Merge e95d411 into 541f78a

NAMESPACE

@@ -108,6 +108,7 @@ export(Parameter)
 export(ParameterList)
 export(Prior)
 export(STAN_BLOCKS)
+export(SimGroup)
 export(StanModel)
 export(StanModule)
 export(Surv)
@@ -172,6 +173,7 @@ exportClasses(LongitudinalSteinFojo)
 exportClasses(Parameter)
 exportClasses(ParameterList)
 exportClasses(Prior)
+exportClasses(SimGroup)
 exportClasses(StanModel)
 exportClasses(StanModule)
 exportClasses(SurvivalExponential)

R/LongitudinalRandomSlope.R

@@ -42,7 +42,7 @@ LongitudinalRandomSlope <- function(
             name = "Random Slope",
             stan = stan,
             parameters = ParameterList(
-                Parameter(name = "lm_rs_intercept", prior = intercept, size = 1),
+                Parameter(name = "lm_rs_intercept", prior = intercept, size = "n_studies"),
                 Parameter(name = "lm_rs_slope_mu", prior = slope_mu, size = "n_arms"),
                 Parameter(name = "lm_rs_slope_sigma", prior = slope_sigma, size = 1),
                 Parameter(name = "lm_rs_sigma", prior = sigma, size = 1),

R/simulations.R

@@ -23,10 +23,64 @@ get_timepoints <- function(x) {
     )
 }
 
+#' Define Simulation Group
+#'
+#' Specifies a simulation group to be used by [`simulate_joint_data()`].
+#'
+#' @param n (`numeric`)\cr number of subjects in the group.
+#' @param arm (`character`)\cr treatment arm.
+#' @param study (`character`)\cr study name.
+#'
+#' @slot n (`numeric`)\cr See arguments.
+#' @slot arm (`character`)\cr See arguments.
+#' @slot study (`character`)\cr See arguments.
+#'
+#' @examples
+#' SimGroup(n = 50, arm = "Arm-A", study = "Study-1")
+#' @name SimGroup-class
+#' @exportClass SimGroup
+.SimGroup <- setClass(
+    "SimGroup",
+    slots = c(
+        n = "numeric",
+        arm = "character",
+        study = "character"
+    )
+)
+
+#' @export
+#' @rdname SimGroup-class
+SimGroup <- function(n, arm, study) {
+    .SimGroup(
+        n = n,
+        arm = arm,
+        study = study
+    )
+}
+
+
+setValidity(
+    "SimGroup",
+    function(object) {
+        if (length(object@n) != 1) {
+            return("`n` must be a length 1 integer")
+        }
+        if (length(object@arm) != 1) {
+            return("`arm` must be a length 1 string")
+        }
+        if (length(object@study) != 1) {
+            return("`study` must be a length 1 string")
+        }
+        if (any(object@n < 1) | any(object@n %% 1 != 0)) {
+            return("`n` must be positive integer")
+        }
+        return(TRUE)
+    }
+)
 
 #' Simulating Joint Longitudinal and Time-to-Event Data
 #'
-#' @param n_arm (`numeric`)\cr numbers of patients per treatment arm.
+#' @param design (`list`)\cr a list of [`SimGroup`] objects. See details.
 #' @param times (`numeric`)\cr time grid, e.g. specifying the days after randomization.
 #' @param lambda_cen (`number`)\cr rate of the exponential censoring distribution.
 #' @param beta_cont (`number`)\cr coefficient for the continuous covariate.
@@ -37,10 +91,26 @@ get_timepoints <- function(x) {
 #' @param .debug (`flag`)\cr whether to enter debug mode such that the function
 #'   would only return a subset of columns.
 #'
+#' @details
+#'
+#' The `design` argument is used to specify how many distinct groups should be simulated
+#' including key information such as the number of subjects within the group as well as
+#' which treatment arm and study the group belongs to. The `design` argument should be a
+#' list of [`SimGroup`] objects e.g.
+#' ```
+#' design = list(
+#'     SimGroup(n = 50, study = "Study-1", arm = "Arm-A"),
+#'     SimGroup(n = 50, study = "Study-1", arm = "Arm-B")
+#' )
+#' ```
+#'
 #' @returns List with simulated `lm` (longitudinal) and `os` (survival) data sets.
 #' @export
 simulate_joint_data <- function(
-    n_arm = c(50, 80),   # Number of arms and number of subjects per arm
+    design = list(
+        SimGroup(n = 50, study = "Study-1", arm = "Arm-A"),
+        SimGroup(n = 50, study = "Study-1", arm = "Arm-B")
+    ),
     times = 0:2000,
     lambda_cen = 1 / 3,
     beta_cont = 0.2,
@@ -50,24 +120,31 @@ simulate_joint_data <- function(
     .silent = FALSE,
     .debug = FALSE
 ) {
-    n <- sum(n_arm)
+
+    assert(
+        all(vapply(design, \(x) is(x, "SimGroup"), logical(1))),
+        msg = "All elements of `design` must be of class `SimGroup`"
+    )
+
+    n_group <- vapply(design, function(x) x@n, numeric(1))
+    arms <- vapply(design, function(x) x@arm, character(1))
+    studies <- vapply(design, function(x) x@study, character(1))
+    n <- sum(n_group)
     u_pts <- sprintf("pt_%05i", seq_len(n))
     bounds <- get_timepoints(times)
 
