In ohdsi-studies/Covid19SusceptibilityAlphaBlockers: A Package Skeleton for Comparative Effectiveness Studies

options(kableExtra.latex.load_packages = FALSE)
library(kableExtra)
knitr::knit_hooks$set(document = function(x) {sub('\\usepackage[]{color}', '\\usepackage[table]{xcolor}', x, fixed = TRUE)})
library(dplyr)
library(tidyr)
library(xtable)
library(gridExtra)
library(cowplot)

readRdsCamelCase <- function(file) {
  result <- readRDS(file)
  colnames(result) <- SqlRender::snakeCaseToCamelCase(colnames(result))
  return(result)
}

\centerline{{\Huge Supplementary Material}} \vspace{1.0em} \centerline{\Large for} \vspace{1.0em} \centerline{\huge Renin-angiotensin system blockers and} \centerline{\huge susceptibility to COVID-19} \vspace{1.0em} \centerline{\Large by} \vspace{1.0em} \centerline{\Large Daniel R. Morales, Mitchell M. Conover, Seng Chan You, et al.}

\tableofcontents

\captionsetup[table]{name=Supplementary Table} \captionsetup[figure]{name=Supplementary Figure} \captionsetup{labelfont=bf}

\renewcommand\theContinuedFloat{\alph{ContinuedFloat}}

cohorts <- read.csv(file = system.file("settings", "CohortsToCreate.csv", package = "Covid19SusceptibilityAlphaBlockers")) %>% select(cohortId, shinyName)

aceMonoId <- cohorts %>% filter(shinyName == "ACE mono") %>% select(cohortId) %>% pull()
arbMonoId <- cohorts %>% filter(shinyName == "ARB mono") %>% select(cohortId) %>% pull()
aceArbMonoId <- cohorts %>% filter(shinyName == "ACE/ARB mono") %>% select(cohortId) %>% pull()
ccbThzMonoId <- cohorts %>% filter(shinyName == "CCB/THZ mono") %>% select(cohortId) %>% pull()

aceComboId <- cohorts %>% filter(shinyName == "ACE +combo") %>% select(cohortId) %>% pull()
arbComboId <- cohorts %>% filter(shinyName == "ARB +combo") %>% select(cohortId) %>% pull()
aceArbComboId <- cohorts %>% filter(shinyName == "ACE/ARB +combo") %>% select(cohortId) %>% pull()
ccbThzComboId <- cohorts %>% filter(shinyName == "CCB/THZ +combo") %>% select(cohortId) %>% pull()

ccbMonoId <- cohorts %>% filter(shinyName == "CCB mono") %>% select(cohortId) %>% pull()
thzMonoId <- cohorts %>% filter(shinyName == "THZ mono") %>% select(cohortId) %>% pull()
ccbComboId <- cohorts %>% filter(shinyName == "CCB +combo") %>% select(cohortId) %>% pull()
thzComboId <- cohorts %>% filter(shinyName == "THZ +combo") %>% select(cohortId) %>% pull()

covidNarrowId <- cohorts %>% filter(shinyName == "COVID-19 diagnosis") %>% select(cohortId) %>% pull()
covidBroadId <- cohorts %>% filter(shinyName == "COVID-19 hospitalization") %>% select(cohortId) %>% pull()

pnaId <- cohorts %>% filter(shinyName == "Pneumonia") %>% select(cohortId) %>% pull()
adverseId <- cohorts %>% filter(shinyName == "PAAS event") %>% select(cohortId) %>% pull()

prettyNames <- data.frame(
  cohortId = c(aceMonoId, arbMonoId, aceArbMonoId, ccbMonoId, thzMonoId, ccbThzMonoId,
               aceComboId, arbComboId, aceArbComboId, ccbComboId, thzComboId, ccbThzComboId),
  cohortName = c("ACE", "ARB", "ACE/ARB", "CCB", "THZ", "CCB/THZ",
                 "ACE+", "ARB+", "ACE/ARB+", "CCB+", "THZ+", "CCB/THZ+"))

primaryAnalysisId <- 5 # Full stratified PS

\clearpage

Sample sizes, total follow-up times and events

sidiapDir <- "/Users/msuchard/Dropbox/Projects/Covid19Icarius/SIDIAP"
cuimcDir <- "/Users/msuchard/Dropbox/Projects/Covid19Icarius/CUIMC"
vaDir <- "/Users/msuchard/Dropbox/Projects/Covid19Icarius/VA-OMOP"

studyFolder <- "/Users/msuchard/Dropbox/Projects/Covid19Icarius"

# dataFolder <- file.path(sidiapDir, "shinyData")
# databaseId <- "SIDIAP"

makeSampleSizeTable <- function(databaseId, 
                                dataFolder,
                                databaseOrder = 1,
                                thisOutcomeId) {
  cmResults <- readRDS(file = file.path(dataFolder, paste0("cohort_method_result_", databaseId, ".rds")))
  colnames(cmResults) <- SqlRender::snakeCaseToCamelCase(colnames(cmResults))
  alpha <- 0.05
  power <- 0.8
  z1MinAlpha <- qnorm(1 - alpha/2)
  zBeta <- -qnorm(1 - power)
  pA <- cmResults$targetSubjects/(cmResults$targetSubjects + cmResults$comparatorSubjects)
  pB <- 1 - pA
  totalEvents <- abs(cmResults$targetOutcomes) + (cmResults$comparatorOutcomes)
  cmResults$mdrr <- exp(sqrt((zBeta + z1MinAlpha)^2/(totalEvents * pA * pB)))
  cmResults$targetYears <- cmResults$targetDays/365.25
  cmResults$comparatorYears <- cmResults$comparatorDays/365.25

  cmResults <- cmResults %>% filter(analysisId == primaryAnalysisId, outcomeId == thisOutcomeId)

  table <- rbind(
    cmResults %>% filter(targetId == aceArbMonoId, comparatorId == ccbThzMonoId) %>% mutate(targetId = "ACE/ARB (m)", comparatorId = "CCB/THZ (m)", order = 1),
    cmResults %>% filter(targetId == aceArbComboId, comparatorId == ccbThzComboId) %>% mutate(targetId = "ACE/ARB (c)", comparatorId = "CCB/THZ (c)", order = 2),

    cmResults %>% filter(targetId == aceArbMonoId, comparatorId == ccbMonoId) %>% mutate(targetId = "ACE/ARB (m)", comparatorId = "CCB (m)", order = 3),
    cmResults %>% filter(targetId == aceArbComboId, comparatorId == ccbComboId) %>% mutate(targetId = "ACE/ARB (c)", comparatorId = "CCB (c)", order = 4),    

    cmResults %>% filter(targetId == aceArbMonoId, comparatorId == thzMonoId) %>% mutate(targetId = "ACE/ARB (m)", comparatorId = "THZ (m)", order = 5),
    cmResults %>% filter(targetId == aceArbComboId, comparatorId == thzComboId) %>% mutate(targetId = "ACE/ARB (c)", comparatorId = "THZ (c)", order = 6),    

    cmResults %>% filter(targetId == aceMonoId, comparatorId == arbMonoId) %>% mutate(targetId = "ACE (m)", comparatorId = "ARB (m)", order = 7),
    cmResults %>% filter(targetId == aceComboId, comparatorId == arbComboId) %>% mutate(targetId = "ACE (c)", comparatorId = "ARB (c)", order = 8),

    cmResults %>% filter(targetId == aceMonoId, comparatorId == ccbThzMonoId) %>% mutate(targetId = "ACE (m)", comparatorId = "CCB/THZ (m)", order = 9),
    cmResults %>% filter(targetId == aceComboId, comparatorId == ccbThzComboId) %>% mutate(targetId = "ACE (c)", comparatorId = "CCB/THZ (c)", order = 10),

    cmResults %>% filter(targetId == aceMonoId, comparatorId == ccbMonoId) %>% mutate(targetId = "ACE (m)", comparatorId = "CCB (m)", order = 11),
    cmResults %>% filter(targetId == aceComboId, comparatorId == ccbComboId) %>% mutate(targetId = "ACE (c)", comparatorId = "CCB (c)", order = 12),

