Internal function. Calculates Inverse Probability of Censoring and Truncation Weights and adds them to a data.frame
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
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$zyg=="MZ")*4
times <- seq(60,100,by=10)
c1 <- timereg::comp.risk(Event(time,cause)~1+cluster(id),data=d,cause=1,
model="fg",times=times,max.clust=NULL,n.sim=0)
mm=model.matrix(~-1+zyg,data=d)
out1<-random.cif(c1,data=d,cause1=1,cause2=1,same.cens=TRUE,theta.des=mm)
summary(out1)
#> Random effect variance for variation due to clusters
#>
#> Cause 1 and cause 1
#>
#>
#> Coef. SE z P-val Cross odds ratio SE
#> zygMZ 1.8051405 0.2489286 7.251639 4.116707e-13 2.805141 0.2489286
#> zygDZ 0.2970522 0.1152042 2.578483 9.923512e-03 1.297052 0.1152042
pc1 <- predict(c1,X=1,se=0)
plot(pc1)
dl <- d[!d$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)
#> Random effect variance for variation due to clusters
#>
#> Cause 1 and cause 1
#>
#>
#> Coef. SE z P-val Cross odds ratio
#> factor(zyg)MZ 1.8252210 0.3957995 4.611479 3.998140e-06 2.825221
#> factor(zyg)DZ 0.3869257 0.1879996 2.058119 3.957867e-02 1.386926
#> SE
#> factor(zyg)MZ 0.3957995
#> factor(zyg)DZ 0.1879996