| psh.test {crrSC} | R Documentation |
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, bingqing.zhou@yale.edu
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))