simJMdata: Simulate single-biomarker joint model data

Description

Simulates single longitudinal biomarker data 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

simJMdata(
  seed = 100,
  N = 200,
  increment = 0.7,
  beta = c(5, 1.5, 2, 1, 2),
  sigma2 = 1,
  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),
  lambda1 = 0.05,
  lambda2 = 0.025,
  CL = 5,
  CU = 10,
  covb = diag(rep(1, 2)),
  CR = TRUE
)

Value

A list with the following components:

ydata: A long-format data frame containing the simulated longitudinal biomarker measurements.
cdata: 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 sub-model.
sigma2: Scalar parameter of error variance for the longitudinal 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.
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.
covb: Variance-covariance matrix for the random effects in the longitudinal sub-model.
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 single-biomarker joint model data