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SuppressionInClosedCohort.R
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SuppressionInClosedCohort.R
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##############
# Suppression and ART start
#
# Joshua Schwab
# jschwab77@berkeley.edu
#---------------------------
set.seed(42642) #for reproducibility with SuperLearner
library(ltmle)
library(data.table)
library(survival)
library(SuperLearner)
rm(list = ls())
source('Utils.R')
source('Stage2_Functions.R')
source('Adapt_Functions.R')
source('Stage1_Functions.R')
SL.library <- list(c("SL.glm", "screen.corRank10"), c("SL.glm", "screen.corP"), c("SL.gam", "screen.corRank10"), c("SL.gam", "screen.corP"), c("SL.glm.interaction", "screen.corRank5"), c("SL.mean", "All"))
community.set <- c("Nyamrisra", "Nyatoto", "Kitare", "Ogongo", "Kisegi", "Magunga",
"Sena", "Tom Mboya", "Bware", "Ongo", "Othoro", "Sibuoche", "Nankoma",
"Nsiinze", "Kamuge", "Kiyunga", "Bugono", "Muyembe", "Kiyeyi",
"Merikit", "Kadama", "Kameke", "Bugamba", "Nsiika", "Mitooma",
"Rugazi", "Kitwe", "Rubaare", "Ruhoko", "Rwashamaire", "Kazo",
"Nyamuyanja")
CreatePopulations <- function() {
AddPop <- function(cascade = "ANY", eart_vl = "ANY", cd4 = "ANY", incorp.self) {
pop <<- rbind(pop, data.table(cascade, eart_vl, cd4, incorp.self))
}
pop <- NULL
if (ART) {
for (incorp.self in c(T, F)) {
AddPop(eart_vl = F, incorp.self = incorp.self)
for (cd4 in 2:4) {
AddPop(cd4 = cd4, eart_vl = F, incorp.self = incorp.self)
}
}
} else {
for (cascade in 1:4) {
AddPop(cascade = cascade, incorp.self = F)
}
}
setkeyv(pop, names(pop))
return(pop)
}
LoadInputs <- function() {
load("outputs-withIntOnly.RData")
return(outputs)
}
GetDT <- function(inputs, cur.outcome, cur.pop) {
dt <- inputs
dt[hiv_0 & is.na(hivtest_date_0) & !is.na(VLdate_0), hivtest_date_0 := VLdate_0]
dt[dead_3 & is.na(death_date_3), death_date_3 := HalfwayDate(hivtest_date_0, trk_end_3)]
dt[outmigrate_3 & is.na(outmigrate_3), outmigrate_date_3 := HalfwayDate(hivtest_date_0, trk_end_3)]
index <- dt[, resident_0 & age_0 >= 15 & hiv_0 %in% TRUE]
if (ART) {
index <- index & dt[, !is.na(hivtest_date_0)]
}
if (cur.pop$cascade != "ANY") {
if (cur.pop$incorp.self) {
if (cur.pop$cascade == 1) {
index <- index & dt[, pdx_vl_0 %in% 0 & pdx_self_0 %in% 0]
} else if (cur.pop$cascade == 2) {
index <- index & dt[, (pdx_vl_0 %in% 1 | pdx_self_0 %in% 1) & eart_vl_0 %in% 0]
} else if (cur.pop$cascade == 3) {
index <- index & dt[, (pdx_vl_0 %in% 1 | pdx_self_0 %in% 1) & (eart_vl_0 %in% 1 | eart_self_0 %in% 1) & supp_0 %in% 1]
} else if (cur.pop$cascade == 4) {
index <- index & dt[, (pdx_vl_0 %in% 1 | pdx_self_0 %in% 1) & (eart_vl_0 %in% 1 | eart_self_0 %in% 1) & supp_0 %in% 0]
} else {
stop("not expected")
}
} else {
if (cur.pop$cascade == 1) {
index <- index & dt[, pdx_vl_0 %in% 0]
} else if (cur.pop$cascade == 2) {
index <- index & dt[, pdx_vl_0 %in% 1 & eart_vl_0 %in% 0]
} else if (cur.pop$cascade == 3) {
index <- index & dt[, eart_vl_0 %in% 1 & supp_0 %in% 1]
} else if (cur.pop$cascade == 4) {
index <- index & dt[, eart_vl_0 %in% 1 & supp_0 %in% 0]
} else {
stop("not expected")
}
}
}
if (cur.pop$cd4 != "ANY") {
if (cur.pop$cd4 == 2) {
index <- index & dt[, !is.na(cd4_0) & cd4_0 < 350]
} else if (cur.pop$cd4 == 3) {
index <- index & dt[, !is.na(cd4_0) & cd4_0 >= 350 & cd4_0 < 500]
} else if (cur.pop$cd4 == 4) {
index <- index & dt[, !is.na(cd4_0) & cd4_0 >= 500]
