mets (version 1.3.5)

survivalG: G-estimator for Cox and Fine-Gray model

Description

Computes G-estimator $$\hat{S}(t,A=a)=n^{-1}\sum _i\hat{S}(t,A=a,Z_i)$$ for the Cox model based on phreg og the Fine-Gray model based on the cifreg function. Gives influence functions of these risk estimates and SE's are based on these. If first covariate is a factor then all contrast are computed, and if continuous then considered covariate values are given by Avalues.

Usage

survivalG(
  x,
  data,
  time = NULL,
  Avalues = c(0, 1),
  varname = NULL,
  same.data = TRUE,
  id = NULL
)

Arguments

x

phreg or cifreg object

data

data frame for risk averaging

time

for estimate

Avalues

values to compare for first covariate A

varname

if given then averages for this variable, default is first variable

same.data

assumes that same data is used for fitting of survival model and averaging.

id

might be given to link to data to iid decomposition of survival data, must be coded as 1,2,..,

Author

Thomas Scheike

Examples

data(bmt); bmt$time <- bmt$time+runif(408)*0.001
bmt$event <- (bmt$cause!=0)*1
dfactor(bmt) <- tcell.f~tcell

fg1 <- cifreg(Event(time,cause)~tcell.f+platelet+age,bmt,cause=1,
              cox.prep=TRUE,propodds=NULL)
summary(survivalG(fg1,bmt,50))

ss <- phreg(Surv(time,event)~tcell.f+platelet+age,bmt) 
summary(survivalG(ss,bmt,50))

sst <- survivalGtime(ss,bmt,n=50)
plot(sst,type=c("survival","risk","survival.ratio")[1])