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Cascade_Functions.R
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Cascade_Functions.R
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#==========================================
# R code for stage I estimation in HIV care cascade
# Laura B. Balzer, PhD MPhil
# lbalzer@umass.edu
# Study Statistician for SEARCH
#==========================================================
Run.Cascade<- function(settings, SL.library='glm', clust.adj){
data.input <- preprocess.cascade()
# Stage 1
stage1 <- Stage1.Cascade.Xsect(data.input, settings=settings,
SL.library=SL.library)
covariates <- stage1$W
data <- stage1$data.clust
raw2 <- stage1$RAW2
primary <- stage1$primary
secondary <- stage1$secondary
est <- arm.pri1 <- arm.pri0 <- raw2.sec1 <- raw2.sec0 <- NULL
# primary analysis weights wrt estimated number of HIV+
alpha.pri <- get.weights(primary, weighting='indv')$alpha
alpha.sec <- get.weights(secondary, weighting='indv')$alpha
# weighting prevalence wrt population size
temp <- data[,c('id', 'N.pop')]
colnames(temp) <- c('id', 'nIndv')
alpha.prev <- get.weights(temp, weighting='indv')$alpha
# calculate arm-specific cascade
arm.pri <- get.subgroup.est(data=primary, alpha= alpha.pri, alpha.prev=alpha.prev)
arm.sec <- get.subgroup.est(data=secondary, alpha=alpha.sec, alpha.prev=alpha.prev)
if( var(data$A)!=0 ){ # then varability in the exposure
# STAGE 2 ANALYSIS:
# First with the primary Yc outcome
data.temp <- reformat.stage2(primary)
Adj.supp_Adj.effect <- Stage2(weighting='indv', data.input=data.temp,
outcome='Y', clust.adj=clust.adj,
do.data.adapt=T)
# secondary yc
data.temp <- reformat.stage2(secondary)
Unadj.supp_Unadj.effect <- Stage2(weighting='indv', data.input=data.temp,
outcome='Y', do.data.adapt=F)
est <- data.frame(rbind(Adj.supp_Adj.effect=Adj.supp_Adj.effect,
Unadj.supp_Unadj.effect=Unadj.supp_Unadj.effect))
txt<- primary$A==1
con<- primary$A==0
arm.pri1 <- get.subgroup.est(data=primary[txt,], alpha= alpha.pri[txt],
alpha.prev=alpha.prev[txt])
arm.pri0 <- get.subgroup.est(data=primary[con,], alpha= alpha.pri[con],
alpha.prev=alpha.prev[con])
# unadjusted output
raw2.sec1 <- get.supp.table(raw2=raw2[raw2$A==1,], pri=arm.pri1)
raw2.sec0 <- get.supp.table(raw2=raw2[raw2$A==0,], pri=arm.pri0)
}
rownames(data) <- rownames(primary) <- rownames(secondary) <- data$community_name
data<- subset(data, select=-c(U,community_name, id))
data[data$region_name=='Western Uganda','region_name'] <- 3
data[data$region_name=='Eastern Uganda', 'region_name'] <- 2
data[data$region_name=='Kenya', 'region_name'] <- 1
colnames(data)[1]<- 'region'
data$region <- as.numeric(data$region)
primary<- subset(primary, select=-c(U,region_name, community_name, id))
secondary<- subset(secondary, select=-c(U,region_name, community_name, id))
list(covariates=covariates, data=data, primary=primary, secondary=secondary, est=est,
arm.pri = arm.pri, arm.sec = arm.sec,
arm.pri1 = arm.pri1, raw2.sec1=raw2.sec1,
