R: Set up weights for delayed-entry competing risks data for...

prep.comp.risk {timereg} | R Documentation

## 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)
    ## }}}

[Package timereg version 1.9.4 Index]