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model_smkdep_tx_v3.R
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model_smkdep_tx_v3.R
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rm(list = ls())
mainDir <- "C:/Users/jamietam/Dropbox/GitHub/smk-dep-model"
setwd(file.path(mainDir))
library(openxlsx)
library(reshape)
library(splines)
library(Bhat)
load("depsmkprevs_2005-2018_v2.rda") # load NSDUH data with 5 age groups
namethisrun = "_v3"
folder = paste0("manuscript",namethisrun,"/") # name the folder where results will be saved
# Read inputs -------------------------------------------------------------
startyear = 1900 # the burn-in period starting point
endyear = 2100 # 2014 or 2050 , still need census projections through 2065
startage = 0
endage = 99
Ny= endyear - startyear + 1
Na= endage - startage + 1
emptycompartment <- matrix(0, nrow = Na, ncol = Ny, dimnames=list(c(startage:endage),c(startyear:endyear))) # create matrix of zeroes for compartments
policystart = 2020
allparamsF = read.xlsx("parameters_tx.xlsx",sheet=paste0("model_females"),rowNames=TRUE,colNames=TRUE) # Adjust parameters in this excel file with estimates to be used in the model
paramsF = as.vector(subset(allparamsF,bhat==1)[['estimate']]) # Parameters where bhat = 1 can be estimated by bhat
paramsnamesF = rownames(subset(allparamsF,bhat==1))
lowervectorF = as.vector(subset(allparamsF,bhat==1)[['lower']]) # lower bounds for bhat parameters
uppervectorF = as.vector(subset(allparamsF,bhat==1)[['upper']]) # upper bounds for bhat parameters
allparamsM = read.xlsx("parameters_tx.xlsx",sheet=paste0("model_males"),rowNames=TRUE,colNames=TRUE) # Adjust parameters in this excel file with estimates to be used in the model
paramsM = as.vector(subset(allparamsM,bhat==1)[['estimate']]) # Parameters where bhat = 1 can be estimated by bhat
paramsnamesM = rownames(subset(allparamsM,bhat==1))
lowervectorM = as.vector(subset(allparamsM,bhat==1)[['lower']]) # lower bounds for bhat parameters
uppervectorM = as.vector(subset(allparamsM,bhat==1)[['upper']]) # upper bounds for bhat parameters
agerownames<-c("18to25", "26to34", "35to49", "50to64", "65plus", "total")
agegroupstart <- c(18,26,35,50,65,18)
agegroupend <- c(25,34,49,64,99,99)
# Get model prevs by age group --------------------------------------------
getmodelprevs <- function(numerator,denominator){
numerator = as.data.frame(numerator)
denominator = as.data.frame(denominator)
prevs = NULL
for (a in c(1:length(agegroupstart))) {
prevs <- rbind(prevs,colSums(numerator[c((agegroupstart[a]+1):(agegroupend[a]+1)), ],na.rm=TRUE)/
colSums(denominator[c((agegroupstart[a]+1):(agegroupend[a]+1)), ],na.rm=TRUE))
}
row.names(prevs)<-agerownames
return(prevs)
}
# Main model --------------------------------------------------------------
main <- function(getmodelprevs, whichgender, allparamsF, paramsF,paramsnamesF, mpc, txeffcess, util){
setwd(file.path(mainDir))
pop = read.xlsx("census_data/np2017_d1.xlsx",sheet=whichgender,rowNames=TRUE, colNames=TRUE, check.names=FALSE)
# smk params --------------------------------------------------------------
smk_init_cisnet = read.xlsx("cisnet_smkrates_nhis2018.xlsx",sheet=paste0(whichgender,"_init"),rowNames=TRUE, colNames=TRUE, check.names=FALSE)
smk_cess_cisnet = read.xlsx("cisnet_smkrates_nhis2018.xlsx",sheet=paste0(whichgender,"_cess"),rowNames=TRUE, colNames=TRUE, check.names=FALSE)
smkinit_SF = matrix(0,100,1)
smkinit_SF[1:18] = ifelse(allparamsF["smkinit_youthSF","bhat"]==0,allparamsF["smkinit_youthSF","estimate"],paramsF[match("smkinit_youthSF",paramsnames)])