arm.pri0 = arm.pri0, raw2.sec0=raw2.sec0)
}
preprocess.cascade <- function(){
load("outputs-withIntOnly.RData")
print( dim(outputs) )
data.input <- outputs
# Exclude anyone that was flagged as an SEARCH-id related error
data.input <- subset(data.input, !(data_flag | dead_0 | move_0) )
# outputs-withIntOnly.Rdata does NOT have community_number
# add now
id<- rep(NA, nrow(data.input ))
comm <- unique(data.input$community_name)
for(j in 1:32){
these.units <- data.input$community_name==comm[j]
id[these.units] <- j
}
data.input <- cbind(data.input,
A= as.numeric(as.logical(data.input$intervention)),
id=id
)
# transform pairs to be numeric
data.input$pair <- as.numeric(as.character(data.input$pair))
# if haven't added a dummy variable for unadjusted
if( sum(grep('U', colnames(data.input)))==0){
data.input <- cbind(U=1, data.input)
}
print('***preprocessing done***')
data.input
}
# Stage1.Cascade.Xsect: get the community-specific estimates of VL suppression
# in open cohort of HIV+ at t
Stage1.Cascade.Xsect <- function(data.input, settings,
SL.library){
# cluster-level covariates
E <- c( 'U', 'region_name', 'community_name', 'id', 'pair', 'A')
# define target population for the analysis
restrict<- get.pop.cascade(data=data.input, settings=settings)
data.input <- subset(data.input, restrict)
print(dim(data.input))
clusters <- unique(data.input$id)
nClust <- length(clusters)
data.clust <- data.frame(matrix(NA, nrow=nClust, ncol=(length(E) + 12 )))
colnames(data.clust) <- c(E, "N.pop", "N.chc", "N.trk", "N.know.HIV",
"N.Delta", "N.know.pos", "N.est.pos" ,"N.know.pDx",
"N.know.eART", "N.know.supp", "N.TstVL", "N.est.supp")
primary <- data.frame(matrix( NA, nrow=nClust, ncol=(length(E) + 19)))
colnames(primary)<- c(E, 'pSupp_0', 'pAdol_0',
"prev_pt","prev_CI.lo", "prev_CI.hi",
"pDx.pos_pt", "pDx.pos_CI.lo", "pDx.pos_CI.hi",
"eART.pDx_pt", "eART.pDx_CI.lo", "eART.pDx_CI.hi",
"supp.eART_pt", "supp.eART_CI.lo", "supp.eART_CI.hi",
"supp.pos_pt", "supp.pos_CI.lo","supp.pos_CI.hi",
"Product", "nIndv")
secondary <- primary
# unadjusted numbers for supplementary table
RAW2 <- data.frame(matrix(NA, nrow=nClust, ncol=length(E)+10))
for(j in 1:nClust){
these.units <- data.input$id==clusters[j]
OC <- data.input[these.units ,]
baseline.pred <- get.X(data=OC, analysis='Cascade',
adj.full=settings$adj.full)
# print(names(baseline.pred))
out<- do.serial.analysis(OC= OC, baseline.pred=baseline.pred,
settings=settings,
SL.library= SL.library)
# aggregate relevant covariates
OC$region_name <- as.character(OC$region_name)
OC$community_name <- as.character(OC$community_name)
data.clust[j,] <- c(OC[1,E], out$RAW)
RAW2[j,] <- c(OC[1,E], out$RAW2)
# create cluster-level dataframe
pSupp_0 <- data.frame(pSupp_0=
mean(OC[OC$hiv_0==1, 'supp_0'], na.rm=T))
pAdol_0 <- data.frame(pAdol_0=
mean(OC[OC$hiv_0==1, 'age_0']<=24, na.rm=T))
# create cluster-level dataframe
primary[j,] <- c(OC[1,E], pSupp_0, pAdol_0, out$Cascade,
nIndv=out$RAW$N.est.pos)
