 5 × 4 #>   USUBJID EXLNKID EXADJ DOSADJFL #>   <chr>   <chr>   <chr> <chr>    #> 1 1       1       AE    Y        #> 2 1       2       NA    NA       #> 3 1       3       NA    Y        #> 4 2       1       NA    NA       #> 5 3       1       NA    Y"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_merged_exist_flag.html","id":null,"dir":"Reference","previous_headings":"","what":"Merge an Existence Flag — derive_var_merged_exist_flag","title":"Merge an Existence Flag — derive_var_merged_exist_flag","text":"Adds flag variable input dataset indicates exists least one observation another dataset fulfilling certain condition. Note: wrapper function generic derive_vars_merged().","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_merged_exist_flag.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Merge an Existence Flag — derive_var_merged_exist_flag","text":"","code":"derive_var_merged_exist_flag(   dataset,   dataset_add,   by_vars,   new_var,   condition,   true_value = \"Y\",   false_value = NA_character_,   missing_value = NA_character_,   filter_add = NULL )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_merged_exist_flag.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Merge an Existence Flag — derive_var_merged_exist_flag","text":"dataset Input dataset variables specified by_vars argument expected dataset. dataset_add Additional dataset variables specified by_vars argument expected. by_vars Grouping variables Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) new_var New variable specified variable added input dataset. condition Condition condition evaluated additional dataset (dataset_add). groups evaluates TRUE least new variable set true value (true_value). groups evaluates FALSE NA observations new variable set false value (false_value). new variable set missing value (missing_value) groups present additional dataset. true_value True value false_value False value missing_value Values used missing information new variable set specified value groups without observations additional dataset. Permitted Value: character scalar filter_add Filter additional data observations fulfilling specified condition taken account flagging. argument specified, observations considered. Permitted Values: condition","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_merged_exist_flag.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Merge an Existence Flag — derive_var_merged_exist_flag","text":"output dataset contains observations variables input dataset additionally variable specified new_var derived additional dataset (dataset_add).","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_merged_exist_flag.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Merge an Existence Flag — derive_var_merged_exist_flag","text":"additional dataset restricted observations matching filter_add condition. new variable added input dataset set true value (true_value) group least one observation exists (restricted) additional dataset condition evaluates TRUE. set false value (false_value) group least one observation exists observations condition evaluates FALSE NA. Otherwise, set missing value (missing_value).","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_merged_exist_flag.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Merge an Existence Flag — derive_var_merged_exist_flag","text":"","code":"library(dplyr, warn.conflicts = FALSE)  dm <- tribble(   ~STUDYID,  ~DOMAIN,  ~USUBJID, ~AGE,   ~AGEU,   \"PILOT01\",    \"DM\", \"01-1028\",   71, \"YEARS\",   \"PILOT01\",    \"DM\", \"04-1127\",   84, \"YEARS\",   \"PILOT01\",    \"DM\", \"06-1049\",   60, \"YEARS\" )  ae <- tribble(   ~STUDYID,  ~DOMAIN,  ~USUBJID,    ~AETERM,     ~AEREL,   \"PILOT01\",    \"AE\", \"01-1028\", \"ERYTHEMA\", \"POSSIBLE\",   \"PILOT01\",    \"AE\", \"01-1028\", \"PRURITUS\", \"PROBABLE\",   \"PILOT01\",    \"AE\", \"06-1049\",  \"SYNCOPE\", \"POSSIBLE\",   \"PILOT01\",    \"AE\", \"06-1049\",  \"SYNCOPE\", \"PROBABLE\" )   derive_var_merged_exist_flag(   dm,   dataset_add = ae,   by_vars = exprs(STUDYID, USUBJID),   new_var = AERELFL,   condition = AEREL == \"PROBABLE\" ) %>%   select(STUDYID, USUBJID, AGE, AGEU, AERELFL) #> # A tibble: 3 × 5 #>   STUDYID USUBJID   AGE AGEU  AERELFL #>   <chr>   <chr>   <dbl> <chr> <chr>   #> 1 PILOT01 01-1028    71 YEARS Y       #> 2 PILOT01 04-1127    84 YEARS NA      #> 3 PILOT01 06-1049    60 YEARS Y        vs <- tribble(   ~STUDYID,  ~DOMAIN,  ~USUBJID,      ~VISIT, ~VSTESTCD, ~VSSTRESN, ~VSBLFL,   \"PILOT01\",    \"VS\", \"01-1028\", \"SCREENING\",  \"HEIGHT\",     177.8,      NA,   \"PILOT01\",    \"VS\", \"01-1028\", \"SCREENING\",  \"WEIGHT\",     98.88,      NA,   \"PILOT01\",    \"VS\", \"01-1028\",  \"BASELINE\",  \"WEIGHT\",     99.34,     \"Y\",   \"PILOT01\",    \"VS\", \"01-1028\",    \"WEEK 4\",  \"WEIGHT\",     98.88,      NA,   \"PILOT01\",    \"VS\", \"04-1127\", \"SCREENING\",  \"HEIGHT\",     165.1,      NA,   \"PILOT01\",    \"VS\", \"04-1127\", \"SCREENING\",  \"WEIGHT\",     42.87,      NA,   \"PILOT01\",    \"VS\", \"04-1127\",  \"BASELINE\",  \"WEIGHT\",     41.05,     \"Y\",   \"PILOT01\",    \"VS\", \"04-1127\",    \"WEEK 4\",  \"WEIGHT\",     41.73,      NA,   \"PILOT01\",    \"VS\", \"06-1049\", \"SCREENING\",  \"HEIGHT\",    167.64,      NA,   \"PILOT01\",    \"VS\", \"06-1049\", \"SCREENING\",  \"WEIGHT\",     57.61,      NA,   \"PILOT01\",    \"VS\", \"06-1049\",  \"BASELINE\",  \"WEIGHT\",     57.83,     \"Y\",   \"PILOT01\",    \"VS\", \"06-1049\",    \"WEEK 4\",  \"WEIGHT\",     58.97,      NA ) derive_var_merged_exist_flag(   dm,   dataset_add = vs,   by_vars = exprs(STUDYID, USUBJID),   filter_add = VSTESTCD == \"WEIGHT\" & VSBLFL == \"Y\",   new_var = WTBLHIFL,   condition = VSSTRESN > 90,   false_value = \"N\",   missing_value = \"M\" ) %>%   select(STUDYID, USUBJID, AGE, AGEU, WTBLHIFL) #> # A tibble: 3 × 5 #>   STUDYID USUBJID   AGE AGEU  WTBLHIFL #>   <chr>   <chr>   <dbl> <chr> <chr>    #> 1 PILOT01 01-1028    71 YEARS Y        #> 2 PILOT01 04-1127    84 YEARS N        #> 3 PILOT01 06-1049    60 YEARS N"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_merged_summary.html","id":null,"dir":"Reference","previous_headings":"","what":"Merge Summary Variables — derive_var_merged_summary","title":"Merge Summary Variables — derive_var_merged_summary","text":"Merge summary variable dataset input dataset.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_merged_summary.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Merge Summary Variables — derive_var_merged_summary","text":"","code":"derive_var_merged_summary(   dataset,   dataset_add,   by_vars,   new_vars = NULL,   filter_add = NULL,   missing_values = NULL )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_merged_summary.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Merge Summary Variables — derive_var_merged_summary","text":"dataset Input dataset variables specified by_vars argument expected dataset. dataset_add Additional dataset variables specified by_vars variables used left hand sides new_vars arguments expected. by_vars Grouping variables expressions left hand sides new_vars evaluated specified variables. resulting values merged input dataset (dataset) specified variables. Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) new_vars New variables add specified variables added input dataset. named list expressions expected: LHS refer variable. RHS refers values set variable. can string, symbol, numeric value, expression NA. summary functions used, values summarized variables specified by_vars. example:   filter_add Filter additional dataset (dataset_add) observations fulfilling specified condition taken account summarizing. argument specified, observations considered. Permitted Values: condition missing_values Values non-matching observations observations input dataset (dataset) matching observation additional dataset (dataset_add) values specified variables set specified value. variables specified new_vars can specified missing_values. Permitted Values: named list expressions, e.g., exprs(BASEC = \"MISSING\", BASE = -1)","code":"new_vars = exprs(     DOSESUM = sum(AVAL),     DOSEMEAN = mean(AVAL)   )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_merged_summary.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Merge Summary Variables — derive_var_merged_summary","text":"output dataset contains observations variables input dataset additionally variables specified new_vars.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_merged_summary.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Merge Summary Variables — derive_var_merged_summary","text":"records additional dataset (dataset_add) restricted matching filter_add condition. new variables (new_vars) created group (by_vars) additional dataset (dataset_add) calling summarize(). .e., observations group summarized single observation. new variables merged input dataset. observations without matching observation additional dataset new variables set NA. Observations additional dataset matching observation input dataset ignored.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_merged_summary.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Merge Summary Variables — derive_var_merged_summary","text":"","code":"library(tibble)  # Add a variable for the mean of AVAL within each visit adbds <- tribble(   ~USUBJID,  ~AVISIT,  ~ASEQ, ~AVAL,   \"1\",      \"WEEK 1\",      1,    10,   \"1\",      \"WEEK 1\",      2,    NA,   \"1\",      \"WEEK 2\",      3,    NA,   \"1\",      \"WEEK 3\",      4,    42,   \"1\",      \"WEEK 4\",      5,    12,   \"1\",      \"WEEK 4\",      6,    12,   \"1\",      \"WEEK 4\",      7,    15,   \"2\",      \"WEEK 1\",      1,    21,   \"2\",      \"WEEK 4\",      2,    22 )  derive_var_merged_summary(   adbds,   dataset_add = adbds,   by_vars = exprs(USUBJID, AVISIT),   new_vars = exprs(     MEANVIS = mean(AVAL, na.rm = TRUE),     MAXVIS = max(AVAL, na.rm = TRUE)   ) ) #> Warning: There was 1 warning in `summarise()`. #> ℹ In argument: `MAXVIS = max(AVAL, na.rm = TRUE)`. #> ℹ In group 2: `USUBJID = \"1\"` and `AVISIT = \"WEEK 2\"`. #> Caused by warning in `max()`: #> ! no non-missing arguments to max; returning -Inf #> # A tibble: 9 × 6 #>   USUBJID AVISIT  ASEQ  AVAL MEANVIS MAXVIS #>   <chr>   <chr>  <dbl> <dbl>   <dbl>  <dbl> #> 1 1       WEEK 1     1    10      10     10 #> 2 1       WEEK 1     2    NA      10     10 #> 3 1       WEEK 2     3    NA     NaN   -Inf #> 4 1       WEEK 3     4    42      42     42 #> 5 1       WEEK 4     5    12      13     15 #> 6 1       WEEK 4     6    12      13     15 #> 7 1       WEEK 4     7    15      13     15 #> 8 2       WEEK 1     1    21      21     21 #> 9 2       WEEK 4     2    22      22     22  # Add a variable listing the lesion ids at baseline adsl <- tribble(   ~USUBJID,   \"1\",   \"2\",   \"3\" )  adtr <- tribble(   ~USUBJID,     ~AVISIT, ~LESIONID,   \"1\",       \"BASELINE\",  \"INV-T1\",   \"1\",       \"BASELINE\",  \"INV-T2\",   \"1\",       \"BASELINE\",  \"INV-T3\",   \"1\",       \"BASELINE\",  \"INV-T4\",   \"1\",         \"WEEK 1\",  \"INV-T1\",   \"1\",         \"WEEK 1\",  \"INV-T2\",   \"1\",         \"WEEK 1\",  \"INV-T4\",   \"2\",       \"BASELINE\",  \"INV-T1\",   \"2\",       \"BASELINE\",  \"INV-T2\",   \"2\",       \"BASELINE\",  \"INV-T3\",   \"2\",         \"WEEK 1\",  \"INV-T1\",   \"2\",         \"WEEK 1\",  \"INV-N1\" )  derive_var_merged_summary(   adsl,   dataset_add = adtr,   by_vars = exprs(USUBJID),   filter_add = AVISIT == \"BASELINE\",   new_vars = exprs(LESIONSBL = paste(LESIONID, collapse = \", \")) ) #> # A tibble: 3 × 2 #>   USUBJID LESIONSBL                      #>   <chr>   <chr>                          #> 1 1       INV-T1, INV-T2, INV-T3, INV-T4 #> 2 2       INV-T1, INV-T2, INV-T3         #> 3 3       NA"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_obs_number.html","id":null,"dir":"Reference","previous_headings":"","what":"Adds a Variable Numbering the Observations Within Each By Group — derive_var_obs_number","title":"Adds a Variable Numbering the Observations Within Each By Group — derive_var_obs_number","text":"Adds variable numbering observations within group","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_obs_number.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adds a Variable Numbering the Observations Within Each By Group — derive_var_obs_number","text":"","code":"derive_var_obs_number(   dataset,   by_vars = NULL,   order = NULL,   new_var = ASEQ,   check_type = \"none\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_obs_number.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adds a Variable Numbering the Observations Within Each By Group — derive_var_obs_number","text":"dataset Input dataset variables specified by_vars order arguments expected dataset. by_vars Grouping variables Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) order Sort order Within group observations ordered specified order. handling NAs sorting variables see Sort Order. Permitted Values: list variables functions variables new_var Name variable create new variable set observation number group. numbering starts 1. Default: ASEQ check_type Check uniqueness? \"warning\" \"error\" specified, specified message issued observations input dataset unique respect variables order. Default: \"none\" Permitted Values: \"none\", \"warning\", \"error\"","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_obs_number.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adds a Variable Numbering the Observations Within Each By Group — derive_var_obs_number","text":"dataset containing observations variables input dataset additionally variable specified new_var parameter.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_obs_number.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Adds a Variable Numbering the Observations Within Each By Group — derive_var_obs_number","text":"group (respect variables specified by_vars parameter) first last observation (respect order specified order parameter mode specified mode parameter) included output dataset.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_obs_number.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Adds a Variable Numbering the Observations Within Each By Group — derive_var_obs_number","text":"","code":"library(dplyr, warn.conflicts = FALSE) vs <- tribble(   ~STUDYID,  ~DOMAIN,      ~USUBJID, ~VSTESTCD, ~VISITNUM, ~VSTPTNUM,   \"PILOT01\",    \"VS\", \"01-703-1182\",   \"DIABP\",         3,       815,   \"PILOT01\",    \"VS\", \"01-703-1182\",   \"DIABP\",         3,       816,   \"PILOT01\",    \"VS\", \"01-703-1182\",   \"DIABP\",         4,       815,   \"PILOT01\",    \"VS\", \"01-703-1182\",   \"DIABP\",         4,       816,   \"PILOT01\",    \"VS\", \"01-703-1182\",   \"PULSE\",         3,       815,   \"PILOT01\",    \"VS\", \"01-703-1182\",   \"PULSE\",         3,       816,   \"PILOT01\",    \"VS\", \"01-703-1182\",   \"PULSE\",         4,       815,   \"PILOT01\",    \"VS\", \"01-703-1182\",   \"PULSE\",         4,       816,   \"PILOT01\",    \"VS\", \"01-703-1182\",   \"SYSBP\",         3,       815,   \"PILOT01\",    \"VS\", \"01-703-1182\",   \"SYSBP\",         3,       816,   \"PILOT01\",    \"VS\", \"01-703-1182\",   \"SYSBP\",         4,       815,   \"PILOT01\",    \"VS\", \"01-703-1182\",   \"SYSBP\",         4,       816,   \"PILOT01\",    \"VS\", \"01-716-1229\",   \"DIABP\",         3,       815,   \"PILOT01\",    \"VS\", \"01-716-1229\",   \"DIABP\",         3,       816,   \"PILOT01\",    \"VS\", \"01-716-1229\",   \"DIABP\",         4,       815,   \"PILOT01\",    \"VS\", \"01-716-1229\",   \"DIABP\",         4,       816,   \"PILOT01\",    \"VS\", \"01-716-1229\",   \"PULSE\",         3,       815,   \"PILOT01\",    \"VS\", \"01-716-1229\",   \"PULSE\",         3,       816,   \"PILOT01\",    \"VS\", \"01-716-1229\",   \"PULSE\",         4,       815,   \"PILOT01\",    \"VS\", \"01-716-1229\",   \"PULSE\",         4,       816,   \"PILOT01\",    \"VS\", \"01-716-1229\",   \"SYSBP\",         3,       815,   \"PILOT01\",    \"VS\", \"01-716-1229\",   \"SYSBP\",         3,       816,   \"PILOT01\",    \"VS\", \"01-716-1229\",   \"SYSBP\",         4,       815,   \"PILOT01\",    \"VS\", \"01-716-1229\",   \"SYSBP\",         4,       816 ) vs %>%   derive_var_obs_number(     by_vars = exprs(USUBJID, VSTESTCD),     order = exprs(VISITNUM, desc(VSTPTNUM))   ) #> # A tibble: 24 × 7 #>    STUDYID DOMAIN USUBJID     VSTESTCD VISITNUM VSTPTNUM  ASEQ #>    <chr>   <chr>  <chr>       <chr>       <dbl>    <dbl> <int> #>  1 PILOT01 VS     01-703-1182 DIABP           3      816     1 #>  2 PILOT01 VS     01-703-1182 DIABP           3      815     2 #>  3 PILOT01 VS     01-703-1182 DIABP           4      816     3 #>  4 PILOT01 VS     01-703-1182 DIABP           4      815     4 #>  5 PILOT01 VS     01-703-1182 PULSE           3      816     1 #>  6 PILOT01 VS     01-703-1182 PULSE           3      815     2 #>  7 PILOT01 VS     01-703-1182 PULSE           4      816     3 #>  8 PILOT01 VS     01-703-1182 PULSE           4      815     4 #>  9 PILOT01 VS     01-703-1182 SYSBP           3      816     1 #> 10 PILOT01 VS     01-703-1182 SYSBP           3      815     2 #> # ℹ 14 more rows"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_ontrtfl.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive On-Treatment Flag Variable — derive_var_ontrtfl","title":"Derive On-Treatment Flag Variable — derive_var_ontrtfl","text":"Derive -treatment flag (ONTRTFL) ADaM dataset single assessment date (e.g ADT) event start end dates (e.g. ASTDT/AENDT).","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_ontrtfl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive On-Treatment Flag Variable — derive_var_ontrtfl","text":"","code":"derive_var_ontrtfl(   dataset,   new_var = ONTRTFL,   start_date,   end_date = NULL,   ref_start_date,   ref_end_date = NULL,   ref_end_window = 0,   ignore_time_for_ref_end_date = TRUE,   filter_pre_timepoint = NULL,   span_period = FALSE )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_ontrtfl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive On-Treatment Flag Variable — derive_var_ontrtfl","text":"dataset Input dataset Required columns start_date, end_date, ref_start_date ref_end_date. new_var -treatment flag variable name created. Default ONTRTFL. start_date start date (e.g. AESDT) assessment date (e.g. ADT) Required; date date-time object column expected. Refer derive_vars_dt() impute derive date date character vector date object. end_date end date assessment/event (e.g. AENDT) date date-time object column expected. Refer derive_vars_dt() impute derive date date character vector date object. Optional; Default null. used date value missing observation, assumed medication ongoing ONTRTFL set \"Y\". ref_start_date lower bound -treatment period Required; date date-time object column expected. Refer derive_vars_dt() impute derive date date character vector date object. ref_end_date upper bound -treatment period date date-time object column expected. Refer derive_vars_dt() impute derive date date character vector date object. Optional; can null everything ref_start_date considered -treatment. Default NULL. ref_end_window window add upper bound ref_end_date measured days (e.g. 7 7 days added upper bound) Optional; default 0. ignore_time_for_ref_end_date argument set TRUE, time part ignored checking event occurred ref_end_window days reference end date. Permitted Values: TRUE, FALSE filter_pre_timepoint expression filter observations -treatment date = ref_start_date. example, observations VSTPT = PRE considered -treatment date = ref_start_date, filter_pre_timepoint used denote -treatment flag set null. Optional; default NULL. span_period logical scalar. TRUE, events started prior ref_start_dateand ongoing end ref_start_date flagged \"Y\". Optional; default FALSE.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_ontrtfl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive On-Treatment Flag Variable — derive_var_ontrtfl","text":"input dataset additional column named ONTRTFL value \"Y\" NA","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_ontrtfl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Derive On-Treatment Flag Variable — derive_var_ontrtfl","text":"-Treatment calculated determining whether assessment date start/stop dates fall 2 dates. following logic used assign -treatment = \"Y\": start_date missing ref_start_dateis non-missing timepoint filter provided (filter_pre_timepoint) start_date ref_start_date non-missing start_date = ref_start_date timepoint provided (filter_pre_timepoint) start_date ref_start_date non-missing start_date = ref_start_date filter provided filter_pre_timepoint true. ref_end_date provided ref_start_date < start_date ref_end_date provided ref_start_date < start_date <= ref_end_date + ref_end_window. end_date provided end_date < ref_start_date ONTRTFL set NULL.applicable cases start_date missing ONTRTFL assigned \"Y\" . span_period TRUE, allows user assign ONTRTFL \"Y\" cases record started prior ref_start_date ongoing ended ref_start_date. date imputations needed done prior calling function.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_ontrtfl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive On-Treatment Flag Variable — derive_var_ontrtfl","text":"","code":"library(tibble) library(dplyr, warn.conflicts = FALSE) library(lubridate, warn.conflicts = FALSE)  advs <- tribble(   ~USUBJID, ~ADT,              ~TRTSDT,           ~TRTEDT,   \"P01\",    ymd(\"2020-02-24\"), ymd(\"2020-01-01\"), ymd(\"2020-03-01\"),   \"P02\",    ymd(\"2020-01-01\"), ymd(\"2020-01-01\"), ymd(\"2020-03-01\"),   \"P03\",    ymd(\"2019-12-31\"), ymd(\"2020-01-01\"), ymd(\"2020-03-01\") ) derive_var_ontrtfl(   advs,   start_date = ADT,   ref_start_date = TRTSDT,   ref_end_date = TRTEDT ) #> # A tibble: 3 × 5 #>   USUBJID ADT        TRTSDT     TRTEDT     ONTRTFL #>   <chr>   <date>     <date>     <date>     <chr>   #> 1 P01     2020-02-24 2020-01-01 2020-03-01 Y       #> 2 P02     2020-01-01 2020-01-01 2020-03-01 Y       #> 3 P03     2019-12-31 2020-01-01 2020-03-01 NA       advs <- tribble(   ~USUBJID, ~ADT,              ~TRTSDT,           ~TRTEDT,   \"P01\",    ymd(\"2020-07-01\"), ymd(\"2020-01-01\"), ymd(\"2020-03-01\"),   \"P02\",    ymd(\"2020-04-30\"), ymd(\"2020-01-01\"), ymd(\"2020-03-01\"),   \"P03\",    ymd(\"2020-03-15\"), ymd(\"2020-01-01\"), ymd(\"2020-03-01\") ) derive_var_ontrtfl(   advs,   start_date = ADT,   ref_start_date = TRTSDT,   ref_end_date = TRTEDT,   ref_end_window = 60 ) #> # A tibble: 3 × 5 #>   USUBJID ADT        TRTSDT     TRTEDT     ONTRTFL #>   <chr>   <date>     <date>     <date>     <chr>   #> 1 P01     2020-07-01 2020-01-01 2020-03-01 NA      #> 2 P02     2020-04-30 2020-01-01 2020-03-01 Y       #> 3 P03     2020-03-15 2020-01-01 2020-03-01 Y        advs <- tribble(   ~USUBJID, ~ADTM,                      ~TRTSDTM,                   ~TRTEDTM,   \"P01\",    ymd_hm(\"2020-01-02T12:00\"), ymd_hm(\"2020-01-01T12:00\"), ymd_hm(\"2020-03-01T12:00\"),   \"P02\",    ymd(\"2020-01-01\"),          ymd_hm(\"2020-01-01T12:00\"), ymd_hm(\"2020-03-01T12:00\"),   \"P03\",    ymd(\"2019-12-31\"),          ymd_hm(\"2020-01-01T12:00\"), ymd_hm(\"2020-03-01T12:00\"), ) %>%   mutate(TPT = c(NA, \"PRE\", NA)) derive_var_ontrtfl(   advs,   start_date = ADTM,   ref_start_date = TRTSDTM,   ref_end_date = TRTEDTM,   filter_pre_timepoint = TPT == \"PRE\" ) #> # A tibble: 3 × 6 #>   USUBJID ADTM                TRTSDTM             TRTEDTM             TPT   #>   <chr>   <dttm>              <dttm>              <dttm>              <chr> #> 1 P01     2020-01-02 12:00:00 2020-01-01 12:00:00 2020-03-01 12:00:00 NA    #> 2 P02     2020-01-01 00:00:00 2020-01-01 12:00:00 2020-03-01 12:00:00 PRE   #> 3 P03     2019-12-31 00:00:00 2020-01-01 12:00:00 2020-03-01 12:00:00 NA    #> # ℹ 1 more variable: ONTRTFL <chr>  advs <- tribble(   ~USUBJID, ~ASTDT,            ~TRTSDT,           ~TRTEDT,           ~AENDT,   \"P01\",    ymd(\"2020-03-15\"), ymd(\"2020-01-01\"), ymd(\"2020-03-01\"), ymd(\"2020-12-01\"),   \"P02\",    ymd(\"2019-04-30\"), ymd(\"2020-01-01\"), ymd(\"2020-03-01\"), ymd(\"2020-03-15\"),   \"P03\",    ymd(\"2019-04-30\"), ymd(\"2020-01-01\"), ymd(\"2020-03-01\"), NA, ) derive_var_ontrtfl(   advs,   start_date = ASTDT,   end_date = AENDT,   ref_start_date = TRTSDT,   ref_end_date = TRTEDT,   ref_end_window = 60,   span_period = TRUE ) #> # A tibble: 3 × 6 #>   USUBJID ASTDT      TRTSDT     TRTEDT     AENDT      ONTRTFL #>   <chr>   <date>     <date>     <date>     <date>     <chr>   #> 1 P01     2020-03-15 2020-01-01 2020-03-01 2020-12-01 Y       #> 2 P02     2019-04-30 2020-01-01 2020-03-01 2020-03-15 Y       #> 3 P03     2019-04-30 2020-01-01 2020-03-01 NA         Y        advs <- tribble(   ~USUBJID, ~ASTDT,            ~AP01SDT,          ~AP01EDT,          ~AENDT,   \"P01\",    ymd(\"2020-03-15\"), ymd(\"2020-01-01\"), ymd(\"2020-03-01\"), ymd(\"2020-12-01\"),   \"P02\",    ymd(\"2019-04-30\"), ymd(\"2020-01-01\"), ymd(\"2020-03-01\"), ymd(\"2020-03-15\"),   \"P03\",    ymd(\"2019-04-30\"), ymd(\"2020-01-01\"), ymd(\"2020-03-01\"), NA, ) derive_var_ontrtfl(   advs,   new_var = ONTR01FL,   start_date = ASTDT,   end_date = AENDT,   ref_start_date = AP01SDT,   ref_end_date = AP01EDT,   span_period = TRUE ) #> # A tibble: 3 × 6 #>   USUBJID ASTDT      AP01SDT    AP01EDT    AENDT      ONTR01FL #>   <chr>   <date>     <date>     <date>     <date>     <chr>    #> 1 P01     2020-03-15 2020-01-01 2020-03-01 2020-12-01 NA       #> 2 P02     2019-04-30 2020-01-01 2020-03-01 2020-03-15 Y        #> 3 P03     2019-04-30 2020-01-01 2020-03-01 NA         Y"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_pchg.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive Percent Change from Baseline — derive_var_pchg","title":"Derive Percent Change from Baseline — derive_var_pchg","text":"Derive percent change baseline (PCHG) BDS dataset","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_pchg.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive Percent Change from Baseline — derive_var_pchg","text":"","code":"derive_var_pchg(dataset)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_pchg.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive Percent Change from Baseline — derive_var_pchg","text":"dataset Input dataset AVAL BASE expected.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_pchg.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive Percent Change from Baseline — derive_var_pchg","text":"input dataset additional column named PCHG","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_pchg.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Derive Percent Change from Baseline — derive_var_pchg","text":"Percent change baseline calculated dividing change baseline absolute value baseline value multiplying result 100.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_pchg.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive Percent Change from Baseline — derive_var_pchg","text":"","code":"library(tibble)  advs <- tribble(   ~USUBJID, ~PARAMCD, ~AVAL, ~ABLFL, ~BASE,   \"P01\",    \"WEIGHT\", 80,    \"Y\",    80,   \"P01\",    \"WEIGHT\", 80.8,  NA,     80,   \"P01\",    \"WEIGHT\", 81.4,  NA,     80,   \"P02\",    \"WEIGHT\", 75.3,  \"Y\",    75.3,   \"P02\",    \"WEIGHT\", 76,    NA,     75.3 ) derive_var_pchg(advs) #> # A tibble: 5 × 6 #>   USUBJID PARAMCD  AVAL ABLFL  BASE  PCHG #>   <chr>   <chr>   <dbl> <chr> <dbl> <dbl> #> 1 P01     WEIGHT   80   Y      80   0     #> 2 P01     WEIGHT   80.8 NA     80   1.00  #> 3 P01     WEIGHT   81.4 NA     80   1.75  #> 4 P02     WEIGHT   75.3 Y      75.3 0     #> 5 P02     WEIGHT   76   NA     75.3 0.930"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_relative_flag.html","id":null,"dir":"Reference","previous_headings":"","what":"Flag Observations Before or After a Condition is Fulfilled — derive_var_relative_flag","title":"Flag Observations Before or After a Condition is Fulfilled — derive_var_relative_flag","text":"Flag observations observation specified condition fulfilled group. example, function called flag subject observations first disease progression flag AEs specific AE.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_relative_flag.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Flag Observations Before or After a Condition is Fulfilled — derive_var_relative_flag","text":"","code":"derive_var_relative_flag(   dataset,   by_vars,   order,   new_var,   condition,   mode,   selection,   inclusive,   flag_no_ref_groups = TRUE,   check_type = \"warning\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_relative_flag.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Flag Observations Before or After a Condition is Fulfilled — derive_var_relative_flag","text":"dataset Input dataset variables specified by_vars order arguments expected dataset. by_vars Grouping variables Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) order Sort order Within group observations ordered specified order. handling NAs sorting variables see Sort Order. Permitted Values: list expressions created exprs(), e.g., exprs(ADT, desc(AVAL)) new_var New variable variable added input dataset set \"Y\" observations condition fulfilled. observations set NA. condition Condition Reference Observation specified condition determines reference observation. output dataset observations (selection argument) reference observation flagged. mode Selection mode (first last) \"first\" specified, group observations (selection argument) observation condition (condition argument) fulfilled first time flagged output dataset. \"last\" specified, group observations (selection argument) observation condition (condition argument) fulfilled last time flagged output dataset. Permitted Values: \"first\", \"last\" selection Flag observations reference observation? Permitted Values: \"\", \"\" inclusive Flag reference observation? Permitted Values: TRUE, FALSE flag_no_ref_groups groups without reference observation flagged? Permitted Values: TRUE, FALSE check_type Check uniqueness? \"warning\" \"error\" specified, specified message issued observations input dataset unique respect variables order. Permitted Values: \"none\", \"warning\", \"error\"","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_relative_flag.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Flag Observations Before or After a Condition is Fulfilled — derive_var_relative_flag","text":"input dataset new variable (new_var) added","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_relative_flag.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Flag Observations Before or After a Condition is Fulfilled — derive_var_relative_flag","text":"group (by_vars argument) observations (selection argument) observations condition (condition argument) fulfilled first last time (order argument mode argument) flagged output dataset.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_relative_flag.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Flag Observations Before or After a Condition is Fulfilled — derive_var_relative_flag","text":"","code":"library(tibble) library(dplyr, warn.conflicts = FALSE)  # Flag all AEs after the first COVID AE adae <- tribble(   ~USUBJID, ~ASTDY, ~ACOVFL, ~AESEQ,   \"1\",           2, NA,           1,   \"1\",           5, \"Y\",          2,   \"1\",           5, NA,           3,   \"1\",          17, NA,           4,   \"1\",          27, \"Y\",          5,   \"1\",          32, NA,           6,   \"2\",           8, NA,           1,   \"2\",          11, NA,           2, )  derive_var_relative_flag(   adae,   by_vars = exprs(USUBJID),   order = exprs(ASTDY, AESEQ),   new_var = PSTCOVFL,   condition = ACOVFL == \"Y\",   mode = \"first\",   selection = \"after\",   inclusive = FALSE,   flag_no_ref_groups = FALSE ) #> # A tibble: 8 × 5 #>   USUBJID ASTDY ACOVFL AESEQ PSTCOVFL #>   <chr>   <dbl> <chr>  <dbl> <chr>    #> 1 1           2 NA         1 NA       #> 2 1           5 Y          2 NA       #> 3 1           5 NA         3 Y        #> 4 1          17 NA         4 Y        #> 5 1          27 Y          5 Y        #> 6 1          32 NA         6 Y        #> 7 2           8 NA         1 NA       #> 8 2          11 NA         2 NA        response <- tribble(   ~USUBJID, ~AVISITN, ~AVALC,   \"1\",      0,        \"PR\",   \"1\",      1,        \"CR\",   \"1\",      2,        \"CR\",   \"1\",      3,        \"SD\",   \"1\",      4,        \"NE\",   \"2\",      0,        \"SD\",   \"2\",      1,        \"PD\",   \"2\",      2,        \"PD\",   \"3\",      0,        \"SD\",   \"4\",      0,        \"SD\",   \"4\",      1,        \"PR\",   \"4\",      2,        \"PD\",   \"4\",      3,        \"SD\",   \"4\",      4,        \"PR\" )  # Flag observations up to first PD for each patient response %>%   derive_var_relative_flag(     by_vars = exprs(USUBJID),     order = exprs(AVISITN),     new_var = ANL02FL,     condition = AVALC == \"PD\",     mode = \"first\",     selection = \"before\",     inclusive = TRUE   ) #> # A tibble: 14 × 4 #>    USUBJID AVISITN AVALC ANL02FL #>    <chr>     <dbl> <chr> <chr>   #>  1 1             0 PR    Y       #>  2 1             1 CR    Y       #>  3 1             2 CR    Y       #>  4 1             3 SD    Y       #>  5 1             4 NE    Y       #>  6 2             0 SD    Y       #>  7 2             1 PD    Y       #>  8 2             2 PD    NA      #>  9 3             0 SD    Y       #> 10 4             0 SD    Y       #> 11 4             1 PR    Y       #> 12 4             2 PD    Y       #> 13 4             3 SD    NA      #> 14 4             4 PR    NA       # Flag observations up to first PD excluding baseline (AVISITN = 0) for each patient response %>%   restrict_derivation(     derivation = derive_var_relative_flag,     args = params(       by_vars = exprs(USUBJID),       order = exprs(AVISITN),       new_var = ANL02FL,       condition = AVALC == \"PD\",       mode = \"first\",       selection = \"before\",       inclusive = TRUE     ),     filter = AVISITN > 0   ) %>%   arrange(USUBJID, AVISITN) #> # A tibble: 14 × 4 #>    USUBJID AVISITN AVALC ANL02FL #>    <chr>     <dbl> <chr> <chr>   #>  1 1             0 PR    NA      #>  2 1             1 CR    Y       #>  3 1             2 CR    Y       #>  4 1             3 SD    Y       #>  5 1             4 NE    Y       #>  6 2             0 SD    NA      #>  7 2             1 PD    Y       #>  8 2             2 PD    NA      #>  9 3             0 SD    NA      #> 10 4             0 SD    NA      #> 11 4             1 PR    Y       #> 12 4             2 PD    Y       #> 13 4             3 SD    NA      #> 14 4             4 PR    NA"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_shift.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive Shift — derive_var_shift","title":"Derive Shift — derive_var_shift","text":"Derives character shift variable containing concatenated shift values based user-defined pairing, e.g., shift baseline analysis value, shift baseline grade analysis grade, ...","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_shift.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive Shift — derive_var_shift","text":"","code":"derive_var_shift(   dataset,   new_var,   from_var,   to_var,   missing_value = \"NULL\",   sep_val = \" to \" )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_shift.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive Shift — derive_var_shift","text":"dataset Input dataset variables specified from_var to_var arguments expected dataset. new_var Name character shift variable create. from_var Variable containing value shift . to_var Variable containing value shift . missing_value Character string replace missing values from_var to_var. Default: \"NULL\" sep_val Character string concatenate values from_var to_var. Default: \" \"","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_shift.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive Shift — derive_var_shift","text":"input dataset character shift variable added","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_shift.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Derive Shift — derive_var_shift","text":"new_var derived concatenating values from_var values to_var (e.g. \"NORMAL HIGH\"). from_var to_var missing value, missing value replaced missing_value (e.g. \"NORMAL NULL\").","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_shift.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive Shift — derive_var_shift","text":"","code":"library(tibble)  data <- tribble(   ~USUBJID, ~PARAMCD, ~AVAL, ~ABLFL, ~BNRIND,  ~ANRIND,   \"P01\",    \"ALB\",       33, \"Y\",    \"LOW\",    \"LOW\",   \"P01\",    \"ALB\",       38, NA,     \"LOW\",    \"NORMAL\",   \"P01\",    \"ALB\",       NA, NA,     \"LOW\",    NA,   \"P02\",    \"ALB\",       37, \"Y\",    \"NORMAL\", \"NORMAL\",   \"P02\",    \"ALB\",       49, NA,     \"NORMAL\", \"HIGH\",   \"P02\",    \"SODIUM\",   147, \"Y\",    \"HIGH\",   \"HIGH\" )  data %>%   convert_blanks_to_na() %>%   derive_var_shift(     new_var = SHIFT1,     from_var = BNRIND,     to_var = ANRIND   ) #> # A tibble: 6 × 7 #>   USUBJID PARAMCD  AVAL ABLFL BNRIND ANRIND SHIFT1           #>   <chr>   <chr>   <dbl> <chr> <chr>  <chr>  <chr>            #> 1 P01     ALB        33 Y     LOW    LOW    LOW to LOW       #> 2 P01     ALB        38 NA    LOW    NORMAL LOW to NORMAL    #> 3 P01     ALB        NA NA    LOW    NA     LOW to NULL      #> 4 P02     ALB        37 Y     NORMAL NORMAL NORMAL to NORMAL #> 5 P02     ALB        49 NA    NORMAL HIGH   NORMAL to HIGH   #> 6 P02     SODIUM    147 Y     HIGH   HIGH   HIGH to HIGH      # or only populate post-baseline records data %>%   convert_blanks_to_na() %>%   restrict_derivation(     derivation = derive_var_shift,     args = params(       new_var = SHIFT1,       from_var = BNRIND,       to_var = ANRIND     ),     filter = is.na(ABLFL)   ) #> # A tibble: 6 × 7 #>   USUBJID PARAMCD  AVAL ABLFL BNRIND ANRIND SHIFT1         #>   <chr>   <chr>   <dbl> <chr> <chr>  <chr>  <chr>          #> 1 P01     ALB        38 NA    LOW    NORMAL LOW to NORMAL  #> 2 P01     ALB        NA NA    LOW    NA     LOW to NULL    #> 3 P02     ALB        49 NA    NORMAL HIGH   NORMAL to HIGH #> 4 P01     ALB        33 Y     LOW    LOW    NA             #> 5 P02     ALB        37 Y     NORMAL NORMAL NA             #> 6 P02     SODIUM    147 Y     HIGH   HIGH   NA"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_trtdurd.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive Total Treatment Duration (Days) — derive_var_trtdurd","title":"Derive Total Treatment Duration (Days) — derive_var_trtdurd","text":"Derives total treatment duration (days) (TRTDURD). Note: wrapper function generic derive_vars_duration().","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_trtdurd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive Total Treatment Duration (Days) — derive_var_trtdurd","text":"","code":"derive_var_trtdurd(dataset, start_date = TRTSDT, end_date = TRTEDT)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_trtdurd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive Total Treatment Duration (Days) — derive_var_trtdurd","text":"dataset Input dataset variables specified start_date end_date arguments expected dataset. start_date start date date date-time object expected. Refer derive_vars_dt() impute derive date date character vector date object. Default: TRTSDT end_date end date date date-time object expected. Refer derive_vars_dt() impute derive date date character vector date object. Default: TRTEDT","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_trtdurd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive Total Treatment Duration (Days) — derive_var_trtdurd","text":"input dataset TRTDURD added","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_trtdurd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Derive Total Treatment Duration (Days) — derive_var_trtdurd","text":"total treatment duration derived number days start end date plus one.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_trtdurd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive Total Treatment Duration (Days) — derive_var_trtdurd","text":"","code":"library(tibble) library(lubridate)  data <- tribble(   ~TRTSDT, ~TRTEDT,   ymd(\"2020-01-01\"), ymd(\"2020-02-24\") )  derive_var_trtdurd(data) #> # A tibble: 1 × 3 #>   TRTSDT     TRTEDT     TRTDURD #>   <date>     <date>       <dbl> #> 1 2020-01-01 2020-02-24      55"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_trtemfl.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive Treatment-emergent Flag — derive_var_trtemfl","title":"Derive Treatment-emergent Flag — derive_var_trtemfl","text":"Derive treatment emergent analysis flag (e.g., TRTEMFL).","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_trtemfl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive Treatment-emergent Flag — derive_var_trtemfl","text":"","code":"derive_var_trtemfl(   dataset,   new_var = TRTEMFL,   start_date = ASTDTM,   end_date = AENDTM,   trt_start_date = TRTSDTM,   trt_end_date = NULL,   end_window = NULL,   ignore_time_for_trt_end = TRUE,   initial_intensity = NULL,   intensity = NULL,   group_var = NULL,   subject_keys = get_admiral_option(\"subject_keys\") )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_trtemfl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive Treatment-emergent Flag — derive_var_trtemfl","text":"dataset Input dataset variables specified start_date, end_date, trt_start_date, trt_end_date, initial_intensity, intensity expected. new_var New variable start_date Event start date Permitted Values: symbol referring date datetime variable input dataset end_date Event end date Permitted Values: symbol referring date datetime variable input dataset trt_start_date Treatment start date Permitted Values: symbol referring date datetime variable input dataset trt_end_date Treatment end date Permitted Values: symbol referring date datetime variable input dataset NULL end_window argument specified ('days'), events starting specified number days end treatment, flagged. Permitted Values: non-negative integer NULL ignore_time_for_trt_end argument set TRUE, time part ignored checking event occurred end_window days end treatment. Permitted Values: TRUE, FALSE initial_intensity Initial severity/intensity toxicity initial_intensity ignored group_var specified. argument specified group_var NULL, events start treatment start end treatment start (ongoing) worsened (.e., intensity greater initial intensity), flagged. values specified variable must comparable usual comparison operators. .e., intensity greater initial intensity initial_intensity < intensity must evaluate TRUE. Permitted Values: symbol referring variable input dataset NULL intensity Severity/intensity toxicity argument specified, events start treatment start end treatment start (ongoing) worsened (.e., intensity greater initial intensity), flagged. values specified variable must comparable usual comparison operators. .e., intensity greater initial intensity initial_intensity < intensity must evaluate TRUE. Permitted Values: symbol referring variable input dataset NULL group_var Grouping variable argument specified, assumes AEs recorded one episode AE multiple lines using grouping variable. Events starting treatment treatment worsening afterward flagged. AE record group flagged, subsequent records treatment window flagged regardless severity. Permitted Values: symbol referring variable input dataset NULL subject_keys Variables uniquely identify subject. list symbols created using exprs() expected. argument used group_var specified.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_trtemfl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive Treatment-emergent Flag — derive_var_trtemfl","text":"input dataset variable specified new_var added","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_trtemfl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Derive Treatment-emergent Flag — derive_var_trtemfl","text":"derivation new variable following cases considered order. first case applies, defines value variable. treated: trt_start_date NA, set NA_character_. event treatment: end_date trt_start_date (end_date NA), set NA_character_. event date: start_date NA, set \"Y\" cases usually considered conservative assume event treatment-emergent. event started treatment: end_window specified: start_date trt_start_date, set \"Y\", end_window specified: start_date trt_start_date start_date trt_end_date + end_window days, set \"Y\", event started treatment (possibly) worsened treatment: initial_intensity, intensity specified group_var specified: initial_intensity < intensity start_date trt_start_date end_date trt_start_date end_date NA, set \"Y\"; group_var specified: previous intensity < intensity start_date trt_start_date end_date trt_start_date end_date NA, set \"Y\"; Otherwise set NA_character_.