smkinit_SF[19:35] = ifelse(allparamsF["smkinit_SF_18to34","bhat"]==0,allparamsF["smkinit_SF_18to34","estimate"],paramsF[match("smkinit_SF_18to34",paramsnamesF)])
smkinit_SF[36:66] = ifelse(allparamsF["smkinit_SF_35to64","bhat"]==0,allparamsF["smkinit_SF_35to64","estimate"],paramsF[match("smkinit_SF_35to64",paramsnamesF)])
smkinit_SF[67:100] = ifelse(allparamsF["smkinit_SF_65plus","bhat"]==0,allparamsF["smkinit_SF_65plus","estimate"],paramsF[match("smkinit_SF_65plus",paramsnamesF)])
smkcess_SF = matrix(0,100,1)
smkcess_SF[1:18] = ifelse(allparamsF["smkcess_youthSF","bhat"]==0,allparamsF["smkcess_youthSF","estimate"],paramsF[match("smkcess_youthSF",paramsnamesF)])
smkcess_SF[19:35] = ifelse(allparamsF["smkcess_SF_18to34","bhat"]==0,allparamsF["smkcess_SF_18to34","estimate"],paramsF[match("smkcess_SF_18to34",paramsnamesF)])
smkcess_SF[36:66] = ifelse(allparamsF["smkcess_SF_35to64","bhat"]==0,allparamsF["smkcess_SF_35to64","estimate"],paramsF[match("smkcess_SF_35to64",paramsnamesF)])
smkcess_SF[67:100] = ifelse(allparamsF["smkcess_SF_65plus","bhat"]==0,allparamsF["smkcess_SF_65plus","estimate"],paramsF[match("smkcess_SF_65plus",paramsnamesF)])
smk_init = smk_init_cisnet*smkinit_SF # scale smoking initiation rates ### CHECK DIMENSIONS might be off by one
smk_cess = smk_cess_cisnet*smkcess_SF # scale smoking cessation rates
smk_cess_txpop = smk_cess # when mpc ==0
death_ns = read.xlsx("cisnet_deathrates.xlsx",sheet=paste0("ns_",whichgender),rowNames=TRUE, colNames=TRUE, check.names=FALSE)
death_cs = read.xlsx("cisnet_deathrates.xlsx",sheet=paste0("cs_",whichgender),rowNames=TRUE, colNames=TRUE, check.names=FALSE)
death_fs = read.xlsx("cisnet_deathrates.xlsx",sheet=paste0("fs_",whichgender),rowNames=TRUE, colNames=TRUE, check.names=FALSE)
LE_ns = read.xlsx("cisnet_deathrates.xlsx",sheet=paste0("LE_ns_",whichgender),rowNames=TRUE, colNames=TRUE, check.names=FALSE)
LE_cs = read.xlsx("cisnet_deathrates.xlsx",sheet=paste0("LE_cs_",whichgender),rowNames=TRUE, colNames=TRUE, check.names=FALSE)
LE_fs = read.xlsx("cisnet_deathrates.xlsx",sheet=paste0("LE_fs_",whichgender),rowNames=TRUE, colNames=TRUE, check.names=FALSE)
ucs_minus_uns = death_cs - death_ns
ufs_minus_uns = death_fs - death_ns
dep1inc = read.xlsx("incidence_eaton.xlsx",sheet=whichgender,rowNames=TRUE, colNames=FALSE, check.names=FALSE)
# model parameters are retrieved from the parameters.xlsx file, if they are bhat=1, then they are read in as a params vector for bhat estimation
RRdepr_death = c(rep(1,17),rep(ifelse(allparamsF["RRdepr_death","bhat"]==0,allparamsF["RRdepr_death","estimate"],paramsF[match("RRdepr_death",paramsnamesF)]),Na-18))
# scale all recovery rates by the scaling factor deprecov_SF
deprecov_rate = c(rep(ifelse(allparamsF["deprecov_rate","bhat"]==0,allparamsF["deprecov_rate","estimate"],paramsF[match("deprecov_rate",paramsnamesF)]),Na-1))
deprecov_SF = matrix(1,99,1)
deprecov_rate = deprecov_rate * deprecov_SF
deprecovSF_cs = c(rep(ifelse(allparamsF["deprecovSF_cs","bhat"]==0,allparamsF["deprecovSF_cs","estimate"],paramsF[match("deprecovSF_cs",paramsnamesF)]),Na-1))
deprecovSF_fs = c(rep(ifelse(allparamsF["deprecovSF_fs","bhat"]==0,allparamsF["deprecovSF_fs","estimate"],paramsF[match("deprecovSF_fs",paramsnamesF)]),Na-1))
deprinc = rep(ifelse(allparamsF["depr_inc","bhat"]==0,allparamsF["depr_inc","estimate"],paramsF[match("depr_inc",paramsnamesF)]),Na-1)
deprinc_SF = matrix(1,99,1)
depr_inc = deprinc * deprinc_SF