secondary[j,] <- c(OC[1,E],pSupp_0, pAdol_0, out$Cascade.sec,
# number estimated to be HIVpositve
nIndv=round(out$RAW$N.pop*out$Cascade.sec$prev_pt))
print(j)
}
colnames(RAW2) <- c(E, colnames(out$RAW2))
W <- names(baseline.pred)
list(W=W, data.clust=data.clust, RAW2=RAW2,
primary=primary,
secondary=secondary )
}
get.pop.cascade <- function(data, settings){
n<- nrow(data)
region <- get.subgroup(data=data, subgroup=settings$region)
# Specifying the subgroups if interest
this.subgroup <- get.subgroup(data=data, subgroup=settings$subgroup,
time=settings$time)
# have to be 15+, cannot have died or outmigrated at t
time <- settings$time
if(time==0){
adult <- data$age_0>14
alive <- rep(T, n)
move <- rep(F,n)
} else if(time==1){
adult <- data$age_0>13
alive <- !data$dead_1
move <- data$move_1
} else if (time==2){
adult <- data$age_0>12
alive <- !data$dead_2
move <- data$move_2
} else if (time==3){
adult <- rep(T, n)
dead <- move <- rep(F, n)
dead[which(data$dead_3)] <- T
alive <- !dead
move[which(data$outmigrate_3)] <-T
}
if(time==1 | time==2){
arm <- data$A==1
} else{
arm <- rep(T, n)
}
if(time==0){
resident <- data$resident_0
} else if (time==1){ # Y1
resident <- data$resident_recensus_1
} else if(time==2){
resident <- data$resident_recensus_2
} else if(time==3){
resident <- data$resident_3
}
restrict<- region & this.subgroup & adult & alive & !move & arm & resident
restrict
}
#*-------
# do.serial.analysis - code to run primary and secondary analyses for
# serial cross-sectional analysis of prevalent HIV+
do.serial.analysis <- function(OC, baseline.pred, settings,
SL.library = NULL, verbose = F) {
# used in deterministic Q adjustment sets
data_0 <- preprocess.serial(data = OC, time=0, settings=settings)
colnames(data_0) <- paste(colnames(data_0), "0", sep = "_")
if(settings$time==2){
data_1 <- preprocess.serial(data = OC, time=1, settings=settings)
colnames(data_1) <- paste(colnames(data_1), "1", sep = "_")
} else{
data_1 <- NULL
}
time <- settings$time
data <- preprocess.serial(data = OC, time=time, settings=settings)
#**************** Prevalence of HIV at time t: P(Y*=1)
Prob.HIVpos <- get.prevalence(baseline.pred=baseline.pred,
data_0=data_0, data_1=data_1,
data=data, time=time,
SL.library=SL.library, verbose=verbose)
#**************** Previous diagnosis at time t: P(pDx=1, Y*=1)
Prob.pDx <- get.pDx(baseline.pred=baseline.pred,
data_0=data_0, data_1=data_1,
data=data, time=time,
SL.library=SL.library, verbose=verbose)
#**************************** Ever ART use at time t: P(eART=1, pDx=1, Y*=1)
Prob.eART <- get.eART( baseline.pred=baseline.pred,
data_0=data_0, data_1=data_1,
data=data, time=time,
SL.library=SL.library, verbose=verbose)
#****************Suppression at t: P(Supp*=1, eART=1, pDx=1, Y*=1)
Prob.joint <- get.joint(baseline.pred=baseline.pred,
data_0=data_0, data_1=data_1,
data=data, time=time,
SL.library=SL.library, verbose=verbose)
#************************** Compiling the results