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_var_trtemfl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive Treatment-emergent Flag — derive_var_trtemfl","text":"","code":"library(tibble) library(dplyr, warn.conflicts = FALSE) library(lubridate)  adae <- tribble(   ~USUBJID, ~ASTDTM,            ~AENDTM,            ~AEITOXGR, ~AETOXGR,   # before treatment   \"1\",      \"2021-12-13T20:15\", \"2021-12-15T12:45\", \"1\",       \"1\",   \"1\",      \"2021-12-14T20:15\", \"2021-12-14T22:00\", \"1\",       \"3\",   # starting before treatment and ending during treatment   \"1\",      \"2021-12-30T20:00\", \"2022-01-14T11:00\", \"1\",       \"3\",   \"1\",      \"2021-12-31T20:15\", \"2022-01-01T01:23\", \"1\",       \"1\",   # starting during treatment   \"1\",      \"2022-01-01T12:00\", \"2022-01-02T23:25\", \"3\",       \"4\",   # after treatment   \"1\",      \"2022-05-10T11:00\", \"2022-05-10T13:05\", \"2\",       \"2\",   \"1\",      \"2022-05-11T11:00\", \"2022-05-11T13:05\", \"2\",       \"2\",   # missing dates   \"1\",      \"\",                 \"\",                 \"3\",       \"4\",   \"1\",      \"2021-12-30T09:00\", \"\",                 \"3\",       \"4\",   \"1\",      \"2021-12-30T11:00\", \"\",                 \"3\",       \"3\",   \"1\",      \"\",                 \"2022-01-04T09:00\", \"3\",       \"4\",   \"1\",      \"\",                 \"2021-12-24T19:00\", \"3\",       \"4\",   \"1\",      \"\",                 \"2022-06-04T09:00\", \"3\",       \"4\",   # without treatment   \"2\",      \"\",                 \"2021-12-03T12:00\", \"1\",       \"2\",   \"2\",      \"2021-12-01T12:00\", \"2021-12-03T12:00\", \"1\",       \"2\",   \"2\",      \"2021-12-06T18:00\", \"\",                 \"1\",       \"2\" ) %>%   mutate(     ASTDTM = ymd_hm(ASTDTM),     AENDTM = ymd_hm(AENDTM),     TRTSDTM = if_else(USUBJID == \"1\", ymd_hm(\"2022-01-01T01:01\"), ymd_hms(\"\")),     TRTEDTM = if_else(USUBJID == \"1\", ymd_hm(\"2022-04-30T23:59\"), ymd_hms(\"\"))   )  # derive TRTEMFL without considering treatment end and worsening derive_var_trtemfl(adae) %>% select(ASTDTM, AENDTM, TRTSDTM, TRTEMFL) #> # A tibble: 16 × 4 #>    ASTDTM              AENDTM              TRTSDTM             TRTEMFL #>    <dttm>              <dttm>              <dttm>              <chr>   #>  1 2021-12-13 20:15:00 2021-12-15 12:45:00 2022-01-01 01:01:00 NA      #>  2 2021-12-14 20:15:00 2021-12-14 22:00:00 2022-01-01 01:01:00 NA      #>  3 2021-12-30 20:00:00 2022-01-14 11:00:00 2022-01-01 01:01:00 NA      #>  4 2021-12-31 20:15:00 2022-01-01 01:23:00 2022-01-01 01:01:00 NA      #>  5 2022-01-01 12:00:00 2022-01-02 23:25:00 2022-01-01 01:01:00 Y       #>  6 2022-05-10 11:00:00 2022-05-10 13:05:00 2022-01-01 01:01:00 Y       #>  7 2022-05-11 11:00:00 2022-05-11 13:05:00 2022-01-01 01:01:00 Y       #>  8 NA                  NA                  2022-01-01 01:01:00 Y       #>  9 2021-12-30 09:00:00 NA                  2022-01-01 01:01:00 NA      #> 10 2021-12-30 11:00:00 NA                  2022-01-01 01:01:00 NA      #> 11 NA                  2022-01-04 09:00:00 2022-01-01 01:01:00 Y       #> 12 NA                  2021-12-24 19:00:00 2022-01-01 01:01:00 NA      #> 13 NA                  2022-06-04 09:00:00 2022-01-01 01:01:00 Y       #> 14 NA                  2021-12-03 12:00:00 NA                  NA      #> 15 2021-12-01 12:00:00 2021-12-03 12:00:00 NA                  NA      #> 16 2021-12-06 18:00:00 NA                  NA                  NA       # derive TRTEM2FL taking treatment end and worsening into account derive_var_trtemfl(   adae,   new_var = TRTEM2FL,   trt_end_date = TRTEDTM,   end_window = 10,   initial_intensity = AEITOXGR,   intensity = AETOXGR ) %>% select(ASTDTM, AENDTM, AEITOXGR, AETOXGR, TRTEM2FL) #> # A tibble: 16 × 5 #>    ASTDTM              AENDTM              AEITOXGR AETOXGR TRTEM2FL #>    <dttm>              <dttm>              <chr>    <chr>   <chr>    #>  1 2021-12-13 20:15:00 2021-12-15 12:45:00 1        1       NA       #>  2 2021-12-14 20:15:00 2021-12-14 22:00:00 1        3       NA       #>  3 2021-12-30 20:00:00 2022-01-14 11:00:00 1        3       Y        #>  4 2021-12-31 20:15:00 2022-01-01 01:23:00 1        1       NA       #>  5 2022-01-01 12:00:00 2022-01-02 23:25:00 3        4       Y        #>  6 2022-05-10 11:00:00 2022-05-10 13:05:00 2        2       Y        #>  7 2022-05-11 11:00:00 2022-05-11 13:05:00 2        2       NA       #>  8 NA                  NA                  3        4       Y        #>  9 2021-12-30 09:00:00 NA                  3        4       Y        #> 10 2021-12-30 11:00:00 NA                  3        3       NA       #> 11 NA                  2022-01-04 09:00:00 3        4       Y        #> 12 NA                  2021-12-24 19:00:00 3        4       NA       #> 13 NA                  2022-06-04 09:00:00 3        4       Y        #> 14 NA                  2021-12-03 12:00:00 1        2       NA       #> 15 2021-12-01 12:00:00 2021-12-03 12:00:00 1        2       NA       #> 16 2021-12-06 18:00:00 NA                  1        2       NA        adae2 <- tribble(   ~USUBJID, ~ASTDTM, ~AENDTM, ~AEITOXGR, ~AETOXGR, ~AEGRPID,   # before treatment   \"1\", \"2021-12-13T20:15\", \"2021-12-15T12:45\", \"1\", \"1\", \"1\",   \"1\", \"2021-12-14T20:15\", \"2021-12-14T22:00\", \"1\", \"3\", \"1\",   # starting before treatment and ending during treatment   \"1\", \"2021-12-30T20:15\", \"2022-01-14T01:23\", \"3\", \"3\", \"2\",   \"1\", \"2022-01-05T20:00\", \"2022-06-01T11:00\", \"3\", \"1\", \"2\",   \"1\", \"2022-01-10T20:15\", \"2022-01-11T01:23\", \"3\", \"2\", \"2\",   \"1\", \"2022-01-13T20:15\", \"2022-03-01T01:23\", \"3\", \"1\", \"2\",   # starting during treatment   \"1\", \"2022-01-01T12:00\", \"2022-01-02T23:25\", \"4\", \"4\", \"3\",    # after treatment   \"1\", \"2022-05-10T11:00\", \"2022-05-10T13:05\", \"2\", \"2\", \"4\",   \"1\", \"2022-05-10T12:00\", \"2022-05-10T13:05\", \"2\", \"2\", \"4\",   \"1\", \"2022-05-11T11:00\", \"2022-05-11T13:05\", \"2\", \"2\", \"4\",   # missing dates   \"1\", \"\", \"\", \"3\", \"4\", \"5\",   \"1\", \"2021-12-30T09:00\", \"\", \"3\", \"4\", \"5\",   \"1\", \"2021-12-30T11:00\", \"\", \"3\", \"3\", \"5\",   \"1\", \"\", \"2022-01-04T09:00\", \"3\", \"4\", \"5\",   \"1\", \"\", \"2021-12-24T19:00\", \"3\", \"4\", \"5\",   \"1\", \"\", \"2022-06-04T09:00\", \"3\", \"4\", \"5\",   # without treatment   \"2\", \"\", \"2021-12-03T12:00\", \"1\", \"2\", \"1\",   \"2\", \"2021-12-01T12:00\", \"2021-12-03T12:00\", \"1\", \"2\", \"2\",   \"2\", \"2021-12-06T18:00\", \"\", \"1\", \"2\", \"3\" ) %>%   mutate(     STUDYID = \"ABC12345\",     ASTDTM = ymd_hm(ASTDTM),     AENDTM = ymd_hm(AENDTM),     TRTSDTM = if_else(USUBJID == \"1\", ymd_hm(\"2022-01-01T01:01\"), ymd_hms(\"\")),     TRTEDTM = if_else(USUBJID == \"1\", ymd_hm(\"2022-04-30T23:59\"), ymd_hms(\"\"))   ) # derive TRTEMFL taking treatment end and worsening into account within a grouping variable derive_var_trtemfl(   adae2,   new_var = TRTEMFL,   trt_end_date = TRTEDTM,   end_window = 10,   intensity = AETOXGR,   group_var = AEGRPID ) %>% select(ASTDTM, AENDTM, AEITOXGR, AETOXGR, AEGRPID, TRTEMFL) #> # A tibble: 19 × 6 #>    ASTDTM              AENDTM              AEITOXGR AETOXGR AEGRPID TRTEMFL #>    <dttm>              <dttm>              <chr>    <chr>   <chr>   <chr>   #>  1 2021-12-13 20:15:00 2021-12-15 12:45:00 1        1       1       NA      #>  2 2021-12-14 20:15:00 2021-12-14 22:00:00 1        3       1       NA      #>  3 2021-12-30 20:15:00 2022-01-14 01:23:00 3        3       2       NA      #>  4 2022-01-05 20:00:00 2022-06-01 11:00:00 3        1       2       NA      #>  5 2022-01-10 20:15:00 2022-01-11 01:23:00 3        2       2       Y       #>  6 2022-01-13 20:15:00 2022-03-01 01:23:00 3        1       2       Y       #>  7 2022-01-01 12:00:00 2022-01-02 23:25:00 4        4       3       Y       #>  8 2022-05-10 11:00:00 2022-05-10 13:05:00 2        2       4       Y       #>  9 2022-05-10 12:00:00 2022-05-10 13:05:00 2        2       4       Y       #> 10 2022-05-11 11:00:00 2022-05-11 13:05:00 2        2       4       NA      #> 11 2021-12-30 09:00:00 NA                  3        4       5       NA      #> 12 2021-12-30 11:00:00 NA                  3        3       5       NA      #> 13 NA                  NA                  3        4       5       Y       #> 14 NA                  2022-01-04 09:00:00 3        4       5       Y       #> 15 NA                  2021-12-24 19:00:00 3        4       5       NA      #> 16 NA                  2022-06-04 09:00:00 3        4       5       Y       #> 17 NA                  2021-12-03 12:00:00 1        2       1       NA      #> 18 2021-12-01 12:00:00 2021-12-03 12:00:00 1        2       2       NA      #> 19 2021-12-06 18:00:00 NA                  1        2       3       NA"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_aage.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive Analysis Age — derive_vars_aage","title":"Derive Analysis Age — derive_vars_aage","text":"Derives analysis age (AAGE) analysis age unit (AAGEU). Note: wrapper function generic derive_vars_duration().","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_aage.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive Analysis Age — derive_vars_aage","text":"","code":"derive_vars_aage(   dataset,   start_date = BRTHDT,   end_date = RANDDT,   age_unit = \"YEARS\",   type = \"interval\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_aage.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive Analysis Age — derive_vars_aage","text":"dataset Input dataset variables specified start_date end_date arguments expected dataset. start_date start date date date-time object expected. Refer derive_vars_dt() impute derive date date character vector date object. Default: BRTHDT end_date end date date date-time object expected. Refer derive_vars_dt() impute derive date date character vector date object. Default: RANDDT age_unit Age unit age derived specified unit Permitted Values (case-insensitive): years: \"year\", \"years\", \"yr\", \"yrs\", \"y\" months: \"month\", \"months\", \"mo\", \"mos\" weeks: \"week\", \"weeks\", \"wk\", \"wks\", \"w\" days: \"day\", \"days\", \"d\" hours: \"hour\", \"hours\", \"hr\", \"hrs\", \"h\" minutes: \"minute\", \"minutes\", \"min\", \"mins\" seconds: \"second\", \"seconds\", \"sec\", \"secs\", \"s\" type lubridate duration type. See details. Default: \"duration\" Permitted Values: \"duration\", \"interval\"","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_aage.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive Analysis Age — derive_vars_aage","text":"input dataset AAGE AAGEU added","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_aage.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Derive Analysis Age — derive_vars_aage","text":"duration derived time start end date specified output unit. end date start date, duration negative. start end date variable must present specified input dataset. lubridate package calculates two types spans two dates: duration interval. calculations largely , unit time period month year result can slightly different. difference arises ambiguity length \"1 month\" \"1 year\". Months may 31, 30, 28, 29 days, years 365 days 366 leap years. Durations intervals help solve ambiguity measures. interval 2000-02-01 2000-03-01 1 (.e. one month). duration two dates 0.95, accounts fact year 2000 leap year, February 29 days, average month length 30.4375, .e. 29 / 30.4375 = 0.95. additional details, review lubridate time span reference page.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_aage.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive Analysis Age — derive_vars_aage","text":"","code":"library(tibble) library(lubridate)  data <- tribble(   ~BRTHDT, ~RANDDT,   ymd(\"1984-09-06\"), ymd(\"2020-02-24\") )  derive_vars_aage(data) #> # A tibble: 1 × 4 #>   BRTHDT     RANDDT      AAGE AAGEU #>   <date>     <date>     <dbl> <chr> #> 1 1984-09-06 2020-02-24    35 YEARS"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_atc.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive ATC Class Variables — derive_vars_atc","title":"Derive ATC Class Variables — derive_vars_atc","text":"Add Anatomical Therapeutic Chemical class variables FACM ADCM. Note: wrapper function generic derive_vars_transposed().","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_atc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive ATC Class Variables — derive_vars_atc","text":"","code":"derive_vars_atc(   dataset,   dataset_facm,   by_vars = exprs(!!!get_admiral_option(\"subject_keys\"), CMREFID = FAREFID),   id_vars = NULL,   value_var = FASTRESC )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_atc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive ATC Class Variables — derive_vars_atc","text":"dataset Input dataset variables specified by_vars argument expected dataset. dataset_facm FACM dataset variables specified by_vars, id_vars, value_var arguments FATESTCD required. variables by_vars, id_vars, FATESTCD must unique key. by_vars Grouping variables Keys used merge dataset_facm dataset. id_vars ID variables Variables (excluding by_vars) uniquely identify observation dataset_merge. Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) value_var variable dataset_facm containing values transposed variables","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_atc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive ATC Class Variables — derive_vars_atc","text":"input dataset ATC variables added","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_atc.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive ATC Class Variables — derive_vars_atc","text":"","code":"library(tibble)  cm <- tribble(   ~STUDYID,  ~USUBJID,       ~CMGRPID, ~CMREFID,  ~CMDECOD,   \"STUDY01\", \"BP40257-1001\", \"14\",     \"1192056\", \"PARACETAMOL\",   \"STUDY01\", \"BP40257-1001\", \"18\",     \"2007001\", \"SOLUMEDROL\",   \"STUDY01\", \"BP40257-1002\", \"19\",     \"2791596\", \"SPIRONOLACTONE\" ) facm <- tribble(   ~STUDYID,  ~USUBJID,       ~FAGRPID, ~FAREFID,  ~FATESTCD,  ~FASTRESC,   \"STUDY01\", \"BP40257-1001\", \"1\",      \"1192056\", \"CMATC1CD\", \"N\",   \"STUDY01\", \"BP40257-1001\", \"1\",      \"1192056\", \"CMATC2CD\", \"N02\",   \"STUDY01\", \"BP40257-1001\", \"1\",      \"1192056\", \"CMATC3CD\", \"N02B\",   \"STUDY01\", \"BP40257-1001\", \"1\",      \"1192056\", \"CMATC4CD\", \"N02BE\",   \"STUDY01\", \"BP40257-1001\", \"1\",      \"2007001\", \"CMATC1CD\", \"D\",   \"STUDY01\", \"BP40257-1001\", \"1\",      \"2007001\", \"CMATC2CD\", \"D10\",   \"STUDY01\", \"BP40257-1001\", \"1\",      \"2007001\", \"CMATC3CD\", \"D10A\",   \"STUDY01\", \"BP40257-1001\", \"1\",      \"2007001\", \"CMATC4CD\", \"D10AA\",   \"STUDY01\", \"BP40257-1001\", \"2\",      \"2007001\", \"CMATC1CD\", \"D\",   \"STUDY01\", \"BP40257-1001\", \"2\",      \"2007001\", \"CMATC2CD\", \"D07\",   \"STUDY01\", \"BP40257-1001\", \"2\",      \"2007001\", \"CMATC3CD\", \"D07A\",   \"STUDY01\", \"BP40257-1001\", \"2\",      \"2007001\", \"CMATC4CD\", \"D07AA\",   \"STUDY01\", \"BP40257-1001\", \"3\",      \"2007001\", \"CMATC1CD\", \"H\",   \"STUDY01\", \"BP40257-1001\", \"3\",      \"2007001\", \"CMATC2CD\", \"H02\",   \"STUDY01\", \"BP40257-1001\", \"3\",      \"2007001\", \"CMATC3CD\", \"H02A\",   \"STUDY01\", \"BP40257-1001\", \"3\",      \"2007001\", \"CMATC4CD\", \"H02AB\",   \"STUDY01\", \"BP40257-1002\", \"1\",      \"2791596\", \"CMATC1CD\", \"C\",   \"STUDY01\", \"BP40257-1002\", \"1\",      \"2791596\", \"CMATC2CD\", \"C03\",   \"STUDY01\", \"BP40257-1002\", \"1\",      \"2791596\", \"CMATC3CD\", \"C03D\",   \"STUDY01\", \"BP40257-1002\", \"1\",      \"2791596\", \"CMATC4CD\", \"C03DA\" )  derive_vars_atc(cm, facm, id_vars = exprs(FAGRPID)) #> # A tibble: 5 × 9 #>   STUDYID USUBJID      CMGRPID CMREFID CMDECOD       ATC1CD ATC2CD ATC3CD ATC4CD #>   <chr>   <chr>        <chr>   <chr>   <chr>         <chr>  <chr>  <chr>  <chr>  #> 1 STUDY01 BP40257-1001 14      1192056 PARACETAMOL   N      N02    N02B   N02BE  #> 2 STUDY01 BP40257-1001 18      2007001 SOLUMEDROL    D      D10    D10A   D10AA  #> 3 STUDY01 BP40257-1001 18      2007001 SOLUMEDROL    D      D07    D07A   D07AA  #> 4 STUDY01 BP40257-1001 18      2007001 SOLUMEDROL    H      H02    H02A   H02AB  #> 5 STUDY01 BP40257-1002 19      2791596 SPIRONOLACTO… C      C03    C03D   C03DA"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_cat.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive Categorization Variables Like AVALCATy and AVALCAyN — derive_vars_cat","title":"Derive Categorization Variables Like AVALCATy and AVALCAyN — derive_vars_cat","text":"Derive Categorization Variables Like AVALCATy AVALCAyN","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_cat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive Categorization Variables Like AVALCATy and AVALCAyN — derive_vars_cat","text":"","code":"derive_vars_cat(dataset, definition, by_vars = NULL)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_cat.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive Categorization Variables Like AVALCATy and AVALCAyN — derive_vars_cat","text":"dataset Input dataset variables specified by_vars definition arguments expected dataset. definition List expressions created exprs(). Must rectangular format specified using syntax creating tibble using tribble() function. definition object converted tibble using tribble() inside function. Must contain: column condition converted logical expression used dataset input. least one additional column new column name category value(s) used logical expression. column specified by_vars (by_vars specified) e.g. by_vars specified:   e.g. by_vars specified exprs(VSTEST):   by_vars list expressions one element. NULL default. Allows specifying groups, e.g. exprs(PARAMCD). Variable must present dataset definition. conditions definition applied records match by_vars. categorization variables set NA records matching groups definition.","code":"exprs(~condition,   ~AVALCAT1, ~AVALCA1N,       AVAL >= 140, \">=140 cm\",         1,       AVAL < 140,   \"<140 cm\",         2) exprs(~VSTEST,   ~condition,  ~AVALCAT1, ~AVALCA1N,       \"Height\", AVAL >= 140, \">=140 cm\",         1,       \"Height\",  AVAL < 140,  \"<140 cm\",         2)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_cat.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive Categorization Variables Like AVALCATy and AVALCAyN — derive_vars_cat","text":"input dataset new variables defined definition added","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_cat.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Derive Categorization Variables Like AVALCATy and AVALCAyN — derive_vars_cat","text":"conditions overlapping, row order definitions must carefully considered. first match determine category. .e. AVAL = 155 definition :   AVALCAT1 \"<=170 cm\", first match AVAL. specify:   AVAL <= 160 lead AVALCAT1 == \"<=160 cm\", AVAL -160 170 lead AVALCAT1 == \"<=170 cm\", AVAL <= 170 lead AVALCAT1 == \">170 cm\". However, suggest explicit defining condition, avoid overlap. case, middle condition : AVAL <= 170 & AVAL > 160","code":"definition <- exprs(   ~VSTEST,   ~condition,  ~AVALCAT1, ~AVALCA1N,   \"Height\",  AVAL > 170,  \">170 cm\",         1,   \"Height\", AVAL <= 170, \"<=170 cm\",         2,   \"Height\", AVAL <= 160, \"<=160 cm\",         3 ) definition <- exprs(   ~VSTEST,   ~condition,  ~AVALCAT1, ~AVALCA1N,   \"Height\", AVAL <= 160, \"<=160 cm\",         3,   \"Height\", AVAL <= 170, \"<=170 cm\",         2,   \"Height\",  AVAL > 170,  \">170 cm\",         1 )"},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_cat.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive Categorization Variables Like AVALCATy and AVALCAyN — derive_vars_cat","text":"","code":"library(dplyr) library(tibble)  advs <- tibble::tribble(   ~USUBJID,       ~VSTEST,  ~AVAL,   \"01-701-1015\", \"Height\", 147.32,   \"01-701-1015\", \"Weight\",  53.98,   \"01-701-1023\", \"Height\", 162.56,   \"01-701-1023\", \"Weight\",     NA,   \"01-701-1028\", \"Height\",     NA,   \"01-701-1028\", \"Weight\",     NA,   \"01-701-1033\", \"Height\", 175.26,   \"01-701-1033\", \"Weight\",  88.45 )  definition <- exprs(   ~condition,                        ~AVALCAT1, ~AVALCA1N,  ~NEWCOL,   VSTEST == \"Height\" & AVAL > 160,   \">160 cm\",         1, \"extra1\",   VSTEST == \"Height\" & AVAL <= 160, \"<=160 cm\",         2, \"extra2\" ) derive_vars_cat(   dataset = advs,   definition = definition ) #> # A tibble: 8 × 6 #>   USUBJID     VSTEST  AVAL AVALCAT1 AVALCA1N NEWCOL #>   <chr>       <chr>  <dbl> <chr>       <dbl> <chr>  #> 1 01-701-1015 Height 147.  <=160 cm        2 extra2 #> 2 01-701-1015 Weight  54.0 NA             NA NA     #> 3 01-701-1023 Height 163.  >160 cm         1 extra1 #> 4 01-701-1023 Weight  NA   NA             NA NA     #> 5 01-701-1028 Height  NA   NA             NA NA     #> 6 01-701-1028 Weight  NA   NA             NA NA     #> 7 01-701-1033 Height 175.  >160 cm         1 extra1 #> 8 01-701-1033 Weight  88.4 NA             NA NA      # Using by_vars: definition2 <- exprs(   ~VSTEST,   ~condition,  ~AVALCAT1, ~AVALCA1N,   \"Height\",  AVAL > 160,  \">160 cm\",         1,   \"Height\", AVAL <= 160, \"<=160 cm\",         2,   \"Weight\",   AVAL > 70,   \">70 kg\",         1,   \"Weight\",  AVAL <= 70,  \"<=70 kg\",         2 )  derive_vars_cat(   dataset = advs,   definition = definition2,   by_vars = exprs(VSTEST) ) #> # A tibble: 8 × 5 #>   USUBJID     VSTEST  AVAL AVALCAT1 AVALCA1N #>   <chr>       <chr>  <dbl> <chr>       <dbl> #> 1 01-701-1015 Height 147.  <=160 cm        2 #> 2 01-701-1015 Weight  54.0 <=70 kg         2 #> 3 01-701-1023 Height 163.  >160 cm         1 #> 4 01-701-1023 Weight  NA   NA             NA #> 5 01-701-1028 Height  NA   NA             NA #> 6 01-701-1028 Weight  NA   NA             NA #> 7 01-701-1033 Height 175.  >160 cm         1 #> 8 01-701-1033 Weight  88.4 >70 kg          1  # With three conditions: definition3 <- exprs(   ~VSTEST,                ~condition,  ~AVALCAT1, ~AVALCA1N,   \"Height\",               AVAL > 170,  \">170 cm\",         1,   \"Height\", AVAL <= 170 & AVAL > 160, \"<=170 cm\",         2,   \"Height\",              AVAL <= 160, \"<=160 cm\",         3 )  derive_vars_cat(   dataset = advs,   definition = definition3,   by_vars = exprs(VSTEST) ) #> # A tibble: 8 × 5 #>   USUBJID     VSTEST  AVAL AVALCAT1 AVALCA1N #>   <chr>       <chr>  <dbl> <chr>       <dbl> #> 1 01-701-1015 Height 147.  <=160 cm        3 #> 2 01-701-1015 Weight  54.0 NA             NA #> 3 01-701-1023 Height 163.  <=170 cm        2 #> 4 01-701-1023 Weight  NA   NA             NA #> 5 01-701-1028 Height  NA   NA             NA #> 6 01-701-1028 Weight  NA   NA             NA #> 7 01-701-1033 Height 175.  >170 cm         1 #> 8 01-701-1033 Weight  88.4 NA             NA  # Let's derive both the MCRITyML and the MCRITyMN variables adlb <- tibble::tribble(   ~USUBJID,     ~PARAM, ~AVAL, ~AVALU,  ~ANRHI,   \"01-701-1015\", \"ALT\",   150,  \"U/L\",      40,   \"01-701-1023\", \"ALT\",    70,  \"U/L\",      40,   \"01-701-1036\", \"ALT\",   130,  \"U/L\",      40,   \"01-701-1048\", \"ALT\",    30,  \"U/L\",      40,   \"01-701-1015\", \"AST\",    50,  \"U/L\",      35 )  definition_mcrit <- exprs(   ~PARAM,                      ~condition,    ~MCRIT1ML, ~MCRIT1MN,   \"ALT\",                    AVAL <= ANRHI,    \"<=ANRHI\",         1,   \"ALT\", ANRHI < AVAL & AVAL <= 3 * ANRHI, \">1-3*ANRHI\",         2,   \"ALT\",                 3 * ANRHI < AVAL,   \">3*ANRHI\",         3 )  adlb %>%   derive_vars_cat(     definition = definition_mcrit,     by_vars = exprs(PARAM)   ) #> # A tibble: 5 × 7 #>   USUBJID     PARAM  AVAL AVALU ANRHI MCRIT1ML   MCRIT1MN #>   <chr>       <chr> <dbl> <chr> <dbl> <chr>         <dbl> #> 1 01-701-1015 ALT     150 U/L      40 >3*ANRHI          3 #> 2 01-701-1023 ALT      70 U/L      40 >1-3*ANRHI        2 #> 3 01-701-1036 ALT     130 U/L      40 >3*ANRHI          3 #> 4 01-701-1048 ALT      30 U/L      40 <=ANRHI           1 #> 5 01-701-1015 AST      50 U/L      35 NA               NA"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_computed.html","id":null,"dir":"Reference","previous_headings":"","what":"Adds Variable(s) Computed from the Analysis Value of one or more Parameters — derive_vars_computed","title":"Adds Variable(s) Computed from the Analysis Value of one or more Parameters — derive_vars_computed","text":"Adds Variable(s) computed analysis value one parameters. expected value new variable defined expression using analysis values parameters, addition/sum, subtraction/difference, multiplication/product, division/ratio, exponentiation/logarithm, formula.  example Body Mass Index Baseline (BMIBL) ADSL can derived HEIGHT WEIGHT parameters ADVS.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_computed.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adds Variable(s) Computed from the Analysis Value of one or more Parameters — derive_vars_computed","text":"","code":"derive_vars_computed(   dataset,   dataset_add,   by_vars,   parameters,   new_vars,   filter_add = NULL,   constant_by_vars = NULL,   constant_parameters = NULL )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_computed.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adds Variable(s) Computed from the Analysis Value of one or more Parameters — derive_vars_computed","text":"dataset variables specified by_vars parameter expected. dataset_add Additional dataset variables specified by_vars parameter expected. variable specified by_vars PARAMCD must unique key additional dataset restricting filter condition (filter_add parameter) parameters specified parameters. by_vars Grouping variables Grouping variables uniquely identifying set records new_vars calculated. Permitted Values: list variables created exprs() parameters Required parameter codes expected parameter codes (PARAMCD) required derive new variable specified parameter constant_parameters parameter. observations considered parameter code, e.g., SDTM dataset used, temporary parameter codes can derived specifying list expressions. name element defines temporary parameter code expression defines condition selecting records. example, parameters = exprs(HGHT = VSTESTCD == \"HEIGHT\") selects observations VSTESTCD == \"HEIGHT\" input data (dataset dataset_add), sets PARAMCD = \"HGHT\" observations, adds observations consider. Unnamed elements list expressions considered parameter codes. example, parameters = exprs(WEIGHT, HGHT = VSTESTCD == \"HEIGHT\") uses parameter code \"WEIGHT\" creates temporary parameter code \"HGHT\". Permitted Values: character vector PARAMCD values list expressions new_vars Name newly created variables specified variables set specified values. values variables parameters specified parameters can accessed using <variable name>.<parameter code>. example   defines value new variable. Variable names expression must contain one dot. Permitted Values: List variable-value pairs filter_add Filter condition additional dataset specified condition applied additional dataset deriving new variable, .e., observations fulfilling condition taken account. Permitted Values: condition constant_by_vars variables constant parameters constant parameters (parameters measured ) merged parameters using specified variables. (Refer Example) Permitted Values: list variables constant_parameters Required constant parameter codes expected parameter codes (PARAMCD) required derive new variable measured specified . example BMI derived height measured weight measured visit. Height specified constant_parameters parameter. (Refer Example) observations considered parameter code, e.g., SDTM dataset used, temporary parameter codes can derived specifying list expressions. name element defines temporary parameter code expression defines condition selecting records. example constant_parameters = exprs(HGHT = VSTESTCD == \"HEIGHT\") selects observations VSTESTCD == \"HEIGHT\" input data (dataset dataset_add), sets PARAMCD = \"HGHT\" observations, adds observations consider. Unnamed elements list expressions considered parameter codes. example, constant_parameters = exprs(WEIGHT, HGHT = VSTESTCD == \"HEIGHT\") uses parameter code \"WEIGHT\" creates temporary parameter code \"HGHT\". Permitted Values: character vector PARAMCD values list expressions","code":"exprs(   BMIBL = (AVAL.WEIGHT / (AVAL.HEIGHT/100)^2) )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_computed.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adds Variable(s) Computed from the Analysis Value of one or more Parameters — derive_vars_computed","text":"input dataset new variables added.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_computed.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Adds Variable(s) Computed from the Analysis Value of one or more Parameters — derive_vars_computed","text":"group (respect variables specified by_vars argument), values new variables (new_vars) computed based parameters additional dataset (dataset_add) new variables merged input dataset (dataset).","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_computed.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Adds Variable(s) Computed from the Analysis Value of one or more Parameters — derive_vars_computed","text":"","code":"library(tibble) library(dplyr)  # Example 1: Derive BMIBL adsl <- tribble(   ~STUDYID,   ~USUBJID, ~AGE,   ~AGEU,   \"PILOT01\", \"01-1302\",   61,   \"YEARS\",   \"PILOT01\", \"17-1344\",   64,   \"YEARS\" )  advs <- tribble(   ~STUDYID,  ~USUBJID,  ~PARAMCD, ~PARAM,        ~VISIT,      ~AVAL, ~AVALU, ~ABLFL,   \"PILOT01\", \"01-1302\", \"HEIGHT\", \"Height (cm)\", \"SCREENING\", 177.8, \"cm\",   \"Y\",   \"PILOT01\", \"01-1302\", \"WEIGHT\", \"Weight (kg)\", \"SCREENING\", 81.19, \"kg\",   NA,   \"PILOT01\", \"01-1302\", \"WEIGHT\", \"Weight (kg)\", \"BASELINE\",   82.1, \"kg\",   \"Y\",   \"PILOT01\", \"01-1302\", \"WEIGHT\", \"Weight (kg)\", \"WEEK 2\",    81.19, \"kg\",   NA,   \"PILOT01\", \"01-1302\", \"WEIGHT\", \"Weight (kg)\", \"WEEK 4\",    82.56, \"kg\",   NA,   \"PILOT01\", \"01-1302\", \"WEIGHT\", \"Weight (kg)\", \"WEEK 6\",    80.74, \"kg\",   NA,   \"PILOT01\", \"17-1344\", \"HEIGHT\", \"Height (cm)\", \"SCREENING\", 163.5, \"cm\",   \"Y\",   \"PILOT01\", \"17-1344\", \"WEIGHT\", \"Weight (kg)\", \"SCREENING\", 58.06, \"kg\",   NA,   \"PILOT01\", \"17-1344\", \"WEIGHT\", \"Weight (kg)\", \"BASELINE\",  58.06, \"kg\",   \"Y\",   \"PILOT01\", \"17-1344\", \"WEIGHT\", \"Weight (kg)\", \"WEEK 2\",    58.97, \"kg\",   NA,   \"PILOT01\", \"17-1344\", \"WEIGHT\", \"Weight (kg)\", \"WEEK 4\",    57.97, \"kg\",   NA,   \"PILOT01\", \"17-1344\", \"WEIGHT\", \"Weight (kg)\", \"WEEK 6\",    58.97, \"kg\",   NA )  derive_vars_computed(   dataset = adsl,   dataset_add = advs,   by_vars = exprs(STUDYID, USUBJID),   parameters = c(\"WEIGHT\", \"HEIGHT\"),   new_vars = exprs(BMIBL = compute_bmi(height = AVAL.HEIGHT, weight = AVAL.WEIGHT)),   filter_add = ABLFL == \"Y\" ) #> # A tibble: 2 × 5 #>   STUDYID USUBJID   AGE AGEU  BMIBL #>   <chr>   <chr>   <dbl> <chr> <dbl> #> 1 PILOT01 01-1302    61 YEARS  26.0 #> 2 PILOT01 17-1344    64 YEARS  21.7"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_crit_flag.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive Criterion Flag Variables CRITy, CRITyFL, and CRITyFN — derive_vars_crit_flag","title":"Derive Criterion Flag Variables CRITy, CRITyFL, and CRITyFN — derive_vars_crit_flag","text":"function derives ADaM compliant criterion flags, e.g., facilitate subgroup analyses. criterion flag derived function, derivation ADaM compliant. helps ensure condition criterion depends variables row, CRITyFL populated valid values, .e, either \"Y\" NA \"Y\", \"N\", NA, CRITy variable populated correctly, .e., set constant value within parameter CRITyFL populated \"Y\", \"N\", NA set constant value within parameter criterion condition fulfilled NA otherwise CRITyFL populated \"Y\",  NA","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_crit_flag.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive Criterion Flag Variables CRITy, CRITyFL, and CRITyFN — derive_vars_crit_flag","text":"","code":"derive_vars_crit_flag(   dataset,   crit_nr = 1,   condition,   description,   values_yn = FALSE,   create_numeric_flag = FALSE )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_crit_flag.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive Criterion Flag Variables CRITy, CRITyFL, and CRITyFN — derive_vars_crit_flag","text":"dataset Input dataset crit_nr criterion number, .e., y CRITy Permitted Values: positive integer condition Condition flagging records See description values_yn argument details CRITyFL variable populated. Permitted Values: unquoted expression evaluates logical (dataset) description description criterion CRITy variable set specified value. expression can specified set value depending parameter. Please note value must constant within parameter. Permitted Values: unquoted expression evaluates character (dataset) values_yn \"Y\" \"N\" used CRITyFL? set TRUE, CRITyFL variable set \"Y\" condition (condition) evaluates TRUE, set \"N\" condition evaluate FALSE, NA evaluates NA. Otherwise, CRITyFL variable set \"Y\" condition (condition) evaluates TRUE, NA otherwise. Permitted Values: TRUE, FALSE create_numeric_flag Create numeric flag? set TRUE, CRITyFN variable created. set 1 CRITyFL == \"Y\", set 0 CRITyFL == \"N\", NA otherwise. Permitted Values: TRUE, FALSE","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_crit_flag.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive Criterion Flag Variables CRITy, CRITyFL, and CRITyFN — derive_vars_crit_flag","text":"input dataset variables CRITy, CRITyFL, optionally CRITyFN added.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_crit_flag.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive Criterion Flag Variables CRITy, CRITyFL, and CRITyFN — derive_vars_crit_flag","text":"","code":"library(tibble) adbds <- tribble(   ~PARAMCD, ~AVAL,   \"AST\",    42,   \"AST\",    52,   \"AST\",    NA_real_,   \"ALT\",    33,   \"ALT\",    51 )  # Create a criterion flag with values \"Y\" and NA derive_vars_crit_flag(   adbds,   condition = AVAL > 50,   description = \"Absolute value > 50\" ) #> # A tibble: 5 × 4 #>   PARAMCD  AVAL CRIT1FL CRIT1               #>   <chr>   <dbl> <chr>   <chr>               #> 1 AST        42 NA      NA                  #> 2 AST        52 Y       Absolute value > 50 #> 3 AST        NA NA      NA                  #> 4 ALT        33 NA      NA                  #> 5 ALT        51 Y       Absolute value > 50  # Create criterion flag with values \"Y\", \"N\", and NA and parameter dependent # criterion description derive_vars_crit_flag(   adbds,   crit_nr = 2,   condition = AVAL > 50,   description = paste(PARAMCD, \"> 50\"),   values_yn = TRUE,   create_numeric_flag = TRUE ) #> # A tibble: 5 × 5 #>   PARAMCD  AVAL CRIT2FL CRIT2    CRIT2FN #>   <chr>   <dbl> <chr>   <chr>      <int> #> 1 AST        42 N       AST > 50       0 #> 2 AST        52 Y       AST > 50       1 #> 3 AST        NA NA      AST > 50      NA #> 4 ALT        33 N       ALT > 50       0 #> 5 ALT        51 Y       ALT > 50       1"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_dt.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive/Impute a Date from a Date Character Vector — derive_vars_dt","title":"Derive/Impute a Date from a Date Character Vector — derive_vars_dt","text":"Derive date ('--DT') date character vector ('--DTC'). date can imputed (see date_imputation argument) date imputation flag ('--DTF') can added.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_dt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive/Impute a Date from a Date Character Vector — derive_vars_dt","text":"","code":"derive_vars_dt(   dataset,   new_vars_prefix,   dtc,   highest_imputation = \"n\",   date_imputation = \"first\",   flag_imputation = \"auto\",   min_dates = NULL,   max_dates = NULL,   preserve = FALSE )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_dt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive/Impute a Date from a Date Character Vector — derive_vars_dt","text":"dataset Input dataset variables specified dtc argument expected dataset. new_vars_prefix Prefix used output variable(s). character scalar expected. date variable \"DT\" appended specified prefix date imputation flag \"DTF\". .e., new_vars_prefix = \"AST\" variables ASTDT ASTDTF created. dtc '--DTC' date impute character date expected format like yyyy-mm-dd yyyy-mm-ddThh:mm:ss. Trailing components can omitted - valid \"missing\" value component. highest_imputation Highest imputation level highest_imputation argument controls components DTC value imputed missing. components specified level imputed. component higher level highest imputation level missing, NA_character_ returned. example, highest_imputation = \"D\" \"2020\" results NA_character_ month missing. \"n\" specified imputation performed, .e., component missing, NA_character_ returned. \"Y\" specified, date_imputation \"first\" \"last\" min_dates max_dates specified respectively. Otherwise, NA_character_ returned year component missing. Permitted Values: \"Y\" (year, highest level), \"M\" (month), \"D\" (day), \"n\" (none, lowest level) date_imputation value impute day/month datepart missing. character value expected, either format month day specified \"mm-dd\": e.g. \"06-15\" 15th June (year can specified; imputing year \"first\" \"last\" together min_dates max_dates argument can used (see examples).), keyword: \"first\", \"mid\", \"last\" impute first/mid/last day/month. \"mid\" specified, missing components imputed middle possible range: month day missing, imputed \"06-30\" (middle year). day missing, imputed \"15\" (middle month). argument ignored highest_imputation less \"D\". flag_imputation Whether date imputation flag must also derived. \"auto\" specified highest_imputation argument \"n\", date imputation flag derived. \"date\" specified, date imputation flag derived. \"none\" specified, date imputation flag derived. Permitted Values: \"auto\", \"date\" \"none\" min_dates Minimum dates list dates expected. ensured imputed date specified dates, e.g., imputed adverse event start date first treatment date. dates range possible dates dtc value considered. possible dates defined missing parts dtc date (see example ). ensures non-missing parts dtc date changed. date date-time object expected. example   returns \"2020-11-11T11:11:11\" possible dates \"2020-11\" range \"2020-11-01T00:00:00\" \"2020-11-30T23:59:59\". Therefore \"2020-12-06T12:12:12\" ignored. Returning \"2020-12-06T12:12:12\" changed month although missing (dtc date). max_dates Maximum dates list dates expected. ensured imputed date specified dates, e.g., imputed date data cut date. dates range possible dates considered. date date-time object expected. preserve Preserve day month missing day present example \"2019---07\" return \"2019-06-07 preserve = TRUE (date_imputation = \"MID\"). Permitted Values: TRUE, FALSE","code":"impute_dtc_dtm(   \"2020-11\",   min_dates = list(    ymd_hms(\"2020-12-06T12:12:12\"),    ymd_hms(\"2020-11-11T11:11:11\")   ),   highest_imputation = \"M\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_dt.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive/Impute a Date from a Date Character Vector — derive_vars_dt","text":"input dataset date '--DT' (date imputation flag '--DTF' requested) added.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_dt.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Derive/Impute a Date from a Date Character Vector — derive_vars_dt","text":"{admiral} allow users pick single part date/time impute, enable impute highest level, .e. choose say impute months, days. presence '--DTF' variable checked already exists input dataset, warning issued '--DTF' overwritten.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_dt.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive/Impute a Date from a Date Character Vector — derive_vars_dt","text":"","code":"library(tibble) library(lubridate)  mhdt <- tribble(   ~MHSTDTC,   \"2019-07-18T15:25:40\",   \"2019-07-18T15:25\",   \"2019-07-18\",   \"2019-02\",   \"2019\",   \"2019---07\",   \"\" )  # Create ASTDT and ASTDTF # No imputation for partial date derive_vars_dt(   mhdt,   new_vars_prefix = \"AST\",   dtc = MHSTDTC ) #> # A tibble: 7 × 2 #>   MHSTDTC               ASTDT      #>   <chr>                 <date>     #> 1 \"2019-07-18T15:25:40\" 2019-07-18 #> 2 \"2019-07-18T15:25\"    2019-07-18 #> 3 \"2019-07-18\"          2019-07-18 #> 4 \"2019-02\"             NA         #> 5 \"2019\"                NA         #> 6 \"2019---07\"           NA         #> 7 \"\"                    NA          # Create ASTDT and ASTDTF # Impute partial dates to first day/month derive_vars_dt(   mhdt,   new_vars_prefix = \"AST\",   dtc = MHSTDTC,   highest_imputation = \"M\" ) #> # A tibble: 7 × 3 #>   MHSTDTC               ASTDT      ASTDTF #>   <chr>                 <date>     <chr>  #> 1 \"2019-07-18T15:25:40\" 2019-07-18 NA     #> 2 \"2019-07-18T15:25\"    2019-07-18 NA     #> 3 \"2019-07-18\"          2019-07-18 NA     #> 4 \"2019-02\"             2019-02-01 D      #> 5 \"2019\"                2019-01-01 M      #> 6 \"2019---07\"           2019-01-01 M      #> 7 \"\"                    NA         NA      # Impute partial dates to 6th of April derive_vars_dt(   mhdt,   new_vars_prefix = \"AST\",   dtc = MHSTDTC,   highest_imputation = \"M\",   date_imputation = \"04-06\" ) #> # A tibble: 7 × 3 #>   MHSTDTC               ASTDT      ASTDTF #>   <chr>                 <date>     <chr>  #> 1 \"2019-07-18T15:25:40\" 2019-07-18 NA     #> 2 \"2019-07-18T15:25\"    2019-07-18 NA     #> 3 \"2019-07-18\"          2019-07-18 NA     #> 4 \"2019-02\"             2019-02-06 D      #> 5 \"2019\"                2019-04-06 M      #> 6 \"2019---07\"           2019-04-06 M      #> 7 \"\"                    NA         NA      # Create AENDT and AENDTF # Impute partial dates to last day/month derive_vars_dt(   mhdt,   new_vars_prefix = \"AEN\",   dtc = MHSTDTC,   highest_imputation = \"M\",   date_imputation = \"last\" ) #> # A tibble: 7 × 3 #>   MHSTDTC               AENDT      AENDTF #>   <chr>                 <date>     <chr>  #> 1 \"2019-07-18T15:25:40\" 2019-07-18 NA     #> 2 \"2019-07-18T15:25\"    2019-07-18 NA     #> 3 \"2019-07-18\"          2019-07-18 NA     #> 4 \"2019-02\"             2019-02-28 D      #> 5 \"2019\"                2019-12-31 M      #> 6 \"2019---07\"           2019-12-31 M      #> 7 \"\"                    NA         NA      # Create BIRTHDT # Impute partial dates to 15th of June. No Date Imputation Flag derive_vars_dt(   mhdt,   new_vars_prefix = \"BIRTH\",   dtc = MHSTDTC,   highest_imputation = \"M\",   date_imputation = \"mid\",   flag_imputation = \"none\" ) #> # A tibble: 7 × 2 #>   MHSTDTC               BIRTHDT    #>   <chr>                 <date>     #> 1 \"2019-07-18T15:25:40\" 2019-07-18 #> 2 \"2019-07-18T15:25\"    2019-07-18 #> 3 \"2019-07-18\"          2019-07-18 #> 4 \"2019-02\"             2019-02-15 #> 5 \"2019\"                2019-06-30 #> 6 \"2019---07\"           2019-06-30 #> 7 \"\"                    NA          # Impute AE start date to the first date and ensure that the imputed date # is not before the treatment start date adae <- tribble(   ~AESTDTC, ~TRTSDTM,   \"2020-12\", ymd_hms(\"2020-12-06T12:12:12\"),   \"2020-11\", ymd_hms(\"2020-12-06T12:12:12\") )  derive_vars_dt(   adae,   dtc = AESTDTC,   new_vars_prefix = \"AST\",   highest_imputation = \"M\",   min_dates = exprs(TRTSDTM) ) #> # A tibble: 2 × 4 #>   AESTDTC TRTSDTM             ASTDT      ASTDTF #>   <chr>   <dttm>              <date>     <chr>  #> 1 2020-12 2020-12-06 12:12:12 2020-12-06 D      #> 2 2020-11 2020-12-06 12:12:12 2020-11-01 D       # A user imputing dates as middle month/day, i.e. date_imputation = \"mid\" can # use preserve argument to \"preserve\" partial dates.  For example, \"2019---07\", # will be displayed as \"2019-06-07\" rather than 2019-06-15 with preserve = TRUE  derive_vars_dt(   mhdt,   new_vars_prefix = \"AST\",   dtc = MHSTDTC,   highest_imputation = \"M\",   date_imputation = \"mid\",   preserve = TRUE ) #> # A tibble: 7 × 3 #>   MHSTDTC               ASTDT      ASTDTF #>   <chr>                 <date>     <chr>  #> 1 \"2019-07-18T15:25:40\" 2019-07-18 NA     #> 2 \"2019-07-18T15:25\"    2019-07-18 NA     #> 3 \"2019-07-18\"          2019-07-18 NA     #> 4 \"2019-02\"             2019-02-15 D      #> 5 \"2019\"                2019-06-30 M      #> 6 \"2019---07\"           2019-06-07 M      #> 7 \"\"                    NA         NA"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_dtm.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive/Impute a Datetime from a Date Character Vector — derive_vars_dtm","title":"Derive/Impute a Datetime from a Date Character Vector — derive_vars_dtm","text":"Derive datetime object ('--DTM') date character vector ('--DTC'). date time can imputed (see date_imputation/time_imputation arguments) date/time imputation flag ('--DTF', '--TMF') can added.