    cmResults %>% filter(targetId == aceMonoId, comparatorId == thzMonoId) %>% mutate(targetId = "ACE (m)", comparatorId = "THZ (m)", order = 13),
    cmResults %>% filter(targetId == aceComboId, comparatorId == thzComboId) %>% mutate(targetId = "ACE (c)", comparatorId = "THZ (c)", order = 14),

    cmResults %>% filter(targetId == arbMonoId, comparatorId == ccbThzMonoId) %>% mutate(targetId = "ARB (m)", comparatorId = "CCB/THZ (m)", order = 15),
    cmResults %>% filter(targetId == arbComboId, comparatorId == ccbThzComboId) %>% mutate(targetId = "ARB (c)", comparatorId = "CCB/THZ (c)", order = 16),

    cmResults %>% filter(targetId == arbMonoId, comparatorId == ccbMonoId) %>% mutate(targetId = "ARB (m)", comparatorId = "CCB (m)", order = 17),
    cmResults %>% filter(targetId == arbComboId, comparatorId == ccbComboId) %>% mutate(targetId = "ARB (c)", comparatorId = "CCB (c)", order = 18),

    cmResults %>% filter(targetId == arbMonoId, comparatorId == thzMonoId) %>% mutate(targetId = "ARB (m)", comparatorId = "THZ (m)", order = 19),
    cmResults %>% filter(targetId == arbComboId, comparatorId == thzComboId) %>% mutate(targetId = "ARB (c)", comparatorId = "THZ (c)", order = 20)

  ) %>% mutate (databaseId = databaseId, databaseOrder = databaseOrder) %>% 
    select(targetId, comparatorId, databaseId, targetSubjects, comparatorSubjects, targetYears, comparatorYears, targetOutcomes, comparatorOutcomes, mdrr, order, databaseOrder)

  table$targetSubjects <- formatC(table$targetSubjects, big.mark = ",", format = "d")
  table$comparatorSubjects <- formatC(table$comparatorSubjects, big.mark = ",", format = "d")
  table$targetYears <- formatC(table$targetYears, big.mark = ",", format = "d")
  table$comparatorYears <- formatC(table$comparatorYears, big.mark = ",", format = "d")
  table$targetOutcomes <- formatC(table$targetOutcomes, big.mark = ",", format = "d")
  table$comparatorOutcomes <- formatC(table$comparatorOutcomes, big.mark = ",", format = "d")
  # table$targetIr <- sprintf("%.2f", table$targetIr)
  # table$comparatorIr <- sprintf("%.2f", table$comparatorIr)
  table$mdrr <- sprintf("%.2f", table$mdrr)
  table$targetSubjects <- gsub("^-", "<", table$targetSubjects)
  table$comparatorSubjects <- gsub("^-", "<", table$comparatorSubjects)
  table$targetOutcomes <- gsub("^-", "<", table$targetOutcomes)
  table$comparatorOutcomes <- gsub("^-", "<", table$comparatorOutcomes)
  # table$targetIr <- gsub("^-", "<", table$targetIr)
  # table$comparatorIr <- gsub("^-", "<", table$comparatorIr)
  return(table)
}

# tableSidiap <- makeSampleSizeTable(databaseId = "SIDIAP", dataFolder = file.path(sidiapDir, "shinyData"), databaseOrder = 1, thisOutcomeId = covidNarrowId)
# tableCuimc <- makeSampleSizeTable(databaseId = "CUIMC", dataFolder = file.path(cuimcDir, "shinyData"), databaseOrder = 2)

tableNarrow <- rbind(
  makeSampleSizeTable(databaseId = "SIDIAP", dataFolder = file.path(sidiapDir, "shinyData"), 
                      databaseOrder = 1, thisOutcomeId = covidNarrowId),
  makeSampleSizeTable(databaseId = "VA-OMOP", dataFolder = file.path(vaDir, "shinyData"), 
                      databaseOrder = 2, thisOutcomeId = covidNarrowId),  
  makeSampleSizeTable(databaseId = "CUIMC", dataFolder = file.path(cuimcDir, "shinyData"), 
                      databaseOrder = 3, thisOutcomeId = covidNarrowId)
  ) %>% arrange(order, databaseOrder) %>% 
  mutate(targetId = "", comparatorId = "") %>% select(-order, -databaseOrder)

tableBroad <- rbind(
  makeSampleSizeTable(databaseId = "SIDIAP", dataFolder = file.path(sidiapDir, "shinyData"), 
                      databaseOrder = 1, thisOutcomeId = covidBroadId),
  makeSampleSizeTable(databaseId = "VA-OMOP", dataFolder = file.path(vaDir, "shinyData"), 
                      databaseOrder = 2, thisOutcomeId = covidBroadId),  
  makeSampleSizeTable(databaseId = "CUIMC", dataFolder = file.path(cuimcDir, "shinyData"), 
                      databaseOrder = 3, thisOutcomeId = covidBroadId)
  ) %>% arrange(order, databaseOrder) %>% 
  mutate(targetId = "", comparatorId = "") %>% select(-order, -databaseOrder)

tablePna <- rbind(
  makeSampleSizeTable(databaseId = "SIDIAP", dataFolder = file.path(sidiapDir, "shinyData"), 
                      databaseOrder = 1, thisOutcomeId = pnaId),
  makeSampleSizeTable(databaseId = "VA-OMOP", dataFolder = file.path(vaDir, "shinyData"), 
                      databaseOrder = 2, thisOutcomeId = pnaId),  
  makeSampleSizeTable(databaseId = "CUIMC", dataFolder = file.path(cuimcDir, "shinyData"), 
                      databaseOrder = 3, thisOutcomeId = pnaId)
  ) %>% arrange(order, databaseOrder) %>% 
  mutate(targetId = "", comparatorId = "") %>% select(-order, -databaseOrder)

tableAdverse <- rbind(
  makeSampleSizeTable(databaseId = "SIDIAP", dataFolder = file.path(sidiapDir, "shinyData"), 
                      databaseOrder = 1, thisOutcomeId = adverseId),
  makeSampleSizeTable(databaseId = "VA-OMOP", dataFolder = file.path(vaDir, "shinyData"), 
                      databaseOrder = 2, thisOutcomeId = adverseId),  
  makeSampleSizeTable(databaseId = "CUIMC", dataFolder = file.path(cuimcDir, "shinyData"), 
                      databaseOrder = 3, thisOutcomeId = adverseId)
  ) %>% arrange(order, databaseOrder) %>% 
  mutate(targetId = "", comparatorId = "") %>% select(-order, -databaseOrder)

COVID-19 diagnoses

\rowcolors{2}{gray!6}{white} \begin{longtable}{p{-2em}p{-2em}rrrrrrrr} \caption{Population size, total exposure time and COVID-19 diagnoses (events) for ACEI, ARB and CCB/THZ monotherapy and in-combination user target (T) and comparator (C) cohorts.} \ \hiderowcolors \toprule & & & \multicolumn{2}{c}{Patients} & \multicolumn{2}{c}{Time (years)} & \multicolumn{2}{c}{Events} \ \cmidrule(lr){4-5} \cmidrule(lr){6-7} \cmidrule(lr){8-9} & & & \multicolumn{1}{c}{T} & \multicolumn{1}{c}{C} & \multicolumn{1}{c}{T} & \multicolumn{1}{c}{C} & \multicolumn{1}{c}{T} & \multicolumn{1}{c}{C} & MDRR \ \midrule \endfirsthead (Continued) & & & \multicolumn{2}{c}{Patients} & \multicolumn{2}{c}{Time (years)} & \multicolumn{2}{c}{Events} \ \cmidrule(lr){4-5} \cmidrule(lr){6-7} \cmidrule(lr){8-9} & & & \multicolumn{1}{c}{T} & \multicolumn{1}{c}{C} & \multicolumn{1}{c}{T} & \multicolumn{1}{c}{C} & \multicolumn{1}{c}{T} & \multicolumn{1}{c}{C} & MDRR \ \midrule \endhead \showrowcolors

print(xtable(tableNarrow, format = "latex"),
      include.rownames = FALSE,
      include.colnames = FALSE,
      hline.after = NULL,
      only.contents = TRUE,
      add.to.row = list(
        pos = as.list(c(0:19) * 3),
        command = c(
          "                       \\multicolumn{9}{l}{\\textbf{ACE/ARB vs CCB/THZ}} \\\\ & \\multicolumn{9}{l}{Monotherapy}  \\\\",
          "                        & \\multicolumn{8}{l}{+ Combination} \\\\",

