simcode: Simulate Weibull survival data from a model, perform...

In PHInfiniteEstimates: Tools for Inference in the Presence of a Monotone Likelihood

Simulate Weibull survival data from a model, perform reduction to remove infinite estimates, and calculate p values.

Description

Operating characteristics for the approximate conditional inferential approach to proportional hazards.

Usage

simcode(
  dataset,
  myformula,
  iv,
  ctime,
  nsamp = 10000,
  add = NULL,
  nobs = NA,
  half = FALSE,
  verbose = FALSE
)

Arguments

dataset	the data set to use
myformula	the formula for the Cox regression
iv	name of the variable of interest, as a character string
ctime	fixed censoring time
nsamp	number of samples.
add	preliminary results, if any.
nobs	number of observations in target models, if different from that of dataset.
half	logical flag triggering a less extreme simulation by dividing the Weibull regression parameters in half.
verbose	logical flag triggering intermediate messaging.

Details

This function is intended to verify the operating characteristics of the approximate conditional inferential approach of \insertCitekz19;textualPHInfiniteEstimates to proportional hazards regression. A Weibull regression model, corresponding to the proportional hazards regression model, is fit to the data, and new data sets are simulated from this model. P-values are calculated for these new data sets, and their empirical distribution is compared to the theoretical uniform distribution.

Value

a list with components

• out matrix with columns corresponding to p-values.

• seed random seed

• bad unused.

• srreg parametric lifetime regression

References

\insertRef

kz19PHInfiniteEstimates

Examples

data(breast)

breasttestp<-simcode(breast,Surv(TIME,CENS)~ T+ N+ G+ CD,"T",72,nsamp=100,verbose=TRUE)

PHInfiniteEstimates documentation built on Nov. 24, 2023, 9:06 a.m.