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_dtm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive/Impute a Datetime from a Date Character Vector — derive_vars_dtm","text":"","code":"derive_vars_dtm(   dataset,   new_vars_prefix,   dtc,   highest_imputation = \"h\",   date_imputation = \"first\",   time_imputation = \"first\",   flag_imputation = \"auto\",   min_dates = NULL,   max_dates = NULL,   preserve = FALSE,   ignore_seconds_flag = FALSE )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_dtm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive/Impute a Datetime from a Date Character Vector — derive_vars_dtm","text":"dataset Input dataset variables specified dtc argument expected dataset. new_vars_prefix Prefix used output variable(s). character scalar expected. date variable \"DT\" appended specified prefix, date imputation flag \"DTF\", time imputation flag \"TMF\". .e., new_vars_prefix = \"AST\" variables ASTDT, ASTDTF, ASTTMF created. dtc '--DTC' date impute character date expected format like yyyy-mm-dd yyyy-mm-ddThh:mm:ss. Trailing components can omitted - valid \"missing\" value component. highest_imputation Highest imputation level highest_imputation argument controls components DTC value imputed missing. components specified level imputed. component higher level highest imputation level missing, NA_character_ returned. example, highest_imputation = \"D\" \"2020\" results NA_character_ month missing. \"n\" specified, imputation performed, .e., component missing, NA_character_ returned. \"Y\" specified, date_imputation \"first\" \"last\" min_dates max_dates specified respectively. Otherwise, NA_character_ returned year component missing. Permitted Values: \"Y\" (year, highest level), \"M\" (month), \"D\" (day), \"h\" (hour), \"m\" (minute), \"s\" (second), \"n\" (none, lowest level) date_imputation value impute day/month datepart missing. character value expected, either format month day specified \"mm-dd\": e.g. \"06-15\" 15th June (year can specified; imputing year \"first\" \"last\" together min_dates max_dates argument can used (see examples).), keyword: \"first\", \"mid\", \"last\" impute first/mid/last day/month. \"mid\" specified, missing components imputed middle possible range: month day missing, imputed \"06-30\" (middle year). day missing, imputed \"15\" (middle month). argument ignored highest_imputation less \"D\". time_imputation value impute time timepart missing. character value expected, either format hour, min sec specified \"hh:mm:ss\": e.g. \"00:00:00\" start day, keyword: \"first\",\"last\" impute start/end day. argument ignored highest_imputation = \"n\". flag_imputation Whether date/time imputation flag(s) must also derived. \"\" \"date\" specified, date imputation flag derived. \"auto\" specified highest_imputation argument greater \"h\", date imputation flag derived. \"\" \"time\" specified, time imputation flag derived. \"auto\" specified highest_imputation argument \"n\", time imputation flag derived. \"none\" specified, date time imputation flag derived. Permitted Values: \"auto\", \"date\", \"time\", \"\", \"none\" min_dates Minimum dates list dates expected. ensured imputed date specified dates, e.g., imputed adverse event start date first treatment date. dates range possible dates dtc value considered. possible dates defined missing parts dtc date (see example ). ensures non-missing parts dtc date changed. date date-time object expected. example   returns \"2020-11-11T11:11:11\" possible dates \"2020-11\" range \"2020-11-01T00:00:00\" \"2020-11-30T23:59:59\". Therefore \"2020-12-06T12:12:12\" ignored. Returning \"2020-12-06T12:12:12\" changed month although missing (dtc date). date variables (datetime) list time imputed \"00:00:00\". Specifying date variables makes sense date imputed. time imputed, date variables affect result. max_dates Maximum dates list dates expected. ensured imputed date specified dates, e.g., imputed date data cut date. dates range possible dates considered. date date-time object expected. date variables (datetime) list time imputed \"23:59:59\". Specifying date variables makes sense date imputed. time imputed, date variables affect result. preserve Preserve lower level date/time part higher order part missing, e.g. preserve day month missing preserve minute hour missing. example \"2019---07\" return \"2019-06-07 preserve = TRUE (date_imputation = \"mid\"). Permitted Values: TRUE, FALSE ignore_seconds_flag ADaM IG states given SDTM ('--DTC') variable, hours minutes ever collected, seconds imputed ('--DTM') 00, necessary set ('--TMF') 'S'. user can set TRUE 'S' Flag dropped ('--TMF'). Permitted Values: logical value","code":"impute_dtc_dtm(   \"2020-11\",   min_dates = list(    ymd_hms(\"2020-12-06T12:12:12\"),    ymd_hms(\"2020-11-11T11:11:11\")   ),   highest_imputation = \"M\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_dtm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive/Impute a Datetime from a Date Character Vector — derive_vars_dtm","text":"input dataset datetime '--DTM' (date/time imputation flag '--DTF', '--TMF') added.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_dtm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Derive/Impute a Datetime from a Date Character Vector — derive_vars_dtm","text":"{admiral} allow users pick single part date/time impute, enable impute highest level, .e. choose say impute months, days. presence '--DTF' variable checked variable derived already exists input dataset. However, '--TMF' already exists input dataset, warning issued '--TMF' overwritten.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_dtm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive/Impute a Datetime from a Date Character Vector — derive_vars_dtm","text":"","code":"library(tibble) library(lubridate)  mhdt <- tribble(   ~MHSTDTC,   \"2019-07-18T15:25:40\",   \"2019-07-18T15:25\",   \"2019-07-18\",   \"2019-02\",   \"2019\",   \"2019---07\",   \"\" )  derive_vars_dtm(   mhdt,   new_vars_prefix = \"AST\",   dtc = MHSTDTC,   highest_imputation = \"M\" ) #> # A tibble: 7 × 4 #>   MHSTDTC               ASTDTM              ASTDTF ASTTMF #>   <chr>                 <dttm>              <chr>  <chr>  #> 1 \"2019-07-18T15:25:40\" 2019-07-18 15:25:40 NA     NA     #> 2 \"2019-07-18T15:25\"    2019-07-18 15:25:00 NA     S      #> 3 \"2019-07-18\"          2019-07-18 00:00:00 NA     H      #> 4 \"2019-02\"             2019-02-01 00:00:00 D      H      #> 5 \"2019\"                2019-01-01 00:00:00 M      H      #> 6 \"2019---07\"           2019-01-01 00:00:00 M      H      #> 7 \"\"                    NA                  NA     NA      # Impute AE end date to the last date and ensure that the imputed date is not # after the death or data cut off date adae <- tribble(   ~AEENDTC, ~DTHDT, ~DCUTDT,   \"2020-12\", ymd(\"2020-12-06\"), ymd(\"2020-12-24\"),   \"2020-11\", ymd(\"2020-12-06\"), ymd(\"2020-12-24\") )  derive_vars_dtm(   adae,   dtc = AEENDTC,   new_vars_prefix = \"AEN\",   highest_imputation = \"M\",   date_imputation = \"last\",   time_imputation = \"last\",   max_dates = exprs(DTHDT, DCUTDT) ) #> # A tibble: 2 × 6 #>   AEENDTC DTHDT      DCUTDT     AENDTM              AENDTF AENTMF #>   <chr>   <date>     <date>     <dttm>              <chr>  <chr>  #> 1 2020-12 2020-12-06 2020-12-24 2020-12-06 23:59:59 D      H      #> 2 2020-11 2020-12-06 2020-12-24 2020-11-30 23:59:59 D      H       # Seconds has been removed from the input dataset.  Function now uses # ignore_seconds_flag to remove the 'S' from the --TMF variable. mhdt <- tribble(   ~MHSTDTC,   \"2019-07-18T15:25\",   \"2019-07-18T15:25\",   \"2019-07-18\",   \"2019-02\",   \"2019\",   \"2019---07\",   \"\" )  derive_vars_dtm(   mhdt,   new_vars_prefix = \"AST\",   dtc = MHSTDTC,   highest_imputation = \"M\",   ignore_seconds_flag = TRUE ) #> # A tibble: 7 × 4 #>   MHSTDTC            ASTDTM              ASTDTF ASTTMF #>   <chr>              <dttm>              <chr>  <chr>  #> 1 \"2019-07-18T15:25\" 2019-07-18 15:25:00 NA     NA     #> 2 \"2019-07-18T15:25\" 2019-07-18 15:25:00 NA     NA     #> 3 \"2019-07-18\"       2019-07-18 00:00:00 NA     H      #> 4 \"2019-02\"          2019-02-01 00:00:00 D      H      #> 5 \"2019\"             2019-01-01 00:00:00 M      H      #> 6 \"2019---07\"        2019-01-01 00:00:00 M      H      #> 7 \"\"                 NA                  NA     NA      # A user imputing dates as middle month/day, i.e. date_imputation = \"MID\" can # use preserve argument to \"preserve\" partial dates.  For example, \"2019---07\", # will be displayed as \"2019-06-07\" rather than 2019-06-15 with preserve = TRUE  derive_vars_dtm(   mhdt,   new_vars_prefix = \"AST\",   dtc = MHSTDTC,   highest_imputation = \"M\",   date_imputation = \"mid\",   preserve = TRUE ) #> # A tibble: 7 × 4 #>   MHSTDTC            ASTDTM              ASTDTF ASTTMF #>   <chr>              <dttm>              <chr>  <chr>  #> 1 \"2019-07-18T15:25\" 2019-07-18 15:25:00 NA     S      #> 2 \"2019-07-18T15:25\" 2019-07-18 15:25:00 NA     S      #> 3 \"2019-07-18\"       2019-07-18 00:00:00 NA     H      #> 4 \"2019-02\"          2019-02-15 00:00:00 D      H      #> 5 \"2019\"             2019-06-30 00:00:00 M      H      #> 6 \"2019---07\"        2019-06-07 00:00:00 M      H      #> 7 \"\"                 NA                  NA     NA"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_dtm_to_dt.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive Date Variables from Datetime Variables — derive_vars_dtm_to_dt","title":"Derive Date Variables from Datetime Variables — derive_vars_dtm_to_dt","text":"function creates date(s) output datetime variable(s)","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_dtm_to_dt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive Date Variables from Datetime Variables — derive_vars_dtm_to_dt","text":"","code":"derive_vars_dtm_to_dt(dataset, source_vars)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_dtm_to_dt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive Date Variables from Datetime Variables — derive_vars_dtm_to_dt","text":"dataset Input dataset variables specified source_vars argument expected dataset. source_vars list datetime variables created using exprs() dates extracted","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_dtm_to_dt.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive Date Variables from Datetime Variables — derive_vars_dtm_to_dt","text":"data frame containing input dataset corresponding date (--DT) variable(s) datetime variables (--DTM) specified source_vars.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_dtm_to_dt.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive Date Variables from Datetime Variables — derive_vars_dtm_to_dt","text":"","code":"library(tibble) library(dplyr, warn.conflicts = FALSE) library(lubridate)  adcm <- tribble(   ~USUBJID, ~TRTSDTM,              ~ASTDTM,               ~AENDTM,   \"PAT01\",  \"2012-02-25 23:00:00\", \"2012-02-28 19:00:00\", \"2012-02-25 23:00:00\",   \"PAT01\",  NA,                    \"2012-02-28 19:00:00\", NA,   \"PAT01\",  \"2017-02-25 23:00:00\", \"2013-02-25 19:00:00\", \"2014-02-25 19:00:00\",   \"PAT01\",  \"2017-02-25 16:00:00\", \"2017-02-25 14:00:00\", \"2017-03-25 23:00:00\",   \"PAT01\",  \"2017-02-25 16:00:00\", \"2017-02-25 14:00:00\", \"2017-04-29 14:00:00\", ) %>%   mutate(     TRTSDTM = as_datetime(TRTSDTM),     ASTDTM = as_datetime(ASTDTM),     AENDTM = as_datetime(AENDTM)   )  adcm %>%   derive_vars_dtm_to_dt(exprs(TRTSDTM, ASTDTM, AENDTM)) %>%   select(USUBJID, starts_with(\"TRT\"), starts_with(\"AST\"), starts_with(\"AEN\")) #> # A tibble: 5 × 7 #>   USUBJID TRTSDTM             TRTSDT     ASTDTM              ASTDT      #>   <chr>   <dttm>              <date>     <dttm>              <date>     #> 1 PAT01   2012-02-25 23:00:00 2012-02-25 2012-02-28 19:00:00 2012-02-28 #> 2 PAT01   NA                  NA         2012-02-28 19:00:00 2012-02-28 #> 3 PAT01   2017-02-25 23:00:00 2017-02-25 2013-02-25 19:00:00 2013-02-25 #> 4 PAT01   2017-02-25 16:00:00 2017-02-25 2017-02-25 14:00:00 2017-02-25 #> 5 PAT01   2017-02-25 16:00:00 2017-02-25 2017-02-25 14:00:00 2017-02-25 #> # ℹ 2 more variables: AENDTM <dttm>, AENDT <date>"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_dtm_to_tm.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive Time Variables from Datetime Variables — derive_vars_dtm_to_tm","title":"Derive Time Variables from Datetime Variables — derive_vars_dtm_to_tm","text":"function creates time variable(s) output datetime variable(s)","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_dtm_to_tm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive Time Variables from Datetime Variables — derive_vars_dtm_to_tm","text":"","code":"derive_vars_dtm_to_tm(dataset, source_vars)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_dtm_to_tm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive Time Variables from Datetime Variables — derive_vars_dtm_to_tm","text":"dataset Input dataset variables specified source_vars argument expected dataset. source_vars list datetime variables created using exprs() time extracted","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_dtm_to_tm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive Time Variables from Datetime Variables — derive_vars_dtm_to_tm","text":"data frame containing input dataset corresponding time (--TM) variable(s) datetime variables (--DTM) specified source_vars correct name.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_dtm_to_tm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Derive Time Variables from Datetime Variables — derive_vars_dtm_to_tm","text":"names newly added variables automatically set replacing --DTM suffix source_vars --TM. --TM variables created using {hms} package.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_dtm_to_tm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive Time Variables from Datetime Variables — derive_vars_dtm_to_tm","text":"","code":"library(tibble) library(dplyr, warn.conflicts = FALSE) library(lubridate)  adcm <- tribble(   ~USUBJID, ~TRTSDTM, ~ASTDTM, ~AENDTM,   \"PAT01\", \"2012-02-25 23:41:10\", \"2012-02-28 19:03:00\", \"2013-02-25 23:32:16\",   \"PAT01\", \"\", \"2012-02-28 19:00:00\", \"\",   \"PAT01\", \"2017-02-25 23:00:02\", \"2013-02-25 19:00:15\", \"2014-02-25 19:00:56\",   \"PAT01\", \"2017-02-25 16:00:00\", \"2017-02-25 14:25:00\", \"2017-03-25 23:00:00\",   \"PAT01\", \"2017-02-25 16:05:17\", \"2017-02-25 14:20:00\", \"2018-04-29 14:06:45\", ) %>%   mutate(     TRTSDTM = as_datetime(TRTSDTM),     ASTDTM = as_datetime(ASTDTM),     AENDTM = as_datetime(AENDTM)   )  adcm %>%   derive_vars_dtm_to_tm(exprs(TRTSDTM)) %>%   select(USUBJID, starts_with(\"TRT\"), everything()) #> # A tibble: 5 × 5 #>   USUBJID TRTSDTM             TRTSTM   ASTDTM              AENDTM              #>   <chr>   <dttm>              <time>   <dttm>              <dttm>              #> 1 PAT01   2012-02-25 23:41:10 23:41:10 2012-02-28 19:03:00 2013-02-25 23:32:16 #> 2 PAT01   NA                        NA 2012-02-28 19:00:00 NA                  #> 3 PAT01   2017-02-25 23:00:02 23:00:02 2013-02-25 19:00:15 2014-02-25 19:00:56 #> 4 PAT01   2017-02-25 16:00:00 16:00:00 2017-02-25 14:25:00 2017-03-25 23:00:00 #> 5 PAT01   2017-02-25 16:05:17 16:05:17 2017-02-25 14:20:00 2018-04-29 14:06:45  adcm %>%   derive_vars_dtm_to_tm(exprs(TRTSDTM, ASTDTM, AENDTM)) %>%   select(USUBJID, starts_with(\"TRT\"), starts_with(\"AS\"), starts_with(\"AE\")) #> # A tibble: 5 × 7 #>   USUBJID TRTSDTM             TRTSTM   ASTDTM              ASTTM    #>   <chr>   <dttm>              <time>   <dttm>              <time>   #> 1 PAT01   2012-02-25 23:41:10 23:41:10 2012-02-28 19:03:00 19:03:00 #> 2 PAT01   NA                        NA 2012-02-28 19:00:00 19:00:00 #> 3 PAT01   2017-02-25 23:00:02 23:00:02 2013-02-25 19:00:15 19:00:15 #> 4 PAT01   2017-02-25 16:00:00 16:00:00 2017-02-25 14:25:00 14:25:00 #> 5 PAT01   2017-02-25 16:05:17 16:05:17 2017-02-25 14:20:00 14:20:00 #> # ℹ 2 more variables: AENDTM <dttm>, AENTM <time>"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_duration.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive Duration — derive_vars_duration","title":"Derive Duration — derive_vars_duration","text":"Derives duration two dates, specified variables present input dataset e.g., duration adverse events, relative day, age, ...","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_duration.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive Duration — derive_vars_duration","text":"","code":"derive_vars_duration(   dataset,   new_var,   new_var_unit = NULL,   start_date,   end_date,   in_unit = \"days\",   out_unit = \"DAYS\",   floor_in = TRUE,   add_one = TRUE,   trunc_out = FALSE,   type = \"duration\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_duration.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive Duration — derive_vars_duration","text":"dataset Input dataset variables specified start_date end_date arguments expected dataset. new_var Name variable create new_var_unit Name unit variable parameter specified, variable unit created. start_date start date date date-time object expected. Refer derive_vars_dt() impute derive date date character vector date object. Refer convert_dtc_to_dt() obtain vector imputed dates. end_date end date date date-time object expected. Refer derive_vars_dt() impute derive date date character vector date object. Refer convert_dtc_to_dt() obtain vector imputed dates. in_unit Input unit See floor_in add_one parameter details. Permitted Values (case-insensitive): years: \"year\", \"years\", \"yr\", \"yrs\", \"y\" months: \"month\", \"months\", \"mo\", \"mos\" days: \"day\", \"days\", \"d\" hours: \"hour\", \"hours\", \"hr\", \"hrs\", \"h\" minutes: \"minute\", \"minutes\", \"min\", \"mins\" seconds: \"second\", \"seconds\", \"sec\", \"secs\", \"s\" out_unit Output unit duration derived specified unit Permitted Values (case-insensitive): years: \"year\", \"years\", \"yr\", \"yrs\", \"y\" months: \"month\", \"months\", \"mo\", \"mos\" weeks: \"week\", \"weeks\", \"wk\", \"wks\", \"w\" days: \"day\", \"days\", \"d\" hours: \"hour\", \"hours\", \"hr\", \"hrs\", \"h\" minutes: \"minute\", \"minutes\", \"min\", \"mins\" seconds: \"second\", \"seconds\", \"sec\", \"secs\", \"s\" floor_in Round input dates? input dates round respect input unit, e.g., input unit 'days', time input dates ignored. Default: TRUE Permitted Values: TRUE, FALSE add_one Add one input unit? duration non-negative, one input unit added. .e., duration can zero. Default: TRUE Permitted Values: TRUE, FALSE trunc_out Return integer part fractional part duration (output unit) removed, .e., integer part returned. Default: FALSE Permitted Values: TRUE, FALSE type lubridate duration type. See details. Default: \"duration\" Permitted Values: \"duration\", \"interval\"","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_duration.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive Duration — derive_vars_duration","text":"input dataset duration unit variable added","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_duration.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Derive Duration — derive_vars_duration","text":"duration derived time start end date specified output unit. end date start date, duration negative. start end date variable must present specified input dataset. lubridate package calculates two types spans two dates: duration interval. calculations largely , unit time period month year result can slightly different. difference arises ambiguity length \"1 month\" \"1 year\". Months may 31, 30, 28, 29 days, years 365 days 366 leap years. Durations intervals help solve ambiguity measures. interval 2000-02-01 2000-03-01 1 (.e. one month). duration two dates 0.95, accounts fact year 2000 leap year, February 29 days, average month length 30.4375, .e. 29 / 30.4375 = 0.95. additional details, review lubridate time span reference page.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_duration.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive Duration — derive_vars_duration","text":"","code":"library(lubridate) library(tibble)  # Derive age in years data <- tribble(   ~USUBJID, ~BRTHDT, ~RANDDT,   \"P01\", ymd(\"1984-09-06\"), ymd(\"2020-02-24\"),   \"P02\", ymd(\"1985-01-01\"), NA,   \"P03\", NA, ymd(\"2021-03-10\"),   \"P04\", NA, NA )  derive_vars_duration(data,   new_var = AAGE,   new_var_unit = AAGEU,   start_date = BRTHDT,   end_date = RANDDT,   out_unit = \"years\",   add_one = FALSE,   trunc_out = TRUE ) #> # A tibble: 4 × 5 #>   USUBJID BRTHDT     RANDDT      AAGE AAGEU #>   <chr>   <date>     <date>     <dbl> <chr> #> 1 P01     1984-09-06 2020-02-24    35 years #> 2 P02     1985-01-01 NA            NA NA    #> 3 P03     NA         2021-03-10    NA NA    #> 4 P04     NA         NA            NA NA     # Derive adverse event duration in days data <- tribble(   ~USUBJID, ~ASTDT, ~AENDT,   \"P01\", ymd(\"2021-03-05\"), ymd(\"2021-03-02\"),   \"P02\", ymd(\"2019-09-18\"), ymd(\"2019-09-18\"),   \"P03\", ymd(\"1985-01-01\"), NA,   \"P04\", NA, NA )  derive_vars_duration(data,   new_var = ADURN,   new_var_unit = ADURU,   start_date = ASTDT,   end_date = AENDT,   out_unit = \"days\" ) #> # A tibble: 4 × 5 #>   USUBJID ASTDT      AENDT      ADURN ADURU #>   <chr>   <date>     <date>     <dbl> <chr> #> 1 P01     2021-03-05 2021-03-02    -3 days  #> 2 P02     2019-09-18 2019-09-18     1 days  #> 3 P03     1985-01-01 NA            NA NA    #> 4 P04     NA         NA            NA NA     # Derive adverse event duration in minutes data <- tribble(   ~USUBJID, ~ADTM, ~TRTSDTM,   \"P01\", ymd_hms(\"2019-08-09T04:30:56\"), ymd_hms(\"2019-08-09T05:00:00\"),   \"P02\", ymd_hms(\"2019-11-11T10:30:00\"), ymd_hms(\"2019-11-11T11:30:00\"),   \"P03\", ymd_hms(\"2019-11-11T00:00:00\"), ymd_hms(\"2019-11-11T04:00:00\"),   \"P04\", NA, ymd_hms(\"2019-11-11T12:34:56\"), )  derive_vars_duration(data,   new_var = ADURN,   new_var_unit = ADURU,   start_date = ADTM,   end_date = TRTSDTM,   in_unit = \"minutes\",   out_unit = \"minutes\",   add_one = FALSE ) #> # A tibble: 4 × 5 #>   USUBJID ADTM                TRTSDTM             ADURN ADURU   #>   <chr>   <dttm>              <dttm>              <dbl> <chr>   #> 1 P01     2019-08-09 04:30:56 2019-08-09 05:00:00    30 minutes #> 2 P02     2019-11-11 10:30:00 2019-11-11 11:30:00    60 minutes #> 3 P03     2019-11-11 00:00:00 2019-11-11 04:00:00   240 minutes #> 4 P04     NA                  2019-11-11 12:34:56    NA NA       # Derive adverse event start time since last dose in hours data <- tribble(   ~USUBJID, ~ASTDTM, ~LDOSEDTM,   \"P01\", ymd_hms(\"2019-08-09T04:30:56\"), ymd_hms(\"2019-08-08T10:05:00\"),   \"P02\", ymd_hms(\"2019-11-11T23:59:59\"), ymd_hms(\"2019-10-11T11:37:00\"),   \"P03\", ymd_hms(\"2019-11-11T00:00:00\"), ymd_hms(\"2019-11-10T23:59:59\"),   \"P04\", ymd_hms(\"2019-11-11T12:34:56\"), NA,   \"P05\", NA, ymd_hms(\"2019-09-28T12:34:56\") ) derive_vars_duration(   data,   new_var = LDRELTM,   new_var_unit = LDRELTMU,   start_date = LDOSEDTM,   end_date = ASTDTM,   in_unit = \"hours\",   out_unit = \"hours\",   add_one = FALSE ) #> # A tibble: 5 × 5 #>   USUBJID ASTDTM              LDOSEDTM            LDRELTM LDRELTMU #>   <chr>   <dttm>              <dttm>                <dbl> <chr>    #> 1 P01     2019-08-09 04:30:56 2019-08-08 10:05:00      18 hours    #> 2 P02     2019-11-11 23:59:59 2019-10-11 11:37:00     756 hours    #> 3 P03     2019-11-11 00:00:00 2019-11-10 23:59:59       1 hours    #> 4 P04     2019-11-11 12:34:56 NA                       NA NA       #> 5 P05     NA                  2019-09-28 12:34:56      NA NA"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_dy.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive Relative Day Variables — derive_vars_dy","title":"Derive Relative Day Variables — derive_vars_dy","text":"Adds relative day variables (--DY) dataset, e.g., ASTDY AENDY.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_dy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive Relative Day Variables — derive_vars_dy","text":"","code":"derive_vars_dy(dataset, reference_date, source_vars)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_dy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive Relative Day Variables — derive_vars_dy","text":"dataset Input dataset variables specified reference_date source_vars arguments expected dataset. reference_date date date-time column, e.g., date first treatment date-time last exposure treatment. Refer derive_vars_dt() impute derive date date character vector date object. source_vars list datetime date variables created using exprs() dates extracted. can either list date(time) variables named --DY variables corresponding –DT(M) variables e.g. exprs(TRTSDTM, ASTDTM, AENDT) exprs(TRTSDT, ASTDTM, AENDT, DEATHDY = DTHDT). source variable end –DT(M), name resulting --DY variable must provided.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_dy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive Relative Day Variables — derive_vars_dy","text":"input dataset --DY corresponding --DTM --DT source variable(s) added","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_dy.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Derive Relative Day Variables — derive_vars_dy","text":"relative day derived number days reference date end date. nonnegative, one added. .e., relative day reference date 1. Unless name explicitly specified, name resulting relative day variable generated source variable name replacing DT (DTM appropriate) DY.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_dy.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive Relative Day Variables — derive_vars_dy","text":"","code":"library(tibble) library(lubridate) library(dplyr, warn.conflicts = FALSE)  datain <- tribble(   ~TRTSDTM, ~ASTDTM, ~AENDT,   \"2014-01-17T23:59:59\", \"2014-01-18T13:09:O9\", \"2014-01-20\" ) %>%   mutate(     TRTSDTM = as_datetime(TRTSDTM),     ASTDTM = as_datetime(ASTDTM),     AENDT = ymd(AENDT)   )  derive_vars_dy(   datain,   reference_date = TRTSDTM,   source_vars = exprs(TRTSDTM, ASTDTM, AENDT) ) #> # A tibble: 1 × 6 #>   TRTSDTM             ASTDTM              AENDT      TRTSDY ASTDY AENDY #>   <dttm>              <dttm>              <date>      <dbl> <dbl> <dbl> #> 1 2014-01-17 23:59:59 2014-01-18 13:09:09 2014-01-20      1     2     4  # specifying name of new variables datain <- tribble(   ~TRTSDT, ~DTHDT,   \"2014-01-17\", \"2014-02-01\" ) %>%   mutate(     TRTSDT = ymd(TRTSDT),     DTHDT = ymd(DTHDT)   )  derive_vars_dy(   datain,   reference_date = TRTSDT,   source_vars = exprs(TRTSDT, DEATHDY = DTHDT) ) #> # A tibble: 1 × 4 #>   TRTSDT     DTHDT      TRTSDY DEATHDY #>   <date>     <date>      <dbl>   <dbl> #> 1 2014-01-17 2014-02-01      1      16"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_extreme_event.html","id":null,"dir":"Reference","previous_headings":"","what":"Add the Worst or Best Observation for Each By Group as New Variables — derive_vars_extreme_event","title":"Add the Worst or Best Observation for Each By Group as New Variables — derive_vars_extreme_event","text":"Add first available record events group new variables, variables selected observation kept. can used selecting extreme observation series user-defined events.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_extreme_event.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add the Worst or Best Observation for Each By Group as New Variables — derive_vars_extreme_event","text":"","code":"derive_vars_extreme_event(   dataset,   by_vars,   events,   tmp_event_nr_var = NULL,   order,   mode,   source_datasets = NULL,   check_type = \"warning\",   new_vars )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_extreme_event.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add the Worst or Best Observation for Each By Group as New Variables — derive_vars_extreme_event","text":"dataset Input dataset variables specified by_vars order arguments expected dataset. by_vars Grouping variables Default: NULL Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) events Conditions new values defining events list event() event_joined() objects expected. observations listed events considered deriving extreme event. multiple records meet filter condition, take first record sorted order. data grouped by_vars, .e., summary functions like () () can used condition. event_joined() events observations selected calling filter_joined(). condition field passed filter_join argument. tmp_event_nr_var Temporary event number variable specified variable added source datasets set number event selecting records event. can used order determine record used records one event selected. variable included output dataset. order Sort order particular event events one observation, within event group, records ordered specified order. handling NAs sorting variables see Sort Order. Permitted Values: list expressions created exprs(), e.g., exprs(ADT, desc(AVAL)) mode Selection mode (first last) particular event events one observation, \"first\"/\"last\" used select first/last record type event sorting order. Permitted Values: \"first\", \"last\" source_datasets Source datasets named list datasets expected. dataset_name field event() event_joined() refers dataset provided list. check_type Check uniqueness? \"warning\" \"error\" specified, specified message issued observations input dataset unique respect variables order. Default: \"warning\" Permitted Values: \"none\", \"warning\", \"error\" new_vars Variables add specified variables events added output dataset. Variables can renamed naming element, .e., new_vars = exprs(<new name> = <old name>).","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_extreme_event.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add the Worst or Best Observation for Each By Group as New Variables — derive_vars_extreme_event","text":"input dataset best worst observation group added new variables.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_extreme_event.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Add the Worst or Best Observation for Each By Group as New Variables — derive_vars_extreme_event","text":"event select observations consider: event class event, observations source dataset restricted condition first last (mode) observation per group (by_vars) selected. event class event_joined, filter_joined() called select observations. variables specified set_values_to field event added selected observations. variable specified tmp_event_nr_var added set number event. selected observations bound together. group (respect variables specified by_vars parameter) first last observation (respect order specified order parameter mode specified mode parameter) selected. variables specified new_vars parameter added selected observations. variables added input dataset.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_extreme_event.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add the Worst or Best Observation for Each By Group as New Variables — derive_vars_extreme_event","text":"","code":"library(tibble) library(dplyr) library(lubridate)  adsl <- tribble(   ~STUDYID, ~USUBJID, ~TRTEDT, ~DTHDT,   \"PILOT01\", \"01-1130\", ymd(\"2014-08-16\"), ymd(\"2014-09-13\"),   \"PILOT01\", \"01-1133\", ymd(\"2013-04-28\"), ymd(\"\"),   \"PILOT01\", \"01-1211\", ymd(\"2013-01-12\"), ymd(\"\"),   \"PILOT01\", \"09-1081\", ymd(\"2014-04-27\"), ymd(\"\"),   \"PILOT01\", \"09-1088\", ymd(\"2014-10-09\"), ymd(\"2014-11-01\"), )  lb <- tribble(   ~STUDYID,  ~DOMAIN,  ~USUBJID, ~LBSEQ,             ~LBDTC,   \"PILOT01\",    \"LB\", \"01-1130\",    219, \"2014-06-07T13:20\",   \"PILOT01\",    \"LB\", \"01-1130\",    322, \"2014-08-16T13:10\",   \"PILOT01\",    \"LB\", \"01-1133\",    268, \"2013-04-18T15:30\",   \"PILOT01\",    \"LB\", \"01-1133\",    304, \"2013-05-01T10:13\",   \"PILOT01\",    \"LB\", \"01-1211\",      8, \"2012-10-30T14:26\",   \"PILOT01\",    \"LB\", \"01-1211\",    162, \"2013-01-08T12:13\",   \"PILOT01\",    \"LB\", \"09-1081\",     47, \"2014-02-01T10:55\",   \"PILOT01\",    \"LB\", \"09-1081\",    219, \"2014-05-10T11:15\",   \"PILOT01\",    \"LB\", \"09-1088\",    283, \"2014-09-27T12:13\",   \"PILOT01\",    \"LB\", \"09-1088\",    322, \"2014-10-09T13:25\" ) %>%   mutate(     ADT = convert_dtc_to_dt(LBDTC)   )  derive_vars_extreme_event(   adsl,   by_vars = exprs(STUDYID, USUBJID),   events = list(     event(       dataset_name = \"adsl\",       condition = !is.na(DTHDT),       set_values_to = exprs(LSTALVDT = DTHDT, DTHFL = \"Y\")     ),     event(       dataset_name = \"lb\",       condition = !is.na(ADT),       order = exprs(ADT),       mode = \"last\",       set_values_to = exprs(LSTALVDT = ADT, DTHFL = \"N\")     ),     event(       dataset_name = \"adsl\",       condition = !is.na(TRTEDT),       order = exprs(TRTEDT),       mode = \"last\",       set_values_to = exprs(LSTALVDT = TRTEDT, DTHFL = \"N\")     )   ),   source_datasets = list(adsl = adsl, lb = lb),   tmp_event_nr_var = event_nr,   order = exprs(LSTALVDT, event_nr),   mode = \"last\",   new_vars = exprs(LSTALVDT, DTHFL) ) #> # A tibble: 5 × 6 #>   STUDYID USUBJID TRTEDT     DTHDT      LSTALVDT   DTHFL #>   <chr>   <chr>   <date>     <date>     <date>     <chr> #> 1 PILOT01 01-1130 2014-08-16 2014-09-13 2014-09-13 Y     #> 2 PILOT01 01-1133 2013-04-28 NA         2013-05-01 N     #> 3 PILOT01 01-1211 2013-01-12 NA         2013-01-12 N     #> 4 PILOT01 09-1081 2014-04-27 NA         2014-05-10 N     #> 5 PILOT01 09-1088 2014-10-09 2014-11-01 2014-11-01 Y      # Derive DTHCAUS from AE and DS domain data adsl <- tribble(   ~STUDYID,  ~USUBJID,   \"STUDY01\", \"PAT01\",   \"STUDY01\", \"PAT02\",   \"STUDY01\", \"PAT03\" ) ae <- tribble(   ~STUDYID, ~USUBJID, ~AESEQ, ~AEDECOD, ~AEOUT, ~AEDTHDTC,   \"STUDY01\", \"PAT01\", 12, \"SUDDEN DEATH\", \"FATAL\", \"2021-04-04\",   \"STUDY01\", \"PAT01\", 13, \"CARDIAC ARREST\", \"FATAL\", \"2021-04-03\", )  ds <- tribble(   ~STUDYID, ~USUBJID, ~DSSEQ, ~DSDECOD, ~DSTERM, ~DSSTDTC,   \"STUDY01\", \"PAT02\", 1, \"INFORMED CONSENT OBTAINED\", \"INFORMED CONSENT OBTAINED\", \"2021-04-03\",   \"STUDY01\", \"PAT02\", 2, \"RANDOMIZATION\", \"RANDOMIZATION\", \"2021-04-11\",   \"STUDY01\", \"PAT02\", 3, \"DEATH\", \"DEATH DUE TO PROGRESSION OF DISEASE\", \"2022-02-01\",   \"STUDY01\", \"PAT03\", 1, \"DEATH\", \"POST STUDY REPORTING OF DEATH\", \"2022-03-03\" )  derive_vars_extreme_event(   adsl,   by_vars = exprs(STUDYID, USUBJID),   events = list(     event(       dataset_name = \"ae\",       condition = AEOUT == \"FATAL\",       set_values_to = exprs(DTHCAUS = AEDECOD, DTHDT = convert_dtc_to_dt(AEDTHDTC)),       order = exprs(DTHDT)     ),     event(       dataset_name = \"ds\",       condition = DSDECOD == \"DEATH\" & grepl(\"DEATH DUE TO\", DSTERM),       set_values_to = exprs(DTHCAUS = DSTERM, DTHDT = convert_dtc_to_dt(DSSTDTC)),       order = exprs(DTHDT)     )   ),   source_datasets = list(ae = ae, ds = ds),   tmp_event_nr_var = event_nr,   order = exprs(DTHDT, event_nr),   mode = \"first\",   new_vars = exprs(DTHCAUS, DTHDT) ) #> # A tibble: 3 × 4 #>   STUDYID USUBJID DTHCAUS                             DTHDT      #>   <chr>   <chr>   <chr>                               <date>     #> 1 STUDY01 PAT01   CARDIAC ARREST                      2021-04-03 #> 2 STUDY01 PAT02   DEATH DUE TO PROGRESSION OF DISEASE 2022-02-01 #> 3 STUDY01 PAT03   NA                                  NA"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_joined.html","id":null,"dir":"Reference","previous_headings":"","what":"Add Variables from an Additional Dataset Based on Conditions from Both Datasets — derive_vars_joined","title":"Add Variables from an Additional Dataset Based on Conditions from Both Datasets — derive_vars_joined","text":"function adds variables additional dataset input dataset. selection observations additional dataset can depend variables datasets. example, add lowest value (nadir) current observation.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_joined.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add Variables from an Additional Dataset Based on Conditions from Both Datasets — derive_vars_joined","text":"","code":"derive_vars_joined(   dataset,   dataset_add,   by_vars = NULL,   order = NULL,   new_vars = NULL,   tmp_obs_nr_var = NULL,   join_vars = NULL,   join_type,   filter_add = NULL,   first_cond_lower = NULL,   first_cond_upper = NULL,   filter_join = NULL,   mode = NULL,   exist_flag = NULL,   true_value = \"Y\",   false_value = NA_character_,   missing_values = NULL,   check_type = \"warning\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_joined.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add Variables from an Additional Dataset Based on Conditions from Both Datasets — derive_vars_joined","text":"dataset Input dataset variables specified by_vars argument expected dataset. dataset_add Additional dataset variables specified by_vars, new_vars, join_vars, order argument expected. by_vars Grouping variables two datasets joined specified variables. Variables can renamed naming element, .e. by_vars = exprs(<name input dataset> = <name additional dataset>), similar dplyr joins. Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) order Sort order argument set non-null value, observation input dataset first last observation joined dataset selected respect specified order. specified variables expected additional dataset (dataset_add). variable available dataset dataset_add, one dataset_add used sorting. expression named, e.g., exprs(EXSTDT = convert_dtc_to_dt(EXSTDTC), EXSEQ), corresponding variable (EXSTDT) added additional dataset can used filter conditions (filter_add, filter_join) join_vars new_vars. variable included output dataset. handling NAs sorting variables see Sort Order. Permitted Values: list expressions created exprs(), e.g., exprs(ADT, desc(AVAL)) NULL new_vars Variables add specified variables additional dataset added output dataset. Variables can renamed naming element, .e., new_vars = exprs(<new name> = <old name>). example new_vars = exprs(var1, var2) adds variables var1 var2 dataset_add input dataset. new_vars = exprs(var1, new_var2 = old_var2) takes var1 old_var2 dataset_add adds input dataset renaming old_var2 new_var2. Values added variables can modified specifying expression. example, new_vars = LASTRSP = exprs(str_to_upper(AVALC)) adds variable LASTRSP dataset sets upper case value AVALC. argument specified set NULL, variables additional dataset (dataset_add) added. Permitted Values: list variables named expressions created exprs() tmp_obs_nr_var Temporary observation number specified variable added input dataset (dataset) additional dataset (dataset_add). set observation number respect order. group (by_vars) observation number starts 1. variable can used conditions (filter_join, first_cond_upper, first_cond_lower). can also used select consecutive observations last observation. variable included output dataset. include specify new_vars. join_vars Variables use additional dataset extra variables required additional dataset filter_join specified argument. Variables specified new_vars need repeated join_vars. specified variable exists input dataset additional dataset, suffix \".join\" added variable additional dataset. expression named, e.g., exprs(EXTDT = convert_dtc_to_dt(EXSTDTC)), corresponding variable added additional dataset can used filter conditions (filter_add, filter_join) new_vars. variable included output dataset. variables included output dataset. Permitted Values: list variables named expressions created exprs() join_type Observations keep joining argument determines joined observations kept respect original observation. example, join_type = \"\" specified observations original observations kept. example confirmed response BOR oncology setting confirmed deterioration questionnaires confirmatory assessment must assessment. Thus join_type = \"\" used. Whereas, sometimes might allow confirmatory observations occur prior observation. example, identify AEs occurring seven days COVID AE. Thus join_type = \"\" used. Permitted Values: \"\", \"\", \"\" filter_add Filter additional dataset (dataset_add) observations dataset_add fulfilling specified condition joined input dataset. argument specified, observations joined. Variables created order new_vars arguments can used condition. condition can include summary functions like () (). additional dataset grouped variables (by_vars). Permitted Values: condition first_cond_lower Condition selecting range data () argument specified, observations restricted first observation current observation specified condition fulfilled current observation. condition fulfilled observations, observations considered. argument specified filter_join contains summary functions apply observations certain observation current observation current observation. example see last example . first_cond_upper Condition selecting range data () argument specified, observations restricted first observation specified condition fulfilled. condition fulfilled observations, observations considered. argument specified filter_join contains summary functions apply observations confirmation assessment. example see last example . filter_join Filter joined dataset specified condition applied joined dataset. Therefore variables datasets dataset dataset_add can used. Variables created order new_vars arguments can used condition. condition can include summary functions like () (). joined dataset grouped original observations. Permitted Values: condition mode Selection mode Determines first last observation selected. order argument specified, mode must non-null. order argument specified, mode argument ignored. Permitted Values: \"first\", \"last\", NULL exist_flag Exist flag argument specified (e.g., exist_flag = FLAG), specified variable (e.g., FLAG) added input dataset. variable value provided true_value selected records dataset_add merged input dataset, value provided false_value otherwise. Permitted Values: Variable name true_value True value value specified variable exist_flag, applicable first last observation (depending mode) group. Permitted Values: atomic scalar false_value False value value specified variable exist_flag, applicable first last observation (depending mode) group. Permitted Values: atomic scalar missing_values Values non-matching observations observations input dataset (dataset) matching observation additional dataset (dataset_add) values specified variables set specified value. variables specified new_vars can specified missing_values. Permitted Values: named list expressions, e.g., exprs(BASEC = \"MISSING\", BASE = -1) check_type Check uniqueness? \"warning\" \"error\" specified, specified message issued observations (restricted) joined dataset unique respect variables order. argument ignored order specified. case error issued independent check_type restricted joined dataset contains one observation observations input dataset. Permitted Values: \"none\", \"warning\", \"error\"","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_joined.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add Variables from an Additional Dataset Based on Conditions from Both Datasets — derive_vars_joined","text":"output dataset contains observations variables input dataset additionally variables specified new_vars additional dataset (dataset_add).","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_joined.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Add Variables from an Additional Dataset Based on Conditions from Both Datasets — derive_vars_joined","text":"variables specified order added additional dataset (dataset_add). variables specified join_vars added additional dataset (dataset_add). records additional dataset (dataset_add) restricted matching filter_add condition. input dataset (restricted) additional dataset left joined grouping variables (by_vars). grouping variables specified, full join performed. first_cond_lower specified, observation input dataset joined dataset restricted observations first observation first_cond_lower fulfilled (observation fulfilling condition included) observation input dataset. observation input dataset condition fulfilled, observation removed. first_cond_upper specified, observation input dataset joined dataset restricted observations first observation first_cond_upper fulfilled (observation fulfilling condition included). observation input dataset condition fulfilled, observation removed. example see last example \"Examples\" section. joined dataset restricted filter_join condition. order specified, observation input dataset first last observation (depending mode) selected. variables specified new_vars created (requested) merged input dataset. .e., output dataset contains observations input dataset. observations without matching observation joined dataset new variables set specified missing_values (NA variables missing_values). Observations additional dataset matching observation input dataset ignored.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_joined.