          "\\rowcolor{white} \\\\ \\multicolumn{9}{l}{\\textbf{ACE/ARB vs CCB}} \\\\ & \\multicolumn{9}{l}{Monotherapy}  \\\\",
          "                        & \\multicolumn{8}{l}{+ Combination} \\\\",     

          "\\rowcolor{white} \\\\ \\multicolumn{9}{l}{\\textbf{ACE/ARB vs THZ}} \\\\ & \\multicolumn{9}{l}{Monotherapy}  \\\\",
          "                        & \\multicolumn{8}{l}{+ Combination} \\\\",            

          "\\rowcolor{white} \\\\ \\multicolumn{9}{l}{\\textbf{ACE vs ARB}} \\\\ & \\multicolumn{9}{l}{Monotherapy}  \\\\",
          "                        & \\multicolumn{8}{l}{+ Combination} \\\\",

          "\\rowcolor{white} \\\\ \\multicolumn{9}{l}{\\textbf{ACE vs CCB/THZ}} \\\\ & \\multicolumn{9}{l}{Monotherapy}  \\\\",
          "                        & \\multicolumn{8}{l}{+ Combination} \\\\",

          "\\rowcolor{white} \\\\ \\multicolumn{9}{l}{\\textbf{ACE vs CCB}} \\\\ & \\multicolumn{9}{l}{Monotherapy}  \\\\",
          "                        & \\multicolumn{8}{l}{+ Combination} \\\\",

          "\\rowcolor{white} \\\\ \\multicolumn{9}{l}{\\textbf{ACE vs THZ}} \\\\ & \\multicolumn{9}{l}{Monotherapy}  \\\\",
          "                        & \\multicolumn{8}{l}{+ Combination} \\\\",          

          "\\rowcolor{white} \\\\ \\multicolumn{9}{l}{\\textbf{ARB vs CCB/THZ}} \\\\ & \\multicolumn{9}{l}{Monotherapy}  \\\\",
          "                        & \\multicolumn{8}{l}{+ Combination} \\\\",

          "\\rowcolor{white} \\\\ \\multicolumn{9}{l}{\\textbf{ARB vs CCB}} \\\\ & \\multicolumn{9}{l}{Monotherapy}  \\\\",
          "                        & \\multicolumn{8}{l}{+ Combination} \\\\",

                    "\\rowcolor{white} \\\\ \\multicolumn{9}{l}{\\textbf{ARB vs THZ}} \\\\ & \\multicolumn{9}{l}{Monotherapy}  \\\\",
          "                        & \\multicolumn{8}{l}{+ Combination} \\\\"
          )
      ),
      sanitize.text.function = identity)

\bottomrule \end{longtable}

\clearpage

COVID-19 hospitalizations

\rowcolors{2}{gray!6}{white} \begin{longtable}{p{-2em}p{-2em}rrrrrrrr} \caption{Population size, total exposure time and COVID-19 hospitalizations (events) for ACEI, ARB and CCB/THZ monotherapy and in-combination user target (T) and comparator (C) cohorts.} \ \hiderowcolors \toprule & & & \multicolumn{2}{c}{Patients} & \multicolumn{2}{c}{Time (years)} & \multicolumn{2}{c}{Events} \ \cmidrule(lr){4-5} \cmidrule(lr){6-7} \cmidrule(lr){8-9} & & & \multicolumn{1}{c}{T} & \multicolumn{1}{c}{C} & \multicolumn{1}{c}{T} & \multicolumn{1}{c}{C} & \multicolumn{1}{c}{T} & \multicolumn{1}{c}{C} & MDRR \ \midrule \endfirsthead (Continued) & & & \multicolumn{2}{c}{Patients} & \multicolumn{2}{c}{Time (years)} & \multicolumn{2}{c}{Events} \ \cmidrule(lr){4-5} \cmidrule(lr){6-7} \cmidrule(lr){8-9} & & & \multicolumn{1}{c}{T} & \multicolumn{1}{c}{C} & \multicolumn{1}{c}{T} & \multicolumn{1}{c}{C} & \multicolumn{1}{c}{T} & \multicolumn{1}{c}{C} & MDRR \ \midrule \endhead \showrowcolors

print(xtable(tableBroad, format = "latex"),
      include.rownames = FALSE,
      include.colnames = FALSE,
      hline.after = NULL,
      only.contents = TRUE,
      add.to.row = list(
        pos = as.list(c(0:19) * 3),
        command = c(
          "                       \\multicolumn{9}{l}{\\textbf{ACE/ARB vs CCB/THZ}} \\\\ & \\multicolumn{9}{l}{Monotherapy}  \\\\",
          "                        & \\multicolumn{8}{l}{+ Combination} \\\\",

          "\\rowcolor{white} \\\\ \\multicolumn{9}{l}{\\textbf{ACE/ARB vs CCB}} \\\\ & \\multicolumn{9}{l}{Monotherapy}  \\\\",
          "                        & \\multicolumn{8}{l}{+ Combination} \\\\",     

          "\\rowcolor{white} \\\\ \\multicolumn{9}{l}{\\textbf{ACE/ARB vs THZ}} \\\\ & \\multicolumn{9}{l}{Monotherapy}  \\\\",
          "                        & \\multicolumn{8}{l}{+ Combination} \\\\",            

          "\\rowcolor{white} \\\\ \\multicolumn{9}{l}{\\textbf{ACE vs ARB}} \\\\ & \\multicolumn{9}{l}{Monotherapy}  \\\\",
          "                        & \\multicolumn{8}{l}{+ Combination} \\\\",

          "\\rowcolor{white} \\\\ \\multicolumn{9}{l}{\\textbf{ACE vs CCB/THZ}} \\\\ & \\multicolumn{9}{l}{Monotherapy}  \\\\",
          "                        & \\multicolumn{8}{l}{+ Combination} \\\\",

          "\\rowcolor{white} \\\\ \\multicolumn{9}{l}{\\textbf{ACE vs CCB}} \\\\ & \\multicolumn{9}{l}{Monotherapy}  \\\\",
          "                        & \\multicolumn{8}{l}{+ Combination} \\\\",

          "\\rowcolor{white} \\\\ \\multicolumn{9}{l}{\\textbf{ACE vs THZ}} \\\\ & \\multicolumn{9}{l}{Monotherapy}  \\\\",
          "                        & \\multicolumn{8}{l}{+ Combination} \\\\",          

          "\\rowcolor{white} \\\\ \\multicolumn{9}{l}{\\textbf{ARB vs CCB/THZ}} \\\\ & \\multicolumn{9}{l}{Monotherapy}  \\\\",
          "                        & \\multicolumn{8}{l}{+ Combination} \\\\",

          "\\rowcolor{white} \\\\ \\multicolumn{9}{l}{\\textbf{ARB vs CCB}} \\\\ & \\multicolumn{9}{l}{Monotherapy}  \\\\",
          "                        & \\multicolumn{8}{l}{+ Combination} \\\\",

                    "\\rowcolor{white} \\\\ \\multicolumn{9}{l}{\\textbf{ARB vs THZ}} \\\\ & \\multicolumn{9}{l}{Monotherapy}  \\\\",
          "                        & \\multicolumn{8}{l}{+ Combination} \\\\"
          )
      ),
      sanitize.text.function = identity)

