Nothing
#### Point and variance for binary treatment in survey observational data##############################################
###########################################################################################
binest_SW<-function(ps.formula=NULL,ps.estimate=NULL,zname=NULL,yname,data,trtgrp=NULL,
survey.design = 'Retrospective', svywtname = NULL,
augmentation=FALSE, augmentation.type = 'WET',
bootstrap=FALSE,R=50,out.formula=NULL,out.estimate=NULL,family=NULL,
weight="overlap",ps.method='glm',ps.control=list(),out.method='glm',out.control=list()){
#preprocess formula and extract y
out.formula<-as.formula(out.formula)
y<-unlist(data[yname])
#extract survey weight
survey.weight <- NULL
if(is.null(svywtname)){
data$survey_weight <- 1
survey.weight <- data$survey_weight
}else{
survey.weight <- as.numeric(unlist(data[svywtname]))
}
#set ordered group
facz<-as.factor(unlist(data[zname]))
#set the treatment label
oldlevel<-levels(facz)
newlevel<-oldlevel
z<-as.numeric(facz)-1
n<-length(z) #total obs
#set group for ATT
if(weight=="treated"){
if(is.null(trtgrp)){
trtgrp<-oldlevel[2]
}else{
newlevel<-rev(unique(c(trtgrp,oldlevel)))
facz<-factor(unlist(data[zname]),levels = newlevel)
z<-as.numeric(facz)-1
}
}
matchlevel<-match(oldlevel,newlevel) #match level for ATT
#reassign value as numeric
data[zname]<-z
data_p<-data[,colnames(data)!=yname] #data without outcome
# obtain ps estimation
# estimate with formula
if(is.null(ps.estimate)){
#estimate population level propensity score
fit<-do.call(PSmethod_SW,list(ps.formula = ps.formula, method=ps.method, data=data_p,survey.weight = survey.weight,ncate=2,ps.control=ps.control))
W<- model.matrix(ps.formula,data_p) # design matrix
e.h <- as.numeric(fit$e.h[,2])
beta.h<-as.numeric(fit$beta.h)
#estimate sample level propensity score
fit.sample<-do.call(PSmethod,list(ps.formula = ps.formula, method=ps.method, data=data_p,ncate=2,ps.control=ps.control))
e.h.sample <- as.numeric(fit.sample$e.h[,2])
beta.h.sample<-as.numeric(fit.sample$beta.h)
#construct ratio for transformation between different types of survey weights
r.z <- ifelse(data_p$trt==1, e.h/e.h.sample, (1-e.h)/(1-e.h.sample))
if(ps.method!='glm'){
ps.estimate<-fit$e.h
ps.estimate.sample<-fit.sample$e.h
}
}else{
stop("External propensity score estimates not supported for population level estimation.")
}
if(weight=="entropy"){
stop("Entropy weight not supported in survey setting.")
}
#tilting function
ftilt<-tiltbin(weight = weight)
if (survey.design == "Retrospective") {
data$balancing.wt <- ifelse(z==1, ftilt(e.h)/e.h, ftilt(e.h)/(1-e.h)) * survey.weight
data$h.p <- ftilt(e.h) * survey.weight * (1/r.z)
} else if (survey.design == "Independent") {
data$balancing.wt <- ifelse(z==1, ftilt(e.h.sample)/e.h.sample, ftilt(e.h.sample)/(1-e.h.sample)) * survey.weight
data$h.p <- ftilt(e.h.sample) * survey.weight
} else if (survey.design == "Prospective") {
data$balancing.wt <- ifelse(z==1, ftilt(e.h)/e.h.sample, ftilt(e.h)/(1-e.h.sample)) * survey.weight
data$h.p <- ftilt(e.h) * survey.weight
} else {
stop("survey.design must be 'Retrospective', 'Independent' or 'Prospective'.")
}
#compute outcome regression for augmentation
if(augmentation){
if(is.null(out.estimate) && length(out.formula) == 0){
stop("When augmentation = TRUE and out.estimate is not provided, a valid out.formula must be supplied.")