-    ARMS <- paste0("Group-", seq_along(n_arm))
-
     os_baseline <- dplyr::tibble(pt = u_pts) |>
         dplyr::mutate(cov_cont = stats::rnorm(n)) |>
         dplyr::mutate(cov_cat = factor(
-            sample(c("A", "B", "C"), replace = TRUE, size = n),
-            levels = c("A", "B", "C")
+            sample(names(beta_cat), replace = TRUE, size = n),
+            levels = names(beta_cat)
         )) |>
         dplyr::mutate(log_haz_cov = .data$cov_cont * beta_cont + beta_cat[.data$cov_cat]) |>
         dplyr::mutate(survival = stats::runif(n)) |>
         dplyr::mutate(chazard_limit = -log(.data$survival)) |>
         dplyr::mutate(time_cen = stats::rexp(n, lambda_cen)) |>
-        dplyr::mutate(study = factor("Study-1")) |>
-        dplyr::mutate(arm = factor(rep(ARMS, times = n_arm), levels = ARMS)) |>
+        dplyr::mutate(arm = factor(rep(arms, times = n_group), levels = unique(arms))) |>
+        dplyr::mutate(study = factor(rep(studies, times = n_group), levels = unique(studies))) |>
         dplyr::select(!dplyr::all_of("survival"))
 
     time_dat <- expand.grid(

R/simulations_gsf.R

@@ -81,6 +81,8 @@ sim_lm_gsf <- function(
 
         assert_that(
             length(unique(lm_base$study)) == length(mu_b),
+            is.factor(lm_base$study),
+            is.factor(lm_base$arm),
             length(mu_b) == 1,
             length(sigma) == 1,
             length(mu_s) == length(unique(lm_base$arm)),
@@ -92,8 +94,8 @@ sim_lm_gsf <- function(
 
         baseline_covs <- lm_base |>
             dplyr::distinct(.data$pt, .data$arm, .data$study) |>
-            dplyr::mutate(study_idx = as.numeric(factor(as.character(.data$study)))) |>
-            dplyr::mutate(arm_idx = as.numeric(factor(as.character(.data$arm)))) |>
+            dplyr::mutate(study_idx = as.numeric(.data$study)) |>
+            dplyr::mutate(arm_idx = as.numeric(.data$arm)) |>
             dplyr::mutate(psi_b = stats::rlnorm(dplyr::n(), log(mu_b[.data$study_idx]), omega_b)) |>
             dplyr::mutate(psi_s = stats::rlnorm(dplyr::n(), log(mu_s[.data$arm_idx]), omega_s)) |>
             dplyr::mutate(psi_g = stats::rlnorm(dplyr::n(), log(mu_g[.data$arm_idx]), omega_g)) |>

R/simulations_rs.R

@@ -1,8 +1,8 @@
 
 #' Construct a Simulation Function for Longitudinal Data from Random Slope Model
 #'
-#' @param intercept (`number`)\cr the mean baseline value.
-#' @param slope_mu (`numeric`)\cr the population slope for the two treatment arms.
+#' @param intercept (`number`)\cr the mean baseline value for each study.
+#' @param slope_mu (`numeric`)\cr the population slope for each treatment arm.
 #' @param slope_sigma (`number`)\cr the random slope standard deviation.
 #' @param sigma (`number`)\cr the variance of the longitudinal values.
 #' @param link_dsld (`number`)\cr the link coefficient for the DSLD contribution.
@@ -23,20 +23,29 @@ sim_lm_random_slope <- function(
     function(lm_base) {
 
         assert_that(
-            length(slope_mu) == 1 | length(slope_mu) == length(unique(lm_base$arm)),
-            msg = "slope_mu should either be length 1 or equal to the length of n_arm"
+            is.factor(lm_base$study),
+            is.factor(lm_base$arm)
         )
 