depinc1 = ifelse(allparamsF["depinc1","bhat"]==0,allparamsF["depinc1","estimate"],paramsF[match("depinc1",paramsnamesF)])
depinc2 = ifelse(allparamsF["depinc2","bhat"]==0,allparamsF["depinc2","estimate"],paramsF[match("depinc2",paramsnamesF)])
depinc3 = ifelse(allparamsF["depinc3","bhat"]==0,allparamsF["depinc3","estimate"],paramsF[match("depinc3",paramsnamesF)])
inc_SF = ifelse(allparamsF["inc_SF","bhat"]==0,allparamsF["inc_SF","estimate"],paramsF[match("inc_SF",paramsnamesF)])
if (whichgender=="females"){
MP=ns(0:21,knots=c(13,18)) ### Matrix of X's
Rps=predict(MP,21)[1,] ## Predicts y value given a set of X's for age 22 # 0.0051285304 anchor at age 22
y=c()
for (j in 0:21){
y=c(y,0.0051285304*exp(sum((MP[j,]-Rps)*c(depinc1,depinc2,depinc3)))) ## multiply by coefficients and sum , exp makes it positive for incidence
}
dep1_inc=dep1inc
dep1_inc[0:22,]<-y
dep1_inc[0:12,]<-rep(0,12) # assumes no 1st MDE before age 12
scaleddep1_inc = dep1_inc
scaleddep1_inc[0:26,]<-scaleddep1_inc[0:26,]*inc_SF
}
if (whichgender=="males"){
MP=ns(0:28,knots=c(13,18))
Rps=predict(MP,28)[1,] ## Predicts y value given a set of X's for age 22 # 0.0019072600 anchor at age 29
y=c()
for (j in 0:28){
y=c(y,0.0019072600*exp(sum((MP[j,]-Rps)*c(depinc1,depinc2,depinc3)))) ## multiply by coefficients and sum , exp makes it positive for incidence
}
dep1_inc=dep1inc
dep1_inc[0:29,]<-y
dep1_inc[0:12,]<-rep(0,12) # assumes no 1st MDE before age 12
scaleddep1_inc = dep1_inc
scaleddep1_inc[0:26,]<-scaleddep1_inc[0:26,]*inc_SF # multiply all incidence probabilities by scaling factor for ages <=25
}
# Age-group categorical forgetting probabilities -------------------------
forget = matrix(NA,99,1)
forget[1:17] = rep(0,17) # assumes no forgetting before age 18
forget[18:25] = ifelse(allparamsF["forget1","bhat"]==0,allparamsF["forget1","estimate"],paramsF[match("forget1",paramsnamesF)])
forget[26:34] = ifelse(allparamsF["forget2","bhat"]==0,allparamsF["forget2","estimate"],paramsF[match("forget2",paramsnamesF)])
forget[35:49] = ifelse(allparamsF["forget3","bhat"]==0,allparamsF["forget3","estimate"],paramsF[match("forget3",paramsnamesF)])
forget[50:64] = ifelse(allparamsF["forget4","bhat"]==0,allparamsF["forget4","estimate"],paramsF[match("forget4",paramsnamesF)])
forget[65:99] = ifelse(allparamsF["forget5","bhat"]==0,allparamsF["forget5","estimate"],paramsF[match("forget5",paramsnamesF)])
# smkdep effects parameters ------------------------------------------
RRcs_dep1 = c(rep(ifelse(allparamsF["RRcs_dep1","bhat"]==0,allparamsF["RRcs_dep1","estimate"],paramsF[match("RRcs_dep1",paramsnamesF)]),Na-1)) # Note: RR estimate comes from adult survey but applies to youth in model
RRfs_dep1 = c(rep(ifelse(allparamsF["RRfs_dep1","bhat"]==0,allparamsF["RRfs_dep1","estimate"],paramsF[match("RRfs_dep1",paramsnamesF)]),Na-1))
Efs_depr = c(rep(ifelse(allparamsF["Efs_depr","bhat"]==0,allparamsF["Efs_depr","estimate"],paramsF[match("Efs_depr",paramsnamesF)]),Na-1))
Ecs_depr = c(rep(ifelse(allparamsF["Ecs_depr","bhat"]==0,allparamsF["Ecs_depr","estimate"],paramsF[match("Ecs_depr",paramsnamesF)]),Na-1))
ORhdep_quit = c(rep(ifelse(allparamsF["ORhdep_quit","bhat"]==0,allparamsF["ORhdep_quit","estimate"],paramsF[match("ORhdep_quit",paramsnamesF)]),Na-1))
Edepr_smkinit = c(rep(ifelse(allparamsF["Edepr_smkinit","bhat"]==0,allparamsF["Edepr_smkinit","estimate"],paramsF[match("Edepr_smkinit",paramsnamesF)]),Na-1))
ORhdep_quit_txpop = ORhdep_quit # when mpc = 0
