simcoxph: Simulate survival data and fit models

Description

Generates simulated clustered survival data by repeatedly generating data, using a shared frailty model, and fitting the models. Respective arguments are passed to genfrail and coxph, and the resulting parameter estimates are aggregated and summarized.

This function is similar to simfrail, except models are fitted using the coxph.

Usage

simcoxph(reps, genfrail.args, coxph.args, Lambda.times, cores = 0)

Value

A simcoxph object that is essentially a data.frame of the resulting parameter estimates. Each row is a single run, and columns are as follows.

seed: the seed used for the run
runtime: the time it took to fit the model
N: number of clusters
mean.K: average cluster size
cens: empirical censorship
beta: true regression coefficients
hat.beta: estimated regression coefficients
se.beta: standard error of each regression coefficient
theta: true frailty distribution parameters
hat.theta: estimated frailty distribution parameters
se.theta: standard error of each frailty distribution parameter (NA since coxph does not currently provide this.)
Lambda: true cumulative baseline hazard at each Lambda.times point
hat.Lambda: estimated cumulative baseline hazard at each Lambda.times point
se.Lambda: standard error at each Lambda.times point (NA since coxph does not currently provide this)

Arguments

reps: number of times to repeat the simulation
genfrail.args: list of arguments to pass to genfrail
coxph.args: list of arguments to pass to coxph
Lambda.times: vector of time points to obtain baseline hazard estimates at
cores: integer; if > 0, the number of cores to use; if < 0, the number of cores not to use; if 0, use all available cores

Author

John. V Monaco, Malka Gorfine, Li Hsu

Examples

Run this code

if (FALSE) {
sim <- simcoxph(reps=100,
                genfrail.args=alist(
                  N=50, K=2,
                  beta=c(log(2),log(3)), 
                  frailty="gamma", theta=2,
                  Lambda_0 = function(t, tau=4.6, C=0.01) (C*t)^tau), 
                coxph.args=alist(
                  formula=Surv(time, status) ~ Z1 + Z2 + cluster(family), 
                  frailty="gamma"),
                Lambda.times=1:120, cores = 0)

# Summarize the results
summary(sim)

# Plot the residuals
plot(sim, "residuals")
}