## R code examples for IPW analyses of nested case-control studies and case-cohort studies
## Materials for the tutorial seminar in the 27th Annual Scientific Meeting of the Japan Epidemiological Association

## 1. Nested case-control studies

require(multipleNCC)

data(CVD_Accidents)
attach(CVD_Accidents)
CVD_Accidents$samplestat2 <- samplestat
CVD_Accidents$samplestat2[samplestat==3] <- 2

wpl(Surv(agestart,agestop,dead24) ~ factor(smoking3gr)+bmi+factor(sex),
 data=CVD_Accidents, samplestat=CVD_Accidents$samplestat2, m=1,
 match.var=cbind(CVD_Accidents$sex, CVD_Accidents$bmi),
 match.int=c(0,0,-2,2), weight.method="KM")  # IPW analysis by design weights
 
wpl(Surv(agestart,agestop,dead24) ~ factor(smoking3gr)+bmi+factor(sex),
 data=CVD_Accidents, samplestat=CVD_Accidents$samplestat2, m=1,
 match.var=cbind(CVD_Accidents$sex, CVD_Accidents$bmi),
 match.int=c(0,0,-2,2), weight.method="glm")  # IPW analysis by estimated weights


 
 
## 2. Case-cohort studies

require(survival)

## The complete Wilms Tumor Data 
## (Breslow and Chatterjee, Applied Statistics, 1999)
## subcohort selected by simple random sampling.
##

subcoh <- nwtco$in.subcohort
selccoh <- with(nwtco, rel==1|subcoh==1)
ccoh.data <- nwtco[selccoh,]
ccoh.data$subcohort <- subcoh[selccoh]
## central-lab histology 
ccoh.data$histol <- factor(ccoh.data$histol,labels=c("FH","UH"))
## tumour stage
ccoh.data$stage <- factor(ccoh.data$stage,labels=c("I","II","III","IV"))
ccoh.data$age <- ccoh.data$age/12 # Age in years

##
## Standard case-cohort analysis: simple random subcohort 
##

fit.ccP <- cch(Surv(edrel, rel) ~ stage + histol + age, data =ccoh.data,
   subcoh = ~subcohort, id=~seqno, cohort.size=4028)
summary(fit.ccP)  # Prentice method

fit.ccSP <- cch(Surv(edrel, rel) ~ stage + histol + age, data =ccoh.data,
   subcoh = ~subcohort, id=~seqno, cohort.size=4028, method="SelfPren")
summary(fit.ccSP)  # Self-Prentice method

fit.ccLY <- cch(Surv(edrel, rel) ~ stage + histol + age, data =ccoh.data,
   subcoh = ~subcohort, id=~seqno, cohort.size=4028, method="LinYing")
summary(fit.ccLY)  # Lin-Ying method

##
## (post-)stratified on instit
##
stratsizes <- table(nwtco$instit)
fit.BI <- cch(Surv(edrel, rel) ~ stage + histol + age, data =ccoh.data,
   subcoh = ~subcohort, id=~seqno, stratum=~instit, cohort.size = stratsizes, method="I.Borgan")
summary(fit.BI)  # Borgan's type-I estimator

fit.BII <- cch(Surv(edrel, rel) ~ stage + histol + age, data =ccoh.data,
   subcoh = ~subcohort, id=~seqno, stratum=~instit, cohort.size = stratsizes, method="II.Borgan")
summary(fit.BII)  # Borgan's type-II estimator