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Cohort Creation.R
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Cohort Creation.R
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rm(list = ls()) #removing any variables in R's memory
options(max.print=10000)
# Processing Initial Data
processsage <- function(inp) {
df <- z <- data.frame("id"=integer(),"age" = integer(),"sex"=integer(),"sbp" = double(),"dbp" = double(),"bmi" = double(),"smk" = integer(),
"iso"=character(),"year" = integer(),"htn_status"=character(),"facility"=integer(),stringsAsFactors=FALSE)
for (row in 1 : nrow(inp)) {
id <- inp[row,"id"]
age <- inp[row,"q1011"]
if (inp[row,"q1009"] == "Male") { #Male - 0, Female - 1
sex <- 0
} else {
sex <- 1
}
sbp <- (inp[row,"q2501_s"] +inp[row,"q2502_s"]+ inp[row,"q2503_s"])/3
dbp <- (inp[row,"q2501_d"] +inp[row,"q2502_d"]+ inp[row,"q2503_d"])/3
bmi <- inp[row,"q2507"] / ((0.01 * inp[row,"q2506"])^2)
if (inp[row,"q3002"] == "Yes, daily" | inp[row,"q3002"] == "Yes, not daily"){
smk <- 1
} else {
smk <- 0
}
if (sbp > 140) {
if (inp[row,"q4060"] == "Yes") {
htn_status = "Uncontrolled"
} else {
htn_status = "Unaware"
}
} else {
if (inp[row,"q4060"] == "Yes") {
htn_status = "Controlled"
} else {
htn_status = "Not hypertensive"
}
}
if (inp[row,"q5004"] == "Public clinic" | inp[row,"q5004"] == "Public hospital") {
facility = 0 # 0 for public sector
} else {
facility = 1 # 1 for any facility
}
iso <- "IND"
year <- 2010
z <- data.frame(id,sex,age,sbp,dbp,bmi,smk,iso,year,htn_status,facility)
df <- rbind(df,z)
}
return(df)
}
#Generating age- and gender-specific cohort
cohortgen <- function(inp,sex,lower_age,samples,facility_choice=1){
id <- lower_age*1000000
htn <- non_htn <- data.frame("id"=integer(),"age" = integer(),"sex"=integer(),"sbp" = double(),"dbp" = double(),"bmi" = double(),"smk" = integer(),
"iso"=character(),"year" = integer(),"htn_status"=character(),"facility"=integer(),stringsAsFactors=FALSE)
df <- z <- data.frame("id"=integer(),"age" = integer(),"sex"=integer(),"sbp" = double(),"bmi" = double(),"smk" = integer(),
"iso"=character(),"year" = integer(),stringsAsFactors=FALSE)
count <- 0
#Categorizing data into Hypertensive vs Not Hypertensive
if (facility_choice == 0) {
for (i in 1:nrow(inp)){
if (inp[i,"sex"] == sex & inp[i,"age"] >= lower_age & inp[i,"age"] < (lower_age+5) & inp[i,"facility"] == 0 ) {
count <- count + 1
if (inp[i,"htn_status"]=="Not hypertensive"){
non_htn <- rbind(non_htn,inp[i,])
} else if (inp[i,"htn_status"]=="Unaware" | inp[i,"htn_status"]=="Uncontrolled" ){
htn <- rbind(htn,inp[i,])
}
}
}
} else {
for (i in 1:nrow(inp)){
if (inp[i,"sex"] == sex & inp[i,"age"] >= lower_age & inp[i,"age"] < (lower_age+5)) {
count <- count + 1
if (inp[i,"htn_status"]=="Not hypertensive"){
non_htn <- rbind(non_htn,inp[i,])
} else if (inp[i,"htn_status"]=="Unaware" | inp[i,"htn_status"]=="Uncontrolled" ){
htn <- rbind(htn,inp[i,])
}
}
}
}
prob_htn <- 1- (nrow(non_htn)/count) #Hypertension prevalence in the cohort
for (i in 1:samples) {
htn_decide <- sample(0:1, size=1, prob = c(prob_htn,1-prob_htn),replace=TRUE) #Deciding if the person is hypertensive or not
if (htn_decide == 0){
line <- sample(1:nrow(htn),1,replace=TRUE)
id <- id + 1
age <- htn[line,"age"]
sex <- htn[line,"sex"]
