diff --git a/NAMESPACE b/NAMESPACE
index a6270a432..0f2bebeeb 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -86,9 +86,15 @@ S3method(coalesceGridTime,GridPrediction)
 S3method(coalesceGridTime,default)
 S3method(compileStanModel,JointModel)
 S3method(dim,Quantities)
+S3method(enableGQ,JointModel)
+S3method(enableGQ,LongitudinalGSF)
+S3method(enableGQ,LongitudinalRandomSlope)
+S3method(enableGQ,LongitudinalSteinFojo)
+S3method(enableGQ,default)
 S3method(enableLink,LongitudinalGSF)
 S3method(enableLink,LongitudinalRandomSlope)
 S3method(enableLink,LongitudinalSteinFojo)
+S3method(enableLink,default)
 S3method(extractVariableNames,DataSubject)
 S3method(extractVariableNames,DataSurvival)
 S3method(generateQuantities,JointModelSamples)
@@ -202,6 +208,7 @@ export(as_stan_list)
 export(autoplot)
 export(brierScore)
 export(compileStanModel)
+export(enableGQ)
 export(enableLink)
 export(generateQuantities)
 export(getParameters)
diff --git a/R/JointModel.R b/R/JointModel.R
index a25e4a1ad..183f130b3 100755
--- a/R/JointModel.R
+++ b/R/JointModel.R
@@ -80,16 +80,24 @@ JointModel <- function(
 }
 
 
+#' @export
+enableGQ.JointModel <- function(object, ...) {
+    merge(
+        enableGQ(object@survival),
+        enableGQ(object@longitudinal)
+    )
+}
+
+
 #' `JointModel` -> `StanModule`
 #'
 #' Converts a [`JointModel`] object to a [`StanModule`] object
 #'
 #' @inheritParams JointModel-Shared
-#' @param include_gq (`logical`)\cr whether to include the generated quantities block.
 #' @family JointModel
 #' @family as.StanModule
 #' @export
-as.StanModule.JointModel <- function(object, include_gq = FALSE, ...) {
+as.StanModule.JointModel <- function(object, ...) {
 
     base_model <- read_stan("base/base.stan")
 
@@ -113,9 +121,6 @@ as.StanModule.JointModel <- function(object, include_gq = FALSE, ...) {
         StanModule(stan_full)
     )
 
-    if (!include_gq) {
-        x@generated_quantities <- ""
-    }
     return(x)
 }
 
diff --git a/R/JointModelSamples.R b/R/JointModelSamples.R
index 9b6ed1dc3..76a1f5b49 100644
--- a/R/JointModelSamples.R
+++ b/R/JointModelSamples.R
@@ -52,9 +52,13 @@ generateQuantities.JointModelSamples <- function(object, generator, type, ...) {
         ) |>
         StanModule()
 
-    stanobj <- merge(
-        as.StanModule(object@model, include_gq = TRUE),
-        quant_stanobj
+    stanobj <- Reduce(
+        merge,
+        list(
+            as.StanModule(object@model),
+            enableGQ(object@model),
+            quant_stanobj
+        )
     )
 
     model <- compileStanModel(stanobj)
diff --git a/R/LongitudinalGSF.R b/R/LongitudinalGSF.R
index f6a3aaab1..26611816e 100755
--- a/R/LongitudinalGSF.R
+++ b/R/LongitudinalGSF.R
@@ -126,6 +126,13 @@ LongitudinalGSF <- function(
 }
 
 
+
+#' @export
+enableGQ.LongitudinalGSF <- function(object, ...) {
+    StanModule("lm-gsf/quantities.stan")
+}
+
+
 #' @export
 enableLink.LongitudinalGSF <- function(object, ...) {
     object@stan <- merge(
diff --git a/R/LongitudinalRandomSlope.R b/R/LongitudinalRandomSlope.R
index e837ff3b4..57b70a45e 100755
--- a/R/LongitudinalRandomSlope.R
+++ b/R/LongitudinalRandomSlope.R
@@ -60,6 +60,11 @@ LongitudinalRandomSlope <- function(
 }
 
 
+#' @export
+enableGQ.LongitudinalRandomSlope <- function(object, ...) {
+    StanModule("lm-random-slope/quantities.stan")
+}
+
 #' @export
 enableLink.LongitudinalRandomSlope <- function(object, ...) {
     object@stan <- merge(
diff --git a/R/LongitudinalSteinFojo.R b/R/LongitudinalSteinFojo.R
index 6b6bbec5b..da5881407 100755
--- a/R/LongitudinalSteinFojo.R
+++ b/R/LongitudinalSteinFojo.R
@@ -111,6 +111,11 @@ LongitudinalSteinFojo <- function(
 