\bottomrule \end{longtable}

\clearpage

Pneumonia

\rowcolors{2}{gray!6}{white} \begin{longtable}{p{-2em}p{-2em}rrrrrrrr} \caption{Population size, total exposure time and pneumonia (events) for ACEI, ARB and CCB/THZ monotherapy and in-combination user target (T) and comparator (C) cohorts.} \ \hiderowcolors \toprule & & & \multicolumn{2}{c}{Patients} & \multicolumn{2}{c}{Time (years)} & \multicolumn{2}{c}{Events} \ \cmidrule(lr){4-5} \cmidrule(lr){6-7} \cmidrule(lr){8-9} & & & \multicolumn{1}{c}{T} & \multicolumn{1}{c}{C} & \multicolumn{1}{c}{T} & \multicolumn{1}{c}{C} & \multicolumn{1}{c}{T} & \multicolumn{1}{c}{C} & MDRR \ \midrule \endfirsthead (Continued) & & & \multicolumn{2}{c}{Patients} & \multicolumn{2}{c}{Time (years)} & \multicolumn{2}{c}{Events} \ \cmidrule(lr){4-5} \cmidrule(lr){6-7} \cmidrule(lr){8-9} & & & \multicolumn{1}{c}{T} & \multicolumn{1}{c}{C} & \multicolumn{1}{c}{T} & \multicolumn{1}{c}{C} & \multicolumn{1}{c}{T} & \multicolumn{1}{c}{C} & MDRR \ \midrule \endhead \showrowcolors

print(xtable(tableAdverse, format = "latex"),
      include.rownames = FALSE,
      include.colnames = FALSE,
      hline.after = NULL,
      only.contents = TRUE,
      add.to.row = list(
        pos = as.list(c(0:19) * 3),
        command = c(
          "                       \\multicolumn{9}{l}{\\textbf{ACE/ARB vs CCB/THZ}} \\\\ & \\multicolumn{9}{l}{Monotherapy}  \\\\",
          "                        & \\multicolumn{8}{l}{+ Combination} \\\\",

          "\\rowcolor{white} \\\\ \\multicolumn{9}{l}{\\textbf{ACE/ARB vs CCB}} \\\\ & \\multicolumn{9}{l}{Monotherapy}  \\\\",
          "                        & \\multicolumn{8}{l}{+ Combination} \\\\",     

          "\\rowcolor{white} \\\\ \\multicolumn{9}{l}{\\textbf{ACE/ARB vs THZ}} \\\\ & \\multicolumn{9}{l}{Monotherapy}  \\\\",
          "                        & \\multicolumn{8}{l}{+ Combination} \\\\",            

          "\\rowcolor{white} \\\\ \\multicolumn{9}{l}{\\textbf{ACE vs ARB}} \\\\ & \\multicolumn{9}{l}{Monotherapy}  \\\\",
          "                        & \\multicolumn{8}{l}{+ Combination} \\\\",

          "\\rowcolor{white} \\\\ \\multicolumn{9}{l}{\\textbf{ACE vs CCB/THZ}} \\\\ & \\multicolumn{9}{l}{Monotherapy}  \\\\",
          "                        & \\multicolumn{8}{l}{+ Combination} \\\\",

          "\\rowcolor{white} \\\\ \\multicolumn{9}{l}{\\textbf{ACE vs CCB}} \\\\ & \\multicolumn{9}{l}{Monotherapy}  \\\\",
          "                        & \\multicolumn{8}{l}{+ Combination} \\\\",

          "\\rowcolor{white} \\\\ \\multicolumn{9}{l}{\\textbf{ACE vs THZ}} \\\\ & \\multicolumn{9}{l}{Monotherapy}  \\\\",
          "                        & \\multicolumn{8}{l}{+ Combination} \\\\",          

          "\\rowcolor{white} \\\\ \\multicolumn{9}{l}{\\textbf{ARB vs CCB/THZ}} \\\\ & \\multicolumn{9}{l}{Monotherapy}  \\\\",
          "                        & \\multicolumn{8}{l}{+ Combination} \\\\",

          "\\rowcolor{white} \\\\ \\multicolumn{9}{l}{\\textbf{ARB vs CCB}} \\\\ & \\multicolumn{9}{l}{Monotherapy}  \\\\",
          "                        & \\multicolumn{8}{l}{+ Combination} \\\\",

                    "\\rowcolor{white} \\\\ \\multicolumn{9}{l}{\\textbf{ARB vs THZ}} \\\\ & \\multicolumn{9}{l}{Monotherapy}  \\\\",
          "                        & \\multicolumn{8}{l}{+ Combination} \\\\"
          )
      ),
      sanitize.text.function = identity)

\bottomrule \end{longtable}

\clearpage

Pneumonia, acute respiratory distress syndrome, acute kidney injury or sepsis (PAAS)

\rowcolors{2}{gray!6}{white} \begin{longtable}{p{-2em}p{-2em}rrrrrrrr} \caption{Population size, total exposure time and PAAS (events) for ACEI, ARB and CCB/THZ monotherapy and in-combination user target (T) and comparator (C) cohorts.} \ \hiderowcolors \toprule & & & \multicolumn{2}{c}{Patients} & \multicolumn{2}{c}{Time (years)} & \multicolumn{2}{c}{Events} \ \cmidrule(lr){4-5} \cmidrule(lr){6-7} \cmidrule(lr){8-9} & & & \multicolumn{1}{c}{T} & \multicolumn{1}{c}{C} & \multicolumn{1}{c}{T} & \multicolumn{1}{c}{C} & \multicolumn{1}{c}{T} & \multicolumn{1}{c}{C} & MDRR \ \midrule \endfirsthead (Continued) & & & \multicolumn{2}{c}{Patients} & \multicolumn{2}{c}{Time (years)} & \multicolumn{2}{c}{Events} \ \cmidrule(lr){4-5} \cmidrule(lr){6-7} \cmidrule(lr){8-9} & & & \multicolumn{1}{c}{T} & \multicolumn{1}{c}{C} & \multicolumn{1}{c}{T} & \multicolumn{1}{c}{C} & \multicolumn{1}{c}{T} & \multicolumn{1}{c}{C} & MDRR \ \midrule \endhead \showrowcolors

print(xtable(tablePna, format = "latex"),
      include.rownames = FALSE,
      include.colnames = FALSE,
      hline.after = NULL,
      only.contents = TRUE,
      add.to.row = list(
        pos = as.list(c(0:19) * 3),
        command = c(
          "                       \\multicolumn{9}{l}{\\textbf{ACE/ARB vs CCB/THZ}} \\\\ & \\multicolumn{9}{l}{Monotherapy}  \\\\",
          "                        & \\multicolumn{8}{l}{+ Combination} \\\\",

          "\\rowcolor{white} \\\\ \\multicolumn{9}{l}{\\textbf{ACE/ARB vs CCB}} \\\\ & \\multicolumn{9}{l}{Monotherapy}  \\\\",
          "                        & \\multicolumn{8}{l}{+ Combination} \\\\",     

          "\\rowcolor{white} \\\\ \\multicolumn{9}{l}{\\textbf{ACE/ARB vs THZ}} \\\\ & \\multicolumn{9}{l}{Monotherapy}  \\\\",
          "                        & \\multicolumn{8}{l}{+ Combination} \\\\",            

          "\\rowcolor{white} \\\\ \\multicolumn{9}{l}{\\textbf{ACE vs ARB}} \\\\ & \\multicolumn{9}{l}{Monotherapy}  \\\\",
          "                        & \\multicolumn{8}{l}{+ Combination} \\\\",

          "\\rowcolor{white} \\\\ \\multicolumn{9}{l}{\\textbf{ACE vs CCB/THZ}} \\\\ & \\multicolumn{9}{l}{Monotherapy}  \\\\",
          "                        & \\multicolumn{8}{l}{+ Combination} \\\\",

          "\\rowcolor{white} \\\\ \\multicolumn{9}{l}{\\textbf{ACE vs CCB}} \\\\ & \\multicolumn{9}{l}{Monotherapy}  \\\\",
          "                        & \\multicolumn{8}{l}{+ Combination} \\\\",

          "\\rowcolor{white} \\\\ \\multicolumn{9}{l}{\\textbf{ACE vs THZ}} \\\\ & \\multicolumn{9}{l}{Monotherapy}  \\\\",
          "                        & \\multicolumn{8}{l}{+ Combination} \\\\",          