-        if (length(slope_mu) == 1) {
-            slope_mu <- rep(slope_mu, length(unique(lm_base$arm)))
-        }
+        assert_that(
+            length(slope_mu) == length(unique(lm_base$arm)),
+            msg = "`length(slope_mu)` should be equal to the number of unique studies"
+        )
+
+        assert_that(
+            length(intercept) == length(unique(lm_base$study)),
+            msg = "`length(intercept)` should be equal to the number of unique studies"
+        )
 
         rs_baseline <- lm_base |>
-            dplyr::distinct(.data$pt, .data$arm) |>
+            dplyr::distinct(.data$pt, .data$arm, .data$study) |>
+            dplyr::mutate(intercept = intercept[as.numeric(.data$study)]) |>
             dplyr::mutate(slope_ind = stats::rnorm(
-                dplyr::n(), slope_mu[as.numeric(factor(as.character(.data$arm)))], sd = slope_sigma
+                n = dplyr::n(),
+                mean = slope_mu[as.numeric(.data$arm)],
+                sd = slope_sigma
             )) |>
-            dplyr::select(!dplyr::any_of("arm"))
+            dplyr::select(!dplyr::any_of(c("arm", "study")))
 
         lm_dat <- lm_base |>
             dplyr::mutate(err = stats::rnorm(dplyr::n(), 0, sigma)) |>

R/simulations_sf.R

@@ -76,15 +76,17 @@ sim_lm_sf <- function(
             length(unique(lm_base$study)) == length(mu_b),
             length(mu_b) == 1,
             length(sigma) == 1,
+            is.factor(lm_base$study),
+            is.factor(lm_base$arm),
             length(mu_s) == length(unique(lm_base$arm)),
             length(mu_s) == length(mu_g),
             length(c(omega_b, omega_s, omega_g)) == 3
         )
 
         baseline_covs <- lm_base |>
             dplyr::distinct(.data$pt, .data$arm, .data$study) |>
-            dplyr::mutate(study_idx = as.numeric(factor(as.character(.data$study)))) |>
-            dplyr::mutate(arm_idx = as.numeric(factor(as.character(.data$arm)))) |>
+            dplyr::mutate(study_idx = as.numeric(.data$study)) |>
+            dplyr::mutate(arm_idx = as.numeric(.data$arm)) |>
             dplyr::mutate(psi_b = stats::rlnorm(dplyr::n(), log(mu_b[.data$study_idx]), omega_b)) |>
             dplyr::mutate(psi_s = stats::rlnorm(dplyr::n(), log(mu_s[.data$arm_idx]), omega_s)) |>
             dplyr::mutate(psi_g = stats::rlnorm(dplyr::n(), log(mu_g[.data$arm_idx]), omega_g))

_pkgdown.yml

@@ -45,6 +45,7 @@ reference:
       - sim_os_exponential
       - sim_os_loglogistic
       - sim_os_weibull
+      - SimGroup
   - title: Prior Distributions
     contents:
       - Prior

design/debug-gsf/compare/model.R

@@ -285,7 +285,9 @@ devtools::load_all(export = FALSE, path = "../../..")
 set.seed(4513)
 jlist <- simulate_joint_data(
     .debug = TRUE,
-    n_arm = c(50),
+    design = list(
+        SimGroup(50, "Arm-A", "Study-X")
+    ),
     times = seq(0, 3, by = (1/365)/2),
     lambda_cen = 1 / 9000,
     beta_cat = c(

design/example_of_use.R

@@ -78,7 +78,10 @@ write_stan(jm, "local/debug.stan")
 
 ## Generate Test data with known parameters
 jlist <- simulate_joint_data(
-    n_arm = c(500, 500),
+    design = list(
+        SimGroup(500, "Arm-A", "Study-X"),
+        SimGroup(500, "Arm-B", "Study-X")
+    ),
     times = 1:2000,
     lambda_cen = 1 / 9000,
     beta_cat = c(

design/tests/gsf-no-link.R

@@ -21,7 +21,10 @@ options("jmpost.cache_dir" = file.path("local", "models"))
 # set.seed(7143)
 # jlist <- simulate_joint_data(
 #     .debug = TRUE,
-#     n_arm = c(70, 90),
+    # design = list(
+    #     SimGroup(70, "Arm-A", "Study-X"),
+    #     SimGroup(90, "Arm-B", "Study-X")
+    # ),
 #     times = seq(0, 3, by = (1/365)/2),
 #     lambda_cen = 1 / 9000,
 #     beta_cat = c(
@@ -48,7 +51,10 @@ options("jmpost.cache_dir" = file.path("local", "models"))
 set.seed(7143)
 jlist <- simulate_joint_data(
     .debug = TRUE,
-    n_arm = c(85, 100),
+    design = list(
+        SimGroup(85, "Arm-A", "Study-X"),
+        SimGroup(100, "Arm-B", "Study-X")
+    ),
     times = seq(0, 3, by = (1/365)/2),
     lambda_cen = 1 / 9000,
     beta_cat = c(

design/tests/gsf-with-link-identity.R

@@ -18,7 +18,9 @@ options("jmpost.cache_dir" = file.path("local", "models"))
 