 
 
+#' @export
+enableGQ.LongitudinalSteinFojo <- function(object, ...) {
+    StanModule("lm-stein-fojo/quantities.stan")
+}
+
 #' @export
 enableLink.LongitudinalSteinFojo <- function(object, ...) {
     object@stan <- merge(
diff --git a/R/brier_score.R b/R/brier_score.R
index 3e1e13b22..c3b319e34 100644
--- a/R/brier_score.R
+++ b/R/brier_score.R
@@ -105,7 +105,7 @@ reverse_km_cen_first <- function(t, times, events) {
         survival::Surv(times, cen_events) ~ 1,
         data = dat
     )
-    preds <- summary(mod, times = t, extend = TRUE)$surv
+    preds <- summary(mod, times = t[order(t)], extend = TRUE)$surv
 
     assert_that(
         length(preds) == length(t)
diff --git a/R/generics.R b/R/generics.R
index 367cd8a91..ec0e45ee7 100755
--- a/R/generics.R
+++ b/R/generics.R
@@ -410,11 +410,32 @@ resolvePromise.default <- function(object, ...) {
 enableLink <- function(object, ...) {
     UseMethod("enableLink")
 }
+#' @export
 enableLink.default <- function(object, ...) {
     object
 }
 
 
+#' Enable Generated Quantities Generic
+#'
+#' @param object ([`StanModel`])\cr to enable generated quantities for.
+#' @param ... Not used.
+#'
+#' Optional hook method that is called on a [`StanModel`] if attempting to use
+#' either [`LongitudinalQuantities`] or [`SurvivalQuantities`]
+#'
+#' @return [`StanModule`] object
+#'
+#' @export
+enableGQ <- function(object, ...) {
+    UseMethod("enableGQ")
+}
+#' @export
+enableGQ.default <- function(object, ...) {
+    StanModule()
+}
+
+
 
 #' Get Prediction Names
 #'
diff --git a/_pkgdown.yml b/_pkgdown.yml
index 7b63965ea..868e3a636 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -168,6 +168,7 @@ reference:
       - sampleObservations
       - sampleSubjects
       - enableLink
+      - enableGQ
       - as_formula
       - getPredictionNames
   
diff --git a/inst/stan/lm-gsf/functions.stan b/inst/stan/lm-gsf/functions.stan
index 0b71cbec4..925abc45f 100644
--- a/inst/stan/lm-gsf/functions.stan
+++ b/inst/stan/lm-gsf/functions.stan
@@ -21,15 +21,5 @@ functions {
         );
         return result;
     }
-
-    vector lm_predict_value(vector time, matrix long_gq_parameters) {
-        return sld(
-            time,
-            long_gq_parameters[,1],
-            long_gq_parameters[,2],
-            long_gq_parameters[,3],
-            long_gq_parameters[,4]
-        );
-    }
 }
 
diff --git a/inst/stan/lm-gsf/model.stan b/inst/stan/lm-gsf/model.stan
index 43f78bca1..560023d6b 100755
--- a/inst/stan/lm-gsf/model.stan
+++ b/inst/stan/lm-gsf/model.stan
@@ -94,21 +94,3 @@ model {
     lm_gsf_psi_phi ~ beta(lm_gsf_a_phi[subject_arm_index], lm_gsf_b_phi[subject_arm_index]);
 }
 