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add Variables from an Additional Dataset Based on Conditions from Both Datasets — derive_vars_joined","text":"","code":"library(tibble) library(lubridate) library(dplyr, warn.conflicts = FALSE) library(tidyr)  # Add AVISIT (based on time windows), AWLO, and AWHI adbds <- tribble(   ~USUBJID, ~ADY,   \"1\",       -33,   \"1\",        -2,   \"1\",         3,   \"1\",        24,   \"2\",        NA, )  windows <- tribble(   ~AVISIT,    ~AWLO, ~AWHI,   \"BASELINE\",   -30,     1,   \"WEEK 1\",       2,     7,   \"WEEK 2\",       8,    15,   \"WEEK 3\",      16,    22,   \"WEEK 4\",      23,    30 )  derive_vars_joined(   adbds,   dataset_add = windows,   join_type = \"all\",   filter_join = AWLO <= ADY & ADY <= AWHI ) #> # A tibble: 5 × 5 #>   USUBJID   ADY AVISIT    AWLO  AWHI #>   <chr>   <dbl> <chr>    <dbl> <dbl> #> 1 1         -33 NA          NA    NA #> 2 1          -2 BASELINE   -30     1 #> 3 1           3 WEEK 1       2     7 #> 4 1          24 WEEK 4      23    30 #> 5 2          NA NA          NA    NA  # derive the nadir after baseline and before the current observation adbds <- tribble(   ~USUBJID, ~ADY, ~AVAL,   \"1\",        -7,    10,   \"1\",         1,    12,   \"1\",         8,    11,   \"1\",        15,     9,   \"1\",        20,    14,   \"1\",        24,    12,   \"2\",        13,     8 )  derive_vars_joined(   adbds,   dataset_add = adbds,   by_vars = exprs(USUBJID),   order = exprs(AVAL),   new_vars = exprs(NADIR = AVAL),   join_vars = exprs(ADY),   join_type = \"all\",   filter_add = ADY > 0,   filter_join = ADY.join < ADY,   mode = \"first\",   check_type = \"none\" ) #> # A tibble: 7 × 4 #>   USUBJID   ADY  AVAL NADIR #>   <chr>   <dbl> <dbl> <dbl> #> 1 1          -7    10    NA #> 2 1           1    12    NA #> 3 1           8    11    12 #> 4 1          15     9    11 #> 5 1          20    14     9 #> 6 1          24    12     9 #> 7 2          13     8    NA  # add highest hemoglobin value within two weeks before AE, # take earliest if more than one adae <- tribble(   ~USUBJID, ~ASTDY,   \"1\",           3,   \"1\",          22,   \"2\",           2 )  adlb <- tribble(   ~USUBJID, ~PARAMCD, ~ADY, ~AVAL,   \"1\",      \"HGB\",       1,   8.5,   \"1\",      \"HGB\",       3,   7.9,   \"1\",      \"HGB\",       5,   8.9,   \"1\",      \"HGB\",       8,   8.0,   \"1\",      \"HGB\",       9,   8.0,   \"1\",      \"HGB\",      16,   7.4,   \"1\",      \"HGB\",      24,   8.1,   \"1\",      \"ALB\",       1,    42, )  derive_vars_joined(   adae,   dataset_add = adlb,   by_vars = exprs(USUBJID),   order = exprs(AVAL, desc(ADY)),   new_vars = exprs(HGB_MAX = AVAL, HGB_DY = ADY),   join_type = \"all\",   filter_add = PARAMCD == \"HGB\",   filter_join = ASTDY - 14 <= ADY & ADY <= ASTDY,   mode = \"last\" ) #> # A tibble: 3 × 4 #>   USUBJID ASTDY HGB_MAX HGB_DY #>   <chr>   <dbl>   <dbl>  <dbl> #> 1 1           3     8.5      1 #> 2 1          22     8        8 #> 3 2           2    NA       NA  # Add APERIOD, APERIODC based on ADSL adsl <- tribble(   ~USUBJID, ~AP01SDT,     ~AP01EDT,     ~AP02SDT,     ~AP02EDT,   \"1\",      \"2021-01-04\", \"2021-02-06\", \"2021-02-07\", \"2021-03-07\",   \"2\",      \"2021-02-02\", \"2021-03-02\", \"2021-03-03\", \"2021-04-01\" ) %>%   mutate(across(ends_with(\"DT\"), ymd)) %>%   mutate(STUDYID = \"xyz\")  period_ref <- create_period_dataset(   adsl,   new_vars = exprs(APERSDT = APxxSDT, APEREDT = APxxEDT) )  period_ref #> # A tibble: 4 × 5 #>   STUDYID USUBJID APERIOD APERSDT    APEREDT    #>   <chr>   <chr>     <int> <date>     <date>     #> 1 xyz     1             1 2021-01-04 2021-02-06 #> 2 xyz     1             2 2021-02-07 2021-03-07 #> 3 xyz     2             1 2021-02-02 2021-03-02 #> 4 xyz     2             2 2021-03-03 2021-04-01  adae <- tribble(   ~USUBJID, ~ASTDT,   \"1\",      \"2021-01-01\",   \"1\",      \"2021-01-05\",   \"1\",      \"2021-02-05\",   \"1\",      \"2021-03-05\",   \"1\",      \"2021-04-05\",   \"2\",      \"2021-02-15\", ) %>%   mutate(     ASTDT = ymd(ASTDT),     STUDYID = \"xyz\"   )  derive_vars_joined(   adae,   dataset_add = period_ref,   by_vars = exprs(STUDYID, USUBJID),   join_vars = exprs(APERSDT, APEREDT),   join_type = \"all\",   filter_join = APERSDT <= ASTDT & ASTDT <= APEREDT ) #> # A tibble: 6 × 6 #>   USUBJID ASTDT      STUDYID APERIOD APERSDT    APEREDT    #>   <chr>   <date>     <chr>     <int> <date>     <date>     #> 1 1       2021-01-01 xyz          NA NA         NA         #> 2 1       2021-01-05 xyz           1 2021-01-04 2021-02-06 #> 3 1       2021-02-05 xyz           1 2021-01-04 2021-02-06 #> 4 1       2021-03-05 xyz           2 2021-02-07 2021-03-07 #> 5 1       2021-04-05 xyz          NA NA         NA         #> 6 2       2021-02-15 xyz           1 2021-02-02 2021-03-02  # Add day since last dose (LDRELD) adae <- tribble(   ~USUBJID, ~ASTDT,       ~AESEQ,   \"1\",      \"2020-02-02\",      1,   \"1\",      \"2020-02-04\",      2 ) %>%   mutate(ASTDT = ymd(ASTDT))  ex <- tribble(   ~USUBJID, ~EXSDTC,   \"1\",      \"2020-01-10\",   \"1\",      \"2020-01\",   \"1\",      \"2020-01-20\",   \"1\",      \"2020-02-03\" )  ## Please note that EXSDT is created via the order argument and then used ## for new_vars, filter_add, and filter_join derive_vars_joined(   adae,   dataset_add = ex,   by_vars = exprs(USUBJID),   order = exprs(EXSDT = convert_dtc_to_dt(EXSDTC)),   join_type = \"all\",   new_vars = exprs(LDRELD = compute_duration(     start_date = EXSDT, end_date = ASTDT   )),   filter_add = !is.na(EXSDT),   filter_join = EXSDT <= ASTDT,   mode = \"last\" ) #> # A tibble: 2 × 4 #>   USUBJID ASTDT      AESEQ LDRELD #>   <chr>   <date>     <dbl>  <dbl> #> 1 1       2020-02-02     1     14 #> 2 1       2020-02-04     2      2  # first_cond_lower and first_cond_upper argument myd <- tribble(   ~subj, ~day, ~val,   \"1\",      1, \"++\",   \"1\",      2, \"-\",   \"1\",      3, \"0\",   \"1\",      4, \"+\",   \"1\",      5, \"++\",   \"1\",      6, \"-\",   \"2\",      1, \"-\",   \"2\",      2, \"++\",   \"2\",      3, \"+\",   \"2\",      4, \"0\",   \"2\",      5, \"-\",   \"2\",      6, \"++\" )  # derive last \"++\" day before \"0\" where all results in between are \"+\" or \"++\" derive_vars_joined(   myd,   dataset_add = myd,   by_vars = exprs(subj),   order = exprs(day),   mode = \"first\",   new_vars = exprs(prev_plus_day = day),   join_vars = exprs(val),   join_type = \"before\",   first_cond_lower = val.join == \"++\",   filter_join = val == \"0\" & all(val.join %in% c(\"+\", \"++\")) ) #> # A tibble: 12 × 4 #>    subj    day val   prev_plus_day #>    <chr> <dbl> <chr>         <dbl> #>  1 1         1 ++               NA #>  2 1         2 -                NA #>  3 1         3 0                NA #>  4 1         4 +                NA #>  5 1         5 ++               NA #>  6 1         6 -                NA #>  7 2         1 -                NA #>  8 2         2 ++               NA #>  9 2         3 +                NA #> 10 2         4 0                 2 #> 11 2         5 -                NA #> 12 2         6 ++               NA  # derive first \"++\" day after \"0\" where all results in between are \"+\" or \"++\" derive_vars_joined(   myd,   dataset_add = myd,   by_vars = exprs(subj),   order = exprs(day),   mode = \"last\",   new_vars = exprs(next_plus_day = day),   join_vars = exprs(val),   join_type = \"after\",   first_cond_upper = val.join == \"++\",   filter_join = val == \"0\" & all(val.join %in% c(\"+\", \"++\")) ) #> # A tibble: 12 × 4 #>    subj    day val   next_plus_day #>    <chr> <dbl> <chr>         <dbl> #>  1 1         1 ++               NA #>  2 1         2 -                NA #>  3 1         3 0                 5 #>  4 1         4 +                NA #>  5 1         5 ++               NA #>  6 1         6 -                NA #>  7 2         1 -                NA #>  8 2         2 ++               NA #>  9 2         3 +                NA #> 10 2         4 0                NA #> 11 2         5 -                NA #> 12 2         6 ++               NA"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_merged.html","id":null,"dir":"Reference","previous_headings":"","what":"Add New Variable(s) to the Input Dataset Based on Variables from Another Dataset — derive_vars_merged","title":"Add New Variable(s) to the Input Dataset Based on Variables from Another Dataset — derive_vars_merged","text":"Add new variable(s) input dataset based variables another dataset. observations merge can selected condition (filter_add argument) /selecting first last observation group (order mode argument).","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_merged.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add New Variable(s) to the Input Dataset Based on Variables from Another Dataset — derive_vars_merged","text":"","code":"derive_vars_merged(   dataset,   dataset_add,   by_vars,   order = NULL,   new_vars = NULL,   filter_add = NULL,   mode = NULL,   exist_flag = NULL,   true_value = \"Y\",   false_value = NA_character_,   missing_values = NULL,   check_type = \"warning\",   duplicate_msg = NULL,   relationship = NULL )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_merged.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add New Variable(s) to the Input Dataset Based on Variables from Another Dataset — derive_vars_merged","text":"dataset Input dataset variables specified by_vars argument expected dataset. dataset_add Additional dataset variables specified by_vars, new_vars, order argument expected. by_vars Grouping variables input dataset selected observations additional dataset merged specified variables. Variables can renamed naming element, .e. by_vars = exprs(<name input dataset> = <name additional dataset>), similar dplyr joins. Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) order Sort order argument set non-null value, group first last observation additional dataset selected respect specified order. Variables defined new_vars argument can used sort order. handling NAs sorting variables see Sort Order. Permitted Values: list expressions created exprs(), e.g., exprs(ADT, desc(AVAL)) NULL new_vars Variables add specified variables additional dataset added output dataset. Variables can renamed naming element, .e., new_vars = exprs(<new name> = <old name>). example new_vars = exprs(var1, var2) adds variables var1 var2 dataset_add input dataset. new_vars = exprs(var1, new_var2 = old_var2) takes var1 old_var2 dataset_add adds input dataset renaming old_var2 new_var2. Values added variables can modified specifying expression. example, new_vars = LASTRSP = exprs(str_to_upper(AVALC)) adds variable LASTRSP dataset sets upper case value AVALC. argument specified set NULL, variables additional dataset (dataset_add) added. Permitted Values: list variables named expressions created exprs() filter_add Filter additional dataset (dataset_add) observations fulfilling specified condition taken account merging. argument specified, observations considered. Variables defined new_vars argument can used filter condition. Permitted Values: condition mode Selection mode Determines first last observation selected. order argument specified, mode must non-null. order argument specified, mode argument ignored. Permitted Values: \"first\", \"last\", NULL exist_flag Exist flag argument specified (e.g., exist_flag = FLAG), specified variable (e.g., FLAG) added input dataset. variable value provided true_value selected records dataset_add merged input dataset, value provided false_value otherwise. Permitted Values: Variable name true_value True value value specified variable exist_flag, applicable first last observation (depending mode) group. Permitted Values: atomic scalar false_value False value value specified variable exist_flag, applicable first last observation (depending mode) group. Permitted Values: atomic scalar missing_values Values non-matching observations observations input dataset (dataset) matching observation additional dataset (dataset_add) values specified variables set specified value. variables specified new_vars can specified missing_values. Permitted Values: named list expressions, e.g., exprs(BASEC = \"MISSING\", BASE = -1) check_type Check uniqueness? \"warning\" \"error\" specified, specified message issued observations (restricted) additional dataset unique respect variables order. order argument specified, check_type argument ignored: observations (restricted) additional dataset unique respect variables, error issued. Permitted Values: \"none\", \"warning\", \"error\" duplicate_msg Message unique check uniqueness check fails, specified message displayed. Default:   relationship Expected merge-relationship by_vars variable(s) dataset (input dataset) dataset_add (additional dataset) containing additional new_vars. argument passed dplyr::left_join() function. See https://dplyr.tidyverse.org/reference/mutate-joins.html#arguments details. Permitted Values: \"one--one\", \"many--one\", NULL.","code":"paste(   \"Dataset {.arg dataset_add} contains duplicate records with respect to\",   \"{.var {vars2chr(by_vars)}}.\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_merged.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add New Variable(s) to the Input Dataset Based on Variables from Another Dataset — derive_vars_merged","text":"output dataset contains observations variables input dataset additionally variables specified new_vars additional dataset (dataset_add).","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_merged.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Add New Variable(s) to the Input Dataset Based on Variables from Another Dataset — derive_vars_merged","text":"new variables (new_vars) added additional dataset (dataset_add). records additional dataset (dataset_add) restricted matching filter_add condition. order specified, group first last observation (depending mode) selected. variables specified new_vars merged input dataset using left_join(). .e., output dataset contains observations input dataset. observations without matching observation additional dataset new variables set specified missing_values (NA variables missing_values). Observations additional dataset matching observation input dataset ignored.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_merged.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add New Variable(s) to the Input Dataset Based on Variables from Another Dataset — derive_vars_merged","text":"","code":"library(dplyr, warn.conflicts = FALSE) vs <- tribble(   ~STUDYID,  ~DOMAIN,  ~USUBJID, ~VSTESTCD,      ~VISIT, ~VSSTRESN, ~VSSTRESU,       ~VSDTC,   \"PILOT01\",    \"VS\", \"01-1302\",  \"HEIGHT\", \"SCREENING\",     177.8,      \"cm\", \"2013-08-20\",   \"PILOT01\",    \"VS\", \"01-1302\",  \"WEIGHT\", \"SCREENING\",     81.19,      \"kg\", \"2013-08-20\",   \"PILOT01\",    \"VS\", \"01-1302\",  \"WEIGHT\",  \"BASELINE\",      82.1,      \"kg\", \"2013-08-29\",   \"PILOT01\",    \"VS\", \"01-1302\",  \"WEIGHT\",    \"WEEK 2\",     81.19,      \"kg\", \"2013-09-15\",   \"PILOT01\",    \"VS\", \"01-1302\",  \"WEIGHT\",    \"WEEK 4\",     82.56,      \"kg\", \"2013-09-24\",   \"PILOT01\",    \"VS\", \"01-1302\",  \"WEIGHT\",    \"WEEK 6\",     80.74,      \"kg\", \"2013-10-08\",   \"PILOT01\",    \"VS\", \"01-1302\",  \"WEIGHT\",    \"WEEK 8\",      82.1,      \"kg\", \"2013-10-22\",   \"PILOT01\",    \"VS\", \"01-1302\",  \"WEIGHT\",   \"WEEK 12\",      82.1,      \"kg\", \"2013-11-05\",   \"PILOT01\",    \"VS\", \"17-1344\",  \"HEIGHT\", \"SCREENING\",     163.5,      \"cm\", \"2014-01-01\",   \"PILOT01\",    \"VS\", \"17-1344\",  \"WEIGHT\", \"SCREENING\",     58.06,      \"kg\", \"2014-01-01\",   \"PILOT01\",    \"VS\", \"17-1344\",  \"WEIGHT\",  \"BASELINE\",     58.06,      \"kg\", \"2014-01-11\",   \"PILOT01\",    \"VS\", \"17-1344\",  \"WEIGHT\",    \"WEEK 2\",     58.97,      \"kg\", \"2014-01-24\",   \"PILOT01\",    \"VS\", \"17-1344\",  \"WEIGHT\",    \"WEEK 4\",     57.97,      \"kg\", \"2014-02-07\",   \"PILOT01\",    \"VS\", \"17-1344\",  \"WEIGHT\",    \"WEEK 6\",     58.97,      \"kg\", \"2014-02-19\",   \"PILOT01\",    \"VS\", \"17-1344\",  \"WEIGHT\",    \"WEEK 8\",     57.79,      \"kg\", \"2014-03-14\" )  dm <- tribble(   ~STUDYID,  ~DOMAIN,  ~USUBJID, ~AGE,   ~AGEU,   \"PILOT01\",    \"DM\", \"01-1302\",   61, \"YEARS\",   \"PILOT01\",    \"DM\", \"17-1344\",   64, \"YEARS\" )   # Merging all dm variables to vs derive_vars_merged(   vs,   dataset_add = select(dm, -DOMAIN),   by_vars = exprs(STUDYID, USUBJID) ) %>%   select(STUDYID, USUBJID, VSTESTCD, VISIT, VSSTRESN, AGE, AGEU) #> # A tibble: 15 × 7 #>    STUDYID USUBJID VSTESTCD VISIT     VSSTRESN   AGE AGEU  #>    <chr>   <chr>   <chr>    <chr>        <dbl> <dbl> <chr> #>  1 PILOT01 01-1302 HEIGHT   SCREENING    178.     61 YEARS #>  2 PILOT01 01-1302 WEIGHT   SCREENING     81.2    61 YEARS #>  3 PILOT01 01-1302 WEIGHT   BASELINE      82.1    61 YEARS #>  4 PILOT01 01-1302 WEIGHT   WEEK 2        81.2    61 YEARS #>  5 PILOT01 01-1302 WEIGHT   WEEK 4        82.6    61 YEARS #>  6 PILOT01 01-1302 WEIGHT   WEEK 6        80.7    61 YEARS #>  7 PILOT01 01-1302 WEIGHT   WEEK 8        82.1    61 YEARS #>  8 PILOT01 01-1302 WEIGHT   WEEK 12       82.1    61 YEARS #>  9 PILOT01 17-1344 HEIGHT   SCREENING    164.     64 YEARS #> 10 PILOT01 17-1344 WEIGHT   SCREENING     58.1    64 YEARS #> 11 PILOT01 17-1344 WEIGHT   BASELINE      58.1    64 YEARS #> 12 PILOT01 17-1344 WEIGHT   WEEK 2        59.0    64 YEARS #> 13 PILOT01 17-1344 WEIGHT   WEEK 4        58.0    64 YEARS #> 14 PILOT01 17-1344 WEIGHT   WEEK 6        59.0    64 YEARS #> 15 PILOT01 17-1344 WEIGHT   WEEK 8        57.8    64 YEARS   # Merge last weight to adsl adsl <- tribble(   ~STUDYID,   ~USUBJID, ~AGE,   ~AGEU,   \"PILOT01\", \"01-1302\",   61, \"YEARS\",   \"PILOT01\", \"17-1344\",   64, \"YEARS\" )   derive_vars_merged(   adsl,   dataset_add = vs,   by_vars = exprs(STUDYID, USUBJID),   order = exprs(convert_dtc_to_dtm(VSDTC)),   mode = \"last\",   new_vars = exprs(LASTWGT = VSSTRESN, LASTWGTU = VSSTRESU),   filter_add = VSTESTCD == \"WEIGHT\",   exist_flag = vsdatafl ) %>%   select(STUDYID, USUBJID, AGE, AGEU, LASTWGT, LASTWGTU, vsdatafl) #> # A tibble: 2 × 7 #>   STUDYID USUBJID   AGE AGEU  LASTWGT LASTWGTU vsdatafl #>   <chr>   <chr>   <dbl> <chr>   <dbl> <chr>    <chr>    #> 1 PILOT01 01-1302    61 YEARS    82.1 kg       Y        #> 2 PILOT01 17-1344    64 YEARS    57.8 kg       Y          # Derive treatment start datetime (TRTSDTM) ex <- tribble(   ~STUDYID,  ~DOMAIN,  ~USUBJID, ~EXSTDY, ~EXENDY,     ~EXSTDTC,     ~EXENDTC,   \"PILOT01\",    \"EX\", \"01-1302\",       1,      18, \"2013-08-29\", \"2013-09-15\",   \"PILOT01\",    \"EX\", \"01-1302\",      19,      69, \"2013-09-16\", \"2013-11-05\",   \"PILOT01\",    \"EX\", \"17-1344\",       1,      14, \"2014-01-11\", \"2014-01-24\",   \"PILOT01\",    \"EX\", \"17-1344\",      15,      63, \"2014-01-25\", \"2014-03-14\" ) ## Impute exposure start date to first date/time ex_ext <- derive_vars_dtm(   ex,   dtc = EXSTDTC,   new_vars_prefix = \"EXST\",   highest_imputation = \"M\", ) ## Add first exposure datetime and imputation flags to adsl derive_vars_merged(   select(dm, STUDYID, USUBJID),   dataset_add = ex_ext,   by_vars = exprs(STUDYID, USUBJID),   new_vars = exprs(TRTSDTM = EXSTDTM, TRTSDTF = EXSTDTF, TRTSTMF = EXSTTMF),   order = exprs(EXSTDTM),   mode = \"first\" ) #> # A tibble: 2 × 5 #>   STUDYID USUBJID TRTSDTM             TRTSDTF TRTSTMF #>   <chr>   <chr>   <dttm>              <chr>   <chr>   #> 1 PILOT01 01-1302 2013-08-29 00:00:00 NA      H       #> 2 PILOT01 17-1344 2014-01-11 00:00:00 NA      H        # Derive treatment end datetime (TRTEDTM) ## Impute exposure end datetime to last time, no date imputation ex_ext <- derive_vars_dtm(   ex,   dtc = EXENDTC,   new_vars_prefix = \"EXEN\",   time_imputation = \"last\", ) ## Add last exposure datetime and imputation flag to adsl derive_vars_merged(   select(adsl, STUDYID, USUBJID),   dataset_add = ex_ext,   filter_add = !is.na(EXENDTM),   by_vars = exprs(STUDYID, USUBJID),   new_vars = exprs(TRTEDTM = EXENDTM, TRTETMF = EXENTMF),   order = exprs(EXENDTM),   mode = \"last\" ) #> # A tibble: 2 × 4 #>   STUDYID USUBJID TRTEDTM             TRTETMF #>   <chr>   <chr>   <dttm>              <chr>   #> 1 PILOT01 01-1302 2013-11-05 23:59:59 H       #> 2 PILOT01 17-1344 2014-03-14 23:59:59 H       # Modify merged values and set value for non matching observations adsl <- tribble(   ~USUBJID, ~SEX, ~COUNTRY,   \"ST42-1\", \"F\",  \"AUT\",   \"ST42-2\", \"M\",  \"MWI\",   \"ST42-3\", \"M\",  \"NOR\",   \"ST42-4\", \"F\",  \"UGA\" )  advs <- tribble(   ~USUBJID, ~PARAMCD, ~AVISIT,    ~AVISITN, ~AVAL,   \"ST42-1\", \"WEIGHT\", \"BASELINE\",        0,    66,   \"ST42-1\", \"WEIGHT\", \"WEEK 2\",          1,    68,   \"ST42-2\", \"WEIGHT\", \"BASELINE\",        0,    88,   \"ST42-3\", \"WEIGHT\", \"WEEK 2\",          1,    55,   \"ST42-3\", \"WEIGHT\", \"WEEK 4\",          2,    50 )  derive_vars_merged(   adsl,   dataset_add = advs,   by_vars = exprs(USUBJID),   new_vars = exprs(     LSTVSCAT = if_else(AVISIT == \"BASELINE\", \"BASELINE\", \"POST-BASELINE\")   ),   order = exprs(AVISITN),   mode = \"last\",   missing_values = exprs(LSTVSCAT = \"MISSING\") ) #> # A tibble: 4 × 4 #>   USUBJID SEX   COUNTRY LSTVSCAT      #>   <chr>   <chr> <chr>   <chr>         #> 1 ST42-1  F     AUT     POST-BASELINE #> 2 ST42-2  M     MWI     BASELINE      #> 3 ST42-3  M     NOR     POST-BASELINE #> 4 ST42-4  F     UGA     MISSING"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_merged_lookup.html","id":null,"dir":"Reference","previous_headings":"","what":"Merge Lookup Table with Source Dataset — derive_vars_merged_lookup","title":"Merge Lookup Table with Source Dataset — derive_vars_merged_lookup","text":"Merge user-defined lookup table input dataset. Optionally print list records input dataset corresponding mapping lookup table.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_merged_lookup.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Merge Lookup Table with Source Dataset — derive_vars_merged_lookup","text":"","code":"derive_vars_merged_lookup(   dataset,   dataset_add,   by_vars,   order = NULL,   new_vars = NULL,   mode = NULL,   filter_add = NULL,   check_type = \"warning\",   duplicate_msg = NULL,   print_not_mapped = TRUE )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_merged_lookup.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Merge Lookup Table with Source Dataset — derive_vars_merged_lookup","text":"dataset Input dataset variables specified by_vars argument expected dataset. dataset_add Lookup table variables specified by_vars argument expected. by_vars Grouping variables input dataset selected observations additional dataset merged specified variables. Variables can renamed naming element, .e. by_vars = exprs(<name input dataset> = <name additional dataset>), similar dplyr joins. Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) order Sort order argument set non-null value, group first last observation additional dataset selected respect specified order. Variables defined new_vars argument can used sort order. handling NAs sorting variables see Sort Order. Permitted Values: list expressions created exprs(), e.g., exprs(ADT, desc(AVAL)) NULL new_vars Variables add specified variables additional dataset added output dataset. Variables can renamed naming element, .e., new_vars = exprs(<new name> = <old name>). example new_vars = exprs(var1, var2) adds variables var1 var2 dataset_add input dataset. new_vars = exprs(var1, new_var2 = old_var2) takes var1 old_var2 dataset_add adds input dataset renaming old_var2 new_var2. Values added variables can modified specifying expression. example, new_vars = LASTRSP = exprs(str_to_upper(AVALC)) adds variable LASTRSP dataset sets upper case value AVALC. argument specified set NULL, variables additional dataset (dataset_add) added. Permitted Values: list variables named expressions created exprs() mode Selection mode Determines first last observation selected. order argument specified, mode must non-null. order argument specified, mode argument ignored. Permitted Values: \"first\", \"last\", NULL filter_add Filter additional dataset (dataset_add) observations fulfilling specified condition taken account merging. argument specified, observations considered. Variables defined new_vars argument can used filter condition. Permitted Values: condition check_type Check uniqueness? \"warning\" \"error\" specified, specified message issued observations (restricted) additional dataset unique respect variables order. order argument specified, check_type argument ignored: observations (restricted) additional dataset unique respect variables, error issued. Permitted Values: \"none\", \"warning\", \"error\" duplicate_msg Message unique check uniqueness check fails, specified message displayed. Default:   print_not_mapped Print list unique by_vars values corresponding records lookup table? Default: TRUE Permitted Values: TRUE, FALSE","code":"paste(   \"Dataset {.arg dataset_add} contains duplicate records with respect to\",   \"{.var {vars2chr(by_vars)}}.\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_merged_lookup.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Merge Lookup Table with Source Dataset — derive_vars_merged_lookup","text":"output dataset contains observations variables input dataset, add variables specified new_vars lookup table specified dataset_add. Optionally prints list unique by_vars values corresponding records lookup table (specifying print_not_mapped = TRUE).","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_merged_lookup.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Merge Lookup Table with Source Dataset — derive_vars_merged_lookup","text":"","code":"library(dplyr, warn.conflicts = FALSE) vs <- tribble(   ~STUDYID,  ~DOMAIN,  ~USUBJID,        ~VISIT, ~VSTESTCD,       ~VSTEST,   \"PILOT01\",    \"VS\", \"01-1028\",   \"SCREENING\",  \"HEIGHT\",      \"Height\",   \"PILOT01\",    \"VS\", \"01-1028\",   \"SCREENING\",    \"TEMP\", \"Temperature\",   \"PILOT01\",    \"VS\", \"01-1028\",    \"BASELINE\",    \"TEMP\", \"Temperature\",   \"PILOT01\",    \"VS\", \"01-1028\",      \"WEEK 4\",    \"TEMP\", \"Temperature\",   \"PILOT01\",    \"VS\", \"01-1028\", \"SCREENING 1\",  \"WEIGHT\",      \"Weight\",   \"PILOT01\",    \"VS\", \"01-1028\",    \"BASELINE\",  \"WEIGHT\",      \"Weight\",   \"PILOT01\",    \"VS\", \"01-1028\",      \"WEEK 4\",  \"WEIGHT\",      \"Weight\",   \"PILOT01\",    \"VS\", \"04-1325\",   \"SCREENING\",  \"HEIGHT\",      \"Height\",   \"PILOT01\",    \"VS\", \"04-1325\",   \"SCREENING\",    \"TEMP\", \"Temperature\",   \"PILOT01\",    \"VS\", \"04-1325\",    \"BASELINE\",    \"TEMP\", \"Temperature\",   \"PILOT01\",    \"VS\", \"04-1325\",      \"WEEK 4\",    \"TEMP\", \"Temperature\",   \"PILOT01\",    \"VS\", \"04-1325\", \"SCREENING 1\",  \"WEIGHT\",      \"Weight\",   \"PILOT01\",    \"VS\", \"04-1325\",    \"BASELINE\",  \"WEIGHT\",      \"Weight\",   \"PILOT01\",    \"VS\", \"04-1325\",      \"WEEK 4\",  \"WEIGHT\",      \"Weight\",   \"PILOT01\",    \"VS\", \"10-1027\",   \"SCREENING\",  \"HEIGHT\",      \"Height\",   \"PILOT01\",    \"VS\", \"10-1027\",   \"SCREENING\",    \"TEMP\", \"Temperature\",   \"PILOT01\",    \"VS\", \"10-1027\",    \"BASELINE\",    \"TEMP\", \"Temperature\",   \"PILOT01\",    \"VS\", \"10-1027\",      \"WEEK 4\",    \"TEMP\", \"Temperature\",   \"PILOT01\",    \"VS\", \"10-1027\", \"SCREENING 1\",  \"WEIGHT\",      \"Weight\",   \"PILOT01\",    \"VS\", \"10-1027\",    \"BASELINE\",  \"WEIGHT\",      \"Weight\",   \"PILOT01\",    \"VS\", \"10-1027\",      \"WEEK 4\",  \"WEIGHT\",      \"Weight\" )  param_lookup <- tribble(   ~VSTESTCD,                 ~VSTEST, ~PARAMCD,                       ~PARAM,   \"SYSBP\", \"Systolic Blood Pressure\",  \"SYSBP\", \"Syst Blood Pressure (mmHg)\",   \"WEIGHT\",                 \"Weight\", \"WEIGHT\",                \"Weight (kg)\",   \"HEIGHT\",                 \"Height\", \"HEIGHT\",                \"Height (cm)\",   \"TEMP\",              \"Temperature\",   \"TEMP\",            \"Temperature (C)\",   \"MAP\",    \"Mean Arterial Pressure\",    \"MAP\",   \"Mean Art Pressure (mmHg)\",   \"BMI\",           \"Body Mass Index\",    \"BMI\",    \"Body Mass Index(kg/m^2)\",   \"BSA\",         \"Body Surface Area\",    \"BSA\",     \"Body Surface Area(m^2)\" )  derive_vars_merged_lookup(   dataset = vs,   dataset_add = param_lookup,   by_vars = exprs(VSTESTCD),   new_vars = exprs(PARAMCD, PARAM),   print_not_mapped = TRUE ) #> All `VSTESTCD` are mapped. #> # A tibble: 21 × 8 #>    STUDYID DOMAIN USUBJID VISIT       VSTESTCD VSTEST      PARAMCD PARAM         #>    <chr>   <chr>  <chr>   <chr>       <chr>    <chr>       <chr>   <chr>         #>  1 PILOT01 VS     01-1028 SCREENING   HEIGHT   Height      HEIGHT  Height (cm)   #>  2 PILOT01 VS     01-1028 SCREENING   TEMP     Temperature TEMP    Temperature … #>  3 PILOT01 VS     01-1028 BASELINE    TEMP     Temperature TEMP    Temperature … #>  4 PILOT01 VS     01-1028 WEEK 4      TEMP     Temperature TEMP    Temperature … #>  5 PILOT01 VS     01-1028 SCREENING 1 WEIGHT   Weight      WEIGHT  Weight (kg)   #>  6 PILOT01 VS     01-1028 BASELINE    WEIGHT   Weight      WEIGHT  Weight (kg)   #>  7 PILOT01 VS     01-1028 WEEK 4      WEIGHT   Weight      WEIGHT  Weight (kg)   #>  8 PILOT01 VS     04-1325 SCREENING   HEIGHT   Height      HEIGHT  Height (cm)   #>  9 PILOT01 VS     04-1325 SCREENING   TEMP     Temperature TEMP    Temperature … #> 10 PILOT01 VS     04-1325 BASELINE    TEMP     Temperature TEMP    Temperature … #> # ℹ 11 more rows"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_period.html","id":null,"dir":"Reference","previous_headings":"","what":"Add Subperiod, Period, or Phase Variables to ADSL — derive_vars_period","title":"Add Subperiod, Period, or Phase Variables to ADSL — derive_vars_period","text":"function adds subperiod, period, phase variables like P01S1SDT, P01S2SDT, AP01SDTM, AP02SDTM, TRT01A, TRT02A, PH1SDT, PH2SDT, ... input dataset. values variables defined period reference dataset one observations per patient subperiod, period, phase.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_period.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add Subperiod, Period, or Phase Variables to ADSL — derive_vars_period","text":"","code":"derive_vars_period(   dataset,   dataset_ref,   new_vars,   subject_keys = get_admiral_option(\"subject_keys\") )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_period.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add Subperiod, Period, or Phase Variables to ADSL — derive_vars_period","text":"dataset Input dataset variables specified subject_keys argument expected dataset. dataset_ref Period reference dataset variables specified new_vars subject_keys expected. subperiod variables requested, APERIOD ASPER expected. period variables requested. APERIOD expected. phase variables requested, APHASEN expected. new_vars New variables named list variables like exprs(PHwSDT = PHSDT, PHwEDT = PHEDT, APHASEw = APHASE) expected. left hand side elements defines set variables (CDISC notation) added output dataset. right hand side defines source variable period reference dataset. lower case letter \"w\"  used refers phase variable, lower case letters \"xx\" used refers period variable, \"xx\" \"w\" used refers subperiod variable. one type must used, e.g., left hand side values must refer period variables. allowed mix example period subperiod variables. period subperiod variables required, separate calls must used. subject_keys Variables uniquely identify subject list expressions expressions symbols returned exprs() expected.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_period.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add Subperiod, Period, or Phase Variables to ADSL — derive_vars_period","text":"input dataset subperiod/period/phase variables added (see \"Details\" section)","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_period.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Add Subperiod, Period, or Phase Variables to ADSL — derive_vars_period","text":"subperiod/period/phase period reference dataset element new_vars variable (LHS value new_vars) added output dataset set value source variable (RHS value new_vars.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_period.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add Subperiod, Period, or Phase Variables to ADSL — derive_vars_period","text":"","code":"library(tibble) library(dplyr, warn.conflicts = FALSE) library(lubridate)  adsl <- tibble(STUDYID = \"xyz\", USUBJID = c(\"1\", \"2\"))  # Add period variables to ADSL period_ref <- tribble(   ~USUBJID, ~APERIOD, ~APERSDT,     ~APEREDT,   \"1\",             1, \"2021-01-04\", \"2021-02-06\",   \"1\",             2, \"2021-02-07\", \"2021-03-07\",   \"2\",             1, \"2021-02-02\", \"2021-03-02\",   \"2\",             2, \"2021-03-03\", \"2021-04-01\" ) %>%   mutate(     STUDYID = \"xyz\",     APERIOD = as.integer(APERIOD),     across(matches(\"APER[ES]DT\"), ymd)   )  derive_vars_period(   adsl,   dataset_ref = period_ref,   new_vars = exprs(APxxSDT = APERSDT, APxxEDT = APEREDT) ) %>%   select(STUDYID, USUBJID, AP01SDT, AP01EDT, AP02SDT, AP02EDT) #> # A tibble: 2 × 6 #>   STUDYID USUBJID AP01SDT    AP01EDT    AP02SDT    AP02EDT    #>   <chr>   <chr>   <date>     <date>     <date>     <date>     #> 1 xyz     1       2021-01-04 2021-02-06 2021-02-07 2021-03-07 #> 2 xyz     2       2021-02-02 2021-03-02 2021-03-03 2021-04-01  # Add phase variables to ADSL phase_ref <- tribble(   ~USUBJID, ~APHASEN, ~PHSDT,       ~PHEDT,       ~APHASE,   \"1\",             1, \"2021-01-04\", \"2021-02-06\", \"TREATMENT\",   \"1\",             2, \"2021-02-07\", \"2021-03-07\", \"FUP\",   \"2\",             1, \"2021-02-02\", \"2021-03-02\", \"TREATMENT\" ) %>%   mutate(     STUDYID = \"xyz\",     APHASEN = as.integer(APHASEN),     across(matches(\"PH[ES]DT\"), ymd)   )  derive_vars_period(   adsl,   dataset_ref = phase_ref,   new_vars = exprs(PHwSDT = PHSDT, PHwEDT = PHEDT, APHASEw = APHASE) ) %>%   select(STUDYID, USUBJID, PH1SDT, PH1EDT, PH2SDT, PH2EDT, APHASE1, APHASE2) #> # A tibble: 2 × 8 #>   STUDYID USUBJID PH1SDT     PH1EDT     PH2SDT     PH2EDT     APHASE1   APHASE2 #>   <chr>   <chr>   <date>     <date>     <date>     <date>     <chr>     <chr>   #> 1 xyz     1       2021-01-04 2021-02-06 2021-02-07 2021-03-07 TREATMENT FUP     #> 2 xyz     2       2021-02-02 2021-03-02 NA         NA         TREATMENT NA       # Add subperiod variables to ADSL subperiod_ref <- tribble(   ~USUBJID, ~APERIOD, ~ASPER, ~ASPRSDT,     ~ASPREDT,   \"1\",             1,      1, \"2021-01-04\", \"2021-01-19\",   \"1\",             1,      2, \"2021-01-20\", \"2021-02-06\",   \"1\",             2,      1, \"2021-02-07\", \"2021-03-07\",   \"2\",             1,      1, \"2021-02-02\", \"2021-03-02\",   \"2\",             2,      1, \"2021-03-03\", \"2021-04-01\" ) %>%   mutate(     STUDYID = \"xyz\",     APERIOD = as.integer(APERIOD),     ASPER = as.integer(ASPER),     across(matches(\"ASPR[ES]DT\"), ymd)   )  derive_vars_period(   adsl,   dataset_ref = subperiod_ref,   new_vars = exprs(PxxSwSDT = ASPRSDT, PxxSwEDT = ASPREDT) ) %>%   select(STUDYID, USUBJID, P01S1SDT, P01S1EDT, P01S2SDT, P01S2EDT, P02S1SDT, P02S1EDT) #> # A tibble: 2 × 8 #>   STUDYID USUBJID P01S1SDT   P01S1EDT   P01S2SDT   P01S2EDT   P02S1SDT   #>   <chr>   <chr>   <date>     <date>     <date>     <date>     <date>     #> 1 xyz     1       2021-01-04 2021-01-19 2021-01-20 2021-02-06 2021-02-07 #> 2 xyz     2       2021-02-02 2021-03-02 NA         NA         2021-03-03 #> # ℹ 1 more variable: P02S1EDT <date>"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_query.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive Query Variables — derive_vars_query","title":"Derive Query Variables — derive_vars_query","text":"Derive Query Variables","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_query.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive Query Variables — derive_vars_query","text":"","code":"derive_vars_query(dataset, dataset_queries)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_query.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive Query Variables — derive_vars_query","text":"dataset Input dataset dataset_queries dataset containing required columns PREFIX, GRPNAME, SRCVAR, TERMCHAR /TERMNUM, optional columns GRPID, SCOPE, SCOPEN. create_query_data() can used create dataset.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_query.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive Query Variables — derive_vars_query","text":"input dataset query variables derived.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_query.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Derive Query Variables — derive_vars_query","text":"function can used derive CDISC variables SMQzzNAM, SMQzzCD, SMQzzSC, SMQzzSCN, CQzzNAM ADAE ADMH, variables SDGzzNAM, SDGzzCD, SDGzzSC ADCM. example usage function can found OCCDS vignette. query dataset expected input function. See Queries Dataset Documentation vignette descriptions, call data(\"queries\") example query dataset. unique element PREFIX, corresponding \"NAM\" variable created. unique PREFIX, GRPID \"\" NA, corresponding \"CD\" variable created; similarly, SCOPE \"\" NA, corresponding \"SC\" variable created; SCOPEN \"\" NA, corresponding \"SCN\" variable created. record dataset, \"NAM\" variable takes value GRPNAME value TERMCHAR TERMNUM dataset_queries matches value respective SRCVAR dataset. Note TERMCHAR dataset_queries dataset may NA TERMNUM non-NA vice versa. matching case insensitive. \"CD\", \"SC\", \"SCN\" variables derived accordingly based GRPID, SCOPE, SCOPEN respectively, whenever missing.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_query.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive Query Variables — derive_vars_query","text":"","code":"library(tibble) data(\"queries\") adae <- tribble(   ~USUBJID, ~ASTDTM, ~AETERM, ~AESEQ, ~AEDECOD, ~AELLT, ~AELLTCD,   \"01\", \"2020-06-02 23:59:59\", \"ALANINE AMINOTRANSFERASE ABNORMAL\",   3, \"Alanine aminotransferase abnormal\", NA_character_, NA_integer_,   \"02\", \"2020-06-05 23:59:59\", \"BASEDOW'S DISEASE\",   5, \"Basedow's disease\", NA_character_, 1L,   \"03\", \"2020-06-07 23:59:59\", \"SOME TERM\",   2, \"Some query\", \"Some term\", NA_integer_,   \"05\", \"2020-06-09 23:59:59\", \"ALVEOLAR PROTEINOSIS\",   7, \"Alveolar proteinosis\", NA_character_, NA_integer_ ) derive_vars_query(adae, queries) #> # A tibble: 4 × 24 #>   USUBJID ASTDTM     AETERM AESEQ AEDECOD AELLT AELLTCD SMQ02NAM SMQ02CD SMQ02SC #>   <chr>   <chr>      <chr>  <dbl> <chr>   <chr>   <int> <chr>      <int> <chr>   #> 1 01      2020-06-0… ALANI…     3 Alanin… NA         NA NA            NA NA      #> 2 02      2020-06-0… BASED…     5 Basedo… NA          1 NA            NA NA      #> 3 03      2020-06-0… SOME …     2 Some q… Some…      NA NA            NA NA      #> 4 05      2020-06-0… ALVEO…     7 Alveol… NA         NA NA            NA NA      #> # ℹ 14 more variables: SMQ02SCN <int>, SMQ03NAM <chr>, SMQ03CD <int>, #> #   SMQ03SC <chr>, SMQ03SCN <int>, SMQ05NAM <chr>, SMQ05CD <int>, #> #   SMQ05SC <chr>, SMQ05SCN <int>, CQ01NAM <chr>, CQ04NAM <chr>, CQ04CD <int>, #> #   CQ06NAM <chr>, CQ06CD <int>"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_transposed.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive Variables by Transposing and Merging a Second Dataset — derive_vars_transposed","title":"Derive Variables by Transposing and Merging a Second Dataset — derive_vars_transposed","text":"Adds variables vertical dataset transposing wide one.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_transposed.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive Variables by Transposing and Merging a Second Dataset — derive_vars_transposed","text":"","code":"derive_vars_transposed(   dataset,   dataset_merge,   by_vars,   id_vars = NULL,   key_var,   value_var,   filter = NULL,   relationship = NULL )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_transposed.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive Variables by Transposing and Merging a Second Dataset — derive_vars_transposed","text":"dataset Input dataset variables specified by_vars argument expected dataset. dataset_merge Dataset transpose merge variables specified by_vars, id_vars, key_var value_var arguments expected. variables by_vars, id_vars, key_var unique key. by_vars Grouping variables Keys used merge dataset_merge dataset. id_vars ID variables Variables (excluding by_vars) uniquely identify observation dataset_merge. Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) key_var variable dataset_merge containing names transposed variables value_var variable dataset_merge containing values transposed variables filter Expression used restrict records dataset_merge prior transposing relationship Expected merge-relationship by_vars variable(s) dataset dataset_merge (transposition) argument passed dplyr::left_join() function. See https://dplyr.tidyverse.org/reference/mutate-joins.html#arguments details. Permitted Values: \"one--one\", \"one--many\", \"many--one\", \"many--many\", NULL","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_transposed.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive Variables by Transposing and Merging a Second Dataset — derive_vars_transposed","text":"input dataset transposed variables dataset_merge added","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_transposed.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Derive Variables by Transposing and Merging a Second Dataset — derive_vars_transposed","text":"filtering dataset_merge based upon condition provided filter, dataset transposed subsequently merged onto dataset using by_vars keys.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/derive_vars_transposed.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive Variables by Transposing and Merging a Second Dataset — derive_vars_transposed","text":"","code":"library(tibble) library(dplyr, warn.conflicts = FALSE)  cm <- tribble(   ~USUBJID,       ~CMGRPID, ~CMREFID,  ~CMDECOD,   \"BP40257-1001\", \"14\",     \"1192056\", \"PARACETAMOL\",   \"BP40257-1001\", \"18\",     \"2007001\", \"SOLUMEDROL\",   \"BP40257-1002\", \"19\",     \"2791596\", \"SPIRONOLACTONE\" ) facm <- tribble(   ~USUBJID,       ~FAGRPID, ~FAREFID,  ~FATESTCD,  ~FASTRESC,   \"BP40257-1001\", \"1\",      \"1192056\", \"CMATC1CD\", \"N\",   \"BP40257-1001\", \"1\",      \"1192056\", \"CMATC2CD\", \"N02\",   \"BP40257-1001\", \"1\",      \"1192056\", \"CMATC3CD\", \"N02B\",   \"BP40257-1001\", \"1\",      \"1192056\", \"CMATC4CD\", \"N02BE\",   \"BP40257-1001\", \"1\",      \"2007001\", \"CMATC1CD\", \"D\",   \"BP40257-1001\", \"1\",      \"2007001\", \"CMATC2CD\", \"D10\",   \"BP40257-1001\", \"1\",      \"2007001\", \"CMATC3CD\", \"D10A\",   \"BP40257-1001\", \"1\",      \"2007001\", \"CMATC4CD\", \"D10AA\",   \"BP40257-1001\", \"2\",      \"2007001\", \"CMATC1CD\", \"D\",   \"BP40257-1001\", \"2\",      \"2007001\", \"CMATC2CD\", \"D07\",   \"BP40257-1001\", \"2\",      \"2007001\", \"CMATC3CD\", \"D07A\",   \"BP40257-1001\", \"2\",      \"2007001\", \"CMATC4CD\", \"D07AA\",   \"BP40257-1001\", \"3\",      \"2007001\", \"CMATC1CD\", \"H\",   \"BP40257-1001\", \"3\",      \"2007001\", \"CMATC2CD\", \"H02\",   \"BP40257-1001\", \"3\",      \"2007001\", \"CMATC3CD\", \"H02A\",   \"BP40257-1001\", \"3\",      \"2007001\", \"CMATC4CD\", \"H02AB\",   \"BP40257-1002\", \"1\",      \"2791596\", \"CMATC1CD\", \"C\",   \"BP40257-1002\", \"1\",      \"2791596\", \"CMATC2CD\", \"C03\",   \"BP40257-1002\", \"1\",      \"2791596\", \"CMATC3CD\", \"C03D\",   \"BP40257-1002\", \"1\",      \"2791596\", \"CMATC4CD\", \"C03DA\" )  cm %>%   derive_vars_transposed(     facm,     by_vars = exprs(USUBJID, CMREFID = FAREFID),     id_vars = exprs(FAGRPID),     key_var = FATESTCD,     value_var = FASTRESC   ) %>%   select(USUBJID, CMDECOD, starts_with(\"CMATC\")) #> # A tibble: 5 × 6 #>   USUBJID      CMDECOD        CMATC1CD CMATC2CD CMATC3CD CMATC4CD #>   <chr>        <chr>          <chr>    <chr>    <chr>    <chr>    #> 1 BP40257-1001 PARACETAMOL    N        N02      N02B     N02BE    #> 2 BP40257-1001 SOLUMEDROL     D        D10      D10A     D10AA    #> 3 BP40257-1001 SOLUMEDROL     D        D07      D07A     D07AA    #> 4 BP40257-1001 SOLUMEDROL     H        H02      H02A     H02AB    #> 5 BP40257-1002 SPIRONOLACTONE C        C03      C03D     C03DA"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/dose_freq_lookup.html","id":null,"dir":"Reference","previous_headings":"","what":"Pre-Defined Dose Frequencies — dose_freq_lookup","title":"Pre-Defined Dose Frequencies — dose_freq_lookup","text":"pre-defined dose frequencies sourced CDISC. number rows generate using create_single_dose_dataset() arguments start_date end_date derived DOSE_COUNT, DOSE_WINDOW, CONVERSION_FACTOR appropriate functions lubridate.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/dose_freq_lookup.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pre-Defined Dose Frequencies — dose_freq_lookup","text":"","code":"dose_freq_lookup"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/dose_freq_lookup.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Pre-Defined Dose Frequencies — dose_freq_lookup","text":"object class tbl_df (inherits tbl, data.frame) 86 rows 5 columns.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/dose_freq_lookup.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Pre-Defined Dose Frequencies — dose_freq_lookup","text":"NCI_CODE CDISC_VALUE included CDISC source traceability. DOSE_COUNT represents number doses received one single unit DOSE_WINDOW. example, CDISC_VALUE==\"10 DAYS PER MONTH\", DOSE_WINDOW==\"MONTH\" DOSE_COUNT==10. Similarly, CDISC_VALUE==\"EVERY 2 WEEKS\", DOSE_WINDOW==\"WEEK\" DOSE_COUNT==0.5 (yield one dose every two weeks). CONVERSION_FACTOR used convert DOSE_WINDOW units \"WEEK\", \"MONTH\", \"YEAR\" unit \"DAY\". example, CDISC_VALUE==\"10 DAYS PER MONTH\", CONVERSION_FACTOR 0.0329. One day month assumed 1 / 30.4375 month (one day assumed 1/365.25 year). Given start_date end_date aggregate dataset, CONVERSION_FACTOR used calculate specific dates forstart_date end_date resulting single dose dataset doses occur. cases, doses assumed occur evenly spaced increments interval. see entire table console, run print(dose_freq_lookup).","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/dt_level.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a dt_level object — dt_level","title":"Create a dt_level object — dt_level","text":"Create dt_level object","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/dt_level.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a dt_level object — dt_level","text":"","code":"dt_level(level)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/dt_level.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a dt_level object — dt_level","text":"level Date level Permitted Values: \"Y\" (year, highest level), \"M\" (month), \"D\" (day), \"n\" (none, lowest level)","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/dt_level.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a dt_level object — dt_level","text":"dt_level object","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/dt_level.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create a dt_level object — dt_level","text":"dt_level object ordered factor, .e., two objects can compared.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/dthcaus_source.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a dthcaus_source Object — dthcaus_source","title":"Create a dthcaus_source Object — dthcaus_source","text":"derive_var_dthcaus() function dthcaus_source() superseded favor derive_vars_extreme_event().","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/dthcaus_source.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a dthcaus_source Object — dthcaus_source","text":"","code":"dthcaus_source(   dataset_name,   filter,   date,   order = NULL,   mode = \"first\",   dthcaus,   set_values_to = NULL )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/dthcaus_source.