analysis/R/indirect_estimates_functions_for_reg.R

In UW-MSDS-DATA-598-Reproducibility-WI20/miao-qian-wang-wei-yang-replication-project: Replication project

###############################################################################
# Name: indirect_estimates_functions.R
# Author: John Quattrochi (john.quattrochi@gmail.com)
# Assistant: Juan Luis Herrera Cortijo (juan.luis.herrera.cortijo@gmail.com)
# Purpose: Defines functions for indirect estimates
# The script assumes the following folder structure:
# Scripts are stored in "[project folder]/R"
# Data are stored in "[project folder]/data"
# Results are stored in "[project folder]/results"
###############################################################################


# R version and load packages and install if necessary

# version
# 
# _                           
# platform       x86_64-apple-darwin15.6.0   
# arch           x86_64                      
# os             darwin15.6.0                
# system         x86_64, darwin15.6.0        
# status                                     
# major          3                           
# minor          5.2                         
# year           2018                        
# month          12                          
# day            20                          
# svn rev        75870                       
# language       R                           
# version.string R version 3.5.2 (2018-12-20)
# nickname       Eggshell Igloo   

if(!require(magrittr)){
  install.packages("magrittr",dependencies = TRUE,repos='http://cran.us.r-project.org')
}


require(magrittr)
packageVersion("magrittr")
# 1.5

if(!require(tidyverse)){
  install.packages("tidyverse",dependencies = TRUE,repos='http://cran.us.r-project.org')
}


require(tidyverse)
packageVersion("tidyverse")
# ── Attaching packages ───────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
# ✔ ggplot2 3.1.0       ✔ purrr   0.3.0  
# ✔ tibble  2.0.1       ✔ dplyr   0.8.0.1
# ✔ tidyr   0.8.2       ✔ stringr 1.4.0  
# ✔ readr   1.1.1       ✔ forcats 0.3.0  
# ── Conflicts ──────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
# ✖ dplyr::filter() masks stats::filter()
# ✖ dplyr::lag()    masks stats::lag()

abc = read.csv("./data/UNgeneral_MLT_ABC.csv", head=TRUE)
efg = read.csv("./data/UNgeneral_MLT_EFG.csv", head=TRUE)


ind_est_computations_core <- function(surv_moms,survey){
  
  #Sum women, CEB, CD by agegroup & caclulate CD/CEB & parity ratios
  
  byagegroup <- surv_moms %>% group_by(agegroup) %>% 
    summarise(totalceb=sum(ceb),cd=sum(cd),women=length(cd)) %>% 
    ungroup() %>%
    mutate(cdceb=cd/totalceb,
           ceb=totalceb/women,
           mpagegroup=seq(17,47,5),
           fxplus2=ceb*mpagegroup
    ) %>% as.data.frame()

  m <- byagegroup %>% summarise(m=sum(fxplus2)/sum(ceb)) %>% pull(m)
  
  
  #Parity ratios
  p1520 = byagegroup[byagegroup$agegroup==1,"ceb"]/byagegroup[byagegroup$agegroup==2,"ceb"]
  p2025 = byagegroup[byagegroup$agegroup==2,"ceb"]/byagegroup[byagegroup$agegroup==3,"ceb"]
  
  # SURVIVING WOMEN: Indirect estimation of U5M 
  nq0=vector(length = 7)
  # Choose life table (1=north, 2=south, 3=east,4=west)
  lt = 1
  # Estimate n_q_0   
  for(i in 1:7){
    nq0[i] = (abc[lt,i+2]+ abc[lt+1,i+2]*p1520 + abc[lt+2,i+2]*p2025 +abc[lt+3,i+2]*m) * byagegroup[byagegroup$agegroup[i],"cdceb"]
  }
  #print(nq0)
  #Estimate time reference
  t_ref = matrix()
  for(i in 1:7){
    t_ref[i] = efg[lt,i+2]+ efg[lt+1,i+2]*p1520 + efg[lt+2,i+2]*p2025 
  }

  #Convert each n q 0 into 5q0
  # Use UN General Female, e(0)=60
  
  l = c(.92688,.91025,.90151,.89193,.89534,.89002,.88195)
  q = 1-l
  logitMLT=.5*log(q/(1-q))
  
  alpha = NA
  fiveq0 = NA
  logit_nq0 = NA
  
  
  for(i in 1:7){
    logit_nq0[i] = .5*log(nq0[i]/(1-nq0[i]))
    alpha[i] = logit_nq0[i] - logitMLT[i]
    fiveq0[i] = exp(2*(alpha[i]+logitMLT[4]))/(1+exp(2*(alpha[i]+logitMLT[4])))
  }
  
  x <- list('fiveq0'=fiveq0,'t_ref'=t_ref,'byagegroup'=byagegroup,'nq0'=nq0)
  return(x)
}


