Wild bootstrap for Cox PH regression
wild bootstrap for uniform bands for Cox models
Bootphreg(
formula,
data,
offset = NULL,
weights = NULL,
B = 1000,
type = c("exp", "poisson", "normal"),
...
)formula |
formula with 'Surv' outcome (see |
data |
data frame |
offset |
offsets for cox model |
weights |
weights for Cox score equations |
B |
bootstraps |
type |
distribution for multiplier |
... |
Additional arguments to lower level funtions |
Klaus K. Holst, Thomas Scheike
Wild bootstrap based confidence intervals for multiplicative hazards models, Dobler, Pauly, and Scheike (2018),
n <- 100 x <- 4*rnorm(n) time1 <- 2*rexp(n)/exp(x*0.3) time2 <- 2*rexp(n)/exp(x*(-0.3)) status <- ifelse(time1<time2,1,2) time <- pmin(time1,time2) rbin <- rbinom(n,1,0.5) cc <-rexp(n)*(rbin==1)+(rbin==0)*rep(3,n) status <- ifelse(time < cc,status,0) time <- ifelse(time < cc,time,cc) data <- data.frame(time=time,status=status,x=x) b1 <- Bootphreg(Surv(time,status==1)~x,data,B=1000) b2 <- Bootphreg(Surv(time,status==2)~x,data,B=1000) c1 <- phreg(Surv(time,status==1)~x,data) c2 <- phreg(Surv(time,status==2)~x,data) ### exp to make all bootstraps positive out <- pred.cif.boot(b1,b2,c1,c2,gplot=0) cif.true <- (1-exp(-out$time))*.5 with(out,plot(time,cif,ylim=c(0,1),type="l")) lines(out$time,cif.true,col=3) with(out,plotConfRegion(time,band.EE,col=1)) with(out,plotConfRegion(time,band.EE.log,col=3)) with(out,plotConfRegion(time,band.EE.log.o,col=2))
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