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a dthcaus_source Object — dthcaus_source","text":"dataset_name name dataset, .e. string, used search death cause. filter expression used filtering dataset. date date datetime variable expression used sorting dataset. order Sort order Additional variables/expressions used sorting dataset. dataset ordered date order. Can used avoid duplicate record warning. Permitted Values: list expressions created exprs(), e.g., exprs(ADT, desc(AVAL)) NULL mode One \"first\" \"last\". Either \"first\" \"last\" observation preserved dataset ordered date. dthcaus variable name, expression, string literal variable name specified, e.g., AEDECOD, variable source dataset used assign values DTHCAUS; expression, e.g., str_to_upper(AEDECOD), evaluated source dataset results assigned DTHCAUS; string literal, e.g. \"Adverse Event\", fixed value assigned DTHCAUS. set_values_to Variables set trace source dataset","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/dthcaus_source.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a dthcaus_source Object — dthcaus_source","text":"object class \"dthcaus_source\".","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/dthcaus_source.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a dthcaus_source Object — dthcaus_source","text":"","code":"# Deaths sourced from AE src_ae <- dthcaus_source(   dataset_name = \"ae\",   filter = AEOUT == \"FATAL\",   date = AEDTHDT,   mode = \"first\",   dthcaus = AEDECOD )  # Deaths sourced from DS src_ds <- dthcaus_source(   dataset_name = \"ds\",   filter = DSDECOD == \"DEATH\",   date = convert_dtc_to_dt(DSSTDTC),   mode = \"first\",   dthcaus = DSTERM )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/dtm_level.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a dtm_level object — dtm_level","title":"Create a dtm_level object — dtm_level","text":"Create dtm_level object","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/dtm_level.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a dtm_level object — dtm_level","text":"","code":"dtm_level(level)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/dtm_level.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a dtm_level object — dtm_level","text":"level Datetime level Permitted Values: \"Y\" (year, highest level), \"M\" (month), \"D\" (day), \"h\" (hour), \"m\" (minute), \"s\" (second, lowest level), \"n\" (none)","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/dtm_level.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a dtm_level object — dtm_level","text":"dtm_level object","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/dtm_level.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create a dtm_level object — dtm_level","text":"dtm_level object ordered factor, .e., two objects can compared.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/event.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a event Object — event","title":"Create a event Object — event","text":"event object used define events input derive_extreme_event() derive_vars_extreme_event() functions.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/event.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a event Object — event","text":"","code":"event(   dataset_name = NULL,   condition = NULL,   mode = NULL,   order = NULL,   set_values_to = NULL,   keep_source_vars = NULL,   description = NULL )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/event.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a event Object — event","text":"dataset_name Dataset name dataset used input event. name refers dataset specified source_datasets derive_extreme_event(). argument specified, input dataset (dataset) derive_extreme_event() used. Permitted Values: character scalar condition unquoted condition selecting observations, contribute extreme event. condition contains summary functions like (), evaluated group separately. Permitted Values: unquoted condition mode specified, first last observation respect order selected group. Permitted Values: \"first\", \"last\", NULL order specified variables expressions used select first last observation mode specified. handling NAs sorting variables see Sort Order. Permitted Values: list expressions created exprs(), e.g., exprs(ADT, desc(AVAL)) NULL set_values_to named list returned exprs() defining variables set event, e.g. exprs(PARAMCD = \"WSP\", PARAM  = \"Worst Sleeping Problems\"). values can symbol, character string, numeric value, NA expression. Permitted Values: named list expressions, e.g., created exprs() keep_source_vars Variables keep source dataset specified variables kept selected observations. variables specified by_vars (derive_extreme_event()) created set_values_to always kept. Permitted Values: list expressions element symbol tidyselect expression, e.g., exprs(VISIT, VISITNUM, starts_with(\"RS\")). description Description event description affect derivations event used. intended documentation . Permitted Values: character scalar","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/event.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a event Object — event","text":"object class event","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/event_joined.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a event_joined Object — event_joined","title":"Create a event_joined Object — event_joined","text":"event_joined object used define events input derive_extreme_event() derive_vars_extreme_event() functions. object used event depend single observation source dataset multiple observations. example, event needs confirmed second observation source dataset. events selected calling filter_joined(). See documentation details.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/event_joined.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a event_joined Object — event_joined","text":"","code":"event_joined(   dataset_name = NULL,   condition,   order = NULL,   join_vars,   join_type,   first_cond_lower = NULL,   first_cond_upper = NULL,   set_values_to = NULL,   keep_source_vars = NULL,   description = NULL )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/event_joined.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a event_joined Object — event_joined","text":"dataset_name Dataset name dataset used input event. name refers dataset specified source_datasets derive_extreme_event(). argument specified, input dataset (dataset) derive_extreme_event() used. Permitted Values: character scalar condition unquoted condition selecting observations, contribute extreme event. condition applied joined dataset selecting confirmed observations. condition can include summary functions like () (). joined dataset grouped original observations. .e., summary function applied observations confirmation observation. example oncology setting using function confirmed best overall response,  condition = AVALC == \"CR\" & (AVALC.join %% c(\"CR\", \"NE\")) & count_vals(var = AVALC.join, val = \"NE\") <= 1 selects observations response \"CR\" observations confirmation observation response \"CR\" \"NE\" one \"NE\". Permitted Values: unquoted condition order specified, specified variables expressions used select first observation. handling NAs sorting variables see Sort Order. Permitted Values: list expressions created exprs(), e.g., exprs(ADT, desc(AVAL)) NULL join_vars Variables keep joined dataset variables needed observations specified parameter. specified variables added joined dataset suffix \".join\". example select observations AVALC == \"Y\" AVALC == \"Y\" least one subsequent visit join_vars = exprs(AVALC, AVISITN) condition = AVALC == \"Y\" & AVALC.join == \"Y\" & AVISITN < AVISITN.join specified. *.join variables included output dataset. Permitted Values: named list expressions, e.g., created exprs() join_type Observations keep joining argument determines joined observations kept respect original observation. example, join_type = \"\" specified observations original observations kept. Permitted Values: \"\", \"\", \"\" first_cond_lower Condition selecting range data () argument specified, observations restricted first observation current observation specified condition fulfilled current observation. condition fulfilled observations, observations considered, .e., observation flagged. parameter specified condition contains summary functions apply observations certain observation current observation current observation. Permitted Values: unquoted condition first_cond_upper Condition selecting range data () argument specified, observations restricted first observation specified condition fulfilled. condition fulfilled observations, observations considered, .e., observation flagged. parameter specified condition contains summary functions apply observations confirmation assessment. Permitted Values: unquoted condition set_values_to named list returned exprs() defining variables set event, e.g. exprs(PARAMCD = \"WSP\", PARAM  = \"Worst Sleeping Problems\"). values can symbol, character string, numeric value, NA expression. Permitted Values: named list expressions, e.g., created exprs() keep_source_vars Variables keep source dataset specified variables kept selected observations. variables specified by_vars (derive_extreme_event()) created set_values_to always kept. Permitted Values: list expressions element symbol tidyselect expression, e.g., exprs(VISIT, VISITNUM, starts_with(\"RS\")). description Description event description affect derivations event used. intended documentation . Permitted Values: character scalar","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/event_joined.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a event_joined Object — event_joined","text":"object class event_joined","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/event_joined.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a event_joined Object — event_joined","text":"","code":"library(tibble) library(dplyr) library(lubridate) # Derive confirmed best overall response (using event_joined()) # CR - complete response, PR - partial response, SD - stable disease # NE - not evaluable, PD - progressive disease adsl <- tribble(   ~USUBJID, ~TRTSDTC,   \"1\",      \"2020-01-01\",   \"2\",      \"2019-12-12\",   \"3\",      \"2019-11-11\",   \"4\",      \"2019-12-30\",   \"5\",      \"2020-01-01\",   \"6\",      \"2020-02-02\",   \"7\",      \"2020-02-02\",   \"8\",      \"2020-02-01\" ) %>%   mutate(TRTSDT = ymd(TRTSDTC))  adrs <- tribble(   ~USUBJID, ~ADTC,        ~AVALC,   \"1\",      \"2020-01-01\", \"PR\",   \"1\",      \"2020-02-01\", \"CR\",   \"1\",      \"2020-02-16\", \"NE\",   \"1\",      \"2020-03-01\", \"CR\",   \"1\",      \"2020-04-01\", \"SD\",   \"2\",      \"2020-01-01\", \"SD\",   \"2\",      \"2020-02-01\", \"PR\",   \"2\",      \"2020-03-01\", \"SD\",   \"2\",      \"2020-03-13\", \"CR\",   \"4\",      \"2020-01-01\", \"PR\",   \"4\",      \"2020-03-01\", \"NE\",   \"4\",      \"2020-04-01\", \"NE\",   \"4\",      \"2020-05-01\", \"PR\",   \"5\",      \"2020-01-01\", \"PR\",   \"5\",      \"2020-01-10\", \"PR\",   \"5\",      \"2020-01-20\", \"PR\",   \"6\",      \"2020-02-06\", \"PR\",   \"6\",      \"2020-02-16\", \"CR\",   \"6\",      \"2020-03-30\", \"PR\",   \"7\",      \"2020-02-06\", \"PR\",   \"7\",      \"2020-02-16\", \"CR\",   \"7\",      \"2020-04-01\", \"NE\",   \"8\",      \"2020-02-16\", \"PD\" ) %>%   mutate(     ADT = ymd(ADTC),     PARAMCD = \"OVR\",     PARAM = \"Overall Response by Investigator\"   ) %>%   derive_vars_merged(     dataset_add = adsl,     by_vars = exprs(USUBJID),     new_vars = exprs(TRTSDT)   )  derive_extreme_event(   adrs,   by_vars = exprs(USUBJID),   order = exprs(ADT),   mode = \"first\",   source_datasets = list(adsl = adsl),   events = list(     event_joined(       description = paste(         \"CR needs to be confirmed by a second CR at least 28 days later\",         \"at most one NE is acceptable between the two assessments\"       ),       join_vars = exprs(AVALC, ADT),       join_type = \"after\",       first_cond_upper = AVALC.join == \"CR\" &         ADT.join >= ADT + 28,       condition = AVALC == \"CR\" &         all(AVALC.join %in% c(\"CR\", \"NE\")) &         count_vals(var = AVALC.join, val = \"NE\") <= 1,       set_values_to = exprs(         AVALC = \"CR\"       )     ),     event_joined(       description = paste(         \"PR needs to be confirmed by a second CR or PR at least 28 days later,\",         \"at most one NE is acceptable between the two assessments\"       ),       join_vars = exprs(AVALC, ADT),       join_type = \"after\",       first_cond_upper = AVALC.join %in% c(\"CR\", \"PR\") &         ADT.join >= ADT + 28,       condition = AVALC == \"PR\" &         all(AVALC.join %in% c(\"CR\", \"PR\", \"NE\")) &         count_vals(var = AVALC.join, val = \"NE\") <= 1,       set_values_to = exprs(         AVALC = \"PR\"       )     ),     event(       description = paste(         \"CR, PR, or SD are considered as SD if occurring at least 28\",         \"after treatment start\"       ),       condition = AVALC %in% c(\"CR\", \"PR\", \"SD\") & ADT >= TRTSDT + 28,       set_values_to = exprs(         AVALC = \"SD\"       )     ),     event(       condition = AVALC == \"PD\",       set_values_to = exprs(         AVALC = \"PD\"       )     ),     event(       condition = AVALC %in% c(\"CR\", \"PR\", \"SD\", \"NE\"),       set_values_to = exprs(         AVALC = \"NE\"       )     ),     event(       description = \"set response to MISSING for patients without records in ADRS\",       dataset_name = \"adsl\",       condition = TRUE,       set_values_to = exprs(         AVALC = \"MISSING\"       ),       keep_source_vars = exprs(TRTSDT)     )   ),   set_values_to = exprs(     PARAMCD = \"CBOR\",     PARAM = \"Best Confirmed Overall Response by Investigator\"   ) ) %>%   filter(PARAMCD == \"CBOR\") #> Warning: Check duplicates: the dataset which consists of all records selected for any of #> the events defined by `events` contains duplicate records with respect to #> `USUBJID` and `ADT` #> ℹ Run `admiral::get_duplicates_dataset()` to access the duplicate records #> # A tibble: 8 × 7 #>   USUBJID ADTC       AVALC   ADT        PARAMCD PARAM                 TRTSDT     #>   <chr>   <chr>      <chr>   <date>     <chr>   <chr>                 <date>     #> 1 1       2020-01-01 PR      2020-01-01 CBOR    Best Confirmed Overa… 2020-01-01 #> 2 2       2020-01-01 NE      2020-01-01 CBOR    Best Confirmed Overa… 2019-12-12 #> 3 3       NA         MISSING NA         CBOR    Best Confirmed Overa… 2019-11-11 #> 4 4       2020-01-01 NE      2020-01-01 CBOR    Best Confirmed Overa… 2019-12-30 #> 5 5       2020-01-01 NE      2020-01-01 CBOR    Best Confirmed Overa… 2020-01-01 #> 6 6       2020-02-06 PR      2020-02-06 CBOR    Best Confirmed Overa… 2020-02-02 #> 7 7       2020-02-06 NE      2020-02-06 CBOR    Best Confirmed Overa… 2020-02-02 #> 8 8       2020-02-16 PD      2020-02-16 CBOR    Best Confirmed Overa… 2020-02-01"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/event_source.html","id":null,"dir":"Reference","previous_headings":"","what":"Create an event_source Object — event_source","title":"Create an event_source Object — event_source","text":"event_source objects used define events input derive_param_tte() function. Note: wrapper function generic tte_source().","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/event_source.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create an event_source Object — event_source","text":"","code":"event_source(dataset_name, filter = NULL, date, set_values_to = NULL)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/event_source.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create an event_source Object — event_source","text":"dataset_name name source dataset name refers dataset provided source_datasets parameter derive_param_tte(). filter unquoted condition selecting observations dataset events possible censoring time points. date variable expression providing date event censoring. date, datetime can specified. unquoted symbol expression expected. Refer derive_vars_dt() convert_dtc_to_dt() impute derive date date character vector date object. set_values_to named list returned exprs() defining variables set event censoring, e.g. exprs(EVENTDESC = \"DEATH\", SRCDOM = \"ADSL\", SRCVAR = \"DTHDT\"). values must symbol, character string, numeric value, expression, NA.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/event_source.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create an event_source Object — event_source","text":"object class event_source, inheriting class tte_source","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/event_source.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create an event_source Object — event_source","text":"","code":"# Death event  event_source(   dataset_name = \"adsl\",   filter = DTHFL == \"Y\",   date = DTHDT,   set_values_to = exprs(     EVNTDESC = \"DEATH\",     SRCDOM = \"ADSL\",     SRCVAR = \"DTHDT\"   ) ) #> <event_source> object #> dataset_name: \"adsl\" #> filter: DTHFL == \"Y\" #> date: DTHDT #> censor: 0 #> set_values_to: #>   EVNTDESC: \"DEATH\" #>   SRCDOM: \"ADSL\" #>   SRCVAR: \"DTHDT\""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/ex_single.html","id":null,"dir":"Reference","previous_headings":"","what":"Single Dose Exposure Dataset — ex_single","title":"Single Dose Exposure Dataset — ex_single","text":"derived dataset single dose per date.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/ex_single.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Single Dose Exposure Dataset — ex_single","text":"","code":"ex_single"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/ex_single.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Single Dose Exposure Dataset — ex_single","text":"object class tbl_df (inherits tbl, data.frame) 22439 rows 17 columns.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/ex_single.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Single Dose Exposure Dataset — ex_single","text":"Derived ex dataset using {admiral} {dplyr} (https://github.com/pharmaverse/admiral/blob/main/inst/example_scripts/derive_single_dose.R)","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/example_qs.html","id":null,"dir":"Reference","previous_headings":"","what":"Example QS Dataset — example_qs","title":"Example QS Dataset — example_qs","text":"example QS dataset based examples CDISC ADaM Supplements Generalized Anxiety Disorder 7-Item Version 2 (GAD-7) Geriatric Depression Scale Short Form (GDS-SF).","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/example_qs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Example QS Dataset — example_qs","text":"","code":"example_qs"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/example_qs.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Example QS Dataset — example_qs","text":"object class tbl_df (inherits tbl, data.frame) 161 rows 11 columns.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/example_qs.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Example QS Dataset — example_qs","text":"Created (https://github.com/pharmaverse/admiral/blob/main/inst/example_scripts/example_qs.R)","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/extend_condition.html","id":null,"dir":"Reference","previous_headings":"","what":"Extend a condition string by adding a new condition based on a variable and its value — extend_condition","title":"Extend a condition string by adding a new condition based on a variable and its value — extend_condition","text":"internal helper function extends condition string appending new condition checks variable equals specific value.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/extend_condition.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extend a condition string by adding a new condition based on a variable and its value — extend_condition","text":"","code":"extend_condition(cond, var, is)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/extend_condition.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extend a condition string by adding a new condition based on a variable and its value — extend_condition","text":"cond character string representing existing condition. var character string representing name variable check. character string representing value variable equal .","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/extend_condition.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extend a condition string by adding a new condition based on a variable and its value — extend_condition","text":"character string representing extended condition.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/extend_condition.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extend a condition string by adding a new condition based on a variable and its value — extend_condition","text":"","code":"# Extend an existing condition to include a check for 'AGE == \"30\"' admiral:::extend_condition(\"SEX == 'M'\", \"AGE\", \"30\") #> [1] \"SEX == 'M' & AGE == '30'\""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/extract_duplicate_records.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract Duplicate Records — extract_duplicate_records","title":"Extract Duplicate Records — extract_duplicate_records","text":"Extract Duplicate Records","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/extract_duplicate_records.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract Duplicate Records — extract_duplicate_records","text":"","code":"extract_duplicate_records(dataset, by_vars)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/extract_duplicate_records.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract Duplicate Records — extract_duplicate_records","text":"dataset Input dataset variables specified by_vars argument expected dataset. by_vars Grouping variables Defines groups records look duplicates. Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT)","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/extract_duplicate_records.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract Duplicate Records — extract_duplicate_records","text":"data.frame duplicate records within dataset","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/extract_duplicate_records.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract Duplicate Records — extract_duplicate_records","text":"","code":"data(admiral_adsl)  # Duplicate the first record adsl <- rbind(admiral_adsl[1L, ], admiral_adsl)  extract_duplicate_records(adsl, exprs(USUBJID)) #> # A tibble: 2 × 54 #>   USUBJID     STUDYID  SUBJID RFSTDTC RFENDTC RFXSTDTC RFXENDTC RFICDTC RFPENDTC #>   <chr>       <chr>    <chr>  <chr>   <chr>   <chr>    <chr>    <chr>   <chr>    #> 1 01-701-1015 CDISCPI… 1015   2014-0… 2014-0… 2014-01… 2014-07… NA      2014-07… #> 2 01-701-1015 CDISCPI… 1015   2014-0… 2014-0… 2014-01… 2014-07… NA      2014-07… #> # ℹ 45 more variables: DTHDTC <chr>, DTHFL <chr>, SITEID <chr>, AGE <dbl>, #> #   AGEU <chr>, SEX <chr>, RACE <chr>, ETHNIC <chr>, ARMCD <chr>, ARM <chr>, #> #   ACTARMCD <chr>, ACTARM <chr>, COUNTRY <chr>, DMDTC <chr>, DMDY <dbl>, #> #   TRT01P <chr>, TRT01A <chr>, TRTSDTM <dttm>, TRTSTMF <chr>, TRTEDTM <dttm>, #> #   TRTETMF <chr>, TRTSDT <date>, TRTEDT <date>, TRTDURD <dbl>, SCRFDT <date>, #> #   EOSDT <date>, EOSSTT <chr>, FRVDT <date>, RANDDT <date>, DTHDT <date>, #> #   DTHDTF <chr>, DTHADY <dbl>, LDDTHELD <dbl>, DTHCAUS <chr>, DTHDOM <chr>, …"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/extract_unit.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract Unit From Parameter Description — extract_unit","title":"Extract Unit From Parameter Description — extract_unit","text":"Extract unit parameter description like \"Param (unit)\".","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/extract_unit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract Unit From Parameter Description — extract_unit","text":"","code":"extract_unit(x)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/extract_unit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract Unit From Parameter Description — extract_unit","text":"x parameter description","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/extract_unit.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract Unit From Parameter Description — extract_unit","text":"string","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/extract_unit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract Unit From Parameter Description — extract_unit","text":"","code":"extract_unit(\"Height (cm)\") #> [1] \"cm\"  extract_unit(\"Diastolic Blood Pressure (mmHg)\") #> [1] \"mmHg\""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_exist.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns records that fit into existing by groups in a filtered source dataset — filter_exist","title":"Returns records that fit into existing by groups in a filtered source dataset — filter_exist","text":"Returns records input dataset belong groups present source dataset, source dataset optionally filtered. example, used return ADSL records subjects experienced certain adverse event course study (per records ADAE).","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_exist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns records that fit into existing by groups in a filtered source dataset — filter_exist","text":"","code":"filter_exist(dataset, dataset_add, by_vars, filter_add = NULL)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_exist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns records that fit into existing by groups in a filtered source dataset — filter_exist","text":"dataset Input dataset variables specified by_vars argument expected dataset. dataset_add Source dataset source dataset, determines groups returned input dataset, based groups exist dataset subset filter_add. variables specified by_vars filter_add parameters expected dataset. by_vars Grouping variables Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) filter_add Filter source dataset filter condition used subset source dataset. Alternatively, filter condition supplied, subsetting source dataset performed. Default: NULL (.e. filtering performed)","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_exist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns records that fit into existing by groups in a filtered source dataset — filter_exist","text":"records input dataset contained within existing group filtered source dataset.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_exist.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Returns records that fit into existing by groups in a filtered source dataset — filter_exist","text":"Returns records dataset match existing group dataset_add, filtered according filter_add. groups exist datasets, empty dataset returned.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_exist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Returns records that fit into existing by groups in a filtered source dataset — filter_exist","text":"","code":"# Get demographic information about subjects who have suffered from moderate or # severe fatigue  library(tibble)  adsl <- tribble(   ~USUBJID,      ~AGE, ~SEX,   \"01-701-1015\", 63,   \"F\",   \"01-701-1034\", 77,   \"F\",   \"01-701-1115\", 84,   \"M\",   \"01-701-1146\", 75,   \"F\",   \"01-701-1444\", 63,   \"M\" )  adae <- tribble(   ~USUBJID,      ~AEDECOD,                    ~AESEV,     ~AESTDTC,   \"01-701-1015\", \"DIARRHOEA\",                 \"MODERATE\", \"2014-01-09\",   \"01-701-1034\", \"FATIGUE\",                   \"SEVERE\",   \"2014-11-02\",   \"01-701-1034\", \"APPLICATION SITE PRURITUS\", \"MODERATE\", \"2014-08-27\",   \"01-701-1115\", \"FATIGUE\",                   \"MILD\",     \"2013-01-14\",   \"01-701-1146\", \"FATIGUE\",                   \"MODERATE\", \"2013-06-03\" )  filter_exist(   dataset = adsl,   dataset_add = adae,   by_vars = exprs(USUBJID),   filter_add = AEDECOD == \"FATIGUE\" & AESEV %in% c(\"MODERATE\", \"SEVERE\") ) #> # A tibble: 2 × 3 #>   USUBJID       AGE SEX   #>   <chr>       <dbl> <chr> #> 1 01-701-1034    77 F     #> 2 01-701-1146    75 F"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_extreme.html","id":null,"dir":"Reference","previous_headings":"","what":"Filter the First or Last Observation for Each By Group — filter_extreme","title":"Filter the First or Last Observation for Each By Group — filter_extreme","text":"Filters first last observation group.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_extreme.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filter the First or Last Observation for Each By Group — filter_extreme","text":"","code":"filter_extreme(dataset, by_vars = NULL, order, mode, check_type = \"warning\")"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_extreme.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filter the First or Last Observation for Each By Group — filter_extreme","text":"dataset Input dataset variables specified by_vars order arguments expected dataset. by_vars Grouping variables Default: NULL Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) order Sort order Within group observations ordered specified order. Permitted Values: list expressions created exprs(), e.g., exprs(ADT, desc(AVAL)) mode Selection mode (first last) \"first\" specified, first observation group included output dataset. \"last\" specified, last observation group included output dataset. Permitted Values:  \"first\", \"last\" check_type Check uniqueness? \"warning\" \"error\" specified, specified message issued observations input dataset unique respect variables order. Default: \"warning\" Permitted Values: \"none\", \"warning\", \"error\"","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_extreme.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filter the First or Last Observation for Each By Group — filter_extreme","text":"dataset containing first last observation group","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_extreme.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Filter the First or Last Observation for Each By Group — filter_extreme","text":"group (respect variables specified by_vars parameter) first last observation (respect order specified order parameter mode specified mode parameter) included output dataset.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_extreme.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Filter the First or Last Observation for Each By Group — filter_extreme","text":"","code":"library(dplyr, warn.conflicts = FALSE)  ex <- tribble(   ~STUDYID,  ~DOMAIN,  ~USUBJID, ~EXSEQ, ~EXDOSE,    ~EXTRT,   \"PILOT01\",    \"EX\", \"01-1442\",      1,      54,    \"XANO\",   \"PILOT01\",    \"EX\", \"01-1442\",      2,      54,    \"XANO\",   \"PILOT01\",    \"EX\", \"01-1442\",      3,      54,    \"XANO\",   \"PILOT01\",    \"EX\", \"01-1444\",      1,      54,    \"XANO\",   \"PILOT01\",    \"EX\", \"01-1444\",      2,      81,    \"XANO\",   \"PILOT01\",    \"EX\", \"05-1382\",      1,      54,    \"XANO\",   \"PILOT01\",    \"EX\", \"08-1213\",      1,      54,    \"XANO\",   \"PILOT01\",    \"EX\", \"10-1053\",      1,      54,    \"XANO\",   \"PILOT01\",    \"EX\", \"10-1053\",      2,      54,    \"XANO\",   \"PILOT01\",    \"EX\", \"10-1183\",      1,       0, \"PLACEBO\",   \"PILOT01\",    \"EX\", \"10-1183\",      2,       0, \"PLACEBO\",   \"PILOT01\",    \"EX\", \"10-1183\",      3,       0, \"PLACEBO\",   \"PILOT01\",    \"EX\", \"11-1036\",      1,       0, \"PLACEBO\",   \"PILOT01\",    \"EX\", \"11-1036\",      2,       0, \"PLACEBO\",   \"PILOT01\",    \"EX\", \"11-1036\",      3,       0, \"PLACEBO\",   \"PILOT01\",    \"EX\", \"14-1425\",      1,      54,    \"XANO\",   \"PILOT01\",    \"EX\", \"15-1319\",      1,      54,    \"XANO\",   \"PILOT01\",    \"EX\", \"15-1319\",      2,      81,    \"XANO\",   \"PILOT01\",    \"EX\", \"16-1151\",      1,      54,    \"XANO\",   \"PILOT01\",    \"EX\", \"16-1151\",      2,      54,    \"XANO\" )   # Select first dose for each patient ex %>%   filter_extreme(     by_vars = exprs(USUBJID),     order = exprs(EXSEQ),     mode = \"first\"   ) %>%   select(USUBJID, EXSEQ) #> # A tibble: 10 × 2 #>    USUBJID EXSEQ #>    <chr>   <dbl> #>  1 01-1442     1 #>  2 01-1444     1 #>  3 05-1382     1 #>  4 08-1213     1 #>  5 10-1053     1 #>  6 10-1183     1 #>  7 11-1036     1 #>  8 14-1425     1 #>  9 15-1319     1 #> 10 16-1151     1  # Select highest dose for each patient on the active drug ex %>%   filter(EXTRT != \"PLACEBO\") %>%   filter_extreme(     by_vars = exprs(USUBJID),     order = exprs(EXDOSE),     mode = \"last\",     check_type = \"none\"   ) %>%   select(USUBJID, EXTRT, EXDOSE) #> # A tibble: 8 × 3 #>   USUBJID EXTRT EXDOSE #>   <chr>   <chr>  <dbl> #> 1 01-1442 XANO      54 #> 2 01-1444 XANO      81 #> 3 05-1382 XANO      54 #> 4 08-1213 XANO      54 #> 5 10-1053 XANO      54 #> 6 14-1425 XANO      54 #> 7 15-1319 XANO      81 #> 8 16-1151 XANO      54"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_joined.html","id":null,"dir":"Reference","previous_headings":"","what":"Filter Observations Taking Other Observations into Account — filter_joined","title":"Filter Observations Taking Other Observations into Account — filter_joined","text":"function filters observation using condition taking observations account. example, select observations AVALC == \"Y\" AVALC == \"Y\" least one subsequent observation. input dataset joined enable conditions taking variables current observation observations account. suffix \".join\" added variables subsequent observations. example usage might checking patient received two required medications within certain timeframe . oncology setting, example, use processing check response value can confirmed subsequent assessment. commonly used endpoints best overall response.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_joined.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filter Observations Taking Other Observations into Account — filter_joined","text":"","code":"filter_joined(   dataset,   dataset_add,   by_vars,   join_vars,   join_type,   first_cond_lower = NULL,   first_cond_upper = NULL,   order,   tmp_obs_nr_var = NULL,   filter_add = NULL,   filter_join,   check_type = \"warning\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_joined.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filter Observations Taking Other Observations into Account — filter_joined","text":"dataset Input dataset variables specified by_vars order arguments expected dataset. dataset_add Additional dataset variables specified by_vars, join_vars, order expected. by_vars variables specified variables used variables joining input dataset . Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) join_vars Variables keep joined dataset variables needed observations specified parameter. specified variables added joined dataset suffix \".join\". example select observations AVALC == \"Y\" AVALC == \"Y\" least one subsequent visit join_vars = exprs(AVALC, AVISITN) filter_join = AVALC == \"Y\" & AVALC.join == \"Y\" & AVISITN < AVISITN.join specified. *.join variables included output dataset. join_type Observations keep joining argument determines joined observations kept respect original observation. example, join_type = \"\" specified observations original observations kept. example confirmed response BOR oncology setting confirmed deterioration questionnaires confirmatory assessment must assessment. Thus join_type = \"\" used. Whereas, sometimes might allow confirmatory observations occur prior observation. example, identify AEs occurring seven days COVID AE. Thus join_type = \"\" used. Permitted Values: \"\", \"\", \"\" first_cond_lower Condition selecting range data () argument specified, observations restricted first observation current observation specified condition fulfilled current observation. condition fulfilled observations, observations considered, .e., observation flagged. parameter specified filter_join contains summary functions apply observations certain observation current observation current observation. example see last example . first_cond_upper Condition selecting range data () argument specified, observations restricted first observation specified condition fulfilled. condition fulfilled observations, observations considered, .e., observation flagged. parameter specified filter_join contains summary functions apply observations confirmation assessment. example see last example . order Order observations ordered specified order. handling NAs sorting variables see Sort Order. Permitted Values: list expressions created exprs(), e.g., exprs(ADT, desc(AVAL)) tmp_obs_nr_var Temporary observation number specified variable added input dataset (dataset) additional dataset (dataset_add). set observation number respect order. group (by_vars) observation number starts 1. variable can used conditions (filter_join, first_cond_upper, first_cond_lower). included output dataset. can also used select consecutive observations last observation (see example ). filter_add Filter additional dataset (dataset_add) observations dataset_add fulfilling specified condition joined input dataset. argument specified, observations joined. Variables created order argument can used condition. condition can include summary functions. additional dataset grouped variables (by_vars). filter_join Condition selecting observations filter applied joined dataset selecting confirmed observations. condition can include summary functions like () (). joined dataset grouped original observations. .e., summary function applied observations confirmation observation. example oncology setting using function confirmed best overall response,  filter_join = AVALC == \"CR\" & (AVALC.join %% c(\"CR\", \"NE\")) & count_vals(var = AVALC.join, val = \"NE\") <= 1 selects observations response \"CR\" observations confirmation observation response \"CR\" \"NE\" one \"NE\". check_type Check uniqueness? \"warning\" \"error\" specified, specified message issued observations input dataset unique respect variables order. Permitted Values: \"none\", \"warning\", \"error\"","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_joined.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filter Observations Taking Other Observations into Account — filter_joined","text":"subset observations input dataset. variables input dataset included output dataset.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_joined.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Filter Observations Taking Other Observations into Account — filter_joined","text":"following steps performed produce output dataset.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_joined.html","id":"step-","dir":"Reference","previous_headings":"","what":"Step 1","title":"Filter Observations Taking Other Observations into Account — filter_joined","text":"variables specified order added additional dataset (dataset_add). variables specified join_vars added additional dataset (dataset_add). records additional dataset (dataset_add) restricted matching filter_add condition.  input dataset (dataset) joined restricted additional dataset variables specified by_vars. additional dataset variables specified join_vars kept. suffix \".join\" added variables also present input dataset. example, by_vars = USUBJID, join_vars = exprs(AVISITN, AVALC) input dataset additional dataset   joined dataset ","code":"# A tibble: 2 x 4 USUBJID AVISITN AVALC  AVAL <chr>     <dbl> <chr> <dbl> 1             1 Y         1 1             2 N         0 A tibble: 4 x 6 USUBJID AVISITN AVALC  AVAL AVISITN.join AVALC.join <chr>     <dbl> <chr> <dbl>        <dbl> <chr> 1             1 Y         1            1 Y 1             1 Y         1            2 N 1             2 N         0            1 Y 1             2 N         0            2 N"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_joined.html","id":"step--1","dir":"Reference","previous_headings":"","what":"Step 2","title":"Filter Observations Taking Other Observations into Account — filter_joined","text":"joined dataset restricted observations respect join_type order. dataset example previous step join_type = \"\" order = exprs(AVISITN) restricted ","code":"A tibble: 4 x 6 USUBJID AVISITN AVALC  AVAL AVISITN.join AVALC.join <chr>     <dbl> <chr> <dbl>        <dbl> <chr> 1             1 Y         1            2 N"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_joined.html","id":"step--2","dir":"Reference","previous_headings":"","what":"Step 3","title":"Filter Observations Taking Other Observations into Account — filter_joined","text":"first_cond_lower specified, observation input dataset joined dataset restricted observations first observation first_cond_lower fulfilled (observation fulfilling condition included) observation input dataset. observation input dataset condition fulfilled, observation removed. first_cond_upper specified, observation input dataset joined dataset restricted observations first observation first_cond_upper fulfilled (observation fulfilling condition included). observation input dataset condition fulfilled, observation removed. example see last example \"Examples\" section.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_joined.html","id":"step--3","dir":"Reference","previous_headings":"","what":"Step 4","title":"Filter Observations Taking Other Observations into Account — filter_joined","text":"joined dataset grouped observations input dataset restricted observations fulfilling condition specified filter_join.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_joined.html","id":"step--4","dir":"Reference","previous_headings":"","what":"Step 5","title":"Filter Observations Taking Other Observations into Account — filter_joined","text":"first observation group selected *.join variables dropped.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_joined.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Filter Observations Taking Other Observations into Account — filter_joined","text":"","code":"library(tibble) library(admiral)  # filter observations with a duration longer than 30 and # on or after 7 days before a COVID AE (ACOVFL == \"Y\") adae <- tribble(   ~USUBJID, ~ADY, ~ACOVFL, ~ADURN,   \"1\",        10, \"N\",          1,   \"1\",        21, \"N\",         50,   \"1\",        23, \"Y\",         14,   \"1\",        32, \"N\",         31,   \"1\",        42, \"N\",         20,   \"2\",        11, \"Y\",         13,   \"2\",        23, \"N\",          2,   \"3\",        13, \"Y\",         12,   \"4\",        14, \"N\",         32,   \"4\",        21, \"N\",         41 )  filter_joined(   adae,   dataset_add = adae,   by_vars = exprs(USUBJID),   join_vars = exprs(ACOVFL, ADY),   join_type = \"all\",   order = exprs(ADY),   filter_join = ADURN > 30 & ACOVFL.join == \"Y\" & ADY >= ADY.join - 7 ) #> # A tibble: 2 × 4 #>   USUBJID   ADY ACOVFL ADURN #>   <chr>   <dbl> <chr>  <dbl> #> 1 1          21 N         50 #> 2 1          32 N         31  # filter observations with AVALC == \"Y\" and AVALC == \"Y\" at a subsequent visit data <- tribble(   ~USUBJID, ~AVISITN, ~AVALC,   \"1\",      1,        \"Y\",   \"1\",      2,        \"N\",   \"1\",      3,        \"Y\",   \"1\",      4,        \"N\",   \"2\",      1,        \"Y\",   \"2\",      2,        \"N\",   \"3\",      1,        \"Y\",   \"4\",      1,        \"N\",   \"4\",      2,        \"N\", )  filter_joined(   data,   dataset_add = data,   by_vars = exprs(USUBJID),   join_vars = exprs(AVALC, AVISITN),   join_type = \"after\",   order = exprs(AVISITN),   filter_join = AVALC == \"Y\" & AVALC.join == \"Y\" & AVISITN < AVISITN.join ) #> # A tibble: 1 × 3 #>   USUBJID AVISITN AVALC #>   <chr>     <dbl> <chr> #> 1 1             1 Y      # select observations with AVALC == \"CR\", AVALC == \"CR\" at a subsequent visit, # only \"CR\" or \"NE\" in between, and at most one \"NE\" in between data <- tribble(   ~USUBJID, ~AVISITN, ~AVALC,   \"1\",      1,        \"PR\",   \"1\",      2,        \"CR\",   \"1\",      3,        \"NE\",   \"1\",      4,        \"CR\",   \"1\",      5,        \"NE\",   \"2\",      1,        \"CR\",   \"2\",      2,        \"PR\",   \"2\",      3,        \"CR\",   \"3\",      1,        \"CR\",   \"4\",      1,        \"CR\",   \"4\",      2,        \"NE\",   \"4\",      3,        \"NE\",   \"4\",      4,        \"CR\",   \"4\",      5,        \"PR\" )  filter_joined(   data,   dataset_add = data,   by_vars = exprs(USUBJID),   join_vars = exprs(AVALC),   join_type = \"after\",   order = exprs(AVISITN),   first_cond_upper = AVALC.join == \"CR\",   filter_join = AVALC == \"CR\" & all(AVALC.join %in% c(\"CR\", \"NE\")) &     count_vals(var = AVALC.join, val = \"NE\") <= 1 ) #> # A tibble: 1 × 3 #>   USUBJID AVISITN AVALC #>   <chr>     <dbl> <chr> #> 1 1             2 CR     # select observations with AVALC == \"PR\", AVALC == \"CR\" or AVALC == \"PR\" # at a subsequent visit at least 20 days later, only \"CR\", \"PR\", or \"NE\" # in between, at most one \"NE\" in between, and \"CR\" is not followed by \"PR\" data <- tribble(   ~USUBJID, ~ADY, ~AVALC,   \"1\",         6, \"PR\",   \"1\",        12, \"CR\",   \"1\",        24, \"NE\",   \"1\",        32, \"CR\",   \"1\",        48, \"PR\",   \"2\",         3, \"PR\",   \"2\",        21, \"CR\",   \"2\",        33, \"PR\",   \"3\",        11, \"PR\",   \"4\",         7, \"PR\",   \"4\",        12, \"NE\",   \"4\",        24, \"NE\",   \"4\",        32, \"PR\",   \"4\",        55, \"PR\" )  filter_joined(   data,   dataset_add = data,   by_vars = exprs(USUBJID),   join_vars = exprs(AVALC, ADY),   join_type = \"after\",   order = exprs(ADY),   first_cond_upper = AVALC.join %in% c(\"CR\", \"PR\") & ADY.join - ADY >= 20,   filter_join = AVALC == \"PR\" &     all(AVALC.join %in% c(\"CR\", \"PR\", \"NE\")) &     count_vals(var = AVALC.join, val = \"NE\") <= 1 &     (       min_cond(var = ADY.join, cond = AVALC.join == \"CR\") >         max_cond(var = ADY.join, cond = AVALC.join == \"PR\") |         count_vals(var = AVALC.join, val = \"CR\") == 0     ) ) #> # A tibble: 1 × 3 #>   USUBJID   ADY AVALC #>   <chr>   <dbl> <chr> #> 1 4          32 PR     # select observations with CRIT1FL == \"Y\" at two consecutive visits or at the last visit data <- tribble(   ~USUBJID, ~AVISITN, ~CRIT1FL,   \"1\",      1,        \"Y\",   \"1\",      2,        \"N\",   \"1\",      3,        \"Y\",   \"1\",      5,        \"N\",   \"2\",      1,        \"Y\",   \"2\",      3,        \"Y\",   \"2\",      5,        \"N\",   \"3\",      1,        \"Y\",   \"4\",      1,        \"Y\",   \"4\",      2,        \"N\", )  filter_joined(   data,   dataset_add = data,   by_vars = exprs(USUBJID),   tmp_obs_nr_var = tmp_obs_nr,   join_vars = exprs(CRIT1FL),   join_type = \"all\",   order = exprs(AVISITN),   filter_join = CRIT1FL == \"Y\" & CRIT1FL.join == \"Y\" &     (tmp_obs_nr + 1 == tmp_obs_nr.join | tmp_obs_nr == max(tmp_obs_nr.join)) ) #> # A tibble: 2 × 3 #>   USUBJID AVISITN CRIT1FL #>   <chr>     <dbl> <chr>   #> 1 2             1 Y       #> 2 3             1 Y        # first_cond_lower and first_cond_upper argument myd <- tribble(   ~subj, ~day, ~val,   \"1\",      1, \"++\",   \"1\",      2, \"-\",   \"1\",      3, \"0\",   \"1\",      4, \"+\",   \"1\",      5, \"++\",   \"1\",      6, \"-\",   \"2\",      1, \"-\",   \"2\",      2, \"++\",   \"2\",      3, \"+\",   \"2\",      4, \"0\",   \"2\",      5, \"-\",   \"2\",      6, \"++\" )  # select \"0\" where all results from the first \"++\" before the \"0\" up to the \"0\" # (excluding the \"0\") are \"+\" or \"++\" filter_joined(   myd,   dataset_add = myd,   by_vars = exprs(subj),   order = exprs(day),   join_vars = exprs(val),   join_type = \"before\",   first_cond_lower = val.join == \"++\",   filter_join = val == \"0\" & all(val.join %in% c(\"+\", \"++\")) ) #> # A tibble: 1 × 3 #>   subj    day val   #>   <chr> <dbl> <chr> #> 1 2         4 0      # select \"0\" where all results from the \"0\" (excluding the \"0\") up to the first # \"++\" after the \"0\" are \"+\" or \"++\" filter_joined(   myd,   dataset_add = myd,   by_vars = exprs(subj),   order = exprs(day),   join_vars = exprs(val),   join_type = \"after\",   first_cond_upper = val.join == \"++\",   filter_join = val == \"0\" & all(val.join %in% c(\"+\", \"++\")) ) #> # A tibble: 1 × 3 #>   subj    day val   #>   <chr> <dbl> <chr> #> 1 1         3 0"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_not_exist.