ind_est_computations <- function(surv_moms,survey){
  
  ind_est_core <- ind_est_computations_core(surv_moms,survey)

  ind_est <- cbind(ind_est_core$fiveq0,ind_est_core$t_ref)
  colnames(ind_est) = c("fiveq0","t_ref")
  
  refdate <- survey-ind_est[,"t_ref"]
  ind_est <- cbind(ind_est_core$fiveq0,ind_est_core$t_ref,refdate,ind_est_core$byagegroup[,"ceb"],ind_est_core$byagegroup[,"women"],ind_est_core$byagegroup[,"cdceb"])
  
  colnames(ind_est) = c("fiveq0","t_ref","refdate","ceb","women","cdceb")
  
  return(ind_est)
}

ind_est <- function(momkidsclean){
  
  # Keep only surviving women
  surv_momsfull = momkidsclean[is.na(momkidsclean$death_date),]
  #dead_moms = momkidsclean[!is.na(momkidsclean$death_date),]
  
  #Create age group variable
  yrend = 2010
  survey = yrend  
  surv_momsfull$endage <- survey-surv_momsfull$dob
  surv_momsfull <- surv_momsfull[surv_momsfull$endage<50,]
  surv_momsfull <- surv_momsfull[surv_momsfull$endage>14,]
  surv_momsfull <- surv_momsfull[surv_momsfull$male==0,]
  
  #Use all surviving women
  surv_moms <- surv_momsfull
  
  cutoffs <- c(49,45, 40, 35, 30, 25, 20, 15)
  surv_moms$agegroup <- cut(surv_moms$endage,cutoffs, right=FALSE, include.lowest=TRUE)
  
  ind_est <- ind_est_computations(surv_moms,survey)
  
  colnames(ind_est) = c("fiveq0_surv","t_ref_surv","refdate_surv","ceb_surv","women_surv","cdceb_surv")
  
  
  return(ind_est)
}




#############################################
### USING ALL WOMEN
###############################################

ind_est_all <- function(momkidsclean){
  
  # RANDOM SAMPLE OF SURVIVING WOMEN: Calculate CEB/CS 
  all_momsfull = momkidsclean
  
  #surv_momsfull = momkidsclean[is.na(momkidsclean$death_date),]
  #dead_moms = momkidsclean[!is.na(momkidsclean$death_date),]
  
  #Create age group variable
  yrend = 2010
  survey = yrend  
  all_momsfull$endage <- survey-all_momsfull$dob
  all_momsfull <- all_momsfull[all_momsfull$endage<50,]
  all_momsfull <- all_momsfull[all_momsfull$endage>14,]
  all_momsfull <- all_momsfull[all_momsfull$male==0,]
  
  #Random sample from living women 15-49 at time of survey
  all_moms <- all_momsfull
  
  cutoffs <- c(49,45, 40, 35, 30, 25, 20, 15)
  all_moms$agegroup <- cut(all_moms$endage,cutoffs, right=FALSE, include.lowest=TRUE)
  
 
  ind_est <- ind_est_computations(all_moms,survey)
  
  colnames(ind_est) = c("fiveq0_all","t_ref_all","refdate_all","ceb_all","women_all","cdceb_all")
  return(ind_est)
}

#############################################################
### USING SURVIVING WOMEN AND WOMEN WHO DIED FROM HIV
#########################################################

ind_est_hiv <- function(momkidsclean){
  
  # Keep surviving moms and moms who died from HIV
  
  #Surviving moms
  surv_momsfull = momkidsclean[is.na(momkidsclean$death_date),]
  
  #Moms who died from HIV
  hiv_momsfull = momkidsclean[momkidsclean$hivdeath==1 & !is.na(momkidsclean$hivdeath),]
  all_momsfull <- rbind(surv_momsfull,hiv_momsfull)
  
  
  #Create age group variable
  yrend = 2010
  survey = yrend  
  all_momsfull$endage <- survey-all_momsfull$dob
  all_momsfull <- all_momsfull[all_momsfull$endage<50,]
  all_momsfull <- all_momsfull[all_momsfull$endage>14,]
  all_momsfull <- all_momsfull[all_momsfull$male==0,]
  
  #Random sample from living women 15-49 at time of survey
  all_moms <- all_momsfull
  
  cutoffs <- c(49,45, 40, 35, 30, 25, 20, 15)
  all_moms$agegroup <- cut(all_moms$endage,cutoffs, right=FALSE, include.lowest=TRUE)
  
  ind_est <- ind_est_computations(all_moms,survey)
  
  colnames(ind_est) = c("fiveq0_hiv","t_ref_hiv","refdate_hiv","ceb_hiv","women_hiv","cdceb_hiv")
  return(ind_est)
}