# Number in the population of interest
N.pop <- nrow(data)
RAW <- data.frame(
# Number in the population of interest
N.pop=N.pop,
# Number seen at CHC
N.chc = sum(data$chc),
N.trk = sum(data$tr),
N.know.HIV = sum(data$TstHIV),
# number seen with known status at CHC/track
N.Delta = sum(Prob.HIVpos$sum.A),
# Number known to be HIV+
N.know.pos = sum(data$HIVpos),
# Number estimated to be HIV+ = (Estimated HIV prevalence) x (Population size)
N.est.pos = round(Prob.HIVpos$pri$e$pt*N.pop, 0),
# Number with prior diagnosis in the population
N.know.pDx = round(Prob.pDx$pri$e$pt*N.pop, 0),
# Number with eART in the population
N.know.eART = round(Prob.eART$pri$e$pt*N.pop, 0),
N.know.supp = sum(data$Supp),
# Number with relevant VL measure
N.TstVL=sum(Prob.joint$sum.A),
# Number estimated to be suppressed = (Estimated Suppression ) x (Population size)
N.est.supp =round(Prob.joint$pri$e$pt * N.pop, 0)
)
Cascade <- do.ratios(Prob.HIVpos = Prob.HIVpos$pri,
Prob.pDx = Prob.pDx$pri,
Prob.eART = Prob.eART$pri,
Prob.joint = Prob.joint$pri,
primary = T)
Cascade.sec <- do.ratios(Prob.HIVpos = Prob.HIVpos$sec,
Prob.pDx = Prob.pDx$sec,
Prob.eART = Prob.eART$sec,
primary = F, data = data, verbose = verbose)
# should correspond to unadjusted estimates
RAW2 <- data.frame(
# all known HIV+ regardless if seen at FUY3
allpos = sum(data$HIVpos),
# all known HIV+ with missed VL - regardless if seen
allpos.noTstVL = sum(data$HIVpos*!data$TstVL),
#
# all folling conditions on being seen at CHC/tracking (Delta=1)
# prevalence = A:delta, Y:HIVpos
HIVpos = sum(data$HIVpos*data$Delta),
# pDx: A=:Delta, Y=pDx
pDx =sum(data$pDx*data$Delta),
# eART: A=Delta, Y=eART
eART = sum(data$eART*data$Delta),
# with VL measured: A=Delta*TstVL Y = eART
eART.TstVL = sum(data$eART*data$Delta*data$TstVL),
# supp with ART: A=Delta*TstVL Y = supp
supp.eART.TstVL = sum(data$Supp*data$eART*data$Delta*data$TstVL),
# supp HIV+: A=Delta*TstVL Y = supp
supp.TstVL = sum(data$Supp*data$Delta*data$TstVL),
# HIV+ with measured VL
HIVpos.TstVL =sum(data$HIVpos*data$Delta*data$TstVL),
# HIV+ with miss VL
HIVpos.NoTstVL = sum(data$HIVpos*data$Delta*!data$TstVL) )
list(RAW = RAW, RAW2=RAW2, Cascade = Cascade,
Cascade.sec = Cascade.sec)
}
preprocess.serial <- function(data, time, settings){
if(time==0 | time==3){
# if using parallel data in intervention and control
HIV.variable <- 'hiv'
pDx.variable <- 'pdx_vl'
eART.variable <- 'eart_vl'
} else{
# if, instead, using interim data in the intervention arm
HIV.variable <- 'all_hiv'
pDx.variable <- 'hiv_preCHC'
eART.variable <- 'i_eart_vl'
}
n <- nrow(data)
pDx <- eART <- Delta <- HIVpos <- TstVL <- Supp <- rep(0, n)
# no evidence of prior Dx or ART == fail
pDx[ which(data[, paste(pDx.variable, time, sep='_')] ==1) ] <-1
eART[ which(data[, paste(eART.variable, time, sep='_')] ==1)] <-1
# if on ART then previously dx-ed
pDx[ eART==1] <- 1
chc <- as.numeric(data[, paste('chc', time, sep='_')] )
tr <- as.numeric(data[, paste('tr', time, sep='_')])