-
-generated quantities {
-    //
-    // Source - lm-gsf/model.stan
-    //
-    matrix[n_subjects, 4] long_gq_parameters;
-    long_gq_parameters[, 1] = lm_gsf_psi_bsld;
-    long_gq_parameters[, 2] = lm_gsf_psi_ks;
-    long_gq_parameters[, 3] = lm_gsf_psi_kg;
-    long_gq_parameters[, 4] = lm_gsf_psi_phi;
-    
-    
-    matrix[gq_n_quant, 4] long_gq_pop_parameters;
-    long_gq_pop_parameters[, 1] = exp(lm_gsf_mu_bsld[gq_long_pop_study_index]);
-    long_gq_pop_parameters[, 2] = exp(lm_gsf_mu_ks[gq_long_pop_arm_index]);
-    long_gq_pop_parameters[, 3] = exp(lm_gsf_mu_kg[gq_long_pop_arm_index]);
-    long_gq_pop_parameters[, 4] = lm_gsf_a_phi[gq_long_pop_arm_index] ./ (lm_gsf_a_phi[gq_long_pop_arm_index] + lm_gsf_b_phi[gq_long_pop_arm_index]);
-}
diff --git a/inst/stan/lm-gsf/quantities.stan b/inst/stan/lm-gsf/quantities.stan
new file mode 100644
index 000000000..285d89511
--- /dev/null
+++ b/inst/stan/lm-gsf/quantities.stan
@@ -0,0 +1,29 @@
+functions {
+    vector lm_predict_value(vector time, matrix long_gq_parameters) {
+        return sld(
+            time,
+            long_gq_parameters[,1],
+            long_gq_parameters[,2],
+            long_gq_parameters[,3],
+            long_gq_parameters[,4]
+        );
+    }
+}
+
+generated quantities {
+    //
+    // Source - lm-gsf/quantities.stan
+    //
+    matrix[n_subjects, 4] long_gq_parameters;
+    long_gq_parameters[, 1] = lm_gsf_psi_bsld;
+    long_gq_parameters[, 2] = lm_gsf_psi_ks;
+    long_gq_parameters[, 3] = lm_gsf_psi_kg;
+    long_gq_parameters[, 4] = lm_gsf_psi_phi;
+    
+    
+    matrix[gq_n_quant, 4] long_gq_pop_parameters;
+    long_gq_pop_parameters[, 1] = exp(lm_gsf_mu_bsld[gq_long_pop_study_index]);
+    long_gq_pop_parameters[, 2] = exp(lm_gsf_mu_ks[gq_long_pop_arm_index]);
+    long_gq_pop_parameters[, 3] = exp(lm_gsf_mu_kg[gq_long_pop_arm_index]);
+    long_gq_pop_parameters[, 4] = lm_gsf_a_phi[gq_long_pop_arm_index] ./ (lm_gsf_a_phi[gq_long_pop_arm_index] + lm_gsf_b_phi[gq_long_pop_arm_index]);
+}
diff --git a/inst/stan/lm-random-slope/model.stan b/inst/stan/lm-random-slope/model.stan
index ddbfe3595..65647c223 100755
--- a/inst/stan/lm-random-slope/model.stan
+++ b/inst/stan/lm-random-slope/model.stan
@@ -1,15 +1,4 @@
 
-functions {
-    //
-    // Source - lm-random-slope/model.stan
-    //
-    vector lm_predict_value(vector time, matrix long_gq_parameters) {
-        int nrow = rows(time);
-        return (
-            long_gq_parameters[, 1] + long_gq_parameters[, 2] .* time
-        );
-    }
-}
 
 
 parameters {
@@ -61,16 +50,3 @@ model {
 }
 
 
-generated quantities {
-    //
-    // Source - lm-random-slope/model.stan
-    //
-    matrix[n_subjects, 2] long_gq_parameters;
-    long_gq_parameters[, 1] = lm_rs_ind_intercept;
-    long_gq_parameters[, 2] = lm_rs_ind_rnd_slope;
-
-
-    matrix[gq_n_quant, 2] long_gq_pop_parameters;
-    long_gq_pop_parameters[, 1] = to_vector(lm_rs_intercept[gq_long_pop_study_index]);
-    long_gq_pop_parameters[, 2] = to_vector(lm_rs_slope_mu[gq_long_pop_arm_index]);
-}
diff --git a/inst/stan/lm-random-slope/quantities.stan b/inst/stan/lm-random-slope/quantities.stan
new file mode 100644
index 000000000..7ba6150b3
--- /dev/null
+++ b/inst/stan/lm-random-slope/quantities.stan
@@ -0,0 +1,26 @@
+
+functions {
+    //
+    // Source - lm-random-slope/quantities.stan
+    //
+    vector lm_predict_value(vector time, matrix long_gq_parameters) {
+        int nrow = rows(time);
+        return (
+            long_gq_parameters[, 1] + long_gq_parameters[, 2] .* time
+        );
+    }
+}
+
+generated quantities {
+    //
+    // Source - lm-random-slope/quantities.stan
+    //
+    matrix[n_subjects, 2] long_gq_parameters;
+    long_gq_parameters[, 1] = lm_rs_ind_intercept;
+    long_gq_parameters[, 2] = lm_rs_ind_rnd_slope;
+
+
+    matrix[gq_n_quant, 2] long_gq_pop_parameters;
+    long_gq_pop_parameters[, 1] = to_vector(lm_rs_intercept[gq_long_pop_study_index]);
+    long_gq_pop_parameters[, 2] = to_vector(lm_rs_slope_mu[gq_long_pop_arm_index]);
+}
diff --git a/inst/stan/lm-stein-fojo/functions.stan b/inst/stan/lm-stein-fojo/functions.stan
index 1c69244e1..f02d2271b 100644
--- a/inst/stan/lm-stein-fojo/functions.stan
+++ b/inst/stan/lm-stein-fojo/functions.stan
@@ -22,13 +22,5 @@ functions {
         );
         return result;
     }
-    vector lm_predict_value(vector time, matrix long_gq_parameters) {
-        return sld(
-            time,
-            long_gq_parameters[,1],
-            long_gq_parameters[,2],
-            long_gq_parameters[,3]
-        );
-    }
 }
 