}
#no outcome estimation provided
if(is.null(out.estimate)){
#fit two outcome regression model for different treatment groups
offset.e<-rep(1,n) #for poisson regression
dataaug<-data[,colnames(data)!=zname]
dataaug0<-dataaug[z==0,]
dataaug1<-dataaug[z==1,]
out.weights0 <- as.numeric(dataaug0$balancing.wt)
out.weights1 <- as.numeric(dataaug1$balancing.wt)
if(augmentation.type == "MOM"){
XY<-model.matrix(formula(out.formula),data=dataaug)
#predict outcome
fitout0<-do.call(OUTmethod_SW,list(out.formula=out.formula,y=y[z==0], out.method=out.method, family=family, datain=dataaug0, dataout=dataaug,out.control=out.control))
m0.h<-fitout0$m.est
gamma0.h<-fitout0$gamma.h
fitout1<-do.call(OUTmethod_SW,list(out.formula=out.formula,y=y[z==1], out.method=out.method, family=family, datain=dataaug1, dataout=dataaug,out.control=out.control))
m1.h<-fitout1$m.est
gamma1.h<-fitout1$gamma.h
}else if (augmentation.type == "CVR"){
out.formula.cvr <- update(out.formula, . ~ . + balancing.wt)
XY<-model.matrix(formula(out.formula.cvr),data=dataaug)
#predict outcome
fitout0<-do.call(OUTmethod_SW,list(out.formula=out.formula.cvr,y=y[z==0], out.method=out.method, family=family, datain=dataaug0, dataout=dataaug,out.control=out.control))
m0.h<-fitout0$m.est
gamma0.h<-fitout0$gamma.h
fitout1<-do.call(OUTmethod_SW,list(out.formula=out.formula.cvr,y=y[z==1], out.method=out.method, family=family, datain=dataaug1, dataout=dataaug,out.control=out.control))
m1.h<-fitout1$m.est
gamma1.h<-fitout1$gamma.h
}else if (augmentation.type == "WET"){
XY<-model.matrix(formula(out.formula),data=dataaug)
#predict outcome
fitout0<-do.call(OUTmethod_SW,list(out.formula=out.formula, out.weights = out.weights0, y=y[z==0], out.method=out.method, family=family, datain=dataaug0, dataout=dataaug, out.control=out.control))
m0.h<-fitout0$m.est
gamma0.h<-fitout0$gamma.h
fitout1<-do.call(OUTmethod_SW,list(out.formula=out.formula, out.weights = out.weights1, y=y[z==1], out.method=out.method, family=family, datain=dataaug1, dataout=dataaug, out.control=out.control))
m1.h<-fitout1$m.est
gamma1.h<-fitout1$gamma.h
}else{
stop("augmentation.type must be 'MOM', 'CVR', or 'WET'.")
}
if(family=='poisson'){
offsetlog<-model.extract(model.frame(out.formula,data = dataaug),'offset')
if(!is.null(offsetlog)){offset.e<-exp(offsetlog)}
}
if(out.method!='glm'){
out.estimate<-cbind(m0.h,m1.h)
colnames(out.estimate)<-newlevel
}
}else{
#the name for the outcome regression
if(!setequal(colnames(out.estimate),newlevel)){
out.estimate<-as.matrix(out.estimate)
m0.h<-out.estimate[,1]
m1.h<-out.estimate[,2]
warning("wrong column name set for out.estimate, treatment set as: ",newlevel[1], " , ", newlevel[2])
}else{
out.estimate<-out.estimate[,match(newlevel,colnames(out.estimate))]
m0.h<-out.estimate[,1]
m1.h<-out.estimate[,2]
}
}
}
##No Augmentation###############################################################
if(augmentation==FALSE){
muhat<-ptbin_SW(e.h,e.h.sample,z,y,ftilt,survey.design,survey.weight,r.z)
##No bootstrap###############################################################
if(bootstrap==FALSE){
conser<-1 #choose conservative or not
#use ps formula
if(is.null(ps.estimate)){
tryCatch( {
if (survey.design == "Retrospective") {
theta.h<-c(muhat,beta.h)
} else if (survey.design == "Independent") {
theta.h<-c(muhat,beta.h.sample)
} else if (survey.design == "Prospective") {
theta.h<-c(muhat,beta.h,beta.h.sample)
} else {
stop("survey.design must be 'Retrospective', 'Independent' or 'Prospective'.")
}
covmu<-sand_bin_SW(z,y,n,ftilt,survey.weight,r.z,theta.h,W,survey.design = survey.design,type='e')
conser<-0 #is pd
},error = function(w) {
warning("The sandwich matrix not pd, therefore not invertable, use conservative variance instead, please double check")
})
}
#use conservative
if(conser==1){
theta.h<-muhat
covmu<-sand_bin_SW(z,y,n,ftilt,survey.weight,r.z,theta.h, eest=e.h, eest.sample =e.h.sample, survey.design = survey.design,type='ec')
}
names(muhat)<-newlevel
colnames(covmu)<-rownames(covmu)<-newlevel
muboot<-NULL
}else{
warning("Bootstrap not supported by PSweight in the survey setting.")
