Inverse Probability of Censoring Weights

Source: R/ipw.R

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

ipw(
  formula,
  data,
  cluster,
  same.cens = FALSE,
  obs.only = FALSE,
  weight.name = "w",
  trunc.prob = FALSE,
  weight.name2 = "wt",
  indi.weight = "pr",
  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

indi.weight

Name of individual censoring weight in the new data.frame

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

Klaus K. Holst

Examples

if (FALSE) {
data("prt",package="mets")
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)
}