simJMWSVdata: Simulate joint model data with heterogeneous within-subject variability

Description

Simulates longitudinal biomarker data with heterogeneous within-subject variability and associated time-to-event data. The survival outcome may be generated either as a single-failure time-to-event outcome or as competing risks data with two failure types.

Usage

simJMWSVdata(
  seed = 100,
  N = 200,
  increment = 0.7,
  beta = c(5, 1.5, 2, 1, 2),
  tau = c(0.5, 0.5, -0.2, 0.2, 0.05),
  gamma1 = c(1, 0.5, 0.5),
  gamma2 = c(-0.5, 0.5, 0.25),
  alpha1 = c(1, 0.7),
  alpha2 = c(-1, -0.5),
  vee1 = 0.5,
  vee2 = -0.5,
  lambda1 = 0.05,
  lambda2 = 0.025,
  CL = 5,
  CU = 10,
  covbw = diag(rep(1, 3)),
  CR = TRUE
)

Value

A list with the following components:

ydatah: A long-format data frame containing the simulated longitudinal biomarker measurements.
cdatah: A data frame containing the simulated event-time data.

Arguments

seed: Integer random seed used for data generation. Default is 100.
N: Integer specifying the sample size. Default is 200.
increment: Numeric value specifying the increment of visit times for longitudinal measurements. Default is 0.7.
beta: Numeric vector of true fixed-effect parameters for the longitudinal mean sub-model.
tau: Numeric vector of true parameters for the within-subject variability sub-model.
gamma1: Numeric vector of true fixed-effect parameters in the cause-specific hazard sub-model for failure type 1.
gamma2: Numeric vector of true fixed-effect parameters in the cause-specific hazard sub-model for failure type 2.
alpha1: Numeric vector of association parameters of individual mean linking the longitudinal process to the cause-specific hazard for failure type 1.
alpha2: Numeric vector of association parameters individual mean linking the longitudinal process to the cause-specific hazard for failure type 2.
vee1: Numeric association parameter of within-subject variability linking the longitudinal process to the cause-specific hazard for failure type 1.
vee2: Numeric association parameter of within-subject variability linking the longitudinal process to the cause-specific hazard for failure type 2.
lambda1: Baseline hazard rate for failure type 1. An exponential baseline hazard with rate lambda1 is assumed.
lambda2: Baseline hazard rate for failure type 2. An exponential baseline hazard with rate lambda2 is assumed.
CL: Lower bound of the uniform distribution used to generate censoring times.
CU: Upper bound of the uniform distribution used to generate censoring times.
covbw: Variance-covariance matrix for the random effects in the longitudinal mean and within-subject variability sub-models.
CR: Logical; if TRUE, competing risks data with two failure types are simulated. If FALSE, a single-failure time-to-event outcome is generated. Default is TRUE.

Details

Simulate joint model data with heterogeneous within-subject variability