heinzeschemper {PHInfiniteEstimates}

Simulate operating characteristics of repaired Cox regression and competitors.

Description

This function is intended to verify the operating characteristics of the approximate conditional inferential approach of Kolassa and Zhang (2019) to proportional hazards regression. An exponential 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.

Usage

heinzeschemper(
  nobs = 50,
  k = 5,
  B = 1,
  c = 0,
  nsamp = 1000,
  beta = NULL,
  add = NULL,
  half = NULL,
  verbose = FALSE,
  smoothfirst = FALSE
)

Arguments

nobs

number of observations in simulated data set.

k

number of covariates in simulated data set. Each covariate is dochotomous.

B

odds of 1 vs. 0 in dichotomous variables.

c

censoring proportion.

nsamp

number of samples.

beta

regression parameters, all zeros if null, and all the same value if a scalar.

add

partial simulation results to be added to, or NULL if de novo.

half

does nothing; provided for compatabilitity with simcode.

verbose

Triggers verbose messages.

smoothfirst

Triggers normal rather than dichotomous interest covariate.

Value

a list with components

• out matrix with columns corresponding to p-values.