          "\\rowcolor{white} \\\\ \\multicolumn{9}{l}{\\textbf{ARB vs CCB/THZ}} \\\\ & \\multicolumn{9}{l}{Monotherapy}  \\\\",
          "                        & \\multicolumn{8}{l}{+ Combination} \\\\",

          "\\rowcolor{white} \\\\ \\multicolumn{9}{l}{\\textbf{ARB vs CCB}} \\\\ & \\multicolumn{9}{l}{Monotherapy}  \\\\",
          "                        & \\multicolumn{8}{l}{+ Combination} \\\\",

                    "\\rowcolor{white} \\\\ \\multicolumn{9}{l}{\\textbf{ARB vs THZ}} \\\\ & \\multicolumn{9}{l}{Monotherapy}  \\\\",
          "                        & \\multicolumn{8}{l}{+ Combination} \\\\"
          )
      ),
      sanitize.text.function = identity)

\bottomrule \end{longtable}

\clearpage

Baseline patient characteristics

source(system.file("shiny", "EvidenceExplorer", "PlotsAndTables.R",
                   package = "Covid19SusceptibilityAlphaBlockers"))
# source(system.file("shiny", "EvidenceExplorer", "DataPulls.R",
#                    package = "Covid19SusceptibilityAlphaBlockers"))

getCovariateBalance <- function(connection,
                                targetId,
                                comparatorId,
                                databaseId,
                                analysisId,
                                covariate,
                                dataFolder,
                                outcomeId = NULL) {
  file <- sprintf("covariate_balance_t%s_c%s_%s.rds", targetId, comparatorId, databaseId)
  balance <- readRDS(file.path(dataFolder, file))
  colnames(balance) <- SqlRender::snakeCaseToCamelCase(colnames(balance))
  balance <- balance[balance$analysisId == analysisId & balance$outcomeId == outcomeId, ]
  balance <- merge(balance, covariate[covariate$databaseId == databaseId & covariate$analysisId == analysisId, 
                                      c("covariateId", "covariateAnalysisId", "covariateName")])
  balance <- balance[ c("covariateId",
                        "covariateName",
                        "covariateAnalysisId", 
                        "targetMeanBefore", 
                        "comparatorMeanBefore", 
                        "stdDiffBefore", 
                        "targetMeanAfter", 
                        "comparatorMeanAfter",
                        "stdDiffAfter")]
  colnames(balance) <- c("covariateId",
                         "covariateName",
                         "analysisId",
                         "beforeMatchingMeanTreated",
                         "beforeMatchingMeanComparator",
                         "beforeMatchingStdDiff",
                         "afterMatchingMeanTreated",
                         "afterMatchingMeanComparator",
                         "afterMatchingStdDiff")
  balance$absBeforeMatchingStdDiff <- abs(balance$beforeMatchingStdDiff)
  balance$absAfterMatchingStdDiff <- abs(balance$afterMatchingStdDiff)
  return(balance)
}


makeCharacteristicsTable <- function(databaseId,
                                     targetId,
                                     comparatorId,
                                     dataFolder,
                                     outcomeId = covidNarrowId) {

covariate <- readRDS(file.path(dataFolder, paste0("covariate_", databaseId, ".rds")))
colnames(covariate) <- SqlRender::snakeCaseToCamelCase(colnames(covariate))

balance <- getCovariateBalance(NULL, targetId, comparatorId, databaseId = databaseId, analysisId = primaryAnalysisId, outcomeId = outcomeId, 
                               covariate = covariate, dataFolder = dataFolder)



#out <- lapply(balances_strat, function(tmp) {

  scale <- 0.55
  header <- readr::read_file("Table1TopSup.tex")
  # targetName <- (firstLineClasses %>% filter(cohortId == tmp$targetId))$shortName
  # comparatorName <- (firstLineClasses %>% filter(cohortId == tmp$comparatorId))$shortName

  # if (!(targetName == "THZ" && comparatorName == "ACEi")) {

  targetName <- cohorts %>% filter(cohortId == targetId) %>% select(shinyName) %>% pull
  comparatorName <- cohorts %>% filter(cohortId == comparatorId) %>% select(shinyName) %>% pull
  title <- sub(pattern = "Patient demographics",
               replacement = paste0("Baseline patient characteristcs for ", targetName," (T) and ", comparatorName, " (C) prevalent-users in the ", databaseId, " data source"),
               x = header)

  title <- sub("0.5\\\\textwidth", paste0(scale, "\\\\textwidth"), x = title)
  title <- sub("-0.5em", "+0.5em", x = title)

  # title <- sub("% Extra row",
  #              paste0("& \\\\multicolumn{3}{c}{$N$ = XXX} & \\\\multicolumn{3}{c}{$N$ = XXX} \\\\\\\\"),
  #              x = title)
  cat(title)

  table <- prepareTable1(balance,pathToCsv = system.file("shiny", "EvidenceExplorer", "Table1Specs.csv", 
                                      package = "Covid19SusceptibilityAlphaBlockers"))
  table <- table[3:nrow(table),]

  print(xtable(table, format = "latex", align = c("l","l","r","r","r","r","r","r")),
        include.rownames = FALSE,
        include.colnames = FALSE,
        hline.after = NULL,
        only.contents = TRUE,
        add.to.row = list(pos = list(nrow(table)), command = c("\\bottomrule")),
        sanitize.text.function = identity)

  footer <- readr::read_file("Table1BottomSup.tex")
  cat(footer)
}

makeAll <- function(db, dbDir) {
  makeCharacteristicsTable(aceArbMonoId, ccbThzMonoId,   databaseId = db, dataFolder = file.path(dbDir, "shinyData"))
  makeCharacteristicsTable(aceArbComboId, ccbThzComboId, databaseId = db, dataFolder = file.path(dbDir, "shinyData"))

  makeCharacteristicsTable(aceArbMonoId, ccbMonoId,   databaseId = db, dataFolder = file.path(dbDir, "shinyData"))
  makeCharacteristicsTable(aceArbComboId, ccbComboId, databaseId = db, dataFolder = file.path(dbDir, "shinyData"))

  makeCharacteristicsTable(aceArbMonoId, thzMonoId,   databaseId = db, dataFolder = file.path(dbDir, "shinyData"))
  makeCharacteristicsTable(aceArbComboId, thzComboId, databaseId = db, dataFolder = file.path(dbDir, "shinyData"))

  makeCharacteristicsTable(aceMonoId, arbMonoId,   databaseId = db, dataFolder = file.path(dbDir, "shinyData"))
  makeCharacteristicsTable(aceComboId, arbComboId, databaseId = db, dataFolder = file.path(dbDir, "shinyData"))

  makeCharacteristicsTable(aceMonoId, ccbThzMonoId, databaseId = db, dataFolder = file.path(dbDir, "shinyData"))
  makeCharacteristicsTable(aceComboId, ccbThzComboId, databaseId = db, dataFolder = file.path(dbDir, "shinyData"))

  makeCharacteristicsTable(aceMonoId, ccbMonoId,   databaseId = db, dataFolder = file.path(dbDir, "shinyData"))
  makeCharacteristicsTable(aceComboId, ccbComboId, databaseId = db, dataFolder = file.path(dbDir, "shinyData"))

  makeCharacteristicsTable(aceMonoId, thzMonoId,   databaseId = db, dataFolder = file.path(dbDir, "shinyData"))
  makeCharacteristicsTable(aceComboId, thzComboId, databaseId = db, dataFolder = file.path(dbDir, "shinyData"))

  makeCharacteristicsTable(arbMonoId, ccbThzMonoId, databaseId = db, dataFolder = file.path(dbDir, "shinyData"))
  makeCharacteristicsTable(arbComboId, ccbThzComboId, databaseId = db, dataFolder = file.path(dbDir, "shinyData"))

  makeCharacteristicsTable(arbMonoId, ccbMonoId,   databaseId = db, dataFolder = file.path(dbDir, "shinyData"))
  makeCharacteristicsTable(arbComboId, ccbComboId, databaseId = db, dataFolder = file.path(dbDir, "shinyData"))

  makeCharacteristicsTable(arbMonoId, thzMonoId,   databaseId = db, dataFolder = file.path(dbDir, "shinyData"))
  makeCharacteristicsTable(arbComboId, thzComboId, databaseId = db, dataFolder = file.path(dbDir, "shinyData"))
}