# mental health service utilization parameters ----------------------------
if ('ahltmde_18to25' %in% paramsnamesF){ # if doing bhat estimation for utilization rates
proptx = matrix(NA,99,1)
# proptx[1:17] = rep(0,17) # assumes no one is in tx before age 18
proptx[1:25] = ifelse(allparamsF["ahltmde_18to25","bhat"]==0,allparamsF["ahltmde_18to25","estimate"],paramsF[match("ahltmde_18to25",paramsnamesF)])
proptx[26:34] = ifelse(allparamsF["ahltmde_26to34","bhat"]==0,allparamsF["ahltmde_26to34","estimate"],paramsF[match("ahltmde_26to34",paramsnamesF)])
proptx[35:49] = ifelse(allparamsF["ahltmde_35to49","bhat"]==0,allparamsF["ahltmde_35to49","estimate"],paramsF[match("ahltmde_35to49",paramsnamesF)])
proptx[50:64] = ifelse(allparamsF["ahltmde_50to64","bhat"]==0,allparamsF["ahltmde_50to64","estimate"],paramsF[match("ahltmde_50to64",paramsnamesF)])
proptx[65:99] = ifelse(allparamsF["ahltmde_65plus","bhat"]==0,allparamsF["ahltmde_65plus","estimate"],paramsF[match("ahltmde_65plus",paramsnamesF)])
}
if (!('ahltmde_18to25'%in% paramsnamesF)){
utilrates = read.xlsx("parameters_tx.xlsx",sheet=paste0("util_", whichgender),rowNames=TRUE,colNames=TRUE)
colnames(utilrates) <- c("1.0","1.1","1.2","100")
proptx = matrix(NA,99,1)
# proptx[1:17] = utilrates["ahltmde_18to25",util] #rep(0,17) # assumes same proportion is in tx before age 18
proptx[1:25] = utilrates["ahltmde_18to25",util]
proptx[26:34] = utilrates["ahltmde_26to34",util]
proptx[35:49] = utilrates["ahltmde_35to49",util]
proptx[50:64] = utilrates["ahltmde_50to64",util]
proptx[65:99] = utilrates["ahltmde_65plus",util]
}
# Compartments / state variables ------------------------------------------
# Initialize population - Model compartments are organized with age 0-99 as rows, and year as columns
matrix.names<-c('ns_nevdep', 'ns_dep1','ns_fdep', 'ns_dep2','ns_recall',
'cs_nevdep', 'cs_dep1_tx','cs_dep1_notx','cs_fdep', 'cs_dep2_tx','cs_dep2_notx','cs_recall',
'fs_nevdep', 'fs_dep1','fs_fdep', 'fs_dep2','fs_recall','ns_deaths', 'cs_deaths', 'fs_deaths')
for (name in matrix.names) assign(name,emptycompartment)
ns_nevdep[paste(startage),1:Ny] <- as.matrix(pop[paste(startage),1:Ny]) # Takes empty compartment and populates the top row of the matrix with the number of 0-yrolds
# Run it ------------------------------------------------------------------
for (y in c((startyear+1):(endyear))){
py = paste(y - 1)
if (y<policystart){
txeff = c(rep(1, Na-1))
} else {
txeff = c(rep(1,17),rep(txeffcess,Na-1-17)) # Treatment effect applies to those ages 18+,
}
if (mpc==1 & y>=policystart){ # when mpc == 1. Under mpc scenario, 100% quitting among treated smkdep pop starting in policy year
ORhdep_quit_txpop = 1 # so that all smokers in treatment quit immediately (1-1=0)
smk_cess_txpop[19:100,120:201]=1 # Treatment effect applies to those ages 18+, 2019-2100 - need to include 2019 because of 'py'
}
if (y>=2016){
usethis = scaleddep1_inc # from 2016-2100, use scaled incidence probabilities
} else {
usethis = dep1_inc # all other years, use the original incidence probabilities
}
# Never MDE
ns_nevdep[2:Na,paste(y)] <- ns_nevdep[1:Na-1,py]*(1-smk_init[(startage+1):endage,py])*(1-usethis[(startage+1):(endage),])*(1-death_ns[(startage+1):endage,py])
cs_nevdep[2:Na,paste(y)] <- cs_nevdep[1:Na-1,py]*(1-smk_cess[(startage+1):endage,py])*(1-RRcs_dep1*usethis[(startage+1):(endage),])*(1-death_cs[(startage+1):endage,py]) + ns_nevdep[1:Na-1,py]*(smk_init[(startage+1):endage,py])
fs_nevdep[2:Na,paste(y)] <- fs_nevdep[1:Na-1,py]*(1-RRfs_dep1*usethis[(startage+1):(endage),])*(1-death_fs[(startage+1):endage,py]) + cs_nevdep[1:Na-1,py]*(smk_cess[(startage+1):endage,py])