# did we actually know your HIV status?
hiv.temp <- data[, paste(HIV.variable, time, sep='_')]
TstHIV <- as.numeric( !is.na(hiv.temp) )
# Delta - require that we saw you at t & had a known status
Delta[ which( (chc==1 | tr==1) & TstHIV==1 )] <-1
# HIV status as =1 if HIV+ and 0 otherwise
HIVpos[ which(hiv.temp) ] <- 1
# Suppression - VL.variable='supp'
supp.temp <- data[, paste('supp', time, sep='_')]
# SET SUPP=NA if not HIVpos
supp.temp[ which( !is.na(supp.temp) & HIVpos==0) ] <- NA
TstVL[ which(!is.na(supp.temp) ) ] <- 1
# suppression as =1 if suppressed and 0 otherwise
Supp[ which(supp.temp) ] <- 1
data.frame(cbind(pDx, eART, chc, tr, TstHIV, Delta, HIVpos, TstVL, Supp))
}
#**************** Prevalence of HIV at time t: P(Y*=1)
get.prevalence <- function(baseline.pred, data_0, data_1, data,
time, SL.library, verbose=F){
# outcome as observed HIV status
Y <- data$HIVpos
# intervention variable: seen at CHC/tracking with HIV status known
A <- data$Delta
# specify adjustment set other than baseline covariates
adj <- get.adjustment(data = data,
baseline.pred = baseline.pred,
data_0 = data_0, data_1=data_1,
time = time)
# using deterministic knowledge about known HIV status at prior time point
Prob.HIVpos <- call.ltmle(pred.A = adj$adj,
A = A, Y = Y, SL.library = SL.library,
deterministicQ = adj$detQ)
if(verbose)print(Prob.HIVpos$est)
#* Secondary analysis
Prob.HIVpos.sec <- do.secondary(A = A, Y = Y, verbose = verbose)
list(sum.A=sum(A), pri=Prob.HIVpos, sec=Prob.HIVpos.sec)
}
#**************** Previous diagnosis at time t: P(pDx=1, Y*=1)
get.pDx <- function(baseline.pred, data_0, data_1, data,
time, SL.library, verbose=F){
Y <- data$pDx
# assumes complete capture
Prob.pDx <- call.ltmle(pred.A = NULL,
A = rep(1, nrow(data)), Y = Y,
SL.library = NULL)
# c(Prob.pDx$est$pt, sum(Y)/length(Y))
if(verbose)print(Prob.pDx$est)
#* Secondary analysis
Prob.pDx.sec <- do.secondary(A = data$Delta,
Y = Y, verbose = verbose)
list(pri=Prob.pDx, sec=Prob.pDx.sec)
}
#**************************** Ever ART use at time t: P(eART=1, pDx=1, Y*=1)
get.eART <- function(baseline.pred, data_0, data_1,
data, time, SL.library, verbose=F){
Y <- data$eART
Prob.eART <- call.ltmle(pred.A = NULL,
A = rep(1, nrow(data)), Y = Y,
SL.library = NULL)
# c(Prob.eART$est$pt, sum(Y)/length(Y))
if(verbose)print(Prob.eART$est)
#* Secondary analysis
Prob.eART.sec <- do.secondary(A = data$Delta,
Y = Y, verbose = verbose)
list(pri=Prob.eART, sec=Prob.eART.sec)
}
#****************Suppression at t: P(Supp*=1, eART=1, pDx=1, Y*=1)
get.joint <- function(baseline.pred, data_0, data_1,
data, time, SL.library, verbose=F){
# last QC that if HIV+&supp, must have started ART
if((sum(data$eART==0 & data$Supp==1)>0)){
Y <- data$eART*data$Supp
}else{
Y <- data$Supp
}
A <- data$TstVL