SIDIAP

makeAll("SIDIAP", sidiapDir)

VA-OMOP

makeAll("VA-OMOP", vaDir)

CUIMC

makeAll("CUIMC", cuimcDir)

tcs <- list(
  list(aceArbMonoId, ccbThzMonoId),
  list(aceArbComboId, ccbThzComboId),

  list(aceArbMonoId, ccbMonoId),
  list(aceArbComboId, ccbComboId),

  list(aceArbMonoId, thzMonoId),
  list(aceArbComboId, thzComboId),

  list(aceMonoId, arbMonoId),
  list(aceComboId, arbComboId),

  list(aceMonoId, ccbThzMonoId),
  list(aceComboId, ccbThzComboId),

  list(aceMonoId, ccbMonoId),
  list(aceComboId, ccbComboId),

  list(aceMonoId, thzMonoId),
  list(aceComboId, thzComboId),

  list(arbMonoId, ccbThzMonoId),
  list(arbComboId, ccbThzComboId),

  list(arbMonoId, ccbMonoId),
  list(arbComboId, ccbComboId),

  list(arbMonoId, thzMonoId),
  list(arbComboId, thzComboId)
)
tcs <- lapply(tcs, function(x) { names(x) <- c("targetId", "comparatorId"); return(x) })

Cohort balance and systematic error diagnostics

plotScatter <- function(controlResults) {
  size <- 2
  labelY <- 1.5
  d <- rbind(data.frame(yGroup = "Uncalibrated",
                        logRr = controlResults$logRr,
                        seLogRr = controlResults$seLogRr,
                        ci95Lb = controlResults$ci95Lb,
                        ci95Ub = controlResults$ci95Ub,
                        trueRr = controlResults$effectSize),
             data.frame(yGroup = "Calibrated",
                        logRr = controlResults$calibratedLogRr,
                        seLogRr = controlResults$calibratedSeLogRr,
                        ci95Lb = controlResults$calibratedCi95Lb,
                        ci95Ub = controlResults$calibratedCi95Ub,
                        trueRr = controlResults$effectSize))
  d <- d[!is.na(d$logRr), ]
  d <- d[!is.na(d$ci95Lb), ]
  d <- d[!is.na(d$ci95Ub), ]
  if (nrow(d) == 0) {
    return(NULL)
  }
  d$Group <- as.factor(d$trueRr)
  d$Significant <- d$ci95Lb > d$trueRr | d$ci95Ub < d$trueRr
  temp1 <- aggregate(Significant ~ Group + yGroup, data = d, length)
  temp2 <- aggregate(Significant ~ Group + yGroup, data = d, mean)
  temp1$nLabel <- paste0(formatC(temp1$Significant, big.mark = ","), " estimates")
  temp1$Significant <- NULL

  temp2$meanLabel <- paste0(formatC(100 * (1 - temp2$Significant), digits = 1, format = "f"),
                            "% of CIs include ",
                            temp2$Group)
  temp2$Significant <- NULL
  dd <- merge(temp1, temp2)
  dd$tes <- as.numeric(as.character(dd$Group))

  breaks <- c(0.1, 0.25, 0.5, 1, 2, 4, 6, 8, 10)
  theme <- ggplot2::element_text(colour = "#000000", size = 12)
  themeRA <- ggplot2::element_text(colour = "#000000", size = 12, hjust = 1)
  themeLA <- ggplot2::element_text(colour = "#000000", size = 12, hjust = 0)

  d$Group <- paste("True hazard ratio =", d$Group)
  dd$Group <- paste("True hazard ratio =", dd$Group)
  alpha <- 1 - min(0.95 * (nrow(d)/nrow(dd)/50000)^0.1, 0.95)
  plot <- ggplot2::ggplot(d, ggplot2::aes(x = logRr, y = seLogRr), environment = environment()) +
          ggplot2::geom_vline(xintercept = log(breaks), colour = "#AAAAAA", lty = 1, size = 0.5) +
          ggplot2::geom_abline(ggplot2::aes(intercept = (-log(tes))/qnorm(0.025), slope = 1/qnorm(0.025)),
                               colour = rgb(0.8, 0, 0),
                               linetype = "dashed",
                               size = 1,
                               alpha = 0.5,
                               data = dd) +
          ggplot2::geom_abline(ggplot2::aes(intercept = (-log(tes))/qnorm(0.975), slope = 1/qnorm(0.975)),
                               colour = rgb(0.8, 0, 0),
                               linetype = "dashed",
                               size = 1,
                               alpha = 0.5,
                               data = dd) +
          ggplot2::geom_point(size = size,
                              color = rgb(0, 0, 0, alpha = 0.05),
                              alpha = alpha,
                              shape = 16) +
          ggplot2::geom_hline(yintercept = 0) +
          ggplot2::geom_label(x = log(0.15),
                              y = 1.9,
                              alpha = 1,
                              hjust = "left",
                              ggplot2::aes(label = nLabel),
                              size = 3,
                              data = dd) +
          ggplot2::geom_label(x = log(0.15),
                              y = labelY,
                              alpha = 1,
                              hjust = "left",
                              ggplot2::aes(label = meanLabel),
                              size = 3,
                              data = dd) +
          ggplot2::scale_x_continuous("Hazard ratio",
                                      limits = log(c(0.1, 10)),
                                      breaks = log(breaks),
                                      labels = breaks) +
          ggplot2::scale_y_continuous("Standard Error", limits = c(0, 2)) +
          ggplot2::facet_grid(yGroup ~ Group) +
          ggplot2::theme(panel.grid.minor = ggplot2::element_blank(),
                         panel.background = ggplot2::element_blank(),
                         panel.grid.major = ggplot2::element_blank(),
                         axis.ticks = ggplot2::element_blank(),
                         axis.text.y = themeRA,
                         axis.text.x = theme,
                         axis.title = theme,
                         legend.key = ggplot2::element_blank(),
                         strip.text.x = theme,
                         strip.text.y = theme,
                         strip.background = ggplot2::element_blank(),
                         legend.position = "none")

  return(plot)
}

getAllControls <- function() {
  pathToCsv <- system.file("settings", "NegativeControls.csv", package = "Covid19SusceptibilityAlphaBlockers")
  allControls <- read.csv(pathToCsv)
  allControls$oldOutcomeId <- allControls$outcomeId
  allControls$targetEffectSize <- rep(1, nrow(allControls))
  return(allControls)
}

getControlResults <- function(tId, cId, aId, dId, negativeControlOutcomeIds, results) {
  results <- results %>% filter(targetId == tId, comparatorId == cId, 
                                analysisId == aId, databaseId == dId, 
                                outcomeId %in% negativeControlOutcomeIds) %>%
    mutate(effectSize = 1)
  return(results)
}

negativeControlOutcomeIds <- unique(getAllControls()$outcomeId)

plotCovariateBalanceScatterPlot <- function(balance, beforeLabel = "Before stratification", afterLabel = "After stratification") {
  limits <- c(min(c(balance$absBeforeMatchingStdDiff, balance$absAfterMatchingStdDiff),
                  na.rm = TRUE),
              max(c(balance$absBeforeMatchingStdDiff, balance$absAfterMatchingStdDiff),
                  na.rm = TRUE))
  theme <- ggplot2::element_text(colour = "#000000", size = 12)
  plot <- ggplot2::ggplot(balance, ggplot2::aes(x = absBeforeMatchingStdDiff, y = absAfterMatchingStdDiff)) +
    ggplot2::geom_point(color = rgb(0, 0, 0.8, alpha = 0.3), shape = 16, size = 2) +
    ggplot2::geom_abline(slope = 1, intercept = 0, linetype = "dashed") +
    ggplot2::geom_hline(yintercept = 0) +
    ggplot2::geom_vline(xintercept = 0) +
    ggplot2::scale_x_continuous(beforeLabel, limits = limits) +
    ggplot2::scale_y_continuous(afterLabel, limits = limits) +
    ggplot2::theme(text = theme)

