predict.crrwt: Compute predictive CIFs for given set of covariates.

In wtcrsk: Competing Risks Regression Models with Covariate-Adjusted Censoring Weights

Description

This is a function to calculate prediction of the cumulative incidence function (CIF) as well as its variance at observed failure times given in original data.

Usage

## S3 method for class 'crrwt'
predict(object, z, ...)

Arguments

object	a 'crrwt' class object obtained from crr.wt function.
z	sets of covariates used for prediction, each row represents a new set of covariates.
...	additional arguments affecting the predictions produced.

Details

More derivations are given in the reference.

Value

z	given sets of covariates.
time	observed failure times.
F1	predicted cumulative incidence probabilities at observed failure times.
F1sd	standard errors of predicted cumulative incidence probabilities.

Author(s)

Peng He

References

He P, Scheike TH and Zhang MJ, A proportional hazards regression model for the subdistribution with covariates adjusted censoring weight for competing risks data, Technical Report #61, Division of Biostatistics, Medical College of Wisconsin, November 2013.

wtcrsk documentation built on Jan. 15, 2017, 10:50 p.m.