# get adjustment set using deterministic knowledge (primary)
pred.A <- get.adjustment.joint(data = data,
baseline.pred = baseline.pred,
data_0 = data_0, data_1=data_1,
time = time)
Prob.joint <- call.ltmle(pred.A = pred.A,
A = A, Y = Y, SL.library = SL.library,
deterministicQ = deterministicQ_NO)
if(verbose)print(Prob.joint$est)
list(sum.A=sum(A), pri=Prob.joint)
}
# get.adjustment: function to get the adjustment set for prevalence
get.adjustment<- function(data, baseline.pred,
data_0, data_1,
time){
# always adjusting for baseline testing
TstHIV_0 <- data_0$TstHIV_0
if(time==0){
adj <- baseline.pred
detQ<- NULL
} else {
if(time==1 | time==3){
# at Y1 or Y3 (not using interim data to be parallel across arms)
adj <- data.frame(baseline.pred, TstHIV_0,
detQ.variable=data_0[, paste('HIVpos', 0, sep='_')])
}else{
# if time=2 adjust for baseline and time-1
adj <- data.frame(baseline.pred, TstHIV_0,
detQ.variable=data_1[, paste('HIVpos', 1, sep='_')])
}
detQ <- deterministicQ_YES
}
list(adj=adj, detQ=detQ)
}
get.adjustment.joint <- function(data, baseline.pred, data_0,
data_1, time){
TstHIV_0 <- data_0$TstHIV_0
if(time==0){
adj <- baseline.pred
} else if( time==1 | time==3 ){
adj <- data.frame(baseline.pred, TstHIV_0,
prior=data_0[, paste('Supp', 0, sep='_')])
}else{
# if time=2 adjust for baseline and time=1
adj <- data.frame(baseline.pred, TstHIV_0,
prior=data_1[, paste('Supp', 1, sep='_')])
}
adj <- data.frame(adj, detQ.variable=data$eART)
adj
}
# call.ltmle: function to call the ltmle
call.ltmle<- function(pred.A=NULL, A, Y,
SL.library=NULL,
deterministicQ=NULL,
observation.weights=NULL,
id=NULL,
verbose=F){
# create temporary data frame
if( is.null(pred.A) ){
data.temp<- data.frame(A, Y)
} else{
data.temp<- data.frame(pred.A, A, Y)
}
est.temp<- ltmle(data=data.temp, Anodes='A', Lnodes=NULL, Ynodes='Y',
abar=1,
stratify = T, SL.library=SL.library,
estimate.time=F,
variance.method='ic',
deterministic.Q.function= deterministicQ,
observation.weights=observation.weights, id=id)
if(verbose){
print(est.temp$fit$g)
print(est.temp$fit$Q)
}
IC<- est.temp$IC$tmle
est<- data.frame(pt=est.temp$estimate["tmle"],
CI.lo=summary(est.temp)$treatment$CI[1],
CI.hi=summary(est.temp)$treatment$CI[2] )
list(est=est,IC=IC)
}
# simple function do the secondary analysis
do.secondary <- function(A, Y, verbose=F){
N.measured <- sum(A)
N.obs.outcome <- sum(A & Y)
out<- call.ltmle(pred.A= NULL, A=A, Y=Y, SL.library=NULL)
if(verbose){
print(c(out$est$pt,N.obs.outcome/ N.measured))
}
out
}
#*====
# USE THE ABOVE FOUR PROBABILITIES TO CALCULATE CASCADE COVERAGE
# USE THE DELTA.METHOD FOR INFERENCE IN PRIMARY ANALYSIS
# See get.var.bayes.bayes function
do.ratios <- function(Prob.HIVpos, Prob.pDx, Prob.eART, Prob.joint,
primary=T, data, verbose=F){
prev<- Prob.HIVpos$est
colnames(prev) <- paste('prev', colnames(prev), sep='_')