# Current MDE (1st episode)
ns_dep1[2:Na,paste(y)] <- ns_dep1[1:Na-1,py]*(1-Edepr_smkinit*smk_init[(startage+1):endage,py])*(1-deprecov_rate)*(1-RRdepr_death*death_ns[(startage+1):endage,py]) + ns_nevdep[1:Na-1,py]*(usethis[(startage+1):(endage),])
cs_dep1_tx[2:Na,paste(y)] <- cs_dep1_tx[1:Na-1,py]*(1-ORhdep_quit_txpop*txeff*smk_cess_txpop[(startage+1):endage,py])*(1-deprecovSF_cs*deprecov_rate)*(1-RRdepr_death*death_cs[(startage+1):endage,py]) + proptx*cs_nevdep[1:Na-1,py]*(RRcs_dep1*usethis[(startage+1):(endage),]) + proptx*ns_dep1[1:Na-1,py]*(Edepr_smkinit*smk_init[(startage+1):endage,py])
cs_dep1_notx[2:Na,paste(y)] <- cs_dep1_notx[1:Na-1,py]*(1-ORhdep_quit*smk_cess[(startage+1):endage,py])*(1-deprecovSF_cs*deprecov_rate)*(1-RRdepr_death*death_cs[(startage+1):endage,py]) + (1-proptx)*cs_nevdep[1:Na-1,py]*(RRcs_dep1*usethis[(startage+1):(endage),]) + (1-proptx)*ns_dep1[1:Na-1,py]*(Edepr_smkinit*smk_init[(startage+1):endage,py])
cs_dep1 = cs_dep1_tx + cs_dep1_notx
fs_dep1[2:Na,paste(y)] <- fs_dep1[1:Na-1,py]*(1-deprecovSF_fs*deprecov_rate)*(1-RRdepr_death*death_fs[(startage+1):endage,py]) + fs_nevdep[1:Na-1,py]*(RRfs_dep1*usethis[(startage+1):(endage),]) + cs_dep1_notx[1:Na-1,py]*(ORhdep_quit*smk_cess[(startage+1):endage,py]) + cs_dep1_tx[1:Na-1,py]*(ORhdep_quit_txpop*txeff*smk_cess_txpop[(startage+1):endage,py])
# Current MDE (Recurrent episode)
ns_dep2[2:Na,paste(y)] <- ns_dep2[1:Na-1,py]*(1-Edepr_smkinit*smk_init[(startage+1):endage,py])*(1-deprecov_rate)*(1-RRdepr_death*death_ns[(startage+1):endage,py]) + ns_fdep[1:Na-1,py]*(depr_inc[(startage+1):(endage),]) + ns_recall[1:Na-1,py]*(depr_inc[(startage+1):(endage),])
cs_dep2_tx[2:Na,paste(y)] <- cs_dep2_tx[1:Na-1,py] *(1-ORhdep_quit_txpop*txeff*smk_cess_txpop[(startage+1):endage,py])*(1-deprecovSF_cs*deprecov_rate)*(1-RRdepr_death*death_cs[(startage+1):endage,py]) + proptx*cs_fdep[1:Na-1,py]*(Ecs_depr*depr_inc[(startage+1):(endage),]) + proptx*ns_dep2[1:Na-1,py]*(Edepr_smkinit*smk_init[(startage+1):endage,py]) + proptx*cs_recall[1:Na-1,py]*(depr_inc[(startage+1):(endage),])
cs_dep2_notx[2:Na,paste(y)] <- cs_dep2_notx[1:Na-1,py] *(1-ORhdep_quit*smk_cess[(startage+1):endage,py])*(1-deprecovSF_cs*deprecov_rate)*(1-RRdepr_death*death_cs[(startage+1):endage,py]) + (1-proptx)*cs_fdep[1:Na-1,py]*(Ecs_depr*depr_inc[(startage+1):(endage),]) + (1-proptx)*ns_dep2[1:Na-1,py]*(Edepr_smkinit*smk_init[(startage+1):endage,py]) + (1-proptx)*cs_recall[1:Na-1,py]*(depr_inc[(startage+1):(endage),])
cs_dep2 = cs_dep2_tx + cs_dep2_notx
fs_dep2[2:Na,paste(y)] <- fs_dep2[1:Na-1,py]*(1-deprecovSF_fs*deprecov_rate)*(1-RRdepr_death*death_fs[(startage+1):endage,py]) + fs_fdep[1:Na-1,py]*(Efs_depr*depr_inc[(startage+1):(endage),]) + cs_dep2_tx[1:Na-1,py]*(ORhdep_quit_txpop*txeff*smk_cess_txpop[(startage+1):endage,py]) +cs_dep2_notx[1:Na-1,py]*(ORhdep_quit*smk_cess[(startage+1):endage,py])+fs_recall[1:Na-1,py]*(depr_inc[(startage+1):(endage),])
# Former MDE - Recovery with risk of recurrence
ns_fdep[2:Na,paste(y)] <- ns_fdep[1:Na-1,py]*(1-smk_init[(startage+1):endage,py])*(1-depr_inc[(startage+1):(endage),])*(1-RRdepr_death*death_ns[(startage+1):endage,py])*(1-forget[(startage+1):(endage)]) + ns_dep1[1:Na-1,py]*(deprecov_rate) + ns_dep2[1:Na-1,py]*(deprecov_rate)