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns records that don't fit into existing by groups in a filtered source dataset — filter_not_exist","title":"Returns records that don't fit into existing by groups in a filtered source dataset — filter_not_exist","text":"Returns records input dataset belong groups present source dataset, source dataset optionally filtered. example, used return ADSL records subjects take certain concomitant medications course study (per records ADCM).","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_not_exist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns records that don't fit into existing by groups in a filtered source dataset — filter_not_exist","text":"","code":"filter_not_exist(dataset, dataset_add, by_vars, filter_add = NULL)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_not_exist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns records that don't fit into existing by groups in a filtered source dataset — filter_not_exist","text":"dataset Input dataset variables specified by_vars argument expected dataset. dataset_add Source dataset source dataset, determines groups returned input dataset, based groups exist dataset subset filter_add. variables specified by_vars filter_add parameters expected dataset. by_vars Grouping variables Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) filter_add Filter source dataset filter condition used subset source dataset. Alternatively, filter condition supplied, subsetting source dataset performed. Default: NULL (.e. filtering performed)","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_not_exist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns records that don't fit into existing by groups in a filtered source dataset — filter_not_exist","text":"records input dataset contained within existing group filtered source dataset.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_not_exist.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Returns records that don't fit into existing by groups in a filtered source dataset — filter_not_exist","text":"Returns records dataset match existing groups dataset_add, filtered according filter_add. groups exist dataset exist dataset_add, empty dataset returned.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_not_exist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Returns records that don't fit into existing by groups in a filtered source dataset — filter_not_exist","text":"","code":"# Get demographic information about subjects who didn't take vitamin supplements # during the study  library(tibble)  adsl <- tribble(   ~USUBJID,      ~AGE, ~SEX,   \"01-701-1015\", 63,   \"F\",   \"01-701-1023\", 64,   \"M\",   \"01-701-1034\", 77,   \"F\",   \"01-701-1118\", 52,   \"M\" )  adcm <- tribble(   ~USUBJID,      ~CMTRT,         ~CMSTDTC,   \"01-701-1015\", \"ASPIRIN\",      \"2013-05-14\",   \"01-701-1023\", \"MYLANTA\",      \"2014-01-04\",   \"01-701-1023\", \"CALCIUM\",      \"2014-02-25\",   \"01-701-1034\", \"VITAMIN C\",    \"2013-12-12\",   \"01-701-1034\", \"CALCIUM\",      \"2013-03-27\",   \"01-701-1118\", \"MULTIVITAMIN\", \"2013-02-21\" )  filter_not_exist(   dataset = adsl,   dataset_add = adcm,   by_vars = exprs(USUBJID),   filter_add = str_detect(CMTRT, \"VITAMIN\") ) #> # A tibble: 2 × 3 #>   USUBJID       AGE SEX   #>   <chr>       <dbl> <chr> #> 1 01-701-1015    63 F     #> 2 01-701-1023    64 M"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_relative.html","id":null,"dir":"Reference","previous_headings":"","what":"Filter the Observations Before or After a Condition is Fulfilled — filter_relative","title":"Filter the Observations Before or After a Condition is Fulfilled — filter_relative","text":"Filters observations observation specified condition fulfilled group. example, function called select subject observations first disease progression.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_relative.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filter the Observations Before or After a Condition is Fulfilled — filter_relative","text":"","code":"filter_relative(   dataset,   by_vars,   order,   condition,   mode,   selection,   inclusive,   keep_no_ref_groups = TRUE,   check_type = \"warning\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_relative.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filter the Observations Before or After a Condition is Fulfilled — filter_relative","text":"dataset Input dataset variables specified by_vars order arguments expected dataset. by_vars Grouping variables Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) order Sort order Within group observations ordered specified order. handling NAs sorting variables see Sort Order. Permitted Values: list expressions created exprs(), e.g., exprs(ADT, desc(AVAL)) condition Condition Reference Observation specified condition determines reference observation. output dataset contains observations (selection parameter) reference observation. mode Selection mode (first last) \"first\" specified, group observations (selection parameter) observation condition (condition parameter) fulfilled first time included output dataset. \"last\" specified, group observations (selection parameter) observation condition (condition parameter) fulfilled last time included output dataset. Permitted Values: \"first\", \"last\" selection Select observations reference observation? Permitted Values: \"\", \"\" inclusive Include reference observation? Permitted Values: TRUE, FALSE keep_no_ref_groups groups without reference observation kept? Permitted Values: TRUE, FALSE check_type Check uniqueness? \"warning\" \"error\" specified, specified message issued observations input dataset unique respect variables order. Permitted Values: \"none\", \"warning\", \"error\"","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_relative.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filter the Observations Before or After a Condition is Fulfilled — filter_relative","text":"dataset containing group observations observation condition fulfilled first last time","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_relative.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Filter the Observations Before or After a Condition is Fulfilled — filter_relative","text":"group ( by_vars parameter) observations (selection parameter) observations condition (condition parameter) fulfilled first last time (order parameter mode parameter) included output dataset.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/filter_relative.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Filter the Observations Before or After a Condition is Fulfilled — filter_relative","text":"","code":"library(tibble)  response <- tribble(   ~USUBJID, ~AVISITN, ~AVALC,   \"1\",      1,        \"PR\",   \"1\",      2,        \"CR\",   \"1\",      3,        \"CR\",   \"1\",      4,        \"SD\",   \"1\",      5,        \"NE\",   \"2\",      1,        \"SD\",   \"2\",      2,        \"PD\",   \"2\",      3,        \"PD\",   \"3\",      1,        \"SD\",   \"4\",      1,        \"SD\",   \"4\",      2,        \"PR\",   \"4\",      3,        \"PD\",   \"4\",      4,        \"SD\",   \"4\",      5,        \"PR\" )  # Select observations up to first PD for each patient response %>%   filter_relative(     by_vars = exprs(USUBJID),     order = exprs(AVISITN),     condition = AVALC == \"PD\",     mode = \"first\",     selection = \"before\",     inclusive = TRUE   ) #> # A tibble: 11 × 3 #>    USUBJID AVISITN AVALC #>    <chr>     <dbl> <chr> #>  1 1             1 PR    #>  2 1             2 CR    #>  3 1             3 CR    #>  4 1             4 SD    #>  5 1             5 NE    #>  6 2             1 SD    #>  7 2             2 PD    #>  8 3             1 SD    #>  9 4             1 SD    #> 10 4             2 PR    #> 11 4             3 PD     # Select observations after last CR, PR, or SD for each patient response %>%   filter_relative(     by_vars = exprs(USUBJID),     order = exprs(AVISITN),     condition = AVALC %in% c(\"CR\", \"PR\", \"SD\"),     mode = \"last\",     selection = \"after\",     inclusive = FALSE   ) #> # A tibble: 3 × 3 #>   USUBJID AVISITN AVALC #>   <chr>     <dbl> <chr> #> 1 1             5 NE    #> 2 2             2 PD    #> 3 2             3 PD     # Select observations from first response to first PD response %>%   filter_relative(     by_vars = exprs(USUBJID),     order = exprs(AVISITN),     condition = AVALC %in% c(\"CR\", \"PR\"),     mode = \"first\",     selection = \"after\",     inclusive = TRUE,     keep_no_ref_groups = FALSE   ) %>%   filter_relative(     by_vars = exprs(USUBJID),     order = exprs(AVISITN),     condition = AVALC == \"PD\",     mode = \"first\",     selection = \"before\",     inclusive = TRUE   ) #> # A tibble: 7 × 3 #>   USUBJID AVISITN AVALC #>   <chr>     <dbl> <chr> #> 1 1             1 PR    #> 2 1             2 CR    #> 3 1             3 CR    #> 4 1             4 SD    #> 5 1             5 NE    #> 6 4             2 PR    #> 7 4             3 PD"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/flag_event.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a flag_event Object — flag_event","title":"Create a flag_event Object — flag_event","text":"flag_event object used define events input derive_var_merged_ef_msrc() function.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/flag_event.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a flag_event Object — flag_event","text":"","code":"flag_event(dataset_name, condition = NULL, by_vars = NULL)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/flag_event.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a flag_event Object — flag_event","text":"dataset_name Dataset name dataset used input event. name refers dataset specified source_datasets derive_var_merged_ef_msrc(). Permitted Values: character scalar condition Condition condition evaluated dataset referenced dataset_name. groups evaluates TRUE least new variable set true value (true_value). by_vars Grouping variables specified, dataset grouped specified variables condition evaluated. named elements used by_vars like by_vars = exprs(USUBJID, EXLNKID = ECLNKID), variables renamed evaluation. by_vars element specified, observations grouped variables specified by_vars argument derive_var_merged_ef_msrc().","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/format.basket_select.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns a Character Representation of a basket_select() Object — format.basket_select","title":"Returns a Character Representation of a basket_select() Object — format.basket_select","text":"function returns character representation basket_select() object. can used error messages example.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/format.basket_select.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns a Character Representation of a basket_select() Object — format.basket_select","text":"","code":"# S3 method for class 'basket_select' format(x, ...)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/format.basket_select.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns a Character Representation of a basket_select() Object — format.basket_select","text":"x basket_select() object ... used","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/format.basket_select.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns a Character Representation of a basket_select() Object — format.basket_select","text":"character representation basket_select() object","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/format.basket_select.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Returns a Character Representation of a basket_select() Object — format.basket_select","text":"","code":"format(basket_select(id = 42, scope = \"NARROW\", type = \"smq\")) #> [1] \"basket_select(name = NULL, id = 42, scope = \\\"NARROW\\\", type = \\\"smq\\\")\""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_admiral_option.html","id":null,"dir":"Reference","previous_headings":"","what":"Get the Value of an Admiral Option — get_admiral_option","title":"Get the Value of an Admiral Option — get_admiral_option","text":"Get Value Admiral Option Can Modified Advanced Users.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_admiral_option.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get the Value of an Admiral Option — get_admiral_option","text":"","code":"get_admiral_option(option)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_admiral_option.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get the Value of an Admiral Option — get_admiral_option","text":"option character scalar commonly used admiral function inputs. now, support available \"subject_keys\" \"signif_digits\". See set_admiral_options() description options.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_admiral_option.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get the Value of an Admiral Option — get_admiral_option","text":"value specified option.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_admiral_option.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get the Value of an Admiral Option — get_admiral_option","text":"function allows flexibility function inputs may need repeated multiple times script, subject_keys.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_admiral_option.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get the Value of an Admiral Option — get_admiral_option","text":"","code":"library(dplyr, warn.conflicts = FALSE) dm <- tribble(   ~STUDYID, ~DOMAIN,  ~USUBJID, ~AGE,   ~AGEU,   \"PILOT01\",   \"DM\", \"01-1302\",   61, \"YEARS\",   \"PILOT01\",   \"DM\", \"17-1344\",   64, \"YEARS\" )  vs <- tribble(   ~STUDYID,  ~DOMAIN,  ~USUBJID, ~VSTESTCD,     ~VISIT,     ~VSTPT, ~VSSTRESN,   \"PILOT01\",    \"VS\", \"01-1302\",   \"DIABP\", \"BASELINE\",    \"LYING\",        76,   \"PILOT01\",    \"VS\", \"01-1302\",   \"DIABP\", \"BASELINE\", \"STANDING\",        87,   \"PILOT01\",    \"VS\", \"01-1302\",   \"DIABP\",   \"WEEK 2\",    \"LYING\",        71,   \"PILOT01\",    \"VS\", \"01-1302\",   \"DIABP\",   \"WEEK 2\", \"STANDING\",        79,   \"PILOT01\",    \"VS\", \"17-1344\",   \"DIABP\", \"BASELINE\",    \"LYING\",        88,   \"PILOT01\",    \"VS\", \"17-1344\",   \"DIABP\", \"BASELINE\", \"STANDING\",        86,   \"PILOT01\",    \"VS\", \"17-1344\",   \"DIABP\",   \"WEEK 2\",    \"LYING\",        84,   \"PILOT01\",    \"VS\", \"17-1344\",   \"DIABP\",   \"WEEK 2\", \"STANDING\",        82 )  # Merging all dm variables to vs derive_vars_merged(   vs,   dataset_add = select(dm, -DOMAIN),   by_vars = get_admiral_option(\"subject_keys\") ) #> # A tibble: 8 × 9 #>   STUDYID DOMAIN USUBJID VSTESTCD VISIT    VSTPT    VSSTRESN   AGE AGEU  #>   <chr>   <chr>  <chr>   <chr>    <chr>    <chr>       <dbl> <dbl> <chr> #> 1 PILOT01 VS     01-1302 DIABP    BASELINE LYING          76    61 YEARS #> 2 PILOT01 VS     01-1302 DIABP    BASELINE STANDING       87    61 YEARS #> 3 PILOT01 VS     01-1302 DIABP    WEEK 2   LYING          71    61 YEARS #> 4 PILOT01 VS     01-1302 DIABP    WEEK 2   STANDING       79    61 YEARS #> 5 PILOT01 VS     17-1344 DIABP    BASELINE LYING          88    64 YEARS #> 6 PILOT01 VS     17-1344 DIABP    BASELINE STANDING       86    64 YEARS #> 7 PILOT01 VS     17-1344 DIABP    WEEK 2   LYING          84    64 YEARS #> 8 PILOT01 VS     17-1344 DIABP    WEEK 2   STANDING       82    64 YEARS"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_duplicates_dataset.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Duplicate Records that Led to a Prior Error — get_duplicates_dataset","title":"Get Duplicate Records that Led to a Prior Error — get_duplicates_dataset","text":"Get Duplicate Records Led Prior Error","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_duplicates_dataset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Duplicate Records that Led to a Prior Error — get_duplicates_dataset","text":"","code":"get_duplicates_dataset()"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_duplicates_dataset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Duplicate Records that Led to a Prior Error — get_duplicates_dataset","text":"data.frame NULL","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_duplicates_dataset.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get Duplicate Records that Led to a Prior Error — get_duplicates_dataset","text":"Many {admiral} function check input dataset contains one record per by_vars group throw error otherwise. get_duplicates_dataset() function allows one retrieve duplicate records lead error. Note function always returns dataset duplicates last error thrown current R session. Thus, restarting R sessions get_duplicates_dataset() return NULL second error thrown, dataset first error can longer accessed (unless saved variable).","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_duplicates_dataset.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get Duplicate Records that Led to a Prior Error — get_duplicates_dataset","text":"","code":"data(admiral_adsl)  # Duplicate the first record adsl <- rbind(admiral_adsl[1L, ], admiral_adsl)  signal_duplicate_records(adsl, exprs(USUBJID), cnd_type = \"warning\") #> Warning: Dataset contains duplicate records with respect to `USUBJID` #> ℹ Run `admiral::get_duplicates_dataset()` to access the duplicate records  get_duplicates_dataset() #> Duplicate records with respect to `USUBJID`. #> # A tibble: 2 × 54 #>   USUBJID     STUDYID  SUBJID RFSTDTC RFENDTC RFXSTDTC RFXENDTC RFICDTC RFPENDTC #> * <chr>       <chr>    <chr>  <chr>   <chr>   <chr>    <chr>    <chr>   <chr>    #> 1 01-701-1015 CDISCPI… 1015   2014-0… 2014-0… 2014-01… 2014-07… NA      2014-07… #> 2 01-701-1015 CDISCPI… 1015   2014-0… 2014-0… 2014-01… 2014-07… NA      2014-07… #> # ℹ 45 more variables: DTHDTC <chr>, DTHFL <chr>, SITEID <chr>, AGE <dbl>, #> #   AGEU <chr>, SEX <chr>, RACE <chr>, ETHNIC <chr>, ARMCD <chr>, ARM <chr>, #> #   ACTARMCD <chr>, ACTARM <chr>, COUNTRY <chr>, DMDTC <chr>, DMDY <dbl>, #> #   TRT01P <chr>, TRT01A <chr>, TRTSDTM <dttm>, TRTSTMF <chr>, TRTEDTM <dttm>, #> #   TRTETMF <chr>, TRTSDT <date>, TRTEDT <date>, TRTDURD <dbl>, SCRFDT <date>, #> #   EOSDT <date>, EOSSTT <chr>, FRVDT <date>, RANDDT <date>, DTHDT <date>, #> #   DTHDTF <chr>, DTHADY <dbl>, LDDTHELD <dbl>, DTHCAUS <chr>, DTHDOM <chr>, …"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_flagged_records.html","id":null,"dir":"Reference","previous_headings":"","what":"Create an Existence Flag — get_flagged_records","title":"Create an Existence Flag — get_flagged_records","text":"Create flag variable input dataset indicates exists least one observation input dataset fulfilling certain condition. Note: helper function derive_vars_merged_exist_flag() inputs result derive_vars_merged().","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_flagged_records.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create an Existence Flag — get_flagged_records","text":"","code":"get_flagged_records(dataset, new_var, condition, filter = NULL)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_flagged_records.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create an Existence Flag — get_flagged_records","text":"dataset Input dataset new_var New variable specified variable added input dataset. condition Condition condition evaluated dataset (dataset). rows evaluates TRUE new variable set 1 new column. Otherwise, set 0. filter Filter additional data observations fulfilling specified condition taken account flagging. argument specified, observations considered. Permitted Values: condition","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_flagged_records.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create an Existence Flag — get_flagged_records","text":"output dataset input dataset filtered filter condition variable specified new_var representing flag condition.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_flagged_records.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create an Existence Flag — get_flagged_records","text":"","code":"library(dplyr, warn.conflicts = FALSE)   ae <- tribble(   ~STUDYID,  ~DOMAIN,  ~USUBJID,    ~AETERM,     ~AEREL,   \"PILOT01\",    \"AE\", \"01-1028\", \"ERYTHEMA\", \"POSSIBLE\",   \"PILOT01\",    \"AE\", \"01-1028\", \"PRURITUS\", \"PROBABLE\",   \"PILOT01\",    \"AE\", \"06-1049\",  \"SYNCOPE\", \"POSSIBLE\",   \"PILOT01\",    \"AE\", \"06-1049\",  \"SYNCOPE\", \"PROBABLE\" )   get_flagged_records(   dataset = ae,   new_var = AERELFL,   condition = AEREL == \"PROBABLE\" ) %>%   select(STUDYID, USUBJID, AERELFL) #> # A tibble: 4 × 3 #>   STUDYID USUBJID AERELFL #>   <chr>   <chr>     <dbl> #> 1 PILOT01 01-1028       0 #> 2 PILOT01 01-1028       1 #> 3 PILOT01 06-1049       0 #> 4 PILOT01 06-1049       1  vs <- tribble(   ~STUDYID,  ~DOMAIN,  ~USUBJID,      ~VISIT, ~VSTESTCD, ~VSSTRESN, ~VSBLFL,   \"PILOT01\",    \"VS\", \"01-1028\", \"SCREENING\",  \"HEIGHT\",     177.8,      NA,   \"PILOT01\",    \"VS\", \"01-1028\", \"SCREENING\",  \"WEIGHT\",     98.88,      NA,   \"PILOT01\",    \"VS\", \"01-1028\",  \"BASELINE\",  \"WEIGHT\",     99.34,     \"Y\",   \"PILOT01\",    \"VS\", \"01-1028\",    \"WEEK 4\",  \"WEIGHT\",     98.88,      NA,   \"PILOT01\",    \"VS\", \"04-1127\", \"SCREENING\",  \"HEIGHT\",     165.1,      NA,   \"PILOT01\",    \"VS\", \"04-1127\", \"SCREENING\",  \"WEIGHT\",     42.87,      NA,   \"PILOT01\",    \"VS\", \"04-1127\",  \"BASELINE\",  \"WEIGHT\",     41.05,     \"Y\",   \"PILOT01\",    \"VS\", \"04-1127\",    \"WEEK 4\",  \"WEIGHT\",     41.73,      NA,   \"PILOT01\",    \"VS\", \"06-1049\", \"SCREENING\",  \"HEIGHT\",    167.64,      NA,   \"PILOT01\",    \"VS\", \"06-1049\", \"SCREENING\",  \"WEIGHT\",     57.61,      NA,   \"PILOT01\",    \"VS\", \"06-1049\",  \"BASELINE\",  \"WEIGHT\",     57.83,     \"Y\",   \"PILOT01\",    \"VS\", \"06-1049\",    \"WEEK 4\",  \"WEIGHT\",     58.97,      NA ) get_flagged_records(   dataset = vs,   new_var = WTBLHIFL,   condition = VSSTRESN > 90,   filter = VSTESTCD == \"WEIGHT\" & VSBLFL == \"Y\" ) %>%   select(STUDYID, USUBJID, WTBLHIFL) #> # A tibble: 3 × 3 #>   STUDYID USUBJID WTBLHIFL #>   <chr>   <chr>      <dbl> #> 1 PILOT01 01-1028        1 #> 2 PILOT01 04-1127        0 #> 3 PILOT01 06-1049        0"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_hori_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Creating Temporary Parameters and <variable>.<parameter> Variables — get_hori_data","title":"Creating Temporary Parameters and <variable>.<parameter> Variables — get_hori_data","text":"function creates temporary parameters variables form <variable>.<parameter>, e.g., AVAL.WEIGHT.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_hori_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creating Temporary Parameters and <variable>.<parameter> Variables — get_hori_data","text":"","code":"get_hori_data(dataset, by_vars, parameters, set_values_to, filter)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_hori_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creating Temporary Parameters and <variable>.<parameter> Variables — get_hori_data","text":"dataset Input dataset variables specified by_vars argument expected dataset. by_vars Grouping variables Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) parameters List parameter codes input dataset restricted specified parameter codes. expression specified, new parameter code added input dataset. name element defines parameter code expression observations select. Permitted Values: character vector PARAMCD values list expressions set_values_to variables form <variable>.<parameter> like AVAL.WEIGHT added input dataset. set value variable parameter. E.g., AVAL.WEIGHT set value AVAL PARAMCD == \"WEIGHT\". Permitted Values: list expressions filter Filter condition used restricting input dataset specified filter condition used warnings . applied input dataset. Permitted Values: unquoted expression","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_hori_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Creating Temporary Parameters and <variable>.<parameter> Variables — get_hori_data","text":"dataset one observation per group. contains variables specified by_vars variables form <variable>.<parameter> occurring set_values_to.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_imputation_target_date.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Date Imputation Targets — get_imputation_target_date","title":"Get Date Imputation Targets — get_imputation_target_date","text":"Get Date Imputation Targets","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_imputation_target_date.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Date Imputation Targets — get_imputation_target_date","text":"","code":"get_imputation_target_date(date_imputation, month)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_imputation_target_date.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Date Imputation Targets — get_imputation_target_date","text":"date_imputation value impute day/month datepart missing. character value expected, either format month day specified \"mm-dd\": e.g. \"06-15\" 15th June, keyword: \"first\", \"mid\", \"last\" impute first/mid/last day/month. month Month component partial date","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_imputation_target_date.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Date Imputation Targets — get_imputation_target_date","text":"list character vectors. elements list named \"year\", \"month\", \"day\".","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_imputation_target_date.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get Date Imputation Targets — get_imputation_target_date","text":"date_imputation = \"first\" \"0000\", \"01\", \"01\" returned. date_imputation = \"mid\" \"xxxx\", \"06\", \"30\" month NA \"15\" otherwise returned. date_imputation = \"last\" \"9999\", \"12\", \"31\" returned. date_imputation = \"<mm>-<dd>\" \"xxxx\", \"<mm>\", \"<dd>\" returned. \"xxxx\" indicates component undefined. undefined component occurs imputed DTC value, imputed DTC value set NA_character_ imputation functions.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_imputation_target_time.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Time Imputation Targets — get_imputation_target_time","title":"Get Time Imputation Targets — get_imputation_target_time","text":"Get Time Imputation Targets","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_imputation_target_time.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Time Imputation Targets — get_imputation_target_time","text":"","code":"get_imputation_target_time(time_imputation)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_imputation_target_time.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Time Imputation Targets — get_imputation_target_time","text":"time_imputation value impute time timepart missing. character value expected, either format hour, min sec specified \"hh:mm:ss\": e.g. \"00:00:00\" start day, keyword: \"first\",\"last\" impute start/end day.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_imputation_target_time.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Time Imputation Targets — get_imputation_target_time","text":"list character vectors. elements list named \"hour\", \"minute\", \"second\".","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_imputation_target_time.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get Time Imputation Targets — get_imputation_target_time","text":"time_imputation = \"first\" \"00\", \"00\", \"00\" returned. time_imputation = \"last\" \"23\", \"59\", \"59\" returned. time_imputation = \"<hh>:<mm>:<ss>\" \"<hh>\", \"<mm>\", \"<ss>\" returned.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_joined_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Join Data for ","title":"Join Data for ","text":"helper function joins data \"joined\" functions. .join variables included output dataset.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_joined_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Join Data for ","text":"","code":"get_joined_data(   dataset,   dataset_add,   by_vars = NULL,   join_vars = NULL,   join_type,   first_cond_lower = NULL,   first_cond_upper = NULL,   order = NULL,   tmp_obs_nr_var = NULL,   filter_add = NULL,   filter_join = NULL,   check_type = \"warning\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_joined_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Join Data for ","text":"dataset Input dataset variables specified by_vars argument expected dataset. dataset_add Additional dataset variables specified by_vars, new_vars, join_vars, order argument expected. by_vars Grouping variables two datasets joined specified variables. Variables can renamed naming element, .e. by_vars = exprs(<name input dataset> = <name additional dataset>), similar dplyr joins. Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) join_vars Variables use additional dataset extra variables required additional dataset filter_join specified argument. Variables specified new_vars need repeated join_vars. specified variable exists input dataset additional dataset, suffix \".join\" added variable additional dataset. expression named, e.g., exprs(EXTDT = convert_dtc_to_dt(EXSTDTC)), corresponding variable added additional dataset can used filter conditions (filter_add, filter_join) new_vars. variable included output dataset. variables included output dataset. Permitted Values: list variables named expressions created exprs() join_type Observations keep joining argument determines joined observations kept respect original observation. example, join_type = \"\" specified observations original observations kept. example confirmed response BOR oncology setting confirmed deterioration questionnaires confirmatory assessment must assessment. Thus join_type = \"\" used. Whereas, sometimes might allow confirmatory observations occur prior observation. example, identify AEs occurring seven days COVID AE. Thus join_type = \"\" used. Permitted Values: \"\", \"\", \"\" first_cond_lower Condition selecting range data () argument specified, observations restricted first observation current observation specified condition fulfilled current observation. condition fulfilled observations, observations considered, .e., observation flagged. argument specified filter_join contains summary functions apply observations certain observation current observation current observation. first_cond_upper Condition selecting range data () argument specified, observations restricted first observation specified condition fulfilled. condition fulfilled observations, observations considered, .e., observation flagged. argument specified filter_join contains summary functions apply observations confirmation assessment. order Sort order argument set non-null value, observation input dataset first last observation joined dataset selected respect specified order. specified variables expected additional dataset (dataset_add). variable available dataset dataset_add, one dataset_add used sorting. expression named, e.g., exprs(EXSTDT = convert_dtc_to_dt(EXSTDTC), EXSEQ), corresponding variable (EXSTDT) added additional dataset can used filter conditions (filter_add, filter_join) join_vars new_vars. variable included output dataset. handling NAs sorting variables see Sort Order. Permitted Values: list expressions created exprs(), e.g., exprs(ADT, desc(AVAL)) NULL tmp_obs_nr_var Temporary observation number specified variable added input dataset (dataset) additional dataset (dataset_add). set observation number respect order. group (by_vars) observation number starts 1. variable can used conditions (filter_join, first_cond_upper, first_cond_lower). can also used select consecutive observations last observation. filter_add Filter additional dataset (dataset_add) observations dataset_add fulfilling specified condition joined input dataset. argument specified, observations joined. Variables created order new_vars arguments can used condition. condition can include summary functions like () (). additional dataset grouped variables (by_vars). Permitted Values: condition filter_join Filter joined dataset specified condition applied joined dataset. Therefore variables datasets dataset dataset_add can used. Variables created order new_vars arguments can used condition. condition can include summary functions like () (). joined dataset grouped original observations. Permitted Values: condition check_type Check uniqueness? \"warning\" \"error\" specified, specified message issued observations (restricted) joined dataset unique respect variables order. argument ignored order specified. case error issued independent check_type restricted joined dataset contains one observation observations input dataset. Permitted Values: \"none\", \"warning\", \"error\"","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_joined_data.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Join Data for ","text":"variables specified order added additional dataset (dataset_add). variables specified join_vars added additional dataset (dataset_add). records additional dataset (dataset_add) restricted matching filter_add condition. input dataset (restricted) additional dataset left joined grouping variables (by_vars). grouping variables specified, full join performed. joined dataset restricted filter_join condition.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_joined_sub_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Join Data for ","title":"Join Data for ","text":"helper function joins data \"joined\" functions. .join variables included output dataset. called get_joined_data() process group separately. reduces memory consumption.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_joined_sub_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Join Data for ","text":"","code":"get_joined_sub_data(   dataset,   dataset_add,   by_vars,   tmp_obs_nr_var,   tmp_obs_nr_left,   join_type,   first_cond_upper,   first_cond_lower,   filter_join )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_joined_sub_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Join Data for ","text":"dataset Input dataset variables specified by_vars argument expected dataset. dataset_add Additional dataset variables specified by_vars, new_vars, join_vars, order argument expected. by_vars Grouping variables two datasets joined specified variables. Variables can renamed naming element, .e. by_vars = exprs(<name input dataset> = <name additional dataset>), similar dplyr joins. Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) tmp_obs_nr_var Temporary observation number specified variable added input dataset (dataset) additional dataset (dataset_add). set observation number respect order. group (by_vars) observation number starts 1. variable can used conditions (filter_join, first_cond_upper, first_cond_lower). can also used select consecutive observations last observation. join_type Observations keep joining argument determines joined observations kept respect original observation. example, join_type = \"\" specified observations original observations kept. example confirmed response BOR oncology setting confirmed deterioration questionnaires confirmatory assessment must assessment. Thus join_type = \"\" used. Whereas, sometimes might allow confirmatory observations occur prior observation. example, identify AEs occurring seven days COVID AE. Thus join_type = \"\" used. Permitted Values: \"\", \"\", \"\" first_cond_upper Condition selecting range data () argument specified, observations restricted first observation specified condition fulfilled. condition fulfilled observations, observations considered, .e., observation flagged. argument specified filter_join contains summary functions apply observations confirmation assessment. first_cond_lower Condition selecting range data () argument specified, observations restricted first observation current observation specified condition fulfilled current observation. condition fulfilled observations, observations considered, .e., observation flagged. argument specified filter_join contains summary functions apply observations certain observation current observation current observation. filter_join Filter joined dataset specified condition applied joined dataset. Therefore variables datasets dataset dataset_add can used. Variables created order new_vars arguments can used condition. condition can include summary functions like () (). joined dataset grouped original observations. Permitted Values: condition","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_joined_sub_data.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Join Data for ","text":"input dataset (dataset) additional dataset (dataset_add) left joined grouping variables (by_vars). grouping variables specified, full join performed. joined dataset restricted specified arguments join_type, first_cond_upper, first_cond_lower. See argument descriptions details. joined dataset restricted filter_join condition.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_many_to_one_dataset.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Many to One Values that Led to a Prior Error — get_many_to_one_dataset","title":"Get Many to One Values that Led to a Prior Error — get_many_to_one_dataset","text":"Get Many One Values Led Prior Error","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_many_to_one_dataset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Many to One Values that Led to a Prior Error — get_many_to_one_dataset","text":"","code":"get_many_to_one_dataset()"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_many_to_one_dataset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Many to One Values that Led to a Prior Error — get_many_to_one_dataset","text":"data.frame NULL","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_many_to_one_dataset.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get Many to One Values that Led to a Prior Error — get_many_to_one_dataset","text":"assert_one_to_one() detects issue, many one values stored dataset. dataset can retrieved get_many_to_one_dataset(). Note function always returns many one values last error thrown current R session. Thus, restarting R sessions get_many_to_one_dataset() return NULL second error thrown, dataset first error can longer accessed (unless saved variable).","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_many_to_one_dataset.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get Many to One Values that Led to a Prior Error — get_many_to_one_dataset","text":"","code":"library(admiraldev, warn.conflicts = FALSE) data(admiral_adsl)  try(   assert_one_to_one(admiral_adsl, exprs(SITEID), exprs(STUDYID)) ) #> Error in eval(expr, envir) :  #>   There is more than one value of \"SITEID\" for some values of \"STUDYID\" #> ℹ Call `admiral::get_many_to_one_dataset()` to get all many-to-one values.  get_many_to_one_dataset() #> # A tibble: 17 × 2 #> # Groups:   STUDYID [1] #>    SITEID STUDYID      #>    <chr>  <chr>        #>  1 701    CDISCPILOT01 #>  2 702    CDISCPILOT01 #>  3 703    CDISCPILOT01 #>  4 704    CDISCPILOT01 #>  5 705    CDISCPILOT01 #>  6 706    CDISCPILOT01 #>  7 707    CDISCPILOT01 #>  8 708    CDISCPILOT01 #>  9 709    CDISCPILOT01 #> 10 710    CDISCPILOT01 #> 11 711    CDISCPILOT01 #> 12 713    CDISCPILOT01 #> 13 714    CDISCPILOT01 #> 14 715    CDISCPILOT01 #> 15 716    CDISCPILOT01 #> 16 717    CDISCPILOT01 #> 17 718    CDISCPILOT01"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_not_mapped.html","id":null,"dir":"Reference","previous_headings":"","what":"Get list of records not mapped from the lookup table. — get_not_mapped","title":"Get list of records not mapped from the lookup table. — get_not_mapped","text":"Get list records mapped lookup table.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_not_mapped.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get list of records not mapped from the lookup table. — get_not_mapped","text":"","code":"get_not_mapped()"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_not_mapped.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get list of records not mapped from the lookup table. — get_not_mapped","text":"data.frame NULL","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_one_to_many_dataset.html","id":null,"dir":"Reference","previous_headings":"","what":"Get One to Many Values that Led to a Prior Error — get_one_to_many_dataset","title":"Get One to Many Values that Led to a Prior Error — get_one_to_many_dataset","text":"Get One Many Values Led Prior Error","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_one_to_many_dataset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get One to Many Values that Led to a Prior Error — get_one_to_many_dataset","text":"","code":"get_one_to_many_dataset()"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_one_to_many_dataset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get One to Many Values that Led to a Prior Error — get_one_to_many_dataset","text":"data.frame NULL","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_one_to_many_dataset.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get One to Many Values that Led to a Prior Error — get_one_to_many_dataset","text":"assert_one_to_one() detects issue, one many values stored dataset. dataset can retrieved get_one_to_many_dataset(). Note function always returns one many values last error thrown current R session. Thus, restarting R sessions get_one_to_many_dataset() return NULL second error thrown, dataset first error can longer accessed (unless saved variable).","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_one_to_many_dataset.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get One to Many Values that Led to a Prior Error — get_one_to_many_dataset","text":"","code":"library(admiraldev, warn.conflicts = FALSE) data(admiral_adsl)  try(   assert_one_to_one(admiral_adsl, exprs(STUDYID), exprs(SITEID)) ) #> Error in eval(expr, envir) :  #>   For some values of \"STUDYID\" there is more than one value of \"SITEID\" #> ℹ Call `admiral::get_one_to_many_dataset()` to get all one-to-many values.  get_one_to_many_dataset() #> # A tibble: 17 × 2 #> # Groups:   STUDYID [1] #>    STUDYID      SITEID #>    <chr>        <chr>  #>  1 CDISCPILOT01 701    #>  2 CDISCPILOT01 702    #>  3 CDISCPILOT01 703    #>  4 CDISCPILOT01 704    #>  5 CDISCPILOT01 705    #>  6 CDISCPILOT01 706    #>  7 CDISCPILOT01 707    #>  8 CDISCPILOT01 708    #>  9 CDISCPILOT01 709    #> 10 CDISCPILOT01 710    #> 11 CDISCPILOT01 711    #> 12 CDISCPILOT01 713    #> 13 CDISCPILOT01 714    #> 14 CDISCPILOT01 715    #> 15 CDISCPILOT01 716    #> 16 CDISCPILOT01 717    #> 17 CDISCPILOT01 718"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_partialdatetime.html","id":null,"dir":"Reference","previous_headings":"","what":"Parse DTC variable and Determine Components — get_partialdatetime","title":"Parse DTC variable and Determine Components — get_partialdatetime","text":"Parse DTC variable Determine Components","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_partialdatetime.