exp_memsim: Memoryless simulation for the exponential change-point hazard...

In cpsurvsim: Simulating Survival Data from Change-Point Hazard Distributions

Memoryless simulation for the exponential change-point hazard distribution

Description

exp_memsim simulates time-to-event data from the exponential change-point hazard distribution by implementing the memoryless method.

Usage

exp_memsim(n, endtime, theta, tau = NA)

Arguments

n	Sample size
endtime	Maximum study time, point at which all participants are censored
theta	Scale parameter \theta
tau	Change-point(s) \tau

Details

This function simulates time-to-event data between K change-points from independent exponential distributions using the inverse CDF implemented in exp_icdf. This method applies Type I right censoring at the endtime specified by the user.

Value

Dataset with n participants including a survival time and censoring indicator (0 = censored, 1 = event).

Examples

nochangepoint <- exp_memsim( n = 10, endtime = 20, theta = 0.05)
onechangepoint <- exp_memsim(n = 10, endtime = 20,
  theta = c(0.05, 0.01), tau = 10)
twochangepoints <- exp_memsim(n = 10, endtime = 20,
  theta = c(0.05, 0.01, 0.05), tau = c(8, 12))

# Pay attention to how you parameterize your model!
# This simulates a decreasing hazard
set.seed(1245)
decreasingHazard <- exp_memsim(n = 10, endtime = 20,
  theta = c(0.05, 0.02, 0.01), tau = c(8, 12))
# This tries to fit an increasing hazard, resulting in biased estimates
cp2.nll <- function(par, tau = tau, dta = dta){
  theta1 <- par[1]
  theta2 <- par[2]
  theta3 <- par[3]
  ll <- log(theta1) * sum(dta$time < tau[1])+
        log(theta2) * sum((tau[1] <= dta$time) * (dta$time < tau[2])) +
        log(theta3) * sum((dta$time >= tau[2]) * dta$censor) -
        theta1 * sum(dta$time * (dta$time < tau[1]) +
          tau[1] * (dta$time >= tau[1])) -
        theta2 * sum((dta$time - tau[1]) * (dta$time >= tau[1]) *
          (dta$time < tau[2]) + (tau[2] - tau[1]) * (dta$time >= tau[2])) -
        theta3 * sum((dta$time - tau[2]) * (dta$time >= tau[2]))
  return(-ll)
}
optim(par = c(0.001, 0.1, 0.5), fn = cp2.nll,
      tau = c(8, 12), dta = decreasingHazard)

cpsurvsim documentation built on Sept. 8, 2023, 5:35 p.m.