 ## Generate Test data with known parameters
 jlist <- simulate_joint_data(
-    n_arm = 200,
+    design = list(
+        SimGroup(200, "Arm-A", "Study-X")
+    ),
     times = seq(0, 1000),
     lambda_cen = 1 / 9000,
     beta_cat = c(

design/tests/gsf-with-link.R

@@ -17,7 +17,10 @@ options("jmpost.cache_dir" = file.path("local", "models"))
 
 ## Generate Test data with known parameters
 jlist <- simulate_joint_data(
-    n_arm = c(80, 80),
+    design = list(
+        SimGroup(80, "Arm-A", "Study-X"),
+        SimGroup(80, "Arm-B", "Study-X")
+    ),
     times = seq(0, 4, by = (1/365)/2),
     lambda_cen = 1 / 9000,
     beta_cat = c(

design/tests/os-brier-score.R

@@ -7,7 +7,10 @@ options("jmpost.cache_dir" = file.path("local", "models"))
 library(bayesplot)
 
 jlist <- simulate_joint_data(
-    n_arm = c(1000, 1000),
+    design = list(
+        SimGroup(1000, "Arm-A", "Study-X"),
+        SimGroup(1000, "Arm-B", "Study-X")
+    ),
     times = seq(1, 1000, by = 0.5),
     lambda_cen = 1 / 9000,
     beta_cat = c(

design/tests/os-exponential.R

@@ -7,7 +7,10 @@ options("jmpost.cache_dir" = file.path("local", "models"))
 library(bayesplot)
 
 jlist <- simulate_joint_data(
-    n_arm = c(1000, 1000),
+    design = list(
+        SimGroup(1000, "Arm-A", "Study-X"),
+        SimGroup(1000, "Arm-B", "Study-X")
+    ),
     times = seq(1, 1000, by = 0.5),
     lambda_cen = 1 / 9000,
     beta_cat = c(

design/tests/os-loglogistic.R

@@ -6,7 +6,10 @@ library(bayesplot)
 options("jmpost.cache_dir" = file.path("local", "models"))
 
 jlist <- simulate_joint_data(
-    n_arm = c(1000, 1000),
+    design = list(
+        SimGroup(1000, "Arm-A", "Study-X"),
+        SimGroup(1000, "Arm-B", "Study-X")
+    ),
     times = 1:2000,
     lambda_cen = 1 / 9000,
     beta_cat = c(

design/tests/os-weibull.R

@@ -8,7 +8,10 @@ options("jmpost.cache_dir" = file.path("local", "models"))
 set.seed(6042)
 
 jlist <- simulate_joint_data(
-    n_arm = c(1000, 1000),
+    design = list(
+        SimGroup(1000, "Arm-A", "Study-X"),
+        SimGroup(1000, "Arm-B", "Study-X")
+    ),
     times = 1:2000,
     lambda_cen = 1 / 9000,
     beta_cat = c(

design/tests/os-zero-covariates.R

@@ -7,7 +7,9 @@ options("jmpost.cache_dir" = file.path("local", "models"))
 true_lambda <- 1/100
 
 jlist <- simulate_joint_data(
-    n_arm = c(500),
+    design = list(
+        SimGroup(500, "Arm-B", "Study-X")
+    ),
     times = seq(1, 1000, by = 0.5),
     lambda_cen = 1 / 9000,
     beta_cat = c( "A" = 0, "B" = 0, "C" = 0),

design/tests/sf-no-link.R

@@ -48,7 +48,10 @@ options("jmpost.cache_dir" = file.path("local", "models"))
 set.seed(7143)
 jlist <- simulate_joint_data(
     .debug = TRUE,
-    n_arm = c(85, 100),
+    design = list(
+        SimGroup(85, "Arm-A", "Study-X"),
+        SimGroup(100, "Arm-B", "Study-X")
+    ),
     times = seq(0, 3, by = (1/365)/2),
     lambda_cen = 1 / 9000,
     beta_cat = c(