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Parse DTC variable and Determine Components — get_partialdatetime","text":"","code":"get_partialdatetime(dtc)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_partialdatetime.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Parse DTC variable and Determine Components — get_partialdatetime","text":"dtc '--DTC' date parse character date expected format like yyyy-mm-dd yyyy-mm-ddThh:mm:ss. Trailing components can omitted - valid value component.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_partialdatetime.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Parse DTC variable and Determine Components — get_partialdatetime","text":"list character vectors. elements list named \"year\", \"month\", \"day\", \"hour\", \"minute\", \"second\". Missing components set NA_character_.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_partialdatetime.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Parse DTC variable and Determine Components — get_partialdatetime","text":"function can replaced parttime parser available.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_summary_records.html","id":null,"dir":"Reference","previous_headings":"","what":"Create Summary Records — get_summary_records","title":"Create Summary Records — get_summary_records","text":"Development get_summary_records() complete, new code recommend switching using dataset_add argument derive_summary_records(). uncommon analysis need whereby one needs derive analysis value (AVAL) multiple records. ADaM basic dataset structure variable DTYPE available indicate new derived records added dataset.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_summary_records.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create Summary Records — get_summary_records","text":"","code":"get_summary_records(dataset, by_vars, filter = NULL, set_values_to = NULL)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_summary_records.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create Summary Records — get_summary_records","text":"dataset Input dataset variables specified by_vars argument expected dataset. by_vars Grouping variables Variables consider generation groupwise summary records. Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) filter Filter condition logical expression apply summary calculation. default, filtering expressions computed within by_vars help aggregating, lagging, ranking function involved. example, filter_rows = (AVAL > mean(AVAL, na.rm = TRUE)) filter AVAL values greater mean AVAL by_vars. filter_rows = (dplyr::n() > 2) filter n count by_vars greater 2. set_values_to Variables set specified variables set specified values new observations. Set list variables specified value new records LHS refer variable. RHS refers values set variable. can string, symbol, numeric value, expression NA. summary functions used, values summarized variables specified by_vars. example:","code":"set_values_to = exprs(     AVAL = sum(AVAL),     PARAMCD = \"TDOSE\",     PARCAT1 = \"OVERALL\"   )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_summary_records.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create Summary Records — get_summary_records","text":"data frame derived records.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_summary_records.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create Summary Records — get_summary_records","text":"function creates derived observations append original dataset observations. like instead, see derive_summary_records() function.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_summary_records.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create Summary Records — get_summary_records","text":"","code":"library(tibble)  adeg <- tribble(   ~USUBJID,   ~EGSEQ, ~PARAM,             ~AVISIT,    ~EGDTC,             ~AVAL, ~TRTA,   \"XYZ-1001\", 1,      \"QTcF Int. (msec)\", \"Baseline\", \"2016-02-24T07:50\", 385,   NA_character_,   \"XYZ-1001\", 2,      \"QTcF Int. (msec)\", \"Baseline\", \"2016-02-24T07:52\", 399,   NA_character_,   \"XYZ-1001\", 3,      \"QTcF Int. (msec)\", \"Baseline\", \"2016-02-24T07:56\", 396,   NA_character_,   \"XYZ-1001\", 4,      \"QTcF Int. (msec)\", \"Visit 2\",  \"2016-03-08T09:45\", 384,   \"Placebo\",   \"XYZ-1001\", 5,      \"QTcF Int. (msec)\", \"Visit 2\",  \"2016-03-08T09:48\", 393,   \"Placebo\",   \"XYZ-1001\", 6,      \"QTcF Int. (msec)\", \"Visit 2\",  \"2016-03-08T09:51\", 388,   \"Placebo\",   \"XYZ-1001\", 7,      \"QTcF Int. (msec)\", \"Visit 3\",  \"2016-03-22T10:45\", 385,   \"Placebo\",   \"XYZ-1001\", 8,      \"QTcF Int. (msec)\", \"Visit 3\",  \"2016-03-22T10:48\", 394,   \"Placebo\",   \"XYZ-1001\", 9,      \"QTcF Int. (msec)\", \"Visit 3\",  \"2016-03-22T10:51\", 402,   \"Placebo\",   \"XYZ-1002\", 1,      \"QTcF Int. (msec)\", \"Baseline\", \"2016-02-22T07:58\", 399,   NA_character_,   \"XYZ-1002\", 2,      \"QTcF Int. (msec)\", \"Baseline\", \"2016-02-22T07:58\", 410,   NA_character_,   \"XYZ-1002\", 3,      \"QTcF Int. (msec)\", \"Baseline\", \"2016-02-22T08:01\", 392,   NA_character_,   \"XYZ-1002\", 4,      \"QTcF Int. (msec)\", \"Visit 2\",  \"2016-03-06T09:50\", 401,   \"Active 20mg\",   \"XYZ-1002\", 5,      \"QTcF Int. (msec)\", \"Visit 2\",  \"2016-03-06T09:53\", 407,   \"Active 20mg\",   \"XYZ-1002\", 6,      \"QTcF Int. (msec)\", \"Visit 2\",  \"2016-03-06T09:56\", 400,   \"Active 20mg\",   \"XYZ-1002\", 7,      \"QTcF Int. (msec)\", \"Visit 3\",  \"2016-03-24T10:50\", 412,   \"Active 20mg\",   \"XYZ-1002\", 8,      \"QTcF Int. (msec)\", \"Visit 3\",  \"2016-03-24T10:53\", 414,   \"Active 20mg\",   \"XYZ-1002\", 9,      \"QTcF Int. (msec)\", \"Visit 3\",  \"2016-03-24T10:56\", 402,   \"Active 20mg\" )  # Summarize the average of the triplicate ECG interval values (AVAL) get_summary_records(   adeg,   by_vars = exprs(USUBJID, PARAM, AVISIT),   set_values_to = exprs(     AVAL = mean(AVAL, na.rm = TRUE),     DTYPE = \"AVERAGE\"   ) ) #> # A tibble: 6 × 5 #>   USUBJID  PARAM            AVISIT    AVAL DTYPE   #>   <chr>    <chr>            <chr>    <dbl> <chr>   #> 1 XYZ-1001 QTcF Int. (msec) Baseline  393. AVERAGE #> 2 XYZ-1001 QTcF Int. (msec) Visit 2   388. AVERAGE #> 3 XYZ-1001 QTcF Int. (msec) Visit 3   394. AVERAGE #> 4 XYZ-1002 QTcF Int. (msec) Baseline  400. AVERAGE #> 5 XYZ-1002 QTcF Int. (msec) Visit 2   403. AVERAGE #> 6 XYZ-1002 QTcF Int. (msec) Visit 3   409. AVERAGE  # Derive more than one summary variable get_summary_records(   adeg,   by_vars = exprs(USUBJID, PARAM, AVISIT),   set_values_to = exprs(     AVAL = mean(AVAL),     ASTDTM = min(convert_dtc_to_dtm(EGDTC)),     AENDTM = max(convert_dtc_to_dtm(EGDTC)),     DTYPE = \"AVERAGE\"   ) ) #> # A tibble: 6 × 7 #>   USUBJID  PARAM      AVISIT  AVAL ASTDTM              AENDTM              DTYPE #>   <chr>    <chr>      <chr>  <dbl> <dttm>              <dttm>              <chr> #> 1 XYZ-1001 QTcF Int.… Basel…  393. 2016-02-24 07:50:00 2016-02-24 07:56:00 AVER… #> 2 XYZ-1001 QTcF Int.… Visit…  388. 2016-03-08 09:45:00 2016-03-08 09:51:00 AVER… #> 3 XYZ-1001 QTcF Int.… Visit…  394. 2016-03-22 10:45:00 2016-03-22 10:51:00 AVER… #> 4 XYZ-1002 QTcF Int.… Basel…  400. 2016-02-22 07:58:00 2016-02-22 08:01:00 AVER… #> 5 XYZ-1002 QTcF Int.… Visit…  403. 2016-03-06 09:50:00 2016-03-06 09:56:00 AVER… #> 6 XYZ-1002 QTcF Int.… Visit…  409. 2016-03-24 10:50:00 2016-03-24 10:56:00 AVER…  # Sample ADEG dataset with triplicate record for only AVISIT = 'Baseline' adeg <- tribble(   ~USUBJID,   ~EGSEQ, ~PARAM,             ~AVISIT,    ~EGDTC,             ~AVAL, ~TRTA,   \"XYZ-1001\", 1,      \"QTcF Int. (msec)\", \"Baseline\", \"2016-02-24T07:50\", 385,   NA_character_,   \"XYZ-1001\", 2,      \"QTcF Int. (msec)\", \"Baseline\", \"2016-02-24T07:52\", 399,   NA_character_,   \"XYZ-1001\", 3,      \"QTcF Int. (msec)\", \"Baseline\", \"2016-02-24T07:56\", 396,   NA_character_,   \"XYZ-1001\", 4,      \"QTcF Int. (msec)\", \"Visit 2\",  \"2016-03-08T09:48\", 393,   \"Placebo\",   \"XYZ-1001\", 5,      \"QTcF Int. (msec)\", \"Visit 2\",  \"2016-03-08T09:51\", 388,   \"Placebo\",   \"XYZ-1001\", 6,      \"QTcF Int. (msec)\", \"Visit 3\",  \"2016-03-22T10:48\", 394,   \"Placebo\",   \"XYZ-1001\", 7,      \"QTcF Int. (msec)\", \"Visit 3\",  \"2016-03-22T10:51\", 402,   \"Placebo\",   \"XYZ-1002\", 1,      \"QTcF Int. (msec)\", \"Baseline\", \"2016-02-22T07:58\", 399,   NA_character_,   \"XYZ-1002\", 2,      \"QTcF Int. (msec)\", \"Baseline\", \"2016-02-22T07:58\", 410,   NA_character_,   \"XYZ-1002\", 3,      \"QTcF Int. (msec)\", \"Baseline\", \"2016-02-22T08:01\", 392,   NA_character_,   \"XYZ-1002\", 4,      \"QTcF Int. (msec)\", \"Visit 2\",  \"2016-03-06T09:53\", 407,   \"Active 20mg\",   \"XYZ-1002\", 5,      \"QTcF Int. (msec)\", \"Visit 2\",  \"2016-03-06T09:56\", 400,   \"Active 20mg\",   \"XYZ-1002\", 6,      \"QTcF Int. (msec)\", \"Visit 3\",  \"2016-03-24T10:53\", 414,   \"Active 20mg\",   \"XYZ-1002\", 7,      \"QTcF Int. (msec)\", \"Visit 3\",  \"2016-03-24T10:56\", 402,   \"Active 20mg\" )  # Compute the average of AVAL only if there are more than 2 records within the # by group get_summary_records(   adeg,   by_vars = exprs(USUBJID, PARAM, AVISIT),   filter = n() > 2,   set_values_to = exprs(     AVAL = mean(AVAL, na.rm = TRUE),     DTYPE = \"AVERAGE\"   ) ) #> # A tibble: 2 × 5 #>   USUBJID  PARAM            AVISIT    AVAL DTYPE   #>   <chr>    <chr>            <chr>    <dbl> <chr>   #> 1 XYZ-1001 QTcF Int. (msec) Baseline  393. AVERAGE #> 2 XYZ-1002 QTcF Int. (msec) Baseline  400. AVERAGE"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_terms_from_db.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Terms from the Queries Database — get_terms_from_db","title":"Get Terms from the Queries Database — get_terms_from_db","text":"function checks requirements access database fulfilled (version access function available, see assert_db_requirements()), reads terms database, checks dataset terms expected format (see assert_terms()).","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_terms_from_db.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Terms from the Queries Database — get_terms_from_db","text":"","code":"get_terms_from_db(   version,   fun,   queries,   definition,   expect_grpname = FALSE,   expect_grpid = FALSE,   i,   temp_env )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_terms_from_db.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Terms from the Queries Database — get_terms_from_db","text":"version Version version must non null. Otherwise, error issued. value passed access function (fun). fun Access function access function must non null. Otherwise, error issued. function called retrieve terms. queries Queries List queries passed create_query_data(). used error messages. definition Definition query definition passed access function. defines terms returned. expect_grpname GRPNAME expected output dataset? expect_grpid GRPID expected output dataset? Index definition queries value used error messages. temp_env Temporary environment value passed access function.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_terms_from_db.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Terms from the Queries Database — get_terms_from_db","text":"Output dataset access function","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_vars_query.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Query Variables — get_vars_query","title":"Get Query Variables — get_vars_query","text":"Create table input dataset binds necessary rows derive_vars_query() call relevant SRCVAR, TERM_NAME_ID temporary index necessary Note: function first step performed derive_vars_query() requested users present independently .","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_vars_query.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Query Variables — get_vars_query","text":"","code":"get_vars_query(dataset, dataset_queries)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_vars_query.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Query Variables — get_vars_query","text":"dataset Input dataset dataset_queries dataset containing required columns PREFIX, GRPNAME, SRCVAR, TERMCHAR /TERMNUM, optional columns GRPID, SCOPE, SCOPEN. create_query_data() can used create dataset.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_vars_query.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Query Variables — get_vars_query","text":"processed query dataset SRCVAR TERM_NAME_ID can merged input dataset execute derivations outlined dataset_queries.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_vars_query.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get Query Variables — get_vars_query","text":"function can used derive CDISC variables SMQzzNAM, SMQzzCD, SMQzzSC, SMQzzSCN, CQzzNAM ADAE ADMH, variables SDGzzNAM, SDGzzCD, SDGzzSC ADCM. example usage function can found OCCDS vignette. query dataset expected input function. See Queries Dataset Documentation vignette descriptions, call data(\"queries\") example query dataset. unique element PREFIX, corresponding \"NAM\" variable created. unique PREFIX, GRPID \"\" NA, corresponding \"CD\" variable created; similarly, SCOPE \"\" NA, corresponding \"SC\" variable created; SCOPEN \"\" NA, corresponding \"SCN\" variable created. record dataset, \"NAM\" variable takes value GRPNAME value TERMCHAR TERMNUM dataset_queries matches value respective SRCVAR dataset. Note TERMCHAR dataset_queries dataset may NA TERMNUM non-NA vice versa. matching case insensitive. \"CD\", \"SC\", \"SCN\" variables derived accordingly based GRPID, SCOPE, SCOPEN respectively, whenever missing.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/get_vars_query.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get Query Variables — get_vars_query","text":"","code":"library(tibble) data(\"queries\") adae <- tribble(   ~USUBJID, ~ASTDTM, ~AETERM, ~AESEQ, ~AEDECOD, ~AELLT, ~AELLTCD,   \"01\", \"2020-06-02 23:59:59\", \"ALANINE AMINOTRANSFERASE ABNORMAL\",   3, \"Alanine aminotransferase abnormal\", NA_character_, NA_integer_,   \"02\", \"2020-06-05 23:59:59\", \"BASEDOW'S DISEASE\",   5, \"Basedow's disease\", NA_character_, 1L,   \"03\", \"2020-06-07 23:59:59\", \"SOME TERM\",   2, \"Some query\", \"Some term\", NA_integer_,   \"05\", \"2020-06-09 23:59:59\", \"ALVEOLAR PROTEINOSIS\",   7, \"Alveolar proteinosis\", NA_character_, NA_integer_ ) get_vars_query(adae, queries) #> # A tibble: 2 × 21 #>   USUBJID ASTDTM AETERM AESEQ SMQ02NAM SMQ02CD SMQ02SC SMQ02SCN SMQ03NAM SMQ03CD #>   <chr>   <chr>  <chr>  <dbl> <chr>      <int> <chr>      <int> <chr>      <int> #> 1 02      2020-… BASED…     5 NA            NA NA            NA NA            NA #> 2 05      2020-… ALVEO…     7 NA            NA NA            NA NA            NA #> # ℹ 11 more variables: SMQ03SC <chr>, SMQ03SCN <int>, SMQ05NAM <chr>, #> #   SMQ05CD <int>, SMQ05SC <chr>, SMQ05SCN <int>, CQ01NAM <chr>, CQ04NAM <chr>, #> #   CQ04CD <int>, CQ06NAM <chr>, CQ06CD <int>"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/impute_dtc_dt.html","id":null,"dir":"Reference","previous_headings":"","what":"Impute Partial Date Portion of a '--DTC' Variable — impute_dtc_dt","title":"Impute Partial Date Portion of a '--DTC' Variable — impute_dtc_dt","text":"Imputation partial date portion '--DTC' variable based user input.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/impute_dtc_dt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Impute Partial Date Portion of a '--DTC' Variable — impute_dtc_dt","text":"","code":"impute_dtc_dt(   dtc,   highest_imputation = \"n\",   date_imputation = \"first\",   min_dates = NULL,   max_dates = NULL,   preserve = FALSE )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/impute_dtc_dt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Impute Partial Date Portion of a '--DTC' Variable — impute_dtc_dt","text":"dtc '--DTC' date impute character date expected format like yyyy-mm-dd yyyy-mm-ddThh:mm:ss. Trailing components can omitted - valid \"missing\" value component. highest_imputation Highest imputation level highest_imputation argument controls components DTC value imputed missing. components specified level imputed. component higher level highest imputation level missing, NA_character_ returned. example, highest_imputation = \"D\" \"2020\" results NA_character_ month missing. \"n\" specified imputation performed, .e., component missing, NA_character_ returned. \"Y\" specified, date_imputation \"first\" \"last\" min_dates max_dates specified respectively. Otherwise, NA_character_ returned year component missing. Permitted Values: \"Y\" (year, highest level), \"M\" (month), \"D\" (day), \"n\" (none, lowest level) date_imputation value impute day/month datepart missing. character value expected, either format month day specified \"mm-dd\": e.g. \"06-15\" 15th June (year can specified; imputing year \"first\" \"last\" together min_dates max_dates argument can used (see examples).), keyword: \"first\", \"mid\", \"last\" impute first/mid/last day/month. \"mid\" specified, missing components imputed middle possible range: month day missing, imputed \"06-30\" (middle year). day missing, imputed \"15\" (middle month). argument ignored highest_imputation less \"D\". min_dates Minimum dates list dates expected. ensured imputed date specified dates, e.g., imputed adverse event start date first treatment date. dates range possible dates dtc value considered. possible dates defined missing parts dtc date (see example ). ensures non-missing parts dtc date changed. date date-time object expected. example   returns \"2020-11-11T11:11:11\" possible dates \"2020-11\" range \"2020-11-01T00:00:00\" \"2020-11-30T23:59:59\". Therefore \"2020-12-06T12:12:12\" ignored. Returning \"2020-12-06T12:12:12\" changed month although missing (dtc date). max_dates Maximum dates list dates expected. ensured imputed date specified dates, e.g., imputed date data cut date. dates range possible dates considered. date date-time object expected. preserve Preserve day month missing day present example \"2019---07\" return \"2019-06-07 preserve = TRUE (date_imputation = \"MID\"). Permitted Values: TRUE, FALSE","code":"impute_dtc_dtm(   \"2020-11\",   min_dates = list(    ymd_hms(\"2020-12-06T12:12:12\"),    ymd_hms(\"2020-11-11T11:11:11\")   ),   highest_imputation = \"M\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/impute_dtc_dt.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Impute Partial Date Portion of a '--DTC' Variable — impute_dtc_dt","text":"character vector","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/impute_dtc_dt.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Impute Partial Date Portion of a '--DTC' Variable — impute_dtc_dt","text":"Usually computation function can used %>%.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/impute_dtc_dt.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Impute Partial Date Portion of a '--DTC' Variable — impute_dtc_dt","text":"","code":"library(lubridate)  dates <- c(   \"2019-07-18T15:25:40\",   \"2019-07-18T15:25\",   \"2019-07-18T15\",   \"2019-07-18\",   \"2019-02\",   \"2019\",   \"2019\",   \"2019---07\",   \"\" )  # No date imputation (highest_imputation defaulted to \"n\") impute_dtc_dt(dtc = dates) #> [1] \"2019-07-18\" \"2019-07-18\" \"2019-07-18\" \"2019-07-18\" NA           #> [6] NA           NA           NA           NA            # Impute to first day/month if date is partial impute_dtc_dt(   dtc = dates,   highest_imputation = \"M\" ) #> [1] \"2019-07-18\" \"2019-07-18\" \"2019-07-18\" \"2019-07-18\" \"2019-02-01\" #> [6] \"2019-01-01\" \"2019-01-01\" \"2019-01-01\" NA           # Same as above impute_dtc_dt(   dtc = dates,   highest_imputation = \"M\",   date_imputation = \"01-01\" ) #> [1] \"2019-07-18\" \"2019-07-18\" \"2019-07-18\" \"2019-07-18\" \"2019-02-01\" #> [6] \"2019-01-01\" \"2019-01-01\" \"2019-01-01\" NA            # Impute to last day/month if date is partial impute_dtc_dt(   dtc = dates,   highest_imputation = \"M\",   date_imputation = \"last\", ) #> [1] \"2019-07-18\" \"2019-07-18\" \"2019-07-18\" \"2019-07-18\" \"2019-02-28\" #> [6] \"2019-12-31\" \"2019-12-31\" \"2019-12-31\" NA            # Impute to mid day/month if date is partial impute_dtc_dt(   dtc = dates,   highest_imputation = \"M\",   date_imputation = \"mid\" ) #> [1] \"2019-07-18\" \"2019-07-18\" \"2019-07-18\" \"2019-07-18\" \"2019-02-15\" #> [6] \"2019-06-30\" \"2019-06-30\" \"2019-06-30\" NA            # Impute a date and ensure that the imputed date is not before a list of # minimum dates impute_dtc_dt(   \"2020-12\",   min_dates = list(     ymd(\"2020-12-06\"),     ymd(\"2020-11-11\")   ),   highest_imputation = \"M\" ) #> [1] \"2020-12-06\"  # Impute completely missing dates (only possible if min_dates or max_dates is specified) impute_dtc_dt(   c(\"2020-12\", NA_character_),   min_dates = list(     ymd(\"2020-12-06\", \"2020-01-01\"),     ymd(\"2020-11-11\", NA)   ),   highest_imputation = \"Y\" ) #> [1] \"2020-12-06\" \"2020-01-01\""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/impute_dtc_dtm.html","id":null,"dir":"Reference","previous_headings":"","what":"Impute Partial Date(-time) Portion of a '--DTC' Variable — impute_dtc_dtm","title":"Impute Partial Date(-time) Portion of a '--DTC' Variable — impute_dtc_dtm","text":"Imputation partial date/time portion '--DTC' variable. based user input.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/impute_dtc_dtm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Impute Partial Date(-time) Portion of a '--DTC' Variable — impute_dtc_dtm","text":"","code":"impute_dtc_dtm(   dtc,   highest_imputation = \"h\",   date_imputation = \"first\",   time_imputation = \"first\",   min_dates = NULL,   max_dates = NULL,   preserve = FALSE )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/impute_dtc_dtm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Impute Partial Date(-time) Portion of a '--DTC' Variable — impute_dtc_dtm","text":"dtc '--DTC' date impute character date expected format like yyyy-mm-dd yyyy-mm-ddThh:mm:ss. Trailing components can omitted - valid \"missing\" value component. highest_imputation Highest imputation level highest_imputation argument controls components DTC value imputed missing. components specified level imputed. component higher level highest imputation level missing, NA_character_ returned. example, highest_imputation = \"D\" \"2020\" results NA_character_ month missing. \"n\" specified, imputation performed, .e., component missing, NA_character_ returned. \"Y\" specified, date_imputation \"first\" \"last\" min_dates max_dates specified respectively. Otherwise, NA_character_ returned year component missing. Permitted Values: \"Y\" (year, highest level), \"M\" (month), \"D\" (day), \"h\" (hour), \"m\" (minute), \"s\" (second), \"n\" (none, lowest level) date_imputation value impute day/month datepart missing. character value expected, either format month day specified \"mm-dd\": e.g. \"06-15\" 15th June (year can specified; imputing year \"first\" \"last\" together min_dates max_dates argument can used (see examples).), keyword: \"first\", \"mid\", \"last\" impute first/mid/last day/month. \"mid\" specified, missing components imputed middle possible range: month day missing, imputed \"06-30\" (middle year). day missing, imputed \"15\" (middle month). argument ignored highest_imputation less \"D\". time_imputation value impute time timepart missing. character value expected, either format hour, min sec specified \"hh:mm:ss\": e.g. \"00:00:00\" start day, keyword: \"first\",\"last\" impute start/end day. argument ignored highest_imputation = \"n\". min_dates Minimum dates list dates expected. ensured imputed date specified dates, e.g., imputed adverse event start date first treatment date. dates range possible dates dtc value considered. possible dates defined missing parts dtc date (see example ). ensures non-missing parts dtc date changed. date date-time object expected. example   returns \"2020-11-11T11:11:11\" possible dates \"2020-11\" range \"2020-11-01T00:00:00\" \"2020-11-30T23:59:59\". Therefore \"2020-12-06T12:12:12\" ignored. Returning \"2020-12-06T12:12:12\" changed month although missing (dtc date). date variables (datetime) list time imputed \"00:00:00\". Specifying date variables makes sense date imputed. time imputed, date variables affect result. max_dates Maximum dates list dates expected. ensured imputed date specified dates, e.g., imputed date data cut date. dates range possible dates considered. date date-time object expected. date variables (datetime) list time imputed \"23:59:59\". Specifying date variables makes sense date imputed. time imputed, date variables affect result. preserve Preserve lower level date/time part higher order part missing, e.g. preserve day month missing preserve minute hour missing. example \"2019---07\" return \"2019-06-07 preserve = TRUE (date_imputation = \"mid\"). Permitted Values: TRUE, FALSE","code":"impute_dtc_dtm(   \"2020-11\",   min_dates = list(    ymd_hms(\"2020-12-06T12:12:12\"),    ymd_hms(\"2020-11-11T11:11:11\")   ),   highest_imputation = \"M\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/impute_dtc_dtm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Impute Partial Date(-time) Portion of a '--DTC' Variable — impute_dtc_dtm","text":"character vector","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/impute_dtc_dtm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Impute Partial Date(-time) Portion of a '--DTC' Variable — impute_dtc_dtm","text":"Usually computation function can used %>%.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/impute_dtc_dtm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Impute Partial Date(-time) Portion of a '--DTC' Variable — impute_dtc_dtm","text":"","code":"library(lubridate)  dates <- c(   \"2019-07-18T15:25:40\",   \"2019-07-18T15:25\",   \"2019-07-18T15\",   \"2019-07-18\",   \"2019-02\",   \"2019\",   \"2019\",   \"2019---07\",   \"\" )  # No date imputation (highest_imputation defaulted to \"h\") # Missing time part imputed with 00:00:00 portion by default impute_dtc_dtm(dtc = dates) #> [1] \"2019-07-18T15:25:40\" \"2019-07-18T15:25:00\" \"2019-07-18T15:00:00\" #> [4] \"2019-07-18T00:00:00\" NA                    NA                    #> [7] NA                    NA                    NA                     # No date imputation (highest_imputation defaulted to \"h\") # Missing time part imputed with 23:59:59 portion impute_dtc_dtm(   dtc = dates,   time_imputation = \"23:59:59\" ) #> [1] \"2019-07-18T15:25:40\" \"2019-07-18T15:25:59\" \"2019-07-18T15:59:59\" #> [4] \"2019-07-18T23:59:59\" NA                    NA                    #> [7] NA                    NA                    NA                     # Same as above impute_dtc_dtm(   dtc = dates,   time_imputation = \"last\" ) #> [1] \"2019-07-18T15:25:40\" \"2019-07-18T15:25:59\" \"2019-07-18T15:59:59\" #> [4] \"2019-07-18T23:59:59\" NA                    NA                    #> [7] NA                    NA                    NA                     # Impute to first day/month if date is partial # Missing time part imputed with 00:00:00 portion by default impute_dtc_dtm(   dtc = dates,   highest_imputation = \"M\" ) #> [1] \"2019-07-18T15:25:40\" \"2019-07-18T15:25:00\" \"2019-07-18T15:00:00\" #> [4] \"2019-07-18T00:00:00\" \"2019-02-01T00:00:00\" \"2019-01-01T00:00:00\" #> [7] \"2019-01-01T00:00:00\" \"2019-01-01T00:00:00\" NA                    # same as above impute_dtc_dtm(   dtc = dates,   highest_imputation = \"M\",   date_imputation = \"01-01\" ) #> [1] \"2019-07-18T15:25:40\" \"2019-07-18T15:25:00\" \"2019-07-18T15:00:00\" #> [4] \"2019-07-18T00:00:00\" \"2019-02-01T00:00:00\" \"2019-01-01T00:00:00\" #> [7] \"2019-01-01T00:00:00\" \"2019-01-01T00:00:00\" NA                     # Impute to last day/month if date is partial # Missing time part imputed with 23:59:59 portion impute_dtc_dtm(   dtc = dates,   date_imputation = \"last\",   time_imputation = \"last\" ) #> [1] \"2019-07-18T15:25:40\" \"2019-07-18T15:25:59\" \"2019-07-18T15:59:59\" #> [4] \"2019-07-18T23:59:59\" NA                    NA                    #> [7] NA                    NA                    NA                     # Impute to mid day/month if date is partial # Missing time part imputed with 00:00:00 portion by default impute_dtc_dtm(   dtc = dates,   highest_imputation = \"M\",   date_imputation = \"mid\" ) #> [1] \"2019-07-18T15:25:40\" \"2019-07-18T15:25:00\" \"2019-07-18T15:00:00\" #> [4] \"2019-07-18T00:00:00\" \"2019-02-15T00:00:00\" \"2019-06-30T00:00:00\" #> [7] \"2019-06-30T00:00:00\" \"2019-06-30T00:00:00\" NA                     # Impute a date and ensure that the imputed date is not before a list of # minimum dates impute_dtc_dtm(   \"2020-12\",   min_dates = list(     ymd_hms(\"2020-12-06T12:12:12\"),     ymd_hms(\"2020-11-11T11:11:11\")   ),   highest_imputation = \"M\" ) #> [1] \"2020-12-06T12:12:12\"  # Impute completely missing dates (only possible if min_dates or max_dates is specified) impute_dtc_dtm(   c(\"2020-12\", NA_character_),   min_dates = list(     ymd_hms(\"2020-12-06T12:12:12\", \"2020-01-01T01:01:01\"),     ymd_hms(\"2020-11-11T11:11:11\", NA)   ),   highest_imputation = \"Y\" ) #> [1] \"2020-12-06T12:12:12\" \"2020-01-01T01:01:01\""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/list_all_templates.html","id":null,"dir":"Reference","previous_headings":"","what":"List All Available ADaM Templates — list_all_templates","title":"List All Available ADaM Templates — list_all_templates","text":"List Available ADaM Templates","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/list_all_templates.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"List All Available ADaM Templates — list_all_templates","text":"","code":"list_all_templates(package = \"admiral\")"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/list_all_templates.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"List All Available ADaM Templates — list_all_templates","text":"package R package look templates. default \"admiral\".","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/list_all_templates.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"List All Available ADaM Templates — list_all_templates","text":"character vector available templates","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/list_all_templates.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"List All Available ADaM Templates — list_all_templates","text":"","code":"list_all_templates() #> Existing ADaM templates in package 'admiral': #> • ADAE #> • ADCM #> • ADEG #> • ADEX #> • ADLB #> • ADLBHY #> • ADMH #> • ADPC #> • ADPP #> • ADPPK #> • ADSL #> • ADVS"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/list_tte_source_objects.html","id":null,"dir":"Reference","previous_headings":"","what":"List all tte_source Objects Available in a Package — list_tte_source_objects","title":"List all tte_source Objects Available in a Package — list_tte_source_objects","text":"List tte_source Objects Available Package","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/list_tte_source_objects.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"List all tte_source Objects Available in a Package — list_tte_source_objects","text":"","code":"list_tte_source_objects(package = \"admiral\")"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/list_tte_source_objects.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"List all tte_source Objects Available in a Package — list_tte_source_objects","text":"package name package search tte_source objects","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/list_tte_source_objects.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"List all tte_source Objects Available in a Package — list_tte_source_objects","text":"data.frame row corresponds one tte_source object NULL package contain tte_source objects","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/list_tte_source_objects.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"List all tte_source Objects Available in a Package — list_tte_source_objects","text":"","code":"list_tte_source_objects() #>              object dataset_name                                        filter #> 1      ae_gr3_event         adae                TRTEMFL == \"Y\" & ATOXGR == \"3\" #> 2       ae_wd_event         adae    TRTEMFL == \"Y\" & AEACN == \"DRUG WITHDRAWN\" #> 3     ae_gr35_event         adae TRTEMFL == \"Y\" & ATOXGR %in% c(\"3\", \"4\", \"5\") #> 4  lastalive_censor         adsl                                          NULL #> 5      ae_gr1_event         adae                TRTEMFL == \"Y\" & ATOXGR == \"1\" #> 6      ae_ser_event         adae                 TRTEMFL == \"Y\" & AESER == \"Y\" #> 7      ae_gr2_event         adae                TRTEMFL == \"Y\" & ATOXGR == \"2\" #> 8          ae_event         adae                                TRTEMFL == \"Y\" #> 9      ae_gr4_event         adae                TRTEMFL == \"Y\" & ATOXGR == \"4\" #> 10     ae_gr5_event         adae                TRTEMFL == \"Y\" & ATOXGR == \"5\" #> 11     ae_sev_event         adae            TRTEMFL == \"Y\" & AESEV == \"SEVERE\" #> 12      death_event         adsl                                  DTHFL == \"Y\" #>        date censor #> 1     ASTDT      0 #> 2     ASTDT      0 #> 3     ASTDT      0 #> 4  LSTALVDT      1 #> 5     ASTDT      0 #> 6     ASTDT      0 #> 7     ASTDT      0 #> 8     ASTDT      0 #> 9     ASTDT      0 #> 10    ASTDT      0 #> 11    ASTDT      0 #> 12    DTHDT      0 #>                                                                                                 set_values_to #> 1                     EVNTDESC: \"GRADE 3 ADVERSE EVENT\"<br>SRCDOM: \"ADAE\"<br>SRCVAR: \"ASTDT\"<br>SRCSEQ: AESEQ #> 2  EVNTDESC: \"ADVERSE EVENT LEADING TO DRUG WITHDRAWAL\"<br>SRCDOM: \"ADAE\"<br>SRCVAR: \"ASTDT\"<br>SRCSEQ: AESEQ #> 3                   EVNTDESC: \"GRADE 3-5 ADVERSE EVENT\"<br>SRCDOM: \"ADAE\"<br>SRCVAR: \"ASTDT\"<br>SRCSEQ: AESEQ #> 4                                                   EVNTDESC: \"ALIVE\"<br>SRCDOM: \"ADSL\"<br>SRCVAR: \"LSTALVDT\" #> 5                     EVNTDESC: \"GRADE 1 ADVERSE EVENT\"<br>SRCDOM: \"ADAE\"<br>SRCVAR: \"ASTDT\"<br>SRCSEQ: AESEQ #> 6                     EVNTDESC: \"SERIOUS ADVERSE EVENT\"<br>SRCDOM: \"ADAE\"<br>SRCVAR: \"ASTDT\"<br>SRCSEQ: AESEQ #> 7                     EVNTDESC: \"GRADE 2 ADVERSE EVENT\"<br>SRCDOM: \"ADAE\"<br>SRCVAR: \"ASTDT\"<br>SRCSEQ: AESEQ #> 8                             EVNTDESC: \"ADVERSE EVENT\"<br>SRCDOM: \"ADAE\"<br>SRCVAR: \"ASTDT\"<br>SRCSEQ: AESEQ #> 9                     EVNTDESC: \"GRADE 4 ADVERSE EVENT\"<br>SRCDOM: \"ADAE\"<br>SRCVAR: \"ASTDT\"<br>SRCSEQ: AESEQ #> 10                    EVNTDESC: \"GRADE 5 ADVERSE EVENT\"<br>SRCDOM: \"ADAE\"<br>SRCVAR: \"ASTDT\"<br>SRCSEQ: AESEQ #> 11                     EVNTDESC: \"SEVERE ADVERSE EVENT\"<br>SRCDOM: \"ADAE\"<br>SRCVAR: \"ASTDT\"<br>SRCSEQ: AESEQ #> 12                                                     EVNTDESC: \"DEATH\"<br>SRCDOM: \"ADSL\"<br>SRCVAR: \"DTHDT\""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/max_cond.html","id":null,"dir":"Reference","previous_headings":"","what":"Maximum Value on a Subset — max_cond","title":"Maximum Value on a Subset — max_cond","text":"function derives maximum value vector/column subset entries/observations.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/max_cond.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Maximum Value on a Subset — max_cond","text":"","code":"max_cond(var, cond)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/max_cond.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Maximum Value on a Subset — max_cond","text":"var vector cond condition","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/max_cond.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Maximum Value on a Subset — max_cond","text":"","code":"library(tibble) library(dplyr, warn.conflicts = FALSE) library(admiral) data <- tribble(   ~USUBJID, ~AVISITN, ~AVALC,   \"1\",      1,        \"PR\",   \"1\",      2,        \"CR\",   \"1\",      3,        \"NE\",   \"1\",      4,        \"CR\",   \"1\",      5,        \"NE\",   \"2\",      1,        \"CR\",   \"2\",      2,        \"PR\",   \"2\",      3,        \"CR\", )  # In oncology setting, when needing to check the first time a patient had # a Complete Response (CR) to compare to see if any Partial Response (PR) # occurred after this add variable indicating if PR occurred after CR group_by(data, USUBJID) %>% mutate(   first_cr_vis = min_cond(var = AVISITN, cond = AVALC == \"CR\"),   last_pr_vis = max_cond(var = AVISITN, cond = AVALC == \"PR\"),   pr_after_cr = last_pr_vis > first_cr_vis ) #> # A tibble: 8 × 6 #> # Groups:   USUBJID [2] #>   USUBJID AVISITN AVALC first_cr_vis last_pr_vis pr_after_cr #>   <chr>     <dbl> <chr>        <dbl>       <dbl> <lgl>       #> 1 1             1 PR               2           1 FALSE       #> 2 1             2 CR               2           1 FALSE       #> 3 1             3 NE               2           1 FALSE       #> 4 1             4 CR               2           1 FALSE       #> 5 1             5 NE               2           1 FALSE       #> 6 2             1 CR               1           2 TRUE        #> 7 2             2 PR               1           2 TRUE        #> 8 2             3 CR               1           2 TRUE"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/min_cond.html","id":null,"dir":"Reference","previous_headings":"","what":"Minimum Value on a Subset — min_cond","title":"Minimum Value on a Subset — min_cond","text":"function derives minimum value vector/column subset entries/observations.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/min_cond.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Minimum Value on a Subset — min_cond","text":"","code":"min_cond(var, cond)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/min_cond.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Minimum Value on a Subset — min_cond","text":"var vector cond condition","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/min_cond.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Minimum Value on a Subset — min_cond","text":"","code":"library(tibble) library(dplyr, warn.conflicts = FALSE) library(admiral) data <- tribble(   ~USUBJID, ~AVISITN, ~AVALC,   \"1\",      1,        \"PR\",   \"1\",      2,        \"CR\",   \"1\",      3,        \"NE\",   \"1\",      4,        \"CR\",   \"1\",      5,        \"NE\",   \"2\",      1,        \"CR\",   \"2\",      2,        \"PR\",   \"2\",      3,        \"CR\", )  # In oncology setting, when needing to check the first time a patient had # a Complete Response (CR) to compare to see if any Partial Response (PR) # occurred after this add variable indicating if PR occurred after CR group_by(data, USUBJID) %>% mutate(   first_cr_vis = min_cond(var = AVISITN, cond = AVALC == \"CR\"),   last_pr_vis = max_cond(var = AVISITN, cond = AVALC == \"PR\"),   pr_after_cr = last_pr_vis > first_cr_vis ) #> # A tibble: 8 × 6 #> # Groups:   USUBJID [2] #>   USUBJID AVISITN AVALC first_cr_vis last_pr_vis pr_after_cr #>   <chr>     <dbl> <chr>        <dbl>       <dbl> <lgl>       #> 1 1             1 PR               2           1 FALSE       #> 2 1             2 CR               2           1 FALSE       #> 3 1             3 NE               2           1 FALSE       #> 4 1             4 CR               2           1 FALSE       #> 5 1             5 NE               2           1 FALSE       #> 6 2             1 CR               1           2 TRUE        #> 7 2             2 PR               1           2 TRUE        #> 8 2             3 CR               1           2 TRUE"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/negate_vars.html","id":null,"dir":"Reference","previous_headings":"","what":"Negate List of Variables — negate_vars","title":"Negate List of Variables — negate_vars","text":"function adds minus sign prefix variable.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/negate_vars.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Negate List of Variables — negate_vars","text":"","code":"negate_vars(vars = NULL)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/negate_vars.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Negate List of Variables — negate_vars","text":"vars List variables created exprs()","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/negate_vars.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Negate List of Variables — negate_vars","text":"list expressions","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/negate_vars.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Negate List of Variables — negate_vars","text":"useful list variables removed dataset, e.g., select(!!!negate_vars(by_vars)) removes variables.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/negate_vars.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Negate List of Variables — negate_vars","text":"","code":"negate_vars(exprs(USUBJID, STUDYID)) #> [[1]] #> -USUBJID #>  #> [[2]] #> -STUDYID #>"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/params.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a Set of Parameters — params","title":"Create a Set of Parameters — params","text":"Create set variable parameters/function arguments used call_derivation().","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a Set of Parameters — params","text":"","code":"params(...)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a Set of Parameters — params","text":"... One named arguments","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/params.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a Set of Parameters — params","text":"object class params","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/params.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a Set of Parameters — params","text":"","code":"library(dplyr, warn.conflicts = FALSE)  adsl <- tribble(   ~STUDYID,   ~USUBJID,      ~TRTSDT,      ~TRTEDT,   \"PILOT01\", \"01-1307\",           NA,           NA,   \"PILOT01\", \"05-1377\", \"2014-01-04\", \"2014-01-25\",   \"PILOT01\", \"06-1384\", \"2012-09-15\", \"2012-09-24\",   \"PILOT01\", \"15-1085\", \"2013-02-16\", \"2013-08-18\",   \"PILOT01\", \"16-1298\", \"2013-04-08\", \"2013-06-28\" ) %>%   mutate(     across(TRTSDT:TRTEDT, as.Date)   )  ae <- tribble(   ~STUDYID,  ~DOMAIN,  ~USUBJID,     ~AESTDTC,     ~AEENDTC,   \"PILOT01\",    \"AE\", \"06-1384\", \"2012-09-15\", \"2012-09-29\",   \"PILOT01\",    \"AE\", \"06-1384\", \"2012-09-15\", \"2012-09-29\",   \"PILOT01\",    \"AE\", \"06-1384\", \"2012-09-23\", \"2012-09-29\",   \"PILOT01\",    \"AE\", \"06-1384\", \"2012-09-23\", \"2012-09-29\",   \"PILOT01\",    \"AE\", \"06-1384\", \"2012-09-15\", \"2012-09-29\",   \"PILOT01\",    \"AE\", \"06-1384\", \"2012-09-15\", \"2012-09-29\",   \"PILOT01\",    \"AE\", \"06-1384\", \"2012-09-15\", \"2012-09-29\",   \"PILOT01\",    \"AE\", \"06-1384\", \"2012-09-15\", \"2012-09-29\",   \"PILOT01\",    \"AE\", \"06-1384\", \"2012-09-23\", \"2012-09-29\",   \"PILOT01\",    \"AE\", \"06-1384\", \"2012-09-23\", \"2012-09-29\",   \"PILOT01\",    \"AE\", \"16-1298\", \"2013-06-08\", \"2013-07-06\",   \"PILOT01\",    \"AE\", \"16-1298\", \"2013-06-08\", \"2013-07-06\",   \"PILOT01\",    \"AE\", \"16-1298\", \"2013-04-22\", \"2013-07-06\",   \"PILOT01\",    \"AE\", \"16-1298\", \"2013-04-22\", \"2013-07-06\",   \"PILOT01\",    \"AE\", \"16-1298\", \"2013-04-22\", \"2013-07-06\",   \"PILOT01\",    \"AE\", \"16-1298\", \"2013-04-22\", \"2013-07-06\" )  adae <- ae %>%   select(USUBJID, AESTDTC, AEENDTC) %>%   derive_vars_merged(     dataset_add = adsl,     new_vars = exprs(TRTSDT, TRTEDT),     by_vars = exprs(USUBJID)   )  ## In order to derive both `ASTDT` and `AENDT` in `ADAE`, one can use `derive_vars_dt()` adae %>%   derive_vars_dt(     new_vars_prefix = \"AST\",     dtc = AESTDTC,     date_imputation = \"first\",     min_dates = exprs(TRTSDT),     max_dates = exprs(TRTEDT)   ) %>%   derive_vars_dt(     new_vars_prefix = \"AEN\",     dtc = AEENDTC,     date_imputation = \"last\",     min_dates = exprs(TRTSDT),     max_dates = exprs(TRTEDT)   ) #> # A tibble: 16 × 7 #>    USUBJID AESTDTC    AEENDTC    TRTSDT     TRTEDT     ASTDT      AENDT      #>    <chr>   <chr>      <chr>      <date>     <date>     <date>     <date>     #>  1 06-1384 2012-09-15 2012-09-29 2012-09-15 2012-09-24 2012-09-15 2012-09-29 #>  2 06-1384 2012-09-15 2012-09-29 2012-09-15 2012-09-24 2012-09-15 2012-09-29 #>  3 06-1384 2012-09-23 2012-09-29 2012-09-15 2012-09-24 2012-09-23 2012-09-29 #>  4 06-1384 2012-09-23 2012-09-29 2012-09-15 2012-09-24 2012-09-23 2012-09-29 #>  5 06-1384 2012-09-15 2012-09-29 2012-09-15 2012-09-24 2012-09-15 2012-09-29 #>  6 06-1384 2012-09-15 2012-09-29 2012-09-15 2012-09-24 2012-09-15 2012-09-29 #>  7 06-1384 2012-09-15 2012-09-29 2012-09-15 2012-09-24 2012-09-15 2012-09-29 #>  8 06-1384 2012-09-15 2012-09-29 2012-09-15 2012-09-24 2012-09-15 2012-09-29 #>  9 06-1384 2012-09-23 2012-09-29 2012-09-15 2012-09-24 2012-09-23 2012-09-29 #> 10 06-1384 2012-09-23 2012-09-29 2012-09-15 2012-09-24 2012-09-23 2012-09-29 #> 11 16-1298 2013-06-08 2013-07-06 2013-04-08 2013-06-28 2013-06-08 2013-07-06 #> 12 16-1298 2013-06-08 2013-07-06 2013-04-08 2013-06-28 2013-06-08 2013-07-06 #> 13 16-1298 2013-04-22 2013-07-06 2013-04-08 2013-06-28 2013-04-22 2013-07-06 #> 14 16-1298 2013-04-22 2013-07-06 2013-04-08 2013-06-28 2013-04-22 2013-07-06 #> 15 16-1298 2013-04-22 2013-07-06 2013-04-08 2013-06-28 2013-04-22 2013-07-06 #> 16 16-1298 2013-04-22 2013-07-06 2013-04-08 2013-06-28 2013-04-22 2013-07-06   ## While `derive_vars_dt()` can only add one variable at a time, using `call_derivation()` ## one can add multiple variables in one go. ## The function arguments which are different from a variable to another (e.g. `new_vars_prefix`, ## `dtc`, and `date_imputation`) are specified as a list of `params()` in the `variable_params` ## argument of `call_derivation()`. All other arguments which are common to all variables ## (e.g. `min_dates` and `max_dates`) are specified outside of `variable_params` (i.e. in `...