diff --git a/inst/stan/lm-stein-fojo/model.stan b/inst/stan/lm-stein-fojo/model.stan
index 4ce7d4f26..b368818f8 100755
--- a/inst/stan/lm-stein-fojo/model.stan
+++ b/inst/stan/lm-stein-fojo/model.stan
@@ -86,17 +86,3 @@ model {
 {%- endif -%}
 }
 
-generated quantities {
-    //
-    // Source - lm-stein-fojo/model.stan
-    //
-    matrix[n_subjects, 3] long_gq_parameters;
-    long_gq_parameters[, 1] = lm_gsf_psi_bsld;
-    long_gq_parameters[, 2] = lm_gsf_psi_ks;
-    long_gq_parameters[, 3] = lm_gsf_psi_kg;
-
-    matrix[gq_n_quant, 3] long_gq_pop_parameters;
-    long_gq_pop_parameters[, 1] = exp(lm_sf_mu_bsld[gq_long_pop_study_index]);
-    long_gq_pop_parameters[, 2] = exp(lm_sf_mu_ks[gq_long_pop_arm_index]);
-    long_gq_pop_parameters[, 3] = exp(lm_sf_mu_kg[gq_long_pop_arm_index]);
-}
diff --git a/inst/stan/lm-stein-fojo/quantities.stan b/inst/stan/lm-stein-fojo/quantities.stan
new file mode 100644
index 000000000..92c07fdb1
--- /dev/null
+++ b/inst/stan/lm-stein-fojo/quantities.stan
@@ -0,0 +1,26 @@
+
+functions {
+    vector lm_predict_value(vector time, matrix long_gq_parameters) {
+        return sld(
+            time,
+            long_gq_parameters[,1],
+            long_gq_parameters[,2],
+            long_gq_parameters[,3]
+        );
+    }
+}
+
+generated quantities {
+    //
+    // Source - lm-stein-fojo/quantities.stan
+    //
+    matrix[n_subjects, 3] long_gq_parameters;
+    long_gq_parameters[, 1] = lm_gsf_psi_bsld;
+    long_gq_parameters[, 2] = lm_gsf_psi_ks;
+    long_gq_parameters[, 3] = lm_gsf_psi_kg;
+
+    matrix[gq_n_quant, 3] long_gq_pop_parameters;
+    long_gq_pop_parameters[, 1] = exp(lm_sf_mu_bsld[gq_long_pop_study_index]);
+    long_gq_pop_parameters[, 2] = exp(lm_sf_mu_ks[gq_long_pop_arm_index]);
+    long_gq_pop_parameters[, 3] = exp(lm_sf_mu_kg[gq_long_pop_arm_index]);
+}
diff --git a/man/as.StanModule.JointModel.Rd b/man/as.StanModule.JointModel.Rd
index 168b278f7..a82198d19 100644
--- a/man/as.StanModule.JointModel.Rd
+++ b/man/as.StanModule.JointModel.Rd
@@ -4,13 +4,11 @@
 \alias{as.StanModule.JointModel}
 \title{\code{JointModel} -> \code{StanModule}}
 \usage{
-\method{as.StanModule}{JointModel}(object, include_gq = FALSE, ...)
+\method{as.StanModule}{JointModel}(object, ...)
 }
 \arguments{
 \item{object}{(\code{\link{JointModel}}) \cr Joint model specification.}
 
