pshtest: Goodness-of-fit test for proportional subdistribution hazards...

In crrSC: Competing Risks Regression for Stratified and Clustered Data

Goodness-of-fit test for proportional subdistribution hazards model

Description

This Goodness-of-fit test proposed a modified weighted Schoenfeld residuals to test the proportionality of subdistribution hazards for the Fine and Gray model

Usage

psh.test(time, fstatus, z, D=c(1,1), tf=function(x) cbind(x,x^2), init)

Arguments

time	vector of failure times
fstatus	failure status =0 if censored
z	covariates
D	components of z that are tested for time-varying effect
tf	functions of t for z being tested on the same location
init	initial values of regression parameters (default=all 0)

Details

The proposed score test employs Schoenfeld residuals adapted to competing risks data. The form of the test is established assuming that the non-proportionality arises via time-dependent coefficients in the Fine-Gray model, similar to the test of Grambsch and Therneau.

Value

Returns a data.frame with percentage of cens, cause 1, Test Statistic, d.f. ,p-value

Author(s)

Bingqing Zhou, [e-mail]

References

Zhou B, Fine JP, Laird, G. (2013). Goodness-of-fit test for proportional subdistribution hazards mode. Statistics in Medicine. In Press.

Examples

data(bce)
attach(bce)
lognodes <- log(nnodes)
Z1 <- cbind(lognodes, tsize/10, age, trt)  
# trt = 0 if placebo, = 0 treatment
# testing for linear time varying effect of trt
psh.test(time=time, fstatus=type, z=Z1, D=c(0,0,0,1), tf=function(x) x)
# testing for quadratic time varying effect of trt
psh.test(time=time, fstatus=type, z=Z1, D=c(0,0,0,1), tf=function(x) x^2)
# testing for log time varying effect of trt
psh.test(time=time, fstatus=type, z=Z1, D=c(0,0,0,1), 
tf=function(x) log(x))
# testing for both linear and quadratic time varying effect of trt
psh.test(time=time, fstatus=type, z=Z1, 
D=matrix(c(0,0,0,1,0,0,0,1), 4,2),  tf=function(x) cbind(x,x^2))

crrSC documentation built on June 11, 2022, 1:05 a.m.