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data_process.R
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data_process.R
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# Generate all required data from different data sources (see Table S1).
library(fst)
library(data.table)
library(parallel)
library("xgboost")
library("dplyr")
library("foreign")
f <- list.files("/nfs/nsaph_ci3/ci3_health_data/medicare/mortality/1999_2016/wu/cache_data/merged_by_year_v2",
pattern = "\\.fst",
full.names = TRUE)
myvars <- c("year", "zip", "sex", "race", "age", "dual", "entry_age_break", "statecode",
"followup_year", "followup_year_plus_one", "dead", "pm25_ensemble",
"mean_bmi", "smoke_rate", "hispanic", "pct_blk", "medhouseholdincome", "medianhousevalue",
"poverty", "education", "popdensity", "pct_owner_occ",
"summer_tmmx", "winter_tmmx", "summer_rmax", "winter_rmax")
national_merged2016 <- rbindlist(lapply(f,
read_fst,
columns = myvars,
as.data.table = TRUE))
national_merged2016 <- as.data.frame(national_merged2016)
national_merged2016$zip <- sprintf("%05d", national_merged2016$zip)
NORTHEAST <- c("NY", "MA", "PA", "RI", "NH", "ME", "VT", "CT", "NJ")
SOUTH <- c("DC", "VA", "NC", "WV", "KY", "SC", "GA", "FL", "AL", "TN", "MS", "AR", "MD", "DE", "OK", "TX", "LA")
MIDWEST <- c("OH", "IN", "MI", "IA", "MO", "WI", "MN", "SD", "ND", "IL", "KS", "NE")
WEST <- c("MT", "CO", "WY", "ID", "UT", "NV", "CA", "OR", "WA", "AZ", "NM")
national_merged2016$region <- ifelse(national_merged2016$state %in% NORTHEAST, "NORTHEAST",
ifelse(national_merged2016$state %in% SOUTH, "SOUTH",
ifelse(national_merged2016$state %in% MIDWEST, "MIDWEST",
ifelse(national_merged2016$state %in% WEST, "WEST",
NA))))
national_merged2016 <- national_merged2016[complete.cases(national_merged2016[, c(1:27)]), ]
#> dim(national_merged2016)
#[1] 573370257 27
# Main analysis
covariates <- aggregate(national_merged2016[, c(12:27)],
by = list(national_merged2016$zip, national_merged2016$year),
FUN = min)
colnames(covariates)[1:2] <- c("zip", "year")
covariates <- subset(covariates[complete.cases(covariates), ])
covariates$year_fac <- as.factor(covariates$year)
covariates$region <- as.factor(covariates$region)
save(covariates, file = "./balance/covariates.RData")
# Fit GPS model via Xgboost machine
GPS_mod <- xgboost(data = data.matrix(covariates[, c(4:19)]),
label = covariates$pm25_ensemble,
nrounds = 50)
mod_sd <- sd(covariates$pm25_ensemble - predict(GPS_mod, data.matrix(covariates[, c(4:19)])))
feature_names <- GPS_mod$feature_names
covariates$GPS <- dnorm(covariates$pm25_ensemble,
mean = predict(GPS_mod, data.matrix(covariates[, c(4:19)])),
sd = sd(covariates$pm25_ensemble - predict(GPS_mod, data.matrix(covariates[, c(4:19)]))))
Nm <- dnorm(covariates$pm25_ensemble,
mean = mean(covariates$pm25_ensemble, na.rm = TRUE),
sd = sd(covariates$pm25_ensemble, na.rm = TRUE))
covariates$IPW <- Nm / (covariates$GPS)
covariates <- covariates[, c("zip", "year", "IPW", "GPS")]
save(GPS_mod,mod_sd, feature_names, covariates, file = "./covariates.RData")
# Generate count data for each individual characteristics and follow-up year
national_merged2016$time_count <- national_merged2016$followup_year_plus_one - national_merged2016$followup_year
dead_personyear <- aggregate(cbind(national_merged2016$dead,
national_merged2016$time_count),
by=list(national_merged2016$zip,
national_merged2016$year,
national_merged2016$sex,
national_merged2016$race,
national_merged2016$dual,
national_merged2016$entry_age_break,
national_merged2016$followup_year),
FUN = sum)
confounders <- aggregate(national_merged2016[,c(12:27)],
by=list(national_merged2016$zip,
national_merged2016$year,
national_merged2016$sex,
national_merged2016$race,
national_merged2016$dual,
national_merged2016$entry_age_break,
national_merged2016$followup_year),
FUN = min)
aggregate_data <- merge(dead_personyear,
confounders,
by = c("Group.1", "Group.2", "Group.3", "Group.4", "Group.5", "Group.6", "Group.7"))
colnames(aggregate_data)[8:9] <- c("dead", "time_count")
colnames(aggregate_data)[1:7] <- c("zip", "year", "sex", "race", "dual", "entry_age_break", "followup_year")
aggregate_data <- subset(aggregate_data[complete.cases(aggregate_data), ])
save(aggregate_data, file = "./aggregate_data.RData")
load("./covariates.RData")
load("./aggregate_data.RData")
aggregate_data <- merge(aggregate_data, covariates, by = c("zip", "year"), all.x = TRUE)
aggregate_data.list <- split(aggregate_data,
list(aggregate_data$sex,
aggregate_data$race,
aggregate_data$dual,
aggregate_data$entry_age_break,
aggregate_data$followup_year))
aggregate_data.list <- aggregate_data.list[lapply(aggregate_data.list, nrow) > 0]
mclapply(aggregate_data.list, function(data) {
write_fst(data,paste0("./FST_data/",
data$sex[1],
data$race[1],
data$dual[1],
data$entry_age_break[1],
data$followup_year[1],
".fst"))
}, mc.cores = 16)
# For analyses, exclude year as a confounder
# Fit GPS model via Xgboost machine
load("./balance/covariates.RData")
GPS_mod <- xgboost(data = data.matrix(covariates[, c(4:18)]),
label = covariates$pm25_ensemble,
nrounds = 50)
mod_sd <- sd(covariates$pm25_ensemble - predict(GPS_mod, data.matrix(covariates[, c(4:18)])))
feature_names <- GPS_mod$feature_names
covariates$GPS <- dnorm(covariates$pm25_ensemble,
mean = predict(GPS_mod, data.matrix(covariates[, c(4:18)])),
sd = sd(covariates$pm25_ensemble - predict(GPS_mod, data.matrix(covariates[, c(4:18)]))))
Nm <- dnorm(covariates$pm25_ensemble,
mean = mean(covariates$pm25_ensemble, na.rm = TRUE),
sd = sd(covariates$pm25_ensemble, na.rm = TRUE))
covariates$IPW <- Nm / (covariates$GPS)
covariates <- covariates[, c("zip", "year", "IPW", "GPS")]
save(GPS_mod, mod_sd, feature_names, covariates, file = "./covariates_noyear.RData")
load("./covariates_noyear.RData")
load("./aggregate_data.RData")
aggregate_data <- merge(aggregate_data, covariates, by = c("zip", "year"), all.x = TRUE)
aggregate_data.list <- split(aggregate_data,
list(aggregate_data$sex,
aggregate_data$race,
aggregate_data$dual,
aggregate_data$entry_age_break,
aggregate_data$followup_year))
aggregate_data.list <- aggregate_data.list[lapply(aggregate_data.list, nrow) > 0]
dir.create(file.path("./FST_data_noyear"), showWarnings = FALSE)
mclapply(aggregate_data.list, function(data) {
write_fst(data,paste0("./FST_data_noyear/",
data$sex[1],
data$race[1],
data$dual[1],
data$entry_age_break[1],
data$followup_year[1],
".fst"))
}, mc.cores = 16)
# For analyses, exclude meteorological variables as confounders
load("./balance/covariates.RData")
GPS_mod <- xgboost(data = data.matrix(covariates[, c(4:13,18:19)]),
label = covariates$pm25_ensemble,
nrounds = 50)
mod_sd <- sd(covariates$pm25_ensemble - predict(GPS_mod, data.matrix(covariates[, c(4:13,18:19)])))
feature_names <- GPS_mod$feature_names
covariates$GPS <- dnorm(covariates$pm25_ensemble,
mean = predict(GPS_mod, data.matrix(covariates[, c(4:13,18:19)])),
sd = sd(covariates$pm25_ensemble - predict(GPS_mod, data.matrix(covariates[, c(4:13,18:19)]))))
Nm <- dnorm(covariates$pm25_ensemble,
mean = mean(covariates$pm25_ensemble, na.rm = TRUE),
sd = sd(covariates$pm25_ensemble, na.rm = TRUE))
covariates$IPW <- Nm / (covariates$GPS)
covariates <- covariates[, c("zip", "year", "IPW", "GPS")]
save(GPS_mod, mod_sd, feature_names, covariates, file = "./covariates_notemp.RData")
load("./covariates_notemp.RData")
load("./aggregate_data.RData")
aggregate_data <- merge(aggregate_data,covariates, by = c("zip", "year"), all.x = TRUE)
aggregate_data.list <- split(aggregate_data,
list(aggregate_data$sex,
aggregate_data$race,
aggregate_data$dual,
aggregate_data$entry_age_break,
aggregate_data$followup_year))
aggregate_data.list <- aggregate_data.list[lapply(aggregate_data.list, nrow) > 0]
dir.create(file.path("./FST_data_notemp"), showWarnings = FALSE)
mclapply(aggregate_data.list, function(data) {
write_fst(data,paste0("./FST_data_notemp/",
data$sex[1],
data$race[1],
data$dual[1],
data$entry_age_break[1],
data$followup_year[1],
".fst"))
}, mc.cores = 16)
# Save the complete data
write.foreign(national_merged2016,
datafile = "./national_merged2016.csv",
codefile = "./national_merged2016.sas",
package = c("SAS"))