} else {
stop("not expected")
}
}
if (cur.pop$eart_vl != "ANY") {
if (cur.pop$incorp.self) {
index <- index & dt[, eart_vl_0 == as.logical(cur.pop$eart_vl) & eart_self_0 == as.logical(cur.pop$eart_vl)]
} else {
index <- index & dt[, eart_vl_0 == as.logical(cur.pop$eart_vl)]
}
}
Y <- GetY(inputs, cur.outcome)
if (ART) {
dt$time <- Y$time
dt$Y <- Y$Y
} else {
dt$Y <- Y
}
dt[, id := as.integer(factor(community_name))]
dt <- dt[index]
return(dt)
}
GetDataFrame <- function(dt) {
if (ART) {
df <- data.frame(time = dt$time, Y = dt$Y)
} else {
df <- data.frame(get.X(as.data.frame(dt), analysis = "HIV"), C = BinaryToCensoring(is.censored = is.na(dt$Y)), Y = dt$Y)
}
return(df)
}
GetY <- function(inputs, cur.outcome) {
if (cur.outcome == "supp") {
inputs[dead_3 %in% FALSE & outmigrate_3 %in% FALSE, Y := supp_3]
return(as.integer(inputs$Y))
} else if (cur.outcome %in% c("ART6", "ART12", "ART24", "ART36")) {
event.time <- inputs[, art_date]
censor.time <- inputs[, pmin(death_date_3, outmigrate_date_3, trk_end_3, na.rm = T)]
event <- DateBefore(event.time, censor.time, strict = F)
time <- as.numeric(pmin(event.time, censor.time, na.rm = T) - inputs[, hivtest_date_0], units = "days")
return(list(time=time, Y=event))
} else {
stop("not expected")
}
}
Est <- function(cur.outcome, cur.pop) {
dt <- GetDT(inputs, cur.outcome, cur.pop)
estimates <- data.table(community_name = community.set, Y = NA_real_)
for (comm in community.set) {
df <- GetDataFrame(dt[community_name == comm])
stopifnot(nrow(df) > 1 & sum(!is.na(df$Y)) > 1)
if (ART) {
stopifnot(!anyNA(df$Y))
stopifnot(!anyNA(df$time))
months <- as.integer(substr(cur.outcome, start = 4, stop = nchar(cur.outcome)))
days <- 365.25 / 12 * months
fit <- survfit(Surv(time, Y) ~ 1, data = df)
est <- 1 - summary(fit, min(days, max(df$time)))$surv
} else {
est <- ltmle(df, Anodes = NULL, abar = NULL, Cnodes = "C", Ynodes = "Y", estimate.time = F, variance.method = "ic", SL.library = SL.library)$estimates["tmle"]
}
estimates[community_name == comm, Y := est]
}
return(estimates)
}
Get1 <- function(z) {
unique.z <- unique(z)
stopifnot(length(unique.z) == 1)
return(unique.z)
}
inputs <- LoadInputs()
for (ART in c(T, F)) {
if (ART) {
outcomes <- paste0("ART", c(6, 12, 24, 36))
} else {
outcomes <- "supp"
}
populations <- CreatePopulations()
results.stage2 <- data.table()
for (outcome.index in 1:length(outcomes)) {
cur.outcome1 <- outcomes[outcome.index]
for (i in 1:nrow(populations)) {
cur.pop1 <- populations[i]
cat("\n\n------ outcome.index = ", outcome.index, " i = ", i, "\n")
print(cur.pop1)
dt1 <- GetDT(inputs, cur.outcome1, cur.pop1)
estimates <- Est(cur.outcome1, cur.pop1)
dt.summary <- dt1[, .(below25 = mean(age_0 < 25), A = Get1(as.integer(intervention)), pair = Get1(as.integer(as.character(pair))), supp = mean(supp_0, na.rm=T), nIndv = .N, U = 1, id = Get1(id)), by = "community_name"]
dt.summary.merged <- merge(estimates, dt.summary, by = "community_name")
stage2 <- Stage2(data.input=as.data.frame(dt.summary.merged), weighting = "individual", outcome = "Y", clust.adj = c("U", "below25", "supp"), do.data.adapt = T, break.match = F)
results.stage2 <- rbind(results.stage2, cbind(cur.pop1, outcome = cur.outcome1, N = nrow(dt1), stage2))
}
}
filestr <- paste("Analysis Outputs/closed cohort outputs - ART =", ART)
write.csv(format(results.stage2, digits = 3), file=paste(filestr, "- stage2.csv"))
}