  return(plot)
}

getPs <- function(dataFolder, targetIds, comparatorIds, analysisId, databaseId) {
  file <- sprintf("preference_score_dist_t%s_c%s_%s.rds", targetIds, comparatorIds, databaseId)
  ps <- readRDS(file.path(dataFolder, file))
  colnames(ps) <- SqlRender::snakeCaseToCamelCase(colnames(ps))
  ps <- ps[ps$analysisId == analysisId, ]
  return(ps)
}

plotPs <- function(ps, targetName, comparatorName) {
  ps <- rbind(data.frame(x = ps$preferenceScore, y = ps$targetDensity, group = targetName),
              data.frame(x = ps$preferenceScore, y = ps$comparatorDensity, group = comparatorName))
  ps$group <- factor(ps$group, levels = c(as.character(targetName), as.character(comparatorName)))
  theme <- ggplot2::element_text(colour = "#000000", size = 12, margin = ggplot2::margin(0, 0.5, 0, 0.1, "cm"))
  plot <- ggplot2::ggplot(ps,
                          ggplot2::aes(x = x, y = y, color = group, group = group, fill = group)) +
          ggplot2::geom_density(stat = "identity") +
          ggplot2::scale_fill_manual(values = c(rgb(0.8, 0, 0, alpha = 0.5),
                                                rgb(0, 0, 0.8, alpha = 0.5))) +
          ggplot2::scale_color_manual(values = c(rgb(0.8, 0, 0, alpha = 0.5),
                                                 rgb(0, 0, 0.8, alpha = 0.5))) +
          ggplot2::scale_x_continuous("Preference score", limits = c(0, 1)) +
          ggplot2::scale_y_continuous("Density") +
          ggplot2::theme(legend.title = ggplot2::element_blank(),
                         panel.grid.major = ggplot2::element_blank(),
                         panel.grid.minor = ggplot2::element_blank(),
                         legend.position = "top",
                         legend.text = theme,
                         axis.text = theme,
                         axis.title = theme)
  return(plot)
}

makeSideBySideBalancePlots <- function(databaseId, covariate, cmResults,
                                       targetId, comparatorId,
                                       thisOutcomeId,
                                       studyFolder) {

  balanceStrat <- getCovariateBalance(NULL, targetId, comparatorId, 
                                      databaseId = databaseId, analysisId = primaryAnalysisId,
                                      outcomeId = thisOutcomeId,
                                      covariate = covariate, 
                                      dataFolder = file.path(studyFolder, 
                                                             databaseId, "shinyData"))

  plotStrat <- plotCovariateBalanceScatterPlot(balanceStrat,
                                               beforeLabel = "Before stratification", 
                                               afterLabel = "After stratification")

  balanceMatch <- getCovariateBalance(NULL, targetId, comparatorId, 
                                      databaseId = databaseId, analysisId = 6,
                                      outcomeId = thisOutcomeId,
                                      covariate = covariate, 
                                      dataFolder = file.path(studyFolder, 
                                                             databaseId, "shinyData"))

  plotMatch <- plotCovariateBalanceScatterPlot(balanceMatch,
                                               beforeLabel = "Before matching", 
                                               afterLabel = "After matching")

  psStrat <- getPs(file.path(studyFolder, databaseId, "shinyData"), targetId, comparatorId, primaryAnalysisId, databaseId)

  targetName <- cohorts %>% filter(cohortId == targetId) %>% select(shinyName) %>% pull()
  comparatorName <- cohorts %>% filter(cohortId == comparatorId) %>% select(shinyName) %>% pull()

  psStrat <- plotPs(psStrat, targetName, comparatorName)  

  # psMatch <- getPs(file.path(studyFolder, databaseId, "shinyData"), targetId, comparatorId, 6, databaseId)
  # 
  # psMatch <- plotPs(psMatch, 
  #                   cohorts %>% filter(cohortId == targetId) %>% select(shinyName) %>% pull(),
  #                   cohorts %>% filter(cohortId == comparatorId) %>% select(shinyName) %>% pull())   

controlsStrat <- getControlResults(targetId, comparatorId, 5, databaseId, negativeControlOutcomeIds,
                                   cmResults)

controlsMatch <- getControlResults(targetId, comparatorId, 6, databaseId, negativeControlOutcomeIds,
                                   cmResults)

errorStrat <- plotScatter(controlsStrat)
errorMatch <- plotScatter(controlsMatch)

  return(list(plotStrat = plotStrat, plotMatch  = plotMatch,
              psStrat = psStrat, 
              # psMatch = psMatch,
              balanceStrat = balanceStrat, balanceMatch = balanceMatch,
              errorStrat = errorStrat, errorMatch = errorMatch,
              targetId = targetId, comparatorId = comparatorId, 
              targetName = targetName, comparatorName = comparatorName,
              outcomeId = thisOutcomeId,
              databaseId = databaseId))
}

saveSideBySidePlots <- function(plot) {
  file <- sprintf("sideBySide_%d_%d_%d_%s.pdf", plot$targetId, plot$comparatorId, plot$outcomeId, 
                  plot$databaseId)
  invisible(save_plot(file,
            plot_grid(
              plot$psStrat, plot$plotStrat, plot$plotMatch, NULL, plot$errorStrat, plot$errorMatch, 
              ncol = 3, rel_heights = c(1, 1)),
            #base_asp = 0.5, 
            #ncol = 5,
            base_height = 8))
  return(list(file = file, targetName = plot$targetName,
              comparatorName = plot$comparatorName,
              databaseName = plot$databaseId))
}

makeExtremeBalanceTable <- function(balance,
                                    targetName, comparatorName, databaseId, designName,
                                    length) {
  originalNrow <- nrow(balance)

  balance <- balance %>% 
    arrange(-absAfterMatchingStdDiff) %>% head(length) %>%
    select(-covariateId, -analysisId, -absBeforeMatchingStdDiff, -absAfterMatchingStdDiff) %>%
    mutate(beforeMatchingMeanTreated = sprintf("%2.1f", beforeMatchingMeanTreated * 100),
           beforeMatchingMeanComparator = sprintf("%2.1f", beforeMatchingMeanComparator * 100),
           afterMatchingMeanTreated = sprintf("%2.1f", afterMatchingMeanTreated * 100),
           afterMatchingMeanComparator = sprintf("%2.1f", afterMatchingMeanComparator * 100))

  header <- readr::read_file("TableBalanceTop.tex")
  header <- gsub(pattern = "CHARACTERISTIC", 
                 paste0("Characteristic (total count = ", originalNrow, ")"),
                 header)
  header <- gsub(pattern = "DESIGN", designName, header)
  header <- gsub(pattern = "DATASOURCE", databaseId, header)
  header <- gsub(pattern = "TARGET", targetName, header)
  header <- gsub(pattern = "COMPARATOR", comparatorName, header)

  cat(header)
  print(xtable(balance, format = "latex"),
        include.rownames = FALSE,
        include.colnames = FALSE,
        hline.after = NULL,
        only.contents = TRUE)
  footer <- readr::read_file("TableBalanceBottom.tex")
  cat(footer)
}

SIDIAP

sidiapCovariate <- readRDS(file.path(studyFolder, "SIDIAP", "shinyData", paste0("covariate_", "SIDIAP", ".rds")))
colnames(sidiapCovariate) <- SqlRender::snakeCaseToCamelCase(colnames(sidiapCovariate))

sidiapResults <- readRDS(file.path(studyFolder, "SIDIAP", "shinyData", paste0("cohort_method_result_", "SIDIAP", ".rds")))
colnames(sidiapResults) <- SqlRender::snakeCaseToCamelCase(colnames(sidiapResults))

devNull <- lapply(tcs, function(tc) {
  balance <- makeSideBySideBalancePlots("SIDIAP", sidiapCovariate, sidiapResults,
                               tc$targetId, tc$comparatorId, covidNarrowId,
                               studyFolder)

  invisible(info <- saveSideBySidePlots(balance))

  figure <- readr::read_file("BalanceFig.tex")
  figure <- sub(pattern = "TARGET", replacement = info$targetName, x = figure)
  figure <- sub(pattern = "COMPARATOR", replacement = info$comparatorName, x = figure)
  figure <- sub(pattern = "DATASOURCE", replacement = info$databaseName, x = figure)
  figure <- sub(pattern = "FILENAME", replacement = info$file, x = figure)
  cat(figure)

  makeExtremeBalanceTable(balance$balanceStrat, balance$targetName, balance$comparatorName, 
                          "SIDIAP", "stratification", length = 10)

  makeExtremeBalanceTable(balance$balanceMatch, balance$targetName, balance$comparatorName, 
                          "SIDIAP", "matching", length = 10)

  cat("\\clearpage")

