gofZ.phreg: GOF for Cox covariates in PH regression

In mets: Analysis of Multivariate Event Times

GOF for Cox covariates in PH regression

Description

That is, computes

U(z,\tau) = \int_0^\tau M(z)^t d \hat M

and resamples its asymptotic distribution.

Usage

gofZ.phreg(
  formula,
  data,
  vars = NULL,
  offset = NULL,
  weights = NULL,
  breaks = 50,
  equi = FALSE,
  n.sim = 1000,
  silent = 1,
  ...
)

Arguments

formula	formula for cox regression
data	data for model
vars	which variables to test for linearity
offset	offset
weights	weights
breaks	number of breaks for cumulatives in covarirate direction
equi	equidistant breaks or not
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

This will show if the residuals are consistent with the model evaulated in the z covariate. M is here chosen based on a grid (z_1, ..., z_m) and the different columns are I(Z_i \leq z_l). for l=1,...,m. The process in z is resampled to find extreme values. The time-points of evuluation is by default 50 points, chosen as 2

The p-value is valid but depends on the chosen grid. When the number of break points are high this will give the orginal test of Lin, Wei and Ying for linearity, that is also computed in the timereg package.

Author(s)

Thomas Scheike and Klaus K. Holst

Examples

library(mets)
data(TRACE)
set.seed(1)
TRACEsam <- blocksample(TRACE,idvar="id",replace=FALSE,100)

## cumulative sums in covariates, via design matrix mm
 ## Reduce Ex.Timings
m1 <- gofZ.phreg(Surv(time,status==9)~strata(vf)+chf+wmi+age,data=TRACEsam)
summary(m1) 
plot(m1,type="z")

mets documentation built on May 29, 2024, 3:51 a.m.