ipw: Inverse Probability of Censoring Weights

In mets: Analysis of Multivariate Event Times

Description

Internal function. Calculates Inverse Probability of Censoring Weights (IPCW) and adds them to a data.frame

Usage

ipw(formula, data, cluster, same.cens = FALSE, obs.only = TRUE,
  weight.name = "w", trunc.prob = FALSE, weight.name2 = "wt",
  cens.model = "aalen", pairs = FALSE, theta.formula = ~1, ...)

Arguments

formula	Formula specifying the censoring model
data	data frame
cluster	clustering variable
same.cens	For clustered data, should same censoring be assumed (bivariate probability calculated as mininum of the marginal probabilities)
obs.only	Return data with uncensored observations only
weight.name	Name of weight variable in the new data.frame
trunc.prob	If TRUE truncation probabilities are also calculated and stored in 'weight.name2' (based on Clayton-Oakes gamma frailty model)
weight.name2	Name of truncation probabilities
cens.model	Censoring model (default Aalens additive model)
pairs	For paired data (e.g. twins) only the complete pairs are returned (With pairs=TRUE)
theta.formula	Model for the dependence parameter in the Clayton-Oakes model (truncation only)
...	Additional arguments to censoring model

Author(s)

Klaus K. Holst

Examples

## Not run: 
data(prt)
prtw <- ipw(Surv(time,status==0)~country, data=prt[sample(nrow(prt),5000),],
            cluster="id",weight.name="w")
plot(0,type="n",xlim=range(prtw$time),ylim=c(0,1),xlab="Age",ylab="Probability")
count <- 0
for (l in unique(prtw$country)) {
    count <- count+1
    prtw <- prtw[order(prtw$time),]
    with(subset(prtw,country==l),
         lines(time,w,col=count,lwd=2))
}
legend("topright",legend=unique(prtw$country),col=1:4,pch=-1,lty=1)

## End(Not run)

Example output

Loading required package: timereg
Loading required package: survival
Loading required package: lava
lava version 1.5
mets version 1.2.2

mets documentation built on May 2, 2019, 4:43 p.m.