# calculate
pDx.pos <- get.var.bayes(mu1=Prob.pDx$est$pt,
IC1=Prob.pDx$IC,
mu0=Prob.HIVpos$est$pt,
IC0=Prob.HIVpos$IC)$est
colnames(pDx.pos) <- paste('pDx.pos', colnames(pDx.pos), sep='_')
eART.pDx <- get.var.bayes(mu1=Prob.eART$est$pt,
IC1=Prob.eART$IC,
mu0=Prob.pDx$est$pt,
IC0=Prob.pDx$IC)$est
colnames(eART.pDx) <- paste('eART.pDx', colnames(eART.pDx), sep='_')
if(primary){
supp.eART <- get.var.bayes(mu1=Prob.joint$est$pt,
IC1=Prob.joint$IC,
mu0=Prob.eART$est$pt,
IC0=Prob.eART$IC)$est
supp.pos <- get.var.bayes(mu1=Prob.joint$est$pt,
IC1=Prob.joint$IC,
mu0=Prob.HIVpos$est$pt,
IC0=Prob.HIVpos$IC)$est
}else{
# identifiability requires only using VL at CHC/tracking
data.sec <- data
data.sec$TstVL <- data.sec$TstVL*data.sec$Delta
data.sec$Supp <- data.sec$Supp*data.sec$TstVL
on.ART<- data.sec$eART==1
supp.eART <- do.secondary(A=data.sec$TstVL[on.ART],
Y=data.sec$Supp[on.ART],
verbose=verbose)$est
supp.pos <- do.secondary(A=data.sec$TstVL,
Y=data.sec$Supp,
verbose=verbose)$est
}
colnames(supp.eART) <- paste('supp.eART', colnames(supp.eART), sep='_')
colnames(supp.pos) <- paste('supp.pos', colnames(supp.pos), sep='_')
Product <- as.numeric(pDx.pos[1])*
as.numeric(eART.pDx[1])*
as.numeric(supp.eART[1])
data.frame(prev, pDx.pos, eART.pDx, supp.eART, supp.pos, Product)
}
#*====
#* STAGE 2
reformat.stage2<- function(data){
colnames(data)[grep('supp.pos_pt', colnames(data) )] <- 'Y'
colnames(data)[grep('nIndv', colnames(data))] <- 'nIndv_Y'
data
}
get.subgroup.est<- function(data, alpha, alpha.prev){
A <- rep(1, nrow(data))
# weight wrt number of indv in population
prev <- call.ltmle(A=A, Y=data$prev_pt, observation.weights=alpha.prev)$est
# weight following wrt number of estimated HIV+
pDx.pos <- call.ltmle(A=A, Y=data$pDx.pos_pt, observation.weights=alpha)$est
eART.pDx <- call.ltmle(A=A, Y=data$eART.pDx_pt, observation.weights=alpha)$est
supp.eART <- call.ltmle(A=A, Y=data$supp.eART_pt, observation.weights=alpha)$est
supp.pos<- call.ltmle(A=A, Y=data$supp.pos_pt, observation.weights=alpha)$est
Product<- call.ltmle(A=A, Y=data$Product, observation.weights=alpha)$est
data.frame(rbind(prev=prev, pDx.pos=pDx.pos, eART.pDx= eART.pDx,
supp.eART= supp.eART, supp.pos= supp.pos, Product=Product))
}
get.file.name.cascade <- function(region='All', subgroup='All',
time=3,
date=NULL){
if(is.null(date)){
date <- format(Sys.time(), "%d%b%Y")
}
adj.full <- subgroup!='Young'
file.name= paste( 'Cascade',
region,
subgroup,
paste('Year', time, sep=''),
paste('FullAdjust', adj.full, sep=''),
paste('v', date, sep=''), sep="_")
file.name
}
#*====
# # alternative calculation for the unadjusted/secondary estimates
supp.table.helper <- function(num, den){
num <- sum(num);
den <- sum(den)
paste( round(num/den*100), '% (', paste(num, den, sep='/'), ')', sep='')
}
