mets (version 1.3.4)

ipw2: Inverse Probability of Censoring Weights

Description

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

Usage

ipw2(
  data,
  times = NULL,
  entrytime = NULL,
  time = "time",
  cause = "cause",
  same.cens = FALSE,
  cluster = NULL,
  pairs = FALSE,
  strata = NULL,
  obs.only = TRUE,
  cens.formula = NULL,
  cens.code = 0,
  pair.cweight = "pcw",
  pair.tweight = "ptw",
  pair.weight = "weights",
  cname = "cweights",
  tname = "tweights",
  weight.name = "indi.weights",
  prec.factor = 100,
  ...
)

Arguments

data

data frame

times

possible time argument for speciying a maximum value of time tau=max(times), to specify when things are considered censored or not.

entrytime

nam of entry-time for truncation.

time

name of time variable on data frame.

cause

name of cause indicator on data frame.

same.cens

For clustered data, should same censoring be assumed and same truncation (bivariate probability calculated as mininum of the marginal probabilities)

cluster

name of clustering variable

pairs

For paired data (e.g. twins) only the complete pairs are returned (With pairs=TRUE)

strata

name of strata variable to get weights stratified.

obs.only

Return data with uncensored observations only

cens.formula

model for Cox models for truncation and right censoring times.

cens.code

censoring.code

pair.cweight

Name of weight variable in the new data.frame for right censorig of pairs

pair.tweight

Name of weight variable in the new data.frame for left truncation of pairs

pair.weight

Name of weight variable in the new data.frame for right censoring and left truncation of pairs

cname

Name of weight variable in the new data.frame for right censoring of individuals

tname

Name of weight variable in the new data.frame for left truncation of individuals

weight.name

Name of weight variable in the new data.frame for right censoring and left truncation of individuals

prec.factor

To let tied censoring and truncation times come after the death times.

...

Additional arguments to censoring model

Author

Thomas Scheike

Examples

library("timereg")
set.seed(1)
d <- simnordic.random(5000,delayed=TRUE,ptrunc=0.7,
      cordz=0.5,cormz=2,lam0=0.3,country=FALSE)
d$strata <- as.numeric(d$country)+(d$zyg=="MZ")*4
times <- seq(60,100,by=10)
c1 <- timereg::comp.risk(Event(time,cause)~1+cluster(id),data=d,cause=1,
	model="fg",times=times,max.clust=NULL,n.sim=0)
mm=model.matrix(~-1+zyg,data=d)
out1<-random.cif(c1,data=d,cause1=1,cause2=1,same.cens=TRUE,theta.des=mm)
summary(out1)
pc1 <- predict(c1,X=1,se=0)
plot(pc1)

dl <- d[!d$truncated,]
dl <- ipw2(dl,cluster="id",same.cens=TRUE,time="time",entrytime="entry",cause="cause",
           strata="strata",prec.factor=100)
cl <- timereg::comp.risk(Event(time,cause)~+1+
		cluster(id),
		data=dl,cause=1,model="fg",
		weights=dl$indi.weights,cens.weights=rep(1,nrow(dl)),
            times=times,max.clust=NULL,n.sim=0)
pcl <- predict(cl,X=1,se=0)
lines(pcl$time,pcl$P1,col=2)
mm=model.matrix(~-1+factor(zyg),data=dl)
out2<-random.cif(cl,data=dl,cause1=1,cause2=1,theta.des=mm,
                 weights=dl$weights,censoring.weights=rep(1,nrow(dl)))
summary(out2)