# gofM.phreg: GOF for Cox covariates in PH regression In mets: Analysis of Multivariate Event Times

## Description

Cumulative residuals after model matrix for Cox PH regression p-values based on Lin, Wei, Ying resampling.

## Usage

  1 2 3 4 5 6 7 8 9 10 gofM.phreg( formula, data, offset = NULL, weights = NULL, modelmatrix = NULL, n.sim = 1000, silent = 1, ... ) 

## Arguments

 formula formula for cox regression data data for model offset offset weights weights modelmatrix matrix for cumulating residuals n.sim number of simulations for score processes silent to keep it absolutely silent, otherwise timing estimate will be prduced for longer jobs. ... Additional arguments to lower level funtions

## Details

That is, computes

U(t) = \int_0^t M^t d \hat M

and resamples its asymptotic distribution.

This will show if the residuals are consistent with the model. Typically, M will be a design matrix for the continous covariates that gives for example the quartiles, and then the plot will show if for the different quartiles of the covariate the risk prediction is consistent over time (time x covariate interaction).

## Author(s)

Thomas Scheike and Klaus K. Holst

## Examples

  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 library(mets) data(TRACE) set.seed(1) TRACEsam <- blocksample(TRACE,idvar="id",replace=FALSE,100) dcut(TRACEsam) <- ~. mm <- model.matrix(~-1+factor(wmicat.4),data=TRACEsam) m1 <- gofM.phreg(Surv(time,status==9)~vf+chf+wmi,data=TRACEsam,modelmatrix=mm) summary(m1) if (interactive()) { par(mfrow=c(2,2)) plot(m1) } m1 <- gofM.phreg(Surv(time,status==9)~strata(vf)+chf+wmi,data=TRACEsam,modelmatrix=mm) summary(m1) ## cumulative sums in covariates, via design matrix mm mm <- cumContr(TRACEsam\$wmi,breaks=10,equi=TRUE) m1 <- gofM.phreg(Surv(time,status==9)~strata(vf)+chf+wmi,data=TRACEsam, modelmatrix=mm,silent=0) summary(m1) 

mets documentation built on Oct. 23, 2020, 5:55 p.m.