Bootphreg: Wild bootstrap for Cox PH regression

In mets: Analysis of Multivariate Event Times

Wild bootstrap for Cox PH regression

Description

wild bootstrap for uniform bands for Cox models

Usage

Bootphreg(
  formula,
  data,
  offset = NULL,
  weights = NULL,
  B = 1000,
  type = c("exp", "poisson", "normal"),
  ...
)

Arguments

formula	formula with 'Surv' outcome (see coxph)
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

Author(s)

Klaus K. Holst, Thomas Scheike

References

Wild bootstrap based confidence intervals for multiplicative hazards models, Dobler, Pauly, and Scheike (2018),

Examples

 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))

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