prep.comp.risk: Set up weights for delayed-entry competing risks data for...

In timereg: Flexible Regression Models for Survival Data

Set up weights for delayed-entry competing risks data for comp.risk function

Description

Computes the weights of Geskus (2011) modified to the setting of the comp.risk function. The returned weights are 1/(H(T_i)*G_c(min(T_i,tau))) and tau is the max of the times argument, here H is the estimator of the truncation distribution and G_c is the right censoring distribution.

Usage

prep.comp.risk(
  data,
  times = NULL,
  entrytime = NULL,
  time = "time",
  cause = "cause",
  cname = "cweight",
  tname = "tweight",
  strata = NULL,
  nocens.out = TRUE,
  cens.formula = NULL,
  cens.code = 0,
  prec.factor = 100,
  trunc.mintau = FALSE
)

Arguments

data	data frame for comp.risk.
times	times for estimating equations.
entrytime	name of delayed entry variable, if not given computes right-censoring case.
time	name of survival time variable.
cause	name of cause indicator
cname	name of censoring weight.
tname	name of truncation weight.
strata	strata variable to obtain stratified weights.
nocens.out	returns only uncensored part of data-frame
cens.formula	censoring model formula for Cox models for the truncation and censoring model.
cens.code	code for censoring among causes.
prec.factor	precision factor, for ties between censoring/even times, truncation times/event times
trunc.mintau	specicies wether the truncation distribution is evaluated in death times or death times minimum max(times), FALSE makes the estimator equivalent to Kaplan-Meier (in the no covariate case).

Value

Returns an object. With the following arguments:

dataw

a data.frame with weights.

The function wants to make two new variables "weights" and "cw" so if these already are in the data frame it tries to add an "_" in the names.

Author(s)

Thomas Scheike

References

Geskus (2011), Cause-Specific Cumulative Incidence Estimation and the Fine and Gray Model Under Both Left Truncation and Right Censoring, Biometrics (2011), pp 39-49.

Shen (2011), Proportional subdistribution hazards regression for left-truncated competing risks data, Journal of Nonparametric Statistics (2011), 23, 885-895

Examples

data(bmt)
nn <- nrow(bmt)
entrytime <- rbinom(nn,1,0.5)*(bmt$time*runif(nn))
bmt$entrytime <- entrytime
times <- seq(5,70,by=1)

### adds weights to uncensored observations
bmtw <- prep.comp.risk(bmt,times=times,time="time",
		       entrytime="entrytime",cause="cause")

#########################################
### nonparametric estimates
#########################################
## {{{ 
### nonparametric estimates, right-censoring only 
out <- comp.risk(Event(time,cause)~+1,data=bmt,
		 cause=1,model="rcif2",
		 times=c(5,30,70),n.sim=0)
out$cum
### same as 
###out <- prodlim(Hist(time,cause)~+1,data=bmt)
###summary(out,cause="1",times=c(5,30,70))

### with truncation 
out <- comp.risk(Event(time,cause)~+1,data=bmtw,cause=1,
  model="rcif2",
  cens.weight=bmtw$cw,weights=bmtw$weights,times=c(5,30,70),
  n.sim=0)
out$cum
### same as
###out <- prodlim(Hist(entry=entrytime,time,cause)~+1,data=bmt)
###summary(out,cause="1",times=c(5,30,70))
## }}} 

#########################################
### Regression 
#########################################
## {{{ 
### with truncation correction
out <- comp.risk(Event(time,cause)~const(tcell)+const(platelet),data=bmtw,
 cause=1,cens.weight=bmtw$cw,
 weights=bmtw$weights,times=times,n.sim=0)
summary(out)

### with only righ-censoring, standard call
outn <- comp.risk(Event(time,cause)~const(tcell)+const(platelet),data=bmt,
	  cause=1,times=times,n.sim=0)
summary(outn)
## }}} 

timereg documentation built on Sept. 11, 2024, 8:35 p.m.