supp.table.helper2 <- function(pri, this){
paste( round(pri[this, 'pt']*100), '% (',
round(pri[this, 'CI.lo']*100), ',',
round(pri[this, 'CI.hi']*100), ')',
sep='')
}
get.supp.table <- function(raw2, pri){
out<- data.frame(rbind(
pDx.pos= c( supp.table.helper(num=raw2$pDx, den=raw2$HIVpos),
supp.table.helper2(pri, this='pDx.pos') ),
eART.pDx= c(supp.table.helper(num=raw2$eART, den=raw2$pDx),
supp.table.helper2(pri, this='eART.pDx') ),
supp.eART= c(supp.table.helper(num=raw2$supp.eART.TstVL,
den=raw2$eART.TstVL),
supp.table.helper2(pri, this='supp.eART') ),
supp.pos = c(supp.table.helper(num=raw2$supp.TstVL,
den=raw2$HIVpos.TstVL),
supp.table.helper2(pri, this='supp.pos') ),
pos.noTstVL = c( supp.table.helper(num=raw2$HIVpos.NoTstVL,
den=raw2$HIVpos),
NA),
allpos.noTstVL = c( supp.table.helper(num=raw2$allpos.noTstVL,
den=raw2$allpos),
NA)
))
colnames(out) <- c('U: % (num/den)', 'A: % (CI in %)' )
out
}
make.pretty.min <- function(out, region='All', subgroup='All',
time=3, date='final'){
file.name<- get.file.name.cascade(region=region,
subgroup=subgroup,
time=time,
date=date)
load(paste(file.name, 'Rdata', sep='.'))
if(subgroup=='All'){
subgroup<- region
} else{
subgroup <- subgroup
}
effect <- out$est['Adj.supp_Adj.effect',]
pool <- out$arm.pri
arm1 <- out$arm.pri1
arm0 <- out$arm.pri0
out.csv <- data.frame(matrix(NA, nrow=3, ncol=9))
colnames(out.csv) <- c('', '', '','pt','CI.lo', 'CI.hi', 'pval', 'N.analysis', 'N.est.HIV+')
out.csv[,1] <- subgroup
out.csv[,3] <- c('Intervention', 'Control', NA)
basic<- c('pt', 'CI.lo', 'CI.hi')
# GET N
txt <- out$data[out$data$A==1,]
con <- out$data[out$data$A==0,]
out.csv[1, c('N.analysis', 'N.est.HIV+')] <- c(sum(txt$N.pop),
round( sum(txt$N.pop)*arm1['prev','pt']))
out.csv[2, c('N.analysis', 'N.est.HIV+')] <- c(sum(con$N.pop),
round( sum(con$N.pop)*arm0['prev','pt']))
out.csv[3,c('N.analysis', 'N.est.HIV+')] <- c(sum(out$data$N.pop), NA)
if(settings$time==0){
#IGNORE EFFECT
out.csv[,2] <- 'Baseline'; out.csv[3,3] <- 'Overall'
out.csv[1, basic] <- arm1['supp.pos', c('pt', 'CI.lo', 'CI.hi')]
out.csv[2, basic] <- arm0['supp.pos', c('pt', 'CI.lo', 'CI.hi')]
out.csv[3, basic] <- pool['supp.pos', c('pt', 'CI.lo', 'CI.hi')]
}else if (settings$time==3){
out.csv[,2] <- 'Year 3'
out.csv[1, basic] <- effect[, c('Txt.est', 'Txt.CI.lo', 'Txt.CI.hi')]
out.csv[2, basic] <- effect[, c('Con.est', 'Con.CI.lo', 'Con.CI.hi')]
out.csv[3, c(basic, 'pval')] <- effect[, c('Effect.est', 'Effect.CI.lo', 'Effect.CI.hi',
'pval')]
out.csv[3,3] <- 'RR'
} else{
if(settings$time==1){
out.csv[,2] <- 'Year 1'
} else if(settings$time==2){
out.csv[,2] <- 'Year 2'
}
out.csv[, basic] <- pool['supp.pos', basic]
out.csv[, c('N.analysis', 'N.est.HIV+')] <- c(sum(out$data$N.pop),
round(sum(out$data$N.pop)*pool['prev','pt']))
out.csv<- out.csv[1,]
}
out.csv
}