}
}
##Augmentation###############################################################
if(augmentation==TRUE){
#calculate point estimate
augest <- ptbin_SW(e.h,e.h.sample,z,y,ftilt,survey.design,survey.weight,r.z,m0.h,m1.h)
muhat <- tail(augest,2)
##No bootstrap###############################################################
if(bootstrap==FALSE){
if(augmentation.type %in% c('MOM','CVR')){
out.weights <- rep(1,n)
}else if(augmentation.type == 'WET'){
out.weights <- data$balancing.wt
}else{
stop("augmentation.type must be 'MOM', 'CVR', or 'WET'.")
}
conser<-1 #choose conservative or not
if(is.null(ps.estimate) & is.null(out.estimate)){
## both with formula
tryCatch({
if (survey.design == "Retrospective") {
theta.h<-c(augest[1:6],beta.h,beta.h.sample,gamma0.h,gamma1.h)
} else if (survey.design == "Independent") {
theta.h<-c(augest[1:6],beta.h.sample,gamma0.h,gamma1.h)
} else if (survey.design == "Prospective") {
theta.h<-c(augest[1:6],beta.h,beta.h.sample,gamma0.h,gamma1.h)
} else {
stop("survey.design must be 'Retrospective', 'Independent' or 'Prospective'.")
}
covmu<-sand_bin_SW(z,y,n,ftilt,survey.weight,r.z,theta.h,W,XY,survey.design=survey.design,out.weights=out.weights,family=family,offset.e=offset.e,type='ea')
conser<-0 #is pd
},error = function(w) {
warning("The sandwich matrix not pd, therefore not invertable, use conservative variance instead, please double check")
})
}else if(!is.null(ps.estimate) & is.null(out.estimate)){
## formula on outcome regression only
tryCatch( {
if (survey.design == "Retrospective") {
theta.h<-c(augest[1:6],gamma0.h,gamma1.h)
} else if (survey.design == "Independent") {
theta.h<-c(augest[1:6],gamma0.h,gamma1.h)
} else if (survey.design == "Prospective") {
theta.h<-c(augest[1:6],gamma0.h,gamma1.h)
} else {
stop("survey.design must be 'Retrospective', 'Independent' or 'Prospective'.")
}
covmu<-sand_bin_SW(z,y,n,ftilt,survey.weight,r.z,theta.h,XY=XY,eest=e.h,eest.sample = e.h.sample,survey.design = survey.design, out.weights = out.weights, family=family,offset.e=offset.e,type='eca')
conser<-0 #is pd
},error = function(w) {
warning("The sandwich matrix not pd, therefore not invertable, use conservative variance instead, please double check")
})
}else if(is.null(ps.estimate) & !is.null(out.estimate)){
## formula on ps only
#when not pd
tryCatch( {
if (survey.design == "Retrospective") {
theta.h<-c(augest[1:6],beta.h,beta.h.sample)
} else if (survey.design == "Independent") {
theta.h<-c(augest[1:6],beta.h.sample)
} else if (survey.design == "Prospective") {
theta.h<-c(augest[1:6],beta.h,beta.h.sample)
} else {
stop("survey.design must be 'Retrospective', 'Independent' or 'Prospective'.")
}
covmu<-sand_bin_SW(z,y,n,ftilt,survey.weight,r.z,theta.h,W,survey.design = survey.design, m0est=m0.h,m1est=m1.h,type='eac')
conser<-0 #is pd
},error = function(w) {
warning("The sandwich matrix not pd, therefore not invertable, use conservative variance instead, please double check")
})
}
#if not pd then use conservative
if(conser==1){
theta.h<-augest[1:6]
covmu<-sand_bin_SW(z,y,n,ftilt,survey.weight,r.z,theta.h,eest=e.h,eest.sample = e.h.sample,survey.design = survey.design,m0est=m0.h,m1est=m1.h,type='ecac')
}
muboot<-NULL
names(muhat)<-newlevel
colnames(covmu)<-rownames(covmu)<-newlevel
}else{
warning("Bootstrap not supported by PSweight in the survey setting.")
}
}
e.h<-cbind(1-e.h,e.h)
colnames(e.h)<-newlevel
e.h.sample<-cbind(1-e.h.sample,e.h.sample)
colnames(e.h.sample)<-newlevel
#match back to original levels
e.h<-e.h[,matchlevel]
e.h.sample<-e.h.sample[,matchlevel]
muhat<-muhat[matchlevel]
covmu<-covmu[matchlevel,matchlevel]
muboot<-muboot[,matchlevel]
out<-list(propensity=e.h, propensity.sample=e.h.sample, muhat=muhat, covmu=covmu, muboot=muboot, group=c(oldlevel), trtgrp=trtgrp)
class(out)<-'PSweight'
out
}
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