GOF for Cox PH regression — gof.phreg • mets

Cumulative score process residuals for Cox PH regression p-values based on Lin, Wei, Ying resampling.

# S3 method for class 'phreg'
gof(object, n.sim = 1000, silent = 1, robust = NULL, ...)

Arguments

object: is phreg object
n.sim: number of simulations for score processes
silent: to show timing estimate will be produced for longer jobs
robust: to control wether robust dM_i(t) or dN_i are used for simulations
...: Additional arguments to lower level funtions

Author

Thomas Scheike and Klaus K. Holst

Examples

library(mets)
data(sTRACE)

m1 <- phreg(Surv(time,status==9)~vf+chf+diabetes,data=sTRACE) 
gg <- gof(m1)
gg
#> Cumulative score process test for Proportionality:
#>          Sup|U(t)|  pval
#> vf        7.276731 0.009
#> chf       8.971263 0.078
#> diabetes  3.044404 0.788
par(mfrow=c(1,3))
plot(gg)


m1 <- phreg(Surv(time,status==9)~strata(vf)+chf+diabetes,data=sTRACE) 
## to get Martingale ~ dN based simulations
gg <- gof(m1)
gg
#> Cumulative score process test for Proportionality:
#>          Sup|U(t)|  pval
#> chf       8.036132 0.132
#> diabetes  3.441389 0.656

## to get Martingale robust simulations, specify cluster in  call 
sTRACE$id <- 1:500
m1 <- phreg(Surv(time,status==9)~vf+chf+diabetes+cluster(id),data=sTRACE) 
gg <- gof(m1)
gg
#> Cumulative score process test for Proportionality:
#>          Sup|U(t)|  pval
#> vf        7.276731 0.004
#> chf       8.971263 0.067
#> diabetes  3.044404 0.790

m1 <- phreg(Surv(time,status==9)~strata(vf)+chf+diabetes+cluster(id),data=sTRACE) 
gg <- gof(m1)
gg
#> Cumulative score process test for Proportionality:
#>          Sup|U(t)|  pval
#> chf       8.036132 0.164
#> diabetes  3.441389 0.666