cs_fdep[2:Na,paste(y)] <- cs_fdep[1:Na-1,py]*(1-ORhdep_quit*smk_cess[(startage+1):endage,py])*(1-Ecs_depr*depr_inc[(startage+1):(endage),])*(1-RRdepr_death*death_cs[(startage+1):endage,py])*(1-forget[(startage+1):(endage)]) + cs_dep1_tx[1:Na-1,py]*(deprecovSF_cs*deprecov_rate) + cs_dep2_tx[1:Na-1,py]*(deprecovSF_cs*deprecov_rate) + cs_dep1_notx[1:Na-1,py]*(deprecovSF_cs*deprecov_rate) + cs_dep2_notx[1:Na-1,py]*(deprecovSF_cs*deprecov_rate) + ns_fdep[1:Na-1,py]*(smk_init[(startage+1):endage,py])
fs_fdep[2:Na,paste(y)] <- fs_fdep[1:Na-1,py]*(1-Efs_depr*depr_inc[(startage+1):(endage),])*(1-RRdepr_death*death_fs[(startage+1):endage,py])*(1-forget[(startage+1):(endage)]) + fs_dep1[1:Na-1,py]*(deprecovSF_fs*deprecov_rate) + fs_dep2[1:Na-1,py]*(deprecovSF_fs*deprecov_rate) + cs_fdep[1:Na-1,py]*(ORhdep_quit*smk_cess[(startage+1):endage,py])
# Recall Error
ns_recall[2:Na,paste(y)] <- ns_recall[1:Na-1,py]*(1-smk_init[(startage+1):endage,py])*(1-depr_inc[(startage+1):(endage),])*(1-RRdepr_death*death_ns[(startage+1):endage,py]) + ns_fdep[1:Na-1,py]*(forget[(startage+1):(endage)])
cs_recall[2:Na,paste(y)] <- cs_recall[1:Na-1,py]*(1-ORhdep_quit*smk_cess[(startage+1):endage,py])*(1-depr_inc[(startage+1):(endage),])*(1-RRdepr_death*death_cs[(startage+1):endage,py]) + ns_recall[1:Na-1,py]*(smk_init[(startage+1):endage,py]) + cs_fdep[1:Na-1,py]*(forget[(startage+1):(endage)])
fs_recall[2:Na,paste(y)] <- fs_recall[1:Na-1,py]*(1-depr_inc[(startage+1):(endage),])*(1-RRdepr_death*death_fs[(startage+1):endage,py])+ cs_recall[1:Na-1,py]*(ORhdep_quit*smk_cess[(startage+1):endage,py]) + fs_fdep[1:Na-1,py]*(forget[(startage+1):(endage)])
}
# Get inputs --------------------------------------------------------------
initiation <- data.frame(cbind(smk_init_cisnet[,"2018"],smk_init_cisnet[,"2018"],smkinit_SF,smk_init["2018"],c(0:99),c(Edepr_smkinit,Edepr_smkinit[99])))
colnames(initiation) <- c("2018", "cisnet2018","SF","scaledrates","age","Edepr_smkinit")
cessation <- data.frame(cbind(smk_cess_cisnet[,"2018"],smk_cess_cisnet[,"2018"],smkcess_SF,smk_cess["2018"],c(0:99),c(ORhdep_quit,Edepr_smkinit[99])))
colnames(cessation) <- c("2018", "cisnet2018","SF","scaledrates","age","ORhdep_quit")
incidence <- data.frame(cbind(dep1inc[-1,],dep1_inc[-1,],scaleddep1_inc[-1,],deprinc,deprinc_SF,data.frame(depr_inc),c(1:99)))
colnames(incidence) <- c("dep1inc","splines","scaleddep1inc_yr", "deprinc","SF_dep2", "scaledrates_dep2", "age")
recovery <- data.frame(cbind(deprecov_rate, deprecov_SF,deprecov_rate,deprecovSF_fs,deprecovSF_cs,c(1:99)))
colnames(recovery) <- c("dep1recov","SF","scaledrates","formersmokers","currentsmokers", "age")
forget <- data.frame(cbind(forget,c(1:99)))
colnames(forget) <- c("forget_prob","age")
# Get population counts for each subpopulation ----------------------------
nspop = (ns_nevdep + ns_recall + ns_dep2 + ns_fdep + ns_dep1)
cspop = (cs_nevdep + cs_recall + cs_dep2 + cs_fdep + cs_dep1)
fspop = (fs_nevdep + fs_recall + fs_dep2 + fs_fdep + fs_dep1)
totalpop = nspop+cspop+fspop
ns_dep <- (ns_dep2+ns_dep1)
cs_dep <- (cs_dep2+cs_dep1)
fs_dep <- (fs_dep2+fs_dep1)
nevdeppop <- (ns_nevdep + cs_nevdep + fs_nevdep)
fdeppop <- (ns_fdep + cs_fdep + fs_fdep + ns_recall + cs_recall + fs_recall)
deppop <- (ns_dep + cs_dep + fs_dep)
recallpop <- ns_recall + cs_recall + fs_recall
cs_everdep = cs_recall + cs_dep2 + cs_fdep + cs_dep1
fs_everdep = fs_recall + fs_dep2 + fs_fdep + fs_dep1
ns_everdep = ns_recall + ns_dep2 + ns_fdep + ns_dep1
cs_dep_tx = cs_dep1_tx + cs_dep2_tx
cs_dep_notx = cs_dep1_notx + cs_dep2_notx
# Get prevalences for model fitting ---------------------------------------
s1 <- getmodelprevs(ns_nevdep,nevdeppop) # smoker prevalence among the entire never depressed population
