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

In mets: Analysis of Multivariate Event Times

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(s)

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))

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