Convert linkGrowth to be log scale for SF and GSF (#357)

R/SimLongitudinalGSF.R

@@ -140,7 +140,7 @@ sampleObservations.SimLongitudinalGSF <- function(object, times_df) {
                 (object@link_dsld * .data$dsld) +
                 (object@link_ttg * .data$ttg) +
                 (object@link_identity * .data$mu_sld) +
-                (object@link_growth * .data$psi_g)
+                (object@link_growth * log(.data$psi_g))
         )
 }
 

R/SimLongitudinalSteinFojo.R

@@ -126,7 +126,7 @@ sampleObservations.SimLongitudinalSteinFojo <- function(object, times_df) {
                 (object@link_dsld * .data$dsld) +
                 (object@link_ttg * .data$ttg) +
                 (object@link_identity * .data$mu_sld) +
-                (object@link_growth * .data$psi_g)
+                (object@link_growth * log(.data$psi_g))
         )
 }
 

inst/stan/lm-gsf/link_growth.stan

@@ -10,6 +10,6 @@ functions {
     ) {
         int nrows = rows(link_function_inputs);
         int ncols = cols(time);
-        return rep_matrix(link_function_inputs[,3], ncols);
+        return rep_matrix(log(link_function_inputs[,3]), ncols);
     }
 }

inst/stan/lm-stein-fojo/link_growth.stan

@@ -10,6 +10,6 @@ functions {
     ) {
         int nrows = rows(link_function_inputs);
         int ncols = cols(time);
-        return rep_matrix(link_function_inputs[,3], ncols);
+        return rep_matrix(log(link_function_inputs[,3]), ncols);
     }
 }

tests/testthat/test-LongitudinalSteinFojo.R

@@ -229,44 +229,68 @@ test_that("Can recover known distributional parameters from a SF joint model wit
 
     skip_if_not(is_full_test())
 
-    set.seed(7738)
+    sim_params <- list(
+        sigma = 0.005,
+        mu_s = log(c(0.15, 0.3)),
+        mu_g = log(c(0.4, 0.25)),
+        mu_b = log(60),
+        omega_b = 0.1,
+        omega_s = 0.1,
+        omega_g = 0.2,
+        link_ttg = 0,
+        link_dsld = 0,
+        link_growth = 1,
+        lambda = 2,
+        lambda_cen = 1 / 9000,
+        beta_cat_b = -0.1,
+        beta_cat_c = 0.5,
+        beta_cont = 0.3
+    )
+
+    set.seed(2338)
     ## Generate Test data with known parameters
     jlist <- SimJointData(
         design = list(
-            SimGroup(170, "Arm-A", "Study-X"),
-            SimGroup(170, "Arm-B", "Study-X")
+            SimGroup(200, "Arm-A", "Study-X"),
+            SimGroup(200, "Arm-B", "Study-X")
         ),
         longitudinal = SimLongitudinalSteinFojo(
             times = c(
-                1, 100, 150, 200, 250, 300, 350, 400, 500, 600, 700, 800, 900,
+                -100, -50, -10, 1, 100, 150, 200, 300, 400, 500, 600, 700, 800, 900,
                 1100, 1300, 1500, 1800
             ) / 365,
-            sigma = 0.005,
-            mu_s = log(c(0.15, 0.3)),
-            mu_g = log(c(0.4, 0.25)),
-            mu_b = log(60),
-            omega_b = 0.1,
-            omega_s = 0.1,
-            omega_g = 0.2,
-            link_ttg = 0,
-            link_dsld = 0,
-            link_growth = 3
+            sigma = sim_params$sigma,
+            mu_s = sim_params$mu_s,
+            mu_g = sim_params$mu_g,
+            mu_b = sim_params$mu_b,
+            omega_b = sim_params$omega_b,
+            omega_s = sim_params$omega_s,
+            omega_g = sim_params$omega_g,
+            link_ttg = sim_params$link_ttg,
+            link_dsld = sim_params$link_dsld,
+            link_growth = sim_params$link_growth
         ),
         survival = SimSurvivalExponential(
             time_max = 4,
             time_step = 1 / 365,
-            lambda = 1,
+            lambda = sim_params$lambda,
             lambda_cen = 1 / 9000,
             beta_cat = c(
                 "A" = 0,
-                "B" = -0.1,
-                "C" = 0.5
+                "B" = sim_params$beta_cat_b,
+                "C" = sim_params$beta_cat_c
             ),
-            beta_cont = 0.3
+            beta_cont = sim_params$beta_cont
         ),
         .silent = TRUE
     )
 
+    # nolint start⁠
+    ### Diagnostics helpers
+    # plot(survival::survfit(Surv(time, event) ~ 1, data = jlist@survival))
+    # median(jlist@survival$time)
+    # nolint end
+
 
     jm <- JointModel(
         longitudinal = LongitudinalSteinFojo(
@@ -284,10 +308,10 @@ test_that("Can recover known distributional parameters from a SF joint model wit
 
         ),
         survival = SurvivalExponential(
-            lambda = prior_lognormal(log(1), 0.5)
+            lambda = prior_lognormal(log(2), 0.5)
         ),
         link = Link(
-            linkGrowth(prior_normal(0, 4))
+            linkGrowth(prior_normal(0, 2))
         )
     )
 
@@ -347,17 +371,19 @@ test_that("Can recover known distributional parameters from a SF joint model wit
         c("lm_sf_mu_bsld", "lm_sf_mu_ks", "lm_sf_mu_kg"),
         TRUE
     )
-    true_values <- c(60, 0.15, 0.3, 0.4, 0.25)
+    true_values <- exp(c(sim_params$mu_b, sim_params$mu_s, sim_params$mu_g))
     expect_true(all(dat$q01 <= true_values))
     expect_true(all(dat$q99 >= true_values))
     expect_true(all(dat$ess_bulk > 100))
 
     dat <- summary_post(
         as.CmdStanMCMC(mp),
-        c("link_growth", "sm_exp_lambda")
+        c("link_growth", "sm_exp_lambda", "beta_os_cov")
+    )
+    true_values <- c(
+        sim_params$link_growth, sim_params$lambda,
+        sim_params$beta_cat_b, sim_params$beta_cat_c, sim_params$beta_cont
     )
-
-    true_values <- c(3, 1)
     expect_true(all(dat$q01 <= true_values))
     expect_true(all(dat$q99 >= true_values))
     expect_true(all(dat$ess_bulk > 100))

vignettes/statistical-specification.Rmd

@@ -229,7 +229,7 @@ Accessible via `linkIdentity()`
 
 $$
 \begin{align*}
-G(t \mid b_i, s_i, g_i) &= g_i
+G(t \mid b_i, s_i, g_i) &= log(g_i)
 \end{align*}
 $$
 
@@ -341,7 +341,7 @@ Accessible via `linkIdentity()`
 
 $$
 \begin{align*}
-G(t \mid b_i, s_i, g_i, \phi_i) &= g_i
+G(t \mid b_i, s_i, g_i, \phi_i) &= log(g_i)
 \end{align*}
 $$