s2 <- getmodelprevs(cs_nevdep,nevdeppop)
s3 <- getmodelprevs(fs_nevdep,nevdeppop)
s4 <- getmodelprevs(ns_fdep,fdeppop) # smoker prevalence among the population w/ history of depression
s5 <- getmodelprevs(cs_fdep,fdeppop)
s6 <- getmodelprevs(fs_fdep,fdeppop)
s7 <- getmodelprevs(ns_dep,deppop) # smoker prevalence among the depressed population
s8 <- getmodelprevs(cs_dep,deppop)
s9 <- getmodelprevs(fs_dep,deppop)
modelsmkprevdata <- list(s1,s2,s3,s4,s5,s6,s7,s8,s9)
d1 <- getmodelprevs(ns_nevdep,nspop) # dep prevalence among never smokers
d2 <- getmodelprevs(ns_fdep,nspop)
d3 <- getmodelprevs(ns_dep,nspop)
d4 <- getmodelprevs(cs_nevdep,cspop) # dep prevalence among current smokers
d5 <- getmodelprevs(cs_fdep,cspop)
d6 <- getmodelprevs(cs_dep,cspop)
d7 <- getmodelprevs(fs_nevdep,fspop) # dep prevalence among former smokers
d8 <- getmodelprevs(fs_fdep,fspop)
d9 <- getmodelprevs(fs_dep,fspop)
modeldepprevdata <- list(d1,d2,d3,d4,d5,d6,d7,d8,d9)
return(list(modelsmkprevdata,modeldepprevdata,
initiation,cessation,incidence,recovery,forget,
totalpop,
nspop, cspop, fspop,
nevdeppop, deppop, fdeppop,recallpop,
ns_nevdep, cs_nevdep, fs_nevdep,
ns_everdep, cs_everdep, fs_everdep,
ns_dep, cs_dep, fs_dep,
ns_fdep, cs_fdep, fs_fdep,
ns_recall, cs_recall, fs_recall,
cs_dep_tx, cs_dep_notx
))
}
# Get NSDUH prevs by age group --------------------------------------------
# Generates the corresponding NSDUH prevalences so that we can compare them to output from the getmodelprevs function
getnsduhprevs <- function(depsmkprevs_by_year,assignedsex,numpop,denompop){
nsduhdata = NULL
nsduhdata <- melt(depsmkprevs_by_year,id.vars=c("group","survey_year","age","gender","subpopulation","status"),measure.vars = c("prev") )
nsduhdata <- cast(nsduhdata, group~survey_year,mean,subset=gender==assignedsex&subpopulation==denompop&status==numpop)
nsduhdata <- nsduhdata[c(-1)] # remove group name column
row.names(nsduhdata)<-agerownames
return(nsduhdata)
}
# Get model sum of squares ------------------------------------------------
getsumdiffs = function(out, whichgender){
modelsmkdata=out[[1]]
modeldepdata=out[[2]]
years<- c("X2005","X2006","X2007","X2008","X2009","X2010","X2011","X2012","X2013","X2014","X2015","X2016","X2017","X2018") # only look at output for years where NSDUH data are available
years2 <- c("2005","2006","2007","2008","2009","2010","2011","2012","2013","2014","2015","2016","2017","2018")
smkdiffs <- rowSums(subset(as.data.frame(modelsmkdata[1]),select=years) - subset(getnsduhprevs(depsmkprevs_by_year,whichgender, "neversmoker","nevdeppop"),select=years2))^2 + # sum of squares
rowSums(subset(as.data.frame(modelsmkdata[2]),select=years) - subset(getnsduhprevs(depsmkprevs_by_year,whichgender,"currentsmoker","nevdeppop"),select=years2))^2 +
rowSums(subset(as.data.frame(modelsmkdata[3]),select=years) - subset(getnsduhprevs(depsmkprevs_by_year,whichgender,"formersmoker","nevdeppop"),select=years2))^2 +
rowSums(subset(as.data.frame(modelsmkdata[4]),select=years) - subset(getnsduhprevs(depsmkprevs_by_year,whichgender,"neversmoker","notdeppop"),select=years2))^2 +
rowSums(subset(as.data.frame(modelsmkdata[5]),select=years) - subset(getnsduhprevs(depsmkprevs_by_year,whichgender,"currentsmoker","notdeppop"),select=years2))^2 +
rowSums(subset(as.data.frame(modelsmkdata[6]),select=years) - subset(getnsduhprevs(depsmkprevs_by_year,whichgender,"formersmoker","notdeppop"),select=years2))^2 +