})

\clearpage

VA-OMOP

vaCovariate <- readRDS(file.path(studyFolder, "VA-OMOP", "shinyData", paste0("covariate_", "VA-OMOP", ".rds")))
colnames(vaCovariate) <- SqlRender::snakeCaseToCamelCase(colnames(vaCovariate))

vaResults <- readRDS(file.path(studyFolder, "VA-OMOP", "shinyData", paste0("cohort_method_result_", "VA-OMOP", ".rds")))
colnames(vaResults) <- SqlRender::snakeCaseToCamelCase(colnames(vaResults))

devNull <- lapply(tcs, function(tc) {
  balance <- makeSideBySideBalancePlots("VA-OMOP", vaCovariate, vaResults,
                               tc$targetId, tc$comparatorId, covidNarrowId,
                               studyFolder)

  invisible(info <- saveSideBySidePlots(balance))

  figure <- readr::read_file("BalanceFig.tex")
  figure <- sub(pattern = "TARGET", replacement = info$targetName, x = figure)
  figure <- sub(pattern = "COMPARATOR", replacement = info$comparatorName, x = figure)
  figure <- sub(pattern = "DATASOURCE", replacement = info$databaseName, x = figure)
  figure <- sub(pattern = "FILENAME", replacement = info$file, x = figure)
  cat(figure)

  makeExtremeBalanceTable(balance$balanceStrat, balance$targetName, balance$comparatorName, 
                          "VA-OMOP", "stratification", length = 10)

  makeExtremeBalanceTable(balance$balanceMatch, balance$targetName, balance$comparatorName, 
                          "VA-OMOP", "matching", length = 10)

  cat("\\clearpage")  
})

\clearpage

CUIMC

cuimcCovariate <- readRDS(file.path(studyFolder, "CUIMC", "shinyData", paste0("covariate_", "CUIMC", ".rds")))
colnames(cuimcCovariate) <- SqlRender::snakeCaseToCamelCase(colnames(cuimcCovariate))

cuimcResults <- readRDS(file.path(studyFolder, "CUIMC", "shinyData", paste0("cohort_method_result_", "CUIMC", ".rds")))
colnames(cuimcResults) <- SqlRender::snakeCaseToCamelCase(colnames(cuimcResults))

devNull <- lapply(tcs, function(tc) {
  balance <- makeSideBySideBalancePlots("CUIMC", cuimcCovariate, cuimcResults,
                               tc$targetId, tc$comparatorId, covidNarrowId,
                               studyFolder)

  invisible(info <- saveSideBySidePlots(balance))

  figure <- readr::read_file("BalanceFig.tex")
  figure <- sub(pattern = "TARGET", replacement = info$targetName, x = figure)
  figure <- sub(pattern = "COMPARATOR", replacement = info$comparatorName, x = figure)
  figure <- sub(pattern = "DATASOURCE", replacement = info$databaseName, x = figure)
  figure <- sub(pattern = "FILENAME", replacement = info$file, x = figure)
  cat(figure)

  makeExtremeBalanceTable(balance$balanceStrat, balance$targetName, balance$comparatorName, 
                          "CUIMC", "stratification", length = 10)

  makeExtremeBalanceTable(balance$balanceMatch, balance$targetName, balance$comparatorName, 
                          "CUIMC", "matching", length = 10)

  cat("\\clearpage")  
})

\clearpage

Effect estimates

sidiapCovariate <- readRdsCamelCase(file.path(sidiapDir, "shinyData", "covariate_SIDIAP.rds"))
cuimcCovariate <- readRdsCamelCase(file.path(cuimcDir, "shinyData", "covariate_CUIMC.rds"))
vaCovariate <- readRdsCamelCase(file.path(vaDir, "shinyData", "covariate_VA-OMOP.rds"))

getBalanceDiagnostic <- function(targetId, comparatorId, databaseId, analysisId, outcomeId, covariate, dataFolder) {
  balance <- getCovariateBalance(NULL, 
                                 targetId, comparatorId, 
                                 databaseId = databaseId, analysisId = analysisId, 
                                 outcomeId = outcomeId, 
                                 covariate = covariate, dataFolder = dataFolder)
  data.frame(databaseId = databaseId, analysisId = analysisId,
             targetId = targetId, comparatorId = comparatorId,
             imbalanced = sum(balance$absAfterMatchingStdDiff > 0.1, na.rm = TRUE))
}

imbalance <- 
  rbind(
    # SIDIAP 
    do.call(
      "rbind",
      lapply(tcs, function(tc) {
        getBalanceDiagnostic(tc$targetId, tc$comparatorId, "SIDIAP", 5, covidNarrowId, sidiapCovariate,
                             file.path(sidiapDir, "shinyData"))
      })),
    do.call(
      "rbind",
      lapply(tcs, function(tc) {
        getBalanceDiagnostic(tc$targetId, tc$comparatorId, "SIDIAP", 6, covidNarrowId, sidiapCovariate,
                             file.path(sidiapDir, "shinyData"))
      })),
    # VA
    do.call(
      "rbind",
      lapply(tcs, function(tc) {
        getBalanceDiagnostic(tc$targetId, tc$comparatorId, "VA-OMOP", 5, covidNarrowId, vaCovariate,
                             file.path(vaDir, "shinyData"))
      })),
    do.call(
      "rbind",
      lapply(tcs, function(tc) {
        getBalanceDiagnostic(tc$targetId, tc$comparatorId, "VA-OMOP", 6, covidNarrowId, vaCovariate,
                             file.path(vaDir, "shinyData"))
      })),    
    # CUIMC
    do.call(
      "rbind",
      lapply(tcs, function(tc) {
        getBalanceDiagnostic(tc$targetId, tc$comparatorId, "CUIMC", 5, covidNarrowId, cuimcCovariate,
                             file.path(cuimcDir, "shinyData"))
      })),
    do.call(
      "rbind",
      lapply(tcs, function(tc) {
        getBalanceDiagnostic(tc$targetId, tc$comparatorId, "CUIMC", 6, covidNarrowId, cuimcCovariate,
                             file.path(cuimcDir, "shinyData"))
      }))    
  ) %>% 
  left_join(prettyNames, by = c("targetId" = "cohortId")) %>% rename(targetName = cohortName) %>%
  left_join(prettyNames, by = c("comparatorId" = "cohortId")) %>% rename(comparatorName = cohortName) %>%
  select(-targetId, -comparatorId) %>%
  select(targetName, comparatorName, everything())  

  # Add meta-analysis
  # meta <- table %>% filter(databaseId == "Meta-analysis-main", outcomeId == covidNarrowId) %>% 
  #   mutate(imbalanced = i2 > 0.40) %>%
  #   select(targetName, comparatorName, databaseId, analysisId, imbalanced)
  # imbalance <- rbind(imbalance, meta) # Comment out to remove meta-analysis

makeEffectTable <- function(outcomeName, designName, databaseName, table,
                            texTop = "SupTabEffectTop.tex",
                            texBottom = "SupTabEffectBottom.tex",
                            imbalance = NULL) {

  table <- table %>% mutate(p = ifelse(p < 0.01, 
                                       "< 0.01", sprintf("%1.2f", p)),
                            calibratedP = ifelse(calibratedP < 0.01, 
                                                 "< 0.01", sprintf("%1.2f", calibratedP)))

  if