`). call_derivation(   dataset = adae,   derivation = derive_vars_dt,   variable_params = list(     params(dtc = AESTDTC, date_imputation = \"first\", new_vars_prefix = \"AST\"),     params(dtc = AEENDTC, date_imputation = \"last\", new_vars_prefix = \"AEN\")   ),   min_dates = exprs(TRTSDT),   max_dates = exprs(TRTEDT) ) #> # A tibble: 16 × 7 #>    USUBJID AESTDTC    AEENDTC    TRTSDT     TRTEDT     ASTDT      AENDT      #>    <chr>   <chr>      <chr>      <date>     <date>     <date>     <date>     #>  1 06-1384 2012-09-15 2012-09-29 2012-09-15 2012-09-24 2012-09-15 2012-09-29 #>  2 06-1384 2012-09-15 2012-09-29 2012-09-15 2012-09-24 2012-09-15 2012-09-29 #>  3 06-1384 2012-09-23 2012-09-29 2012-09-15 2012-09-24 2012-09-23 2012-09-29 #>  4 06-1384 2012-09-23 2012-09-29 2012-09-15 2012-09-24 2012-09-23 2012-09-29 #>  5 06-1384 2012-09-15 2012-09-29 2012-09-15 2012-09-24 2012-09-15 2012-09-29 #>  6 06-1384 2012-09-15 2012-09-29 2012-09-15 2012-09-24 2012-09-15 2012-09-29 #>  7 06-1384 2012-09-15 2012-09-29 2012-09-15 2012-09-24 2012-09-15 2012-09-29 #>  8 06-1384 2012-09-15 2012-09-29 2012-09-15 2012-09-24 2012-09-15 2012-09-29 #>  9 06-1384 2012-09-23 2012-09-29 2012-09-15 2012-09-24 2012-09-23 2012-09-29 #> 10 06-1384 2012-09-23 2012-09-29 2012-09-15 2012-09-24 2012-09-23 2012-09-29 #> 11 16-1298 2013-06-08 2013-07-06 2013-04-08 2013-06-28 2013-06-08 2013-07-06 #> 12 16-1298 2013-06-08 2013-07-06 2013-04-08 2013-06-28 2013-06-08 2013-07-06 #> 13 16-1298 2013-04-22 2013-07-06 2013-04-08 2013-06-28 2013-04-22 2013-07-06 #> 14 16-1298 2013-04-22 2013-07-06 2013-04-08 2013-06-28 2013-04-22 2013-07-06 #> 15 16-1298 2013-04-22 2013-07-06 2013-04-08 2013-06-28 2013-04-22 2013-07-06 #> 16 16-1298 2013-04-22 2013-07-06 2013-04-08 2013-06-28 2013-04-22 2013-07-06  ## The above call using `call_derivation()` is equivalent to the call using `derive_vars_dt()` ## to derive variables `ASTDT` and `AENDT` separately at the beginning."},{"path":"https://pharmaverse.github.io/admiral/dev/reference/pipe.html","id":null,"dir":"Reference","previous_headings":"","what":"Pipe operator — %>%","title":"Pipe operator — %>%","text":"See magrittr::%>% details.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/pipe.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pipe operator — %>%","text":"","code":"lhs %>% rhs"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/pipe.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pipe operator — %>%","text":"lhs value magrittr placeholder. rhs function call using magrittr semantics.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/print.adam_templates.html","id":null,"dir":"Reference","previous_headings":"","what":"Print adam_templates Objects — print.adam_templates","title":"Print adam_templates Objects — print.adam_templates","text":"Print adam_templates Objects","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/print.adam_templates.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print adam_templates Objects — print.adam_templates","text":"","code":"# S3 method for class 'adam_templates' print(x, ...)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/print.adam_templates.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print adam_templates Objects — print.adam_templates","text":"x adam_templates object ... used","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/print.adam_templates.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print adam_templates Objects — print.adam_templates","text":"return value, called side effects","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/print.adam_templates.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Print adam_templates Objects — print.adam_templates","text":"","code":"templates <- list_all_templates() print(templates) #> Existing ADaM templates in package 'admiral': #> • ADAE #> • ADCM #> • ADEG #> • ADEX #> • ADLB #> • ADLBHY #> • ADMH #> • ADPC #> • ADPP #> • ADPPK #> • ADSL #> • ADVS"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/print.duplicates.html","id":null,"dir":"Reference","previous_headings":"","what":"Print duplicates Objects — print.duplicates","title":"Print duplicates Objects — print.duplicates","text":"Print duplicates Objects","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/print.duplicates.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print duplicates Objects — print.duplicates","text":"","code":"# S3 method for class 'duplicates' print(x, ...)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/print.duplicates.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print duplicates Objects — print.duplicates","text":"x duplicates object ... used","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/print.duplicates.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print duplicates Objects — print.duplicates","text":"return value, called side effects","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/print.source.html","id":null,"dir":"Reference","previous_headings":"","what":"Print source Objects — print.source","title":"Print source Objects — print.source","text":"Print source Objects","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/print.source.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print source Objects — print.source","text":"","code":"# S3 method for class 'source' print(x, ...)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/print.source.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print source Objects — print.source","text":"x source object ... indent = <numeric value> specified output indented specified number characters.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/print.source.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print source Objects — print.source","text":"return value, called side effects","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/print.source.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Print source Objects — print.source","text":"","code":"print(death_event) #> <event_source> object #> dataset_name: \"adsl\" #> filter: DTHFL == \"Y\" #> date: DTHDT #> censor: 0 #> set_values_to: #>   EVNTDESC: \"DEATH\" #>   SRCDOM: \"ADSL\" #>   SRCVAR: \"DTHDT\""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/print_named_list.html","id":null,"dir":"Reference","previous_headings":"","what":"Print Named List — print_named_list","title":"Print Named List — print_named_list","text":"Print Named List","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/print_named_list.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print Named List — print_named_list","text":"","code":"print_named_list(list, indent = 0)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/print_named_list.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print Named List — print_named_list","text":"list named list indent Indent output indented specified number characters.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/print_named_list.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print Named List — print_named_list","text":"return value, called side effects","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/print_named_list.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Print Named List — print_named_list","text":"","code":"print_named_list(death_event) #> dataset_name: \"adsl\" #> filter: DTHFL == \"Y\" #> date: DTHDT #> censor: 0 #> set_values_to: #>   EVNTDESC: \"DEATH\" #>   SRCDOM: \"ADSL\" #>   SRCVAR: \"DTHDT\""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/queries.html","id":null,"dir":"Reference","previous_headings":"","what":"Queries Dataset — queries","title":"Queries Dataset — queries","text":"Queries Dataset","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/queries.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Queries Dataset — queries","text":"","code":"queries"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/queries.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Queries Dataset — queries","text":"object class tbl_df (inherits tbl, data.frame) 15 rows 8 columns.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/queries.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Queries Dataset — queries","text":"example standard query dataset used deriving Standardized MedDRA Query variables ADAE","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/queries_mh.html","id":null,"dir":"Reference","previous_headings":"","what":"Queries MH Dataset — queries_mh","title":"Queries MH Dataset — queries_mh","text":"Queries MH Dataset","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/queries_mh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Queries MH Dataset — queries_mh","text":"","code":"queries_mh"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/queries_mh.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Queries MH Dataset — queries_mh","text":"object class tbl_df (inherits tbl, data.frame) 14 rows 8 columns.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/queries_mh.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Queries MH Dataset — queries_mh","text":"example standard query MH dataset used deriving Standardized MedDRA Query variables ADMH","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/query.html","id":null,"dir":"Reference","previous_headings":"","what":"Create an query object — query","title":"Create an query object — query","text":"query object defines query, e.g., Standard MedDRA Query (SMQ), Standardized Drug Grouping (SDG), customized query (CQ). used input create_query_data().","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/query.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create an query object — query","text":"","code":"query(prefix, name = auto, id = NULL, add_scope_num = FALSE, definition = NULL)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/query.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create an query object — query","text":"prefix value used populate PREFIX output dataset create_query_data(), e.g., \"SMQ03\" name value used populate GRPNAME output dataset create_query_data(). auto keyword specified, variable set name query SMQ/SDG database. Permitted Values: character scalar auto keyword. auto keyword permitted queries defined basket_select() object. id value used populate GRPID output dataset create_query_data(). auto keyword specified, variable set id query SMQ/SDG database. Permitted Values: integer scalar auto keyword. auto keyword permitted queries defined basket_select() object. add_scope_num Determines  SCOPEN output dataset create_query_data() populated parameter set TRUE, definition must basket_select() object. Default: FALSE Permitted Values: TRUE, FALSE definition Definition terms belonging query three different ways define terms: basket_select() object specified select query SMQ database. data frame columns SRCVAR TERMCHAR TERMNUM can specified define terms customized query. SRCVAR set name variable used select terms, e.g., \"AEDECOD\" \"AELLTCD\". SRCVAR need constant within query. example query can based AEDECOD AELLT. SRCVAR refers character variable, TERMCHAR set value variable. refers numeric variable, TERMNUM set value variable. character variables numeric variables used, TERMNUM TERMCHAR respectively can omitted. list data frames basket_select() objects can specified define customized query based custom terms SMQs. data frames must structure described previous item. Permitted Values: basket_select() object, data frame, list data frames basket_select() objects.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/query.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create an query object — query","text":"object class query.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/query.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create an query object — query","text":"","code":"# create a query for an SMQ library(tibble) library(dplyr, warn.conflicts = FALSE)  # create a query for a SMQ query(   prefix = \"SMQ02\",   id = auto,   definition = basket_select(     name = \"Pregnancy and neonatal topics (SMQ)\",     scope = \"NARROW\",     type = \"smq\"   ) ) #> <query> object #> prefix: \"SMQ02\" #> name: auto #> id: auto #> add_scope_num: FALSE #> definition: #>   <basket_select> object #>   name: \"Pregnancy and neonatal topics (SMQ)\" #>   id: NULL #>   scope: \"NARROW\" #>   type: \"smq\"  # create a query for an SDG query(   prefix = \"SDG01\",   id = auto,   definition = basket_select(     name = \"5-aminosalicylates for ulcerative colitis\",     scope = NA_character_,     type = \"sdg\"   ) ) #> <query> object #> prefix: \"SDG01\" #> name: auto #> id: auto #> add_scope_num: FALSE #> definition: #>   <basket_select> object #>   name: \"5-aminosalicylates for ulcerative colitis\" #>   id: NULL #>   scope: \"NA\" #>   type: \"sdg\"  # creating a query for a customized query cqterms <- tribble(   ~TERMCHAR, ~TERMNUM,   \"APPLICATION SITE ERYTHEMA\", 10003041L,   \"APPLICATION SITE PRURITUS\", 10003053L ) %>%   mutate(SRCVAR = \"AEDECOD\")  query(   prefix = \"CQ01\",   name = \"Application Site Issues\",   definition = cqterms ) #> <query> object #> prefix: \"CQ01\" #> name: \"Application Site Issues\" #> add_scope_num: FALSE #> definition: #> # A tibble: 2 × 3 #>   TERMCHAR                   TERMNUM SRCVAR  #>   <chr>                        <int> <chr>   #> 1 APPLICATION SITE ERYTHEMA 10003041 AEDECOD #> 2 APPLICATION SITE PRURITUS 10003053 AEDECOD  # creating a customized query based on SMQs and additional terms query(   prefix = \"CQ03\",   name = \"Special issues of interest\",   definition = list(     cqterms,     basket_select(       name = \"Pregnancy and neonatal topics (SMQ)\",       scope = \"NARROW\",       type = \"smq\"     ),     basket_select(       id = 8050L,       scope = \"BROAD\",       type = \"smq\"     )   ) ) #> <query> object #> prefix: \"CQ03\" #> name: \"Special issues of interest\" #> add_scope_num: FALSE #> definition: #>   c(\"APPLICATION SITE ERYTHEMA\", \"APPLICATION SITE PRURITUS\") #> c(10003041, 10003053) #> c(\"AEDECOD\", \"AEDECOD\") #>   Pregnancy and neonatal topics (SMQ) #> NULL #> NARROW #> smq #>   NULL #> 8050 #> BROAD #> smq"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/records_source.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a records_source Object — records_source","title":"Create a records_source Object — records_source","text":"records_source object used find extreme records interest.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/records_source.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a records_source Object — records_source","text":"","code":"records_source(dataset_name, filter = NULL, new_vars)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/records_source.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a records_source Object — records_source","text":"dataset_name name source dataset name refers dataset provided source_datasets argument derive_param_extreme_record(). filter unquoted condition selecting observations dataset. new_vars Variables add specified variables source datasets added output dataset. Variables can renamed naming element, .e., new_vars = exprs(<new name> = <old name>). example new_vars = exprs(var1, var2) adds variables var1 var2 input dataset. new_vars = exprs(var1, new_var2 = old_var2) takes var1 old_var2 source dataset adds input dataset renaming old_var2 new_var2. Expressions can used create new variables (see example new_vars argument derive_vars_merged()). Permitted Values: list expressions created exprs(), e.g., exprs(ADT, desc(AVAL))","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/records_source.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a records_source Object — records_source","text":"object class records_source","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/reexport-desc.html","id":null,"dir":"Reference","previous_headings":"","what":"dplyr desc — desc","title":"dplyr desc — desc","text":"See dplyr::desc details.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/reexport-exprs.html","id":null,"dir":"Reference","previous_headings":"","what":"rlang exprs — exprs","title":"rlang exprs — exprs","text":"See rlang::exprs details.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/restrict_derivation.html","id":null,"dir":"Reference","previous_headings":"","what":"Execute a Derivation on a Subset of the Input Dataset — restrict_derivation","title":"Execute a Derivation on a Subset of the Input Dataset — restrict_derivation","text":"Execute derivation subset input dataset.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/restrict_derivation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Execute a Derivation on a Subset of the Input Dataset — restrict_derivation","text":"","code":"restrict_derivation(dataset, derivation, args = NULL, filter)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/restrict_derivation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Execute a Derivation on a Subset of the Input Dataset — restrict_derivation","text":"dataset Input dataset derivation Derivation function performs specific derivation expected. derivation adds variables observations dataset. first argument derivation must expect dataset derivation must return dataset. function must provide dataset argument arguments specified params() objects passed arg argument. args Arguments derivation params() object expected. filter Filter condition","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/restrict_derivation.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Execute a Derivation on a Subset of the Input Dataset — restrict_derivation","text":"","code":"library(tibble)  adlb <- tribble(   ~USUBJID, ~AVISITN, ~AVAL, ~ABLFL,   \"1\",            -1,   113, NA_character_,   \"1\",             0,   113, \"Y\",   \"1\",             3,   117, NA_character_,   \"2\",             0,    95, \"Y\",   \"3\",             0,   111, \"Y\",   \"3\",             1,   101, NA_character_,   \"3\",             2,   123, NA_character_ )  # Derive BASE for post-baseline records only (derive_var_base() can not be used in this case # as it requires the baseline observation to be in the input dataset) restrict_derivation(   adlb,   derivation = derive_vars_merged,   args = params(     by_vars = exprs(USUBJID),     dataset_add = adlb,     filter_add = ABLFL == \"Y\",     new_vars = exprs(BASE = AVAL)   ),   filter = AVISITN > 0 ) #> # A tibble: 7 × 5 #>   USUBJID AVISITN  AVAL ABLFL  BASE #>   <chr>     <dbl> <dbl> <chr> <dbl> #> 1 1             3   117 NA      113 #> 2 3             1   101 NA      111 #> 3 3             2   123 NA      111 #> 4 1            -1   113 NA       NA #> 5 1             0   113 Y        NA #> 6 2             0    95 Y        NA #> 7 3             0   111 Y        NA  # Derive BASE for baseline and post-baseline records only restrict_derivation(   adlb,   derivation = derive_var_base,   args = params(     by_vars = exprs(USUBJID)   ),   filter = AVISITN >= 0 ) %>%   # Derive CHG for post-baseline records only   restrict_derivation(     derivation = derive_var_chg,     filter = AVISITN > 0   ) #> # A tibble: 7 × 6 #>   USUBJID AVISITN  AVAL ABLFL  BASE   CHG #>   <chr>     <dbl> <dbl> <chr> <dbl> <dbl> #> 1 1             3   117 NA      113     4 #> 2 3             1   101 NA      111   -10 #> 3 3             2   123 NA      111    12 #> 4 1             0   113 Y       113    NA #> 5 2             0    95 Y        95    NA #> 6 3             0   111 Y       111    NA #> 7 1            -1   113 NA       NA    NA"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/restrict_imputed_dtc_dt.html","id":null,"dir":"Reference","previous_headings":"","what":"Restrict Imputed DTC date to Minimum/Maximum Dates — restrict_imputed_dtc_dt","title":"Restrict Imputed DTC date to Minimum/Maximum Dates — restrict_imputed_dtc_dt","text":"Restrict Imputed DTC date Minimum/Maximum Dates","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/restrict_imputed_dtc_dt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Restrict Imputed DTC date to Minimum/Maximum Dates — restrict_imputed_dtc_dt","text":"","code":"restrict_imputed_dtc_dt(dtc, imputed_dtc, min_dates, max_dates)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/restrict_imputed_dtc_dt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Restrict Imputed DTC date to Minimum/Maximum Dates — restrict_imputed_dtc_dt","text":"dtc '--DTC' date impute character date expected format like yyyy-mm-dd yyyy-mm-ddThh:mm:ss. Trailing components can omitted - valid \"missing\" value component. imputed_dtc imputed DTC date min_dates Minimum dates list dates expected. ensured imputed date specified dates, e.g., imputed adverse event start date first treatment date. dates range possible dates dtc value considered. possible dates defined missing parts dtc date (see example ). ensures non-missing parts dtc date changed. date date-time object expected. example   returns \"2020-11-11T11:11:11\" possible dates \"2020-11\" range \"2020-11-01T00:00:00\" \"2020-11-30T23:59:59\". Therefore \"2020-12-06T12:12:12\" ignored. Returning \"2020-12-06T12:12:12\" changed month although missing (dtc date). max_dates Maximum dates list dates expected. ensured imputed date specified dates, e.g., imputed date data cut date. dates range possible dates considered. date date-time object expected.","code":"impute_dtc_dtm(   \"2020-11\",   min_dates = list(    ymd_hms(\"2020-12-06T12:12:12\"),    ymd_hms(\"2020-11-11T11:11:11\")   ),   highest_imputation = \"M\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/restrict_imputed_dtc_dt.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Restrict Imputed DTC date to Minimum/Maximum Dates — restrict_imputed_dtc_dt","text":"last minimum dates (min_dates) range partial DTC date (dtc) first maximum dates (max_dates) range partial DTC date (dtc) imputed_dtc partial DTC date (dtc) range minimum maximum dates.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/restrict_imputed_dtc_dtm.html","id":null,"dir":"Reference","previous_headings":"","what":"Restrict Imputed DTC date to Minimum/Maximum Dates — restrict_imputed_dtc_dtm","title":"Restrict Imputed DTC date to Minimum/Maximum Dates — restrict_imputed_dtc_dtm","text":"Restrict Imputed DTC date Minimum/Maximum Dates","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/restrict_imputed_dtc_dtm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Restrict Imputed DTC date to Minimum/Maximum Dates — restrict_imputed_dtc_dtm","text":"","code":"restrict_imputed_dtc_dtm(dtc, imputed_dtc, min_dates, max_dates)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/restrict_imputed_dtc_dtm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Restrict Imputed DTC date to Minimum/Maximum Dates — restrict_imputed_dtc_dtm","text":"dtc '--DTC' date impute character date expected format like yyyy-mm-dd yyyy-mm-ddThh:mm:ss. Trailing components can omitted - valid \"missing\" value component. imputed_dtc imputed DTC date min_dates Minimum dates list dates expected. ensured imputed date specified dates, e.g., imputed adverse event start date first treatment date. dates range possible dates dtc value considered. possible dates defined missing parts dtc date (see example ). ensures non-missing parts dtc date changed. date date-time object expected. example   returns \"2020-11-11T11:11:11\" possible dates \"2020-11\" range \"2020-11-01T00:00:00\" \"2020-11-30T23:59:59\". Therefore \"2020-12-06T12:12:12\" ignored. Returning \"2020-12-06T12:12:12\" changed month although missing (dtc date). date variables (datetime) list time imputed \"00:00:00\". Specifying date variables makes sense date imputed. time imputed, date variables affect result. max_dates Maximum dates list dates expected. ensured imputed date specified dates, e.g., imputed date data cut date. dates range possible dates considered. date date-time object expected. date variables (datetime) list time imputed \"23:59:59\". Specifying date variables makes sense date imputed. time imputed, date variables affect result.","code":"impute_dtc_dtm(   \"2020-11\",   min_dates = list(    ymd_hms(\"2020-12-06T12:12:12\"),    ymd_hms(\"2020-11-11T11:11:11\")   ),   highest_imputation = \"M\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/restrict_imputed_dtc_dtm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Restrict Imputed DTC date to Minimum/Maximum Dates — restrict_imputed_dtc_dtm","text":"last minimum dates (min_dates) range partial DTC date (dtc) first maximum dates (max_dates) range partial DTC date (dtc) imputed_dtc partial DTC date (dtc) range minimum maximum dates.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/set_admiral_options.html","id":null,"dir":"Reference","previous_headings":"","what":"Set the Value of Admiral Options — set_admiral_options","title":"Set the Value of Admiral Options — set_admiral_options","text":"Set Values Admiral Options Can Modified Advanced Users.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/set_admiral_options.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set the Value of Admiral Options — set_admiral_options","text":"","code":"set_admiral_options(subject_keys, signif_digits)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/set_admiral_options.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set the Value of Admiral Options — set_admiral_options","text":"subject_keys Variables uniquely identify subject, defaults exprs(STUDYID, USUBJID). option used default value subject_keys argument admiral functions. signif_digits Holds number significant digits comparing numeric variables, defaults 15. option used default value  signif_dig argument admiral functions derive_var_atoxgr_dir() derive_var_anrind().","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/set_admiral_options.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set the Value of Admiral Options — set_admiral_options","text":"return value, called side effects.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/set_admiral_options.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Set the Value of Admiral Options — set_admiral_options","text":"Modify admiral option, e.g subject_keys, automatically affects downstream function inputs get_admiral_option() called derive_param_exist_flag().","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/set_admiral_options.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Set the Value of Admiral Options — set_admiral_options","text":"","code":"library(lubridate) library(dplyr, warn.conflicts = FALSE) library(tibble) set_admiral_options(subject_keys = exprs(STUDYID, USUBJID2))  # Derive a new parameter for measurable disease at baseline adsl <- tribble(   ~USUBJID2,   \"1\",   \"2\",   \"3\" ) %>%   mutate(STUDYID = \"XX1234\")  tu <- tribble(   ~USUBJID2,      ~VISIT,    ~TUSTRESC,   \"1\",       \"SCREENING\",     \"TARGET\",   \"1\",          \"WEEK 1\",     \"TARGET\",   \"1\",          \"WEEK 5\",     \"TARGET\",   \"1\",          \"WEEK 9\", \"NON-TARGET\",   \"2\",       \"SCREENING\", \"NON-TARGET\",   \"2\",       \"SCREENING\", \"NON-TARGET\" ) %>%   mutate(     STUDYID = \"XX1234\",     TUTESTCD = \"TUMIDENT\"   )  derive_param_exist_flag(   dataset_ref = adsl,   dataset_add = tu,   filter_add = TUTESTCD == \"TUMIDENT\" & VISIT == \"SCREENING\",   condition = TUSTRESC == \"TARGET\",   false_value = \"N\",   missing_value = \"N\",   set_values_to = exprs(     PARAMCD = \"MDIS\",     PARAM = \"Measurable Disease at Baseline\"   ) ) #> # A tibble: 3 × 5 #>   USUBJID2 STUDYID AVALC PARAMCD PARAM                          #>   <chr>    <chr>   <chr> <chr>   <chr>                          #> 1 1        XX1234  Y     MDIS    Measurable Disease at Baseline #> 2 2        XX1234  N     MDIS    Measurable Disease at Baseline #> 3 3        XX1234  N     MDIS    Measurable Disease at Baseline  set_admiral_options(signif_digits = 14)  # Derive ANRIND for ADVS advs <- tribble(   ~PARAMCD, ~AVAL, ~ANRLO, ~ANRHI,   \"DIABP\",     59,     60,     80,   \"SYSBP\",    120,     90,    130,   \"RESP\",      21,      8,     20, )  derive_var_anrind(advs) #> # A tibble: 3 × 5 #>   PARAMCD  AVAL ANRLO ANRHI ANRIND #>   <chr>   <dbl> <dbl> <dbl> <chr>  #> 1 DIABP      59    60    80 LOW    #> 2 SYSBP     120    90   130 NORMAL #> 3 RESP       21     8    20 HIGH"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/signal_duplicate_records.html","id":null,"dir":"Reference","previous_headings":"","what":"Signal Duplicate Records — signal_duplicate_records","title":"Signal Duplicate Records — signal_duplicate_records","text":"Signal Duplicate Records","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/signal_duplicate_records.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Signal Duplicate Records — signal_duplicate_records","text":"","code":"signal_duplicate_records(   dataset,   by_vars,   msg = paste(\"Dataset contains duplicate records\", \"with respect to\",     \"{.var {replace_values_by_names(by_vars)}}\"),   cnd_type = \"error\",   class = NULL )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/signal_duplicate_records.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Signal Duplicate Records — signal_duplicate_records","text":"dataset Input dataset variables specified by_vars argument expected dataset. by_vars Grouping variables Defines groups records look duplicates. Permitted Values: list variables created exprs() e.g. exprs(USUBJID, VISIT) msg condition message cnd_type Type condition signal detecting duplicate records. One \"message\", \"warning\" \"error\". Default \"error\". class Class condition specified classes added classes condition. c(\"duplicate_records\", \"assert-admiral\") always added.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/signal_duplicate_records.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Signal Duplicate Records — signal_duplicate_records","text":"return value, called side effects","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/signal_duplicate_records.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Signal Duplicate Records — signal_duplicate_records","text":"","code":"data(admiral_adsl)  # Duplicate the first record adsl <- rbind(admiral_adsl[1L, ], admiral_adsl)  signal_duplicate_records(adsl, exprs(USUBJID), cnd_type = \"message\") #> Dataset contains duplicate records with respect to `USUBJID` #> ℹ Run `admiral::get_duplicates_dataset()` to access the duplicate records"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/slice_derivation.html","id":null,"dir":"Reference","previous_headings":"","what":"Execute a Derivation with Different Arguments for Subsets of the Input Dataset — slice_derivation","title":"Execute a Derivation with Different Arguments for Subsets of the Input Dataset — slice_derivation","text":"input dataset split slices (subsets) slice derivation called separately. arguments derivation may vary depending slice.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/slice_derivation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Execute a Derivation with Different Arguments for Subsets of the Input Dataset — slice_derivation","text":"","code":"slice_derivation(dataset, derivation, args = NULL, ...)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/slice_derivation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Execute a Derivation with Different Arguments for Subsets of the Input Dataset — slice_derivation","text":"dataset Input dataset derivation Derivation function performs specific derivation expected. derivation adds variables observations dataset. first argument derivation must expect dataset derivation must return dataset. function must provide dataset argument arguments specified params() objects passed arg argument. Please note possible specify {dplyr} functions like mutate() summarize(). args Arguments derivation params() object expected. ... derivation_slice() object expected slice defines subset input dataset parameters derivation. derivation called subset parameters specified args parameter args field derivation_slice() object. parameter specified , value derivation_slice() overwrites one args.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/slice_derivation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Execute a Derivation with Different Arguments for Subsets of the Input Dataset — slice_derivation","text":"input dataset variables derived derivation added","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/slice_derivation.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Execute a Derivation with Different Arguments for Subsets of the Input Dataset — slice_derivation","text":"slice derivation called subset defined filter field derivation_slice() object parameters specified args parameter args field derivation_slice() object. parameter specified , value derivation_slice() overwrites one args. Observations match one slice considered first matching slice. Observations match slices included output dataset derivation called .","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/slice_derivation.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Execute a Derivation with Different Arguments for Subsets of the Input Dataset — slice_derivation","text":"","code":"library(tibble) library(stringr) advs <- tribble(   ~USUBJID, ~VSDTC,       ~VSTPT,   \"1\",      \"2020-04-16\", NA_character_,   \"1\",      \"2020-04-16\", \"BEFORE TREATMENT\" )  # For the second slice filter is set to TRUE. Thus derive_vars_dtm is called # with time_imputation = \"last\" for all observations which do not match for the # first slice. slice_derivation(   advs,   derivation = derive_vars_dtm,   args = params(     dtc = VSDTC,     new_vars_prefix = \"A\"   ),   derivation_slice(     filter = str_detect(VSTPT, \"PRE|BEFORE\"),     args = params(time_imputation = \"first\")   ),   derivation_slice(     filter = TRUE,     args = params(time_imputation = \"last\")   ) ) #> # A tibble: 2 × 5 #>   USUBJID VSDTC      VSTPT            ADTM                ATMF  #>   <chr>   <chr>      <chr>            <dttm>              <chr> #> 1 1       2020-04-16 NA               2020-04-16 23:59:59 H     #> 2 1       2020-04-16 BEFORE TREATMENT 2020-04-16 00:00:00 H"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/transform_range.html","id":null,"dir":"Reference","previous_headings":"","what":"Transform Range — transform_range","title":"Transform Range — transform_range","text":"Transforms results source range target range. example, transforming source values 1, 2, 3, 4, 5 0, 25, 50, 75, 100.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/transform_range.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transform Range — transform_range","text":"","code":"transform_range(   source,   source_range,   target_range,   flip_direction = FALSE,   outside_range = \"NA\" )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/transform_range.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transform Range — transform_range","text":"source vector values transformed numeric vector expected. source_range permitted source range numeric vector containing two elements expected, representing lower upper bounds permitted source range. target_range target range numeric vector containing two elements expected, representing lower upper bounds target range. flip_direction Flip direction range? transformed values reversed within target range, e.g. within range 0 100, 25 reversed 75. Permitted Values: TRUE, FALSE outside_range Handling values outside source range Values outside source range (source_range) transformed NA. \"warning\" \"error\" specified, warning error issued source includes values outside source range. Permitted Values: \"NA\", \"warning\", \"error\"","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/transform_range.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transform Range — transform_range","text":"source linearly transformed target range","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/transform_range.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transform Range — transform_range","text":"Returns values source linearly transformed source range (source_range) target range (target_range). Values outside source range set NA.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/transform_range.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transform Range — transform_range","text":"","code":"transform_range(   source = c(1, 4, 3, 6, 5),   source_range = c(1, 5),   target_range = c(0, 100) ) #> [1]   0  75  50  NA 100  transform_range(   source = c(1, 4, 3, 6, 5),   source_range = c(1, 5),   target_range = c(0, 100),   flip_direction = TRUE ) #> [1] 100  25  50  NA   0"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/tte_source.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a tte_source Object — tte_source","title":"Create a tte_source Object — tte_source","text":"tte_source object used define events possible censorings.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/tte_source.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a tte_source Object — tte_source","text":"","code":"tte_source(dataset_name, filter = NULL, date, censor = 0, set_values_to = NULL)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/tte_source.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a tte_source Object — tte_source","text":"dataset_name name source dataset name refers dataset provided source_datasets parameter derive_param_tte(). filter unquoted condition selecting observations dataset events possible censoring time points. date variable expression providing date event censoring. date, datetime can specified. unquoted symbol expression expected. Refer derive_vars_dt() convert_dtc_to_dt() impute derive date date character vector date object. censor Censoring value CDISC strongly recommends using 0 events positive integers censoring. set_values_to named list returned exprs() defining variables set event censoring, e.g. exprs(EVENTDESC = \"DEATH\", SRCDOM = \"ADSL\", SRCVAR = \"DTHDT\"). values must symbol, character string, numeric value, expression, NA.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/tte_source.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a tte_source Object — tte_source","text":"object class tte_source","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/tte_source_objects.html","id":null,"dir":"Reference","previous_headings":"","what":"Pre-Defined Time-to-Event Source Objects — death_event","title":"Pre-Defined Time-to-Event Source Objects — death_event","text":"pre-defined tte_source objects can used input derive_param_tte().","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/tte_source_objects.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pre-Defined Time-to-Event Source Objects — death_event","text":"","code":"death_event  lastalive_censor  ae_event  ae_ser_event  ae_gr1_event  ae_gr2_event  ae_gr3_event  ae_gr4_event  ae_gr5_event  ae_gr35_event  ae_sev_event  ae_wd_event"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/tte_source_objects.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Pre-Defined Time-to-Event Source Objects — death_event","text":"see definition various objects simply print object R console, e.g. print(death_event). details use objects please refer derive_param_tte().","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/tte_source_objects.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pre-Defined Time-to-Event Source Objects — death_event","text":"","code":"# This shows the definition of all pre-defined `tte_source` objects that ship # with {admiral} for (obj in list_tte_source_objects()$object) {   cat(obj, \"\\n\")   print(get(obj))   cat(\"\\n\") } #> ae_gr3_event  #> <event_source> object #> dataset_name: \"adae\" #> filter: TRTEMFL == \"Y\" & ATOXGR == \"3\" #> date: ASTDT #> censor: 0 #> set_values_to: #>   EVNTDESC: \"GRADE 3 ADVERSE EVENT\" #>   SRCDOM: \"ADAE\" #>   SRCVAR: \"ASTDT\" #>   SRCSEQ: AESEQ #>  #> ae_wd_event  #> <event_source> object #> dataset_name: \"adae\" #> filter: TRTEMFL == \"Y\" & AEACN == \"DRUG WITHDRAWN\" #> date: ASTDT #> censor: 0 #> set_values_to: #>   EVNTDESC: \"ADVERSE EVENT LEADING TO DRUG WITHDRAWAL\" #>   SRCDOM: \"ADAE\" #>   SRCVAR: \"ASTDT\" #>   SRCSEQ: AESEQ #>  #> ae_gr35_event  #> <event_source> object #> dataset_name: \"adae\" #> filter: TRTEMFL == \"Y\" & ATOXGR %in% c(\"3\", \"4\", \"5\") #> date: ASTDT #> censor: 0 #> set_values_to: #>   EVNTDESC: \"GRADE 3-5 ADVERSE EVENT\" #>   SRCDOM: \"ADAE\" #>   SRCVAR: \"ASTDT\" #>   SRCSEQ: AESEQ #>  #> lastalive_censor  #> <censor_source> object #> dataset_name: \"adsl\" #> filter: NULL #> date: LSTALVDT #> censor: 1 #> set_values_to: #>   EVNTDESC: \"ALIVE\" #>   SRCDOM: \"ADSL\" #>   SRCVAR: \"LSTALVDT\" #>  #> ae_gr1_event  #> <event_source> object #> dataset_name: \"adae\" #> filter: TRTEMFL == \"Y\" & ATOXGR == \"1\" #> date: ASTDT #> censor: 0 #> set_values_to: #>   EVNTDESC: \"GRADE 1 ADVERSE EVENT\" #>   SRCDOM: \"ADAE\" #>   SRCVAR: \"ASTDT\" #>   SRCSEQ: AESEQ #>  #> ae_ser_event  #> <event_source> object #> dataset_name: \"adae\" #> filter: TRTEMFL == \"Y\" & AESER == \"Y\" #> date: ASTDT #> censor: 0 #> set_values_to: #>   EVNTDESC: \"SERIOUS ADVERSE EVENT\" #>   SRCDOM: \"ADAE\" #>   SRCVAR: \"ASTDT\" #>   SRCSEQ: AESEQ #>  #> ae_gr2_event  #> <event_source> object #> dataset_name: \"adae\" #> filter: TRTEMFL == \"Y\" & ATOXGR == \"2\" #> date: ASTDT #> censor: 0 #> set_values_to: #>   EVNTDESC: \"GRADE 2 ADVERSE EVENT\" #>   SRCDOM: \"ADAE\" #>   SRCVAR: \"ASTDT\" #>   SRCSEQ: AESEQ #>  #> ae_event  #> <event_source> object #> dataset_name: \"adae\" #> filter: TRTEMFL == \"Y\" #> date: ASTDT #> censor: 0 #> set_values_to: #>   EVNTDESC: \"ADVERSE EVENT\" #>   SRCDOM: \"ADAE\" #>   SRCVAR: \"ASTDT\" #>   SRCSEQ: AESEQ #>  #> ae_gr4_event  #> <event_source> object #> dataset_name: \"adae\" #> filter: TRTEMFL == \"Y\" & ATOXGR == \"4\" #> date: ASTDT #> censor: 0 #> set_values_to: #>   EVNTDESC: \"GRADE 4 ADVERSE EVENT\" #>   SRCDOM: \"ADAE\" #>   SRCVAR: \"ASTDT\" #>   SRCSEQ: AESEQ #>  #> ae_gr5_event  #> <event_source> object #> dataset_name: \"adae\" #> filter: TRTEMFL == \"Y\" & ATOXGR == \"5\" #> date: ASTDT #> censor: 0 #> set_values_to: #>   EVNTDESC: \"GRADE 5 ADVERSE EVENT\" #>   SRCDOM: \"ADAE\" #>   SRCVAR: \"ASTDT\" #>   SRCSEQ: AESEQ #>  #> ae_sev_event  #> <event_source> object #> dataset_name: \"adae\" #> filter: TRTEMFL == \"Y\" & AESEV == \"SEVERE\" #> date: ASTDT #> censor: 0 #> set_values_to: #>   EVNTDESC: \"SEVERE ADVERSE EVENT\" #>   SRCDOM: \"ADAE\" #>   SRCVAR: \"ASTDT\" #>   SRCSEQ: AESEQ #>  #> death_event  #> <event_source> object #> dataset_name: \"adsl\" #> filter: DTHFL == \"Y\" #> date: DTHDT #> censor: 0 #> set_values_to: #>   EVNTDESC: \"DEATH\" #>   SRCDOM: \"ADSL\" #>   SRCVAR: \"DTHDT\" #>"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/use_ad_template.html","id":null,"dir":"Reference","previous_headings":"","what":"Open an ADaM Template Script — use_ad_template","title":"Open an ADaM Template Script — use_ad_template","text":"Open ADaM Template Script","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/use_ad_template.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Open an ADaM Template Script — use_ad_template","text":"","code":"use_ad_template(   adam_name = \"adsl\",   save_path = paste0(\"./\", adam_name, \".R\"),   package = \"admiral\",   overwrite = FALSE,   open = interactive() )"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/use_ad_template.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Open an ADaM Template Script — use_ad_template","text":"adam_name ADaM dataset name. can use available dataset names \"ADAE\", \"ADCM\", \"ADEG\", \"ADEX\", \"ADLB\", \"ADLBHY\", \"ADMH\", \"ADPC\", \"ADPP\", \"ADPPK\", \"ADSL\", \"ADVS\". dataset name case-insensitive. default dataset name \"ADSL\". save_path Path save script. package R package look templates. default \"admiral\". overwrite Whether overwrite existing file named save_path. open Whether open script right away.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/use_ad_template.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Open an ADaM Template Script — use_ad_template","text":"return values, called side effects","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/use_ad_template.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Open an ADaM Template Script — use_ad_template","text":"Running without arguments use_ad_template() auto-generates adsl.R current path. Use list_all_templates() discover templates available.","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/use_ad_template.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Open an ADaM Template Script — use_ad_template","text":"","code":"if (interactive()) {   use_ad_template(\"adsl\") }"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/yn_to_numeric.html","id":null,"dir":"Reference","previous_headings":"","what":"Map ","title":"Map ","text":"Map \"Y\" \"N\" numeric values.","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/yn_to_numeric.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Map ","text":"","code":"yn_to_numeric(arg)"},{"path":"https://pharmaverse.github.io/admiral/dev/reference/yn_to_numeric.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Map ","text":"arg Character vector","code":""},{"path":"https://pharmaverse.github.io/admiral/dev/reference/yn_to_numeric.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Map ","text":"1 arg equals \"Y\", 0 arg equals \"N\", NA_real_ otherwise","code":""},{"path":[]},{"path":"https://pharmaverse.github.io/admiral/dev/reference/yn_to_numeric.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Map ","text":"","code":"yn_to_numeric(c(\"Y\", \"N\", NA_character_)) #> [1]  1  0 NA"}]