rowSums(subset(as.data.frame(modelsmkdata[7]),select=years) - subset(getnsduhprevs(depsmkprevs_by_year,whichgender,"neversmoker","deppop"),select=years2))^2 +
rowSums(subset(as.data.frame(modelsmkdata[8]),select=years) - subset(getnsduhprevs(depsmkprevs_by_year,whichgender,"currentsmoker","deppop"),select=years2))^2 +
rowSums(subset(as.data.frame(modelsmkdata[9]),select=years) - subset(getnsduhprevs(depsmkprevs_by_year,whichgender,"formersmoker","deppop"),select=years2))^2
depdiffs <- rowSums(subset(as.data.frame(modeldepdata[1]),select=years) - subset(getnsduhprevs(depsmkprevs_by_year,whichgender,"nevdep","neversmokers"),select=years2))^2 +
rowSums(subset(as.data.frame(modeldepdata[2]),select=years) - subset(getnsduhprevs(depsmkprevs_by_year,whichgender,"notdep","neversmokers"),select=years2))^2 +
rowSums(subset(as.data.frame(modeldepdata[3]),select=years) - subset(getnsduhprevs(depsmkprevs_by_year,whichgender,"dep","neversmokers"),select=years2))^2 +
rowSums(subset(as.data.frame(modeldepdata[4]),select=years) - subset(getnsduhprevs(depsmkprevs_by_year,whichgender,"nevdep","currentsmokers"),select=years2))^2 +
rowSums(subset(as.data.frame(modeldepdata[5]),select=years) - subset(getnsduhprevs(depsmkprevs_by_year,whichgender,"notdep","currentsmokers"),select=years2))^2 +
rowSums(subset(as.data.frame(modeldepdata[6]),select=years) - subset(getnsduhprevs(depsmkprevs_by_year,whichgender,"dep","currentsmokers"),select=years2))^2 +
rowSums(subset(as.data.frame(modeldepdata[7]),select=years) - subset(getnsduhprevs(depsmkprevs_by_year,whichgender,"nevdep","formersmokers"),select=years2))^2 +
rowSums(subset(as.data.frame(modeldepdata[8]),select=years) - subset(getnsduhprevs(depsmkprevs_by_year,whichgender,"notdep","formersmokers"),select=years2))^2 +
rowSums(subset(as.data.frame(modeldepdata[9]),select=years) - subset(getnsduhprevs(depsmkprevs_by_year,whichgender,"dep","formersmokers"),select=years2))^2
return(c(sum(smkdiffs),sum(depdiffs)))
}
getsumdiffs_util = function(out, whichgender,util){
if (whichgender=="females"){
ahltmde <- as.numeric(util)*c(0.4955417, 0.6229463, 0.7067596, 0.7326142, 0.6762736, 0.6576061)
}
if (whichgender=="males"){
ahltmde <- as.numeric(util)*c(0.3750369,0.4482582, 0.5409039,0.6605244,0.5895799,0.5128584 )
}
modelsmkdata=out[[1]]
txdiffs <- abs(subset(as.data.frame(modelsmkdata[13]),select="X2018")[1:6,] - ahltmde) #absolute value of diffs
return(sum(txdiffs))
}
# Bhat estimation ---------------------------------------------------------
xF <- list(label=paramsnamesF, est=paramsF,low=lowervectorF,upp=uppervectorF) # est = parameter starting values
xM <- list(label=paramsnamesM, est=paramsM,low=lowervectorM,upp=uppervectorM) # est = parameter starting values
ML_bhatF=function(paramsF){
out = main(getmodelprevs,"females",allparamsF,paramsF,paramsnamesF, mpc = 0, txeffcess = 1.0,util="1.0")
# LL = sum(getsumdiffs(out,"females")) #Least squares
LL = sum(getsumdiffs_util(out,"females",util="1.0")) #Least squares
cat(LL,paramsF,'\n')
return(LL)
}
# resbhatF=dfp(xF,ML_bhatF)
# paramsF=resbhatF$est
ML_bhatM=function(paramsM){
out = main(getmodelprevs,"males",allparamsM,paramsM,paramsnamesM, mpc = 0, txeffcess = 1.0,util="1.0")
# LL = sum(getsumdiffs(out,"males")) #Least squares
LL = sum(getsumdiffs_util(out,"males",util="1.0"))
cat(LL,paramsM,'\n')
return(LL)
}
# resbhatM=dfp(xM,ML_bhatM)
# paramsM=resbhatM$est
mainDir <- "C:/Users/jamietam/Dropbox/GitHub/smk-dep-model/"
dir.create(file.path(mainDir, folder), showWarnings = FALSE)
setwd(file.path(mainDir,folder)) # save all output to this subdirectory