simfrail function

Simulate survival data and fit models

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 fitfrail, and the resulting parameter estimates are aggregated and summarized.

simfrail(reps, genfrail.args, fitfrail.args, Lambda.times, 
         vcov.args = list(), cores = 0, skip.SE = FALSE)

Arguments

• reps: number of times to repeat the simulation
• genfrail.args: list of arguments to pass to genfrail
• fitfrail.args: list of arguments to pass to fitfrail
• Lambda.times: vector of time points to obtain baseline hazard estimates at
• vcov.args: list of arguments to pass to vcov.fitfrail for variance estimates. This is mainly used to specify whether bootstrap or estimated variances should be obtained.
• 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
• skip.SE: logical value, whether to skip the standard error estimates (saves time)

Returns

A simfrail 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

• 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

Author(s)

John. V Monaco, Malka Gorfine, Li Hsu

See Also

genfrail, fitfrail

Examples

## Not run:

sim <- simfrail(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), 
                fitfrail.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")
## End(Not run)