Last data update: 2014.03.03
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R: Inverse Probability of Censoring Weights
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
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times |
possible time argument for speciying a maximum value of time tau=max(times), to specify when things are considered censored or not.
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entrytime |
nam of entry-time for truncation.
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time |
name of time variable on data frame.
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cause |
name of cause indicator on data frame.
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same.cens |
For clustered data, should same censoring be assumed and same truncation (bivariate probability calculated as mininum of the marginal probabilities)
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cluster |
name of clustering variable
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pairs |
For paired data (e.g. twins) only the complete pairs are returned (With pairs=TRUE)
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strata |
name of strata variable to get weights stratified.
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obs.only |
Return data with uncensored observations only
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cens.formula |
model for Cox models for truncation and right censoring times.
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cens.code |
censoring.code
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pair.cweight |
Name of weight variable in the new data.frame for right censorig of pairs
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pair.tweight |
Name of weight variable in the new data.frame for left truncation of pairs
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pair.weight |
Name of weight variable in the new data.frame for right censoring and left truncation of pairs
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cname |
Name of weight variable in the new data.frame for right censoring of individuals
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tname |
Name of weight variable in the new data.frame for left truncation of individuals
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weight.name |
Name of weight variable in the new data.frame for right censoring and left truncation of individuals
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prec.factor |
To let tied censoring and truncation times come after the death times.
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... |
Additional arguments to censoring model
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Author(s)
Thomas Scheike
Examples
d <- simnordic.random(3000,delayed=TRUE,ptrunc=0.7,
cordz=0.5,cormz=2,lam0=0.3,country=FALSE)
d$strata <- as.numeric(d$country)+(d$zyg=="MZ")*4
times <- seq(60,100,by=10)
c1 <- comp.risk(Event(time,cause)~1+cluster(id),data=d,cause=1,
model="fg",times=times,max.clust=NULL,n.sim=0)
mm=model.matrix(~-1+zyg,data=d)
out1<-random.cif(c1,data=d,cause1=1,cause2=1,same.cens=TRUE,theta.des=mm)
summary(out1)
pc1 <- predict(c1,X=1,se=0)
plot(pc1)
dl <- d[!d$truncated,]
dl <- ipw2(dl,cluster="id",same.cens=TRUE,time="time",entrytime="entry",cause="cause",
strata="strata",prec.factor=100)
cl <- 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)
Results
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