-\item{include_gq}{(\code{logical})\cr whether to include the generated quantities block.}
-
 \item{...}{Not Used.}
 }
 \description{
diff --git a/man/enableGQ.Rd b/man/enableGQ.Rd
new file mode 100644
index 000000000..0c808bb03
--- /dev/null
+++ b/man/enableGQ.Rd
@@ -0,0 +1,22 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/generics.R
+\name{enableGQ}
+\alias{enableGQ}
+\title{Enable Generated Quantities Generic}
+\usage{
+enableGQ(object, ...)
+}
+\arguments{
+\item{object}{(\code{\link{StanModel}})\cr to enable generated quantities for.}
+
+\item{...}{Not used.
+
+Optional hook method that is called on a \code{\link{StanModel}} if attempting to use
+either \code{\link{LongitudinalQuantities}} or \code{\link{SurvivalQuantities}}}
+}
+\value{
+\code{\link{StanModule}} object
+}
+\description{
+Enable Generated Quantities Generic
+}
diff --git a/vignettes/custom-model-gq.stan b/vignettes/custom-model-gq.stan
new file mode 100644
index 000000000..2fe67f241
--- /dev/null
+++ b/vignettes/custom-model-gq.stan
@@ -0,0 +1,29 @@
+
+functions {
+    // Define required function for enabling generated quantities
+    vector lm_predict_value(vector time, matrix long_gq_parameters) {
+        return sld(
+            time,
+            long_gq_parameters[,1],  // baseline
+            long_gq_parameters[,2],  // shrinkage
+            long_gq_parameters[,3]   // growth
+        );
+    }
+}
+
+
+generated quantities {
+    // Enable individual subject predictions / quantities e.g.
+    // `GridFixed()` / `GridObservation()` / `GridGrouped()` / `GridEven
+    matrix[n_subjects, 3] long_gq_parameters;
+    long_gq_parameters[, 1] = baseline_idv;
+    long_gq_parameters[, 2] = shrinkage_idv;
+    long_gq_parameters[, 3] = growth_idv;
+
+    // Enable Population level predictions / quantities by taking the median of the
+    // hierarchical distribution e.g. `GridPopulation()`
+    matrix[gq_n_quant, 3] long_gq_pop_parameters;
+    long_gq_pop_parameters[, 1] = rep_vector(mu_baseline, gq_n_quant);
+    long_gq_pop_parameters[, 2] = rep_vector(mu_shrinkage, gq_n_quant);
+    long_gq_pop_parameters[, 3] = rep_vector(mu_growth, gq_n_quant);
+}
diff --git a/vignettes/custom-model.Rmd b/vignettes/custom-model.Rmd
index 75ab46440..77c3c4dc1 100644
--- a/vignettes/custom-model.Rmd
+++ b/vignettes/custom-model.Rmd
@@ -126,7 +126,7 @@ sampleSubjects.SimWang <- function(object, subjects_df) {
     subjects_df
 }
 
-# Method to generate observations for each indiviudal subject
+# Method to generate observations for each individual subject
 sampleObservations.SimWang <- function(object, times_df) {
     nobs <- nrow(times_df)
     calc_mu <- function(time, b, s, g) b * exp(-s * time) + g * time
@@ -327,12 +327,26 @@ as.CmdStanMCMC(model_samples)$summary(vars)
 
 ## Generating Quantities of Interest
 
-If you look at the Stan code for the above model you will notice that several of the
-requirement elements for the generated quantities have been defined thus enabling us to
-generate quantities both at a population and individual subject level.
+In order to enable the generation of both population and individual level quantities of interest
+we need to implement the required
+generated quantity objects and functions as outlined in the "Extending jmpost" vignette.
 
+This can be done as follows:
 
-This can be done at the subject level via:
+```{R}
+enableGQ.WangModel <- function(object, ...) {
+    StanModule("custom-model-gq.stan")
+}
+```
+
+Where the Stan code for the `custom-model-gq.stan` file is as follows:
+```{R, results='asis', echo=FALSE}
+x <- readLines("./custom-model-gq.stan")
+cat(c("\```stan", x, "\```"), sep = "\n")
+```
+
+With the above in place we are now able to generate quantities as needed;
+this can be done at the subject level via:
 
 ```{R}
 selected_subjects <- head(dat_os$subject, 4)
diff --git a/vignettes/custom-model.stan b/vignettes/custom-model.stan
index c21621bde..2935da7f5 100644
--- a/vignettes/custom-model.stan
+++ b/vignettes/custom-model.stan
@@ -7,16 +7,6 @@ functions {
                        growth .* tumour_time;
         return tumour_value;
     }
-
-    // Define required function for enabling generated quantities
-    vector lm_predict_value(vector time, matrix long_gq_parameters) {
-        return sld(
-            time,
-            long_gq_parameters[,1],  // baseline
-            long_gq_parameters[,2],  // shrinkage
-            long_gq_parameters[,3]   // growth
-        );
-    }
 }
 
 parameters{
@@ -64,19 +54,3 @@ model {
     growth_idv ~ lognormal(log(mu_growth), sigma_growth);
 }
 
-
-generated quantities {
-    // Enable individual subject predictions / quantities e.g.
-    // `GridFixed()` / `GridObservation()` / `GridGrouped()` / `GridEven
-    matrix[n_subjects, 3] long_gq_parameters;
-    long_gq_parameters[, 1] = baseline_idv;
-    long_gq_parameters[, 2] = shrinkage_idv;
-    long_gq_parameters[, 3] = growth_idv;
-
-    // Enable Population level predictions / quantities by taking the median of the
-    // hierarchical distribution e.g. `GridPopulation()`
-    matrix[gq_n_quant, 3] long_gq_pop_parameters;
-    long_gq_pop_parameters[, 1] = rep_vector(mu_baseline, gq_n_quant);
-    long_gq_pop_parameters[, 2] = rep_vector(mu_shrinkage, gq_n_quant);
-    long_gq_pop_parameters[, 3] = rep_vector(mu_growth, gq_n_quant);
-}
diff --git a/vignettes/extending-jmpost.Rmd b/vignettes/extending-jmpost.Rmd
index ce836002c..92a3aa79d 100644
--- a/vignettes/extending-jmpost.Rmd
+++ b/vignettes/extending-jmpost.Rmd
@@ -180,6 +180,9 @@ Note that the `long_gq_parameters` matrix should be structured
 as your `lm_predict_value()` function would expect it to be for the
 `long_gq_parameters` argument.
 
+Please see "Custom Generated Quantities" section below for implementation details for inserting
+custom generated quantity code.
+
 ### 3) Population Generated Quantity Integration
 
 A common use case is to calculate the quantities based on the "population" level parameters which is
@@ -217,6 +220,9 @@ Note that the `long_gq_pop_parameters` matrix should be structured
 as your `lm_predict_value()` function would expect it to be for the
 `long_gq_parameters` argument.
 
+Please see "Custom Generated Quantities" section below for implementation details for inserting
+custom generated quantity code.
+
 ## Prior Specification
 
 When writing your own custom longitudinal or survival model it is important to understand
@@ -241,6 +247,54 @@ as these are handled by the `parameters` slot of the model object as mentioned a
 The main reason for using this approach is that `jmpost` implements the priors in such a way
 that users can change them without having to re-compile the Stan model.
 
+
+## Custom Generated Quantities
+
+In order to avoid unnecessary processing, code that is solely used for generation of post sampling
+quantities is
+excluded from the Stan program when initially sampling from the joint model. Instead this code
+is only included when generating quantities via the `LongitidunalQuantities` or `SurvivalQuantities`
+constructors.
+
+To facilitate this, when using `LongitidunalQuantities` or `SurvivalQuantities`,
+a dedicated model method `enableGQ()` is called on the user provided longitudinal and
+survival models.
+This model specific
+method is responsible for returning a `StanModule` object that contains all the relevant
+code required to generate the quantities for that given model. The following is a rough
+implementation of this method for the Random-Slope model which implements both feature (2) and (3)
+outlined in the above "Custom Longitudinal Model" section:
+
+```R
+enableGQ.LongitudinalRandomSlope <- function() {
+    StanModule("
+        functions {
+            vector lm_predict_value(vector time, matrix long_gq_parameters) {
+                int nrow = rows(time);
+                return (
+                    long_gq_parameters[, 1] + long_gq_parameters[, 2] .* time
+                );
+            }
+        }
+
+        generated quantities {
+            matrix[n_subjects, 2] long_gq_parameters;
+            long_gq_parameters[, 1] = lm_rs_ind_intercept;
+            long_gq_parameters[, 2] = lm_rs_ind_rnd_slope;
+
+            matrix[gq_n_quant, 2] long_gq_pop_parameters;
+            long_gq_pop_parameters[, 1] = to_vector(lm_rs_intercept[gq_long_pop_study_index]);
+            long_gq_pop_parameters[, 2] = to_vector(lm_rs_slope_mu[gq_long_pop_arm_index]);
+        }
+    ")
+}
+```
+
+Note that whilst it is possible to provide an `enableGQ()` method for the survival model it is not
+required. This is because the underlying framework for creating survival quantities
+is distribution agnostic and does not require any model specific code.
+
+
 ## Custom Link Functions