sbp <- htn[line,"sbp"]
bmi <- htn[line,"bmi"]
smk <- htn[line,"smk"]
iso <- "IND"
year<- 2010
}
else {
line <- sample(1:nrow(non_htn),1,replace=TRUE)
id <- id + 1
age <- non_htn[line,"age"]
sex <- non_htn[line,"sex"]
sbp <- non_htn[line,"sbp"]
bmi <- non_htn[line,"bmi"]
smk <- non_htn[line,"smk"]
iso <- "IND"
year<- 2010
}
z <- data.frame(id,sex,age,sbp,bmi,smk,iso,year)
df <- rbind(df,z)
}
return(df)
}
#Generating unified sex-based cohort
samplegen <- function(input,gender,n_samples,fac) { #Gender: 0 fo Male, and 1 for Female, Facility: 0 for Public, 1 for All
out <- s <- data.frame("id"=integer(),"age" = integer(),"sex"=integer(),"sbp" = double(),"bmi" = double(),"smk" = integer(),
"iso"=character(),"year" = integer(),stringsAsFactors=FALSE)
for (i in 1:8)
{
age <- 40 + (i-1)*5
if (gender==0) {
s <- cohortgen(input,gender,age,round(n_samples*male_dist[i]),fac)
} else {
s <- cohortgen(input,gender,age,round(n_samples*female_dist[i]),fac)
}
out <- rbind(out,s)
}
return(out)
}
# Franklin Implentation to increase blood pressure with age
franklin_sbpinc <- function (sample,age_till) {
dfM1 <- sim_sample <- data.frame("id" = integer(),"age" = integer(),"sex" = character(),"sbp" = double(),
"smk"= integer(), "bmi"= double(),"iso" = character(),"year"=integer(),stringsAsFactors=FALSE)
sim_sample <- sample
cur <- 0
cur_calib <- 0
slp <- 0
slp_calib <- 0
for( row in 1: nrow(sim_sample))
{
age <- sim_sample[row,"age"]
sex <- sim_sample[row,"sex"]
sbp <- sim_sample[row,"sbp"]
smk <- sim_sample[row,"smk"]
bmi <- sim_sample[row,"bmi"]
id <- sim_sample[row,"id"]
iso <- "IND"
year<- 2010
if (age %% 5 != 0)
{
while (age %% 5 !=0)
{
dfM1 <- rbind(dfM1, data.frame(id,age,sex,sbp,smk,bmi,iso,year))
if (sbp < 120) {
cur <- 0.014
cur_calib <- 0.35
slp <- 0.57
slp_calib <- 0.6
} else if (sbp >= 120 & sbp < 140) {
cur <- 0.007
cur_calib <- 0.35
slp <- 0.75
slp_calib <- 0.6
} else if (sbp >= 140 & sbp <160) {
cur <- 0.001
cur_calib <- 0.35
slp <- 1.18
slp_calib <- 0.45
} else {
cur <- 0.013
cur_calib <- 0.35
slp <- 1.97
slp_calib <- 0.4
}
inc = 2*cur_calib*cur*(age-60) + slp_calib*slp
sbp = sbp + inc
age = age + 1
}
}
while (age <= age_till)
{
dfM1 <- rbind(dfM1, data.frame(id,age,sex,sbp,smk,bmi,iso,year))
if (sbp < 120) {
cur <- 0.014
cur_calib <- 0.35
slp <- 0.57
slp_calib <- 0.6
} else if (sbp >= 120 & sbp < 140) {
cur <- 0.007
cur_calib <- 0.35
slp <- 0.75
slp_calib <- 0.6
} else if (sbp >= 140 & sbp <160) {
cur <- 0.001
cur_calib <- 0.35
slp <- 1.18
slp_calib <- 0.45
} else {
cur <- 0.013
cur_calib <- 0.35
slp <- 1.97
slp_calib <- 0.4
}
inc = (2*cur_calib*cur*(age-60) + slp_calib*slp)*5
sbp = sbp + inc
age <- age + 5
}
}
return(dfM1)
}
sim_inp <- read.csv("SAGE_Cleaned_19Oct.csv") #Cleaned SAGE File as input
male_dist <- c(0.229,0.199,0.165,0.132,0.113,0.079,0.053,0.03)
female_dist <- c(0.224,0.199,0.158,0.136,0.114,0.081,0.055,0.033)
output <- processsage(sim_inp)
female_samp <- samplegen(output,1,100,1)
male_samp <- samplegen(output,0,100,1)
female_samples <- franklin_sbpinc(female_samp,age_till = 120)
male_samples <- franklin_sbpinc(male_samp,age_till = 120)
#Output files which will be input for thr Globorisk calculator
write.csv(female_samples,"Females_Globorisk_Input.csv")
write.csv(male_samples,"Males_Globorisk_Input.csv")