# NOT RUN { ## two-phase simple random sampling. data(pbc, package="survival") pbc$randomized<-with(pbc, !is.na(trt) & trt>0) pbc$id<-1:nrow(pbc) d2pbc<-twophase(id=list(~id,~id), data=pbc, subset=~randomized) svymean(~bili, d2pbc) ## two-stage sampling as two-phase data(mu284) ii<-with(mu284, c(1:15, rep(1:5,n2[1:5]-3))) mu284.1<-mu284[ii,] mu284.1$id<-1:nrow(mu284.1) mu284.1$sub<-rep(c(TRUE,FALSE),c(15,34-15)) dmu284<-svydesign(id=~id1+id2,fpc=~n1+n2, data=mu284) ## first phase cluster sample, second phase stratified within cluster d2mu284<-twophase(id=list(~id1,~id),strata=list(NULL,~id1), fpc=list(~n1,NULL),data=mu284.1,subset=~sub) svytotal(~y1, dmu284) svytotal(~y1, d2mu284) svymean(~y1, dmu284) svymean(~y1, d2mu284) ## case-cohort design: this example requires R 2.2.0 or later library("survival") data(nwtco) ## stratified on case status dcchs<-twophase(id=list(~seqno,~seqno), strata=list(NULL,~rel), subset=~I(in.subcohort | rel), data=nwtco) svycoxph(Surv(edrel,rel)~factor(stage)+factor(histol)+I(age/12), design=dcchs) ## Using survival::cch subcoh <- nwtco$in.subcohort selccoh <- with(nwtco, rel==1|subcoh==1) ccoh.data <- nwtco[selccoh,] ccoh.data$subcohort <- subcoh[selccoh] cch(Surv(edrel, rel) ~ factor(stage) + factor(histol) + I(age/12), data =ccoh.data, subcoh = ~subcohort, id=~seqno, cohort.size=4028, method="LinYing") ## two-phase case-control ## Similar to Breslow & Chatterjee, Applied Statistics (1999) but with ## a slightly different version of the data set nwtco$incc2<-as.logical(with(nwtco, ifelse(rel | instit==2,1,rbinom(nrow(nwtco),1,.1)))) dccs2<-twophase(id=list(~seqno,~seqno),strata=list(NULL,~interaction(rel,instit)), data=nwtco, subset=~incc2) dccs8<-twophase(id=list(~seqno,~seqno),strata=list(NULL,~interaction(rel,stage,instit)), data=nwtco, subset=~incc2) summary(glm(rel~factor(stage)*factor(histol),data=nwtco,family=binomial())) summary(svyglm(rel~factor(stage)*factor(histol),design=dccs2,family=quasibinomial())) summary(svyglm(rel~factor(stage)*factor(histol),design=dccs8,family=quasibinomial())) ## Stratification on stage is really post-stratification, so we should use calibrate() gccs8<-calibrate(dccs2, phase=2, formula=~interaction(rel,stage,instit)) summary(svyglm(rel~factor(stage)*factor(histol),design=gccs8,family=quasibinomial())) ## For this saturated model calibration is equivalent to estimating weights. pccs8<-calibrate(dccs2, phase=2,formula=~interaction(rel,stage,instit), calfun="rrz") summary(svyglm(rel~factor(stage)*factor(histol),design=pccs8,family=quasibinomial())) ## Since sampling is SRS at phase 1 and stratified RS at phase 2, we ## can use method="simple" to save memory. dccs8_simple<-twophase(id=list(~seqno,~seqno),strata=list(NULL,~interaction(rel,stage,instit)), data=nwtco, subset=~incc2,method="simple") summary(svyglm(rel~factor(stage)*factor(histol),design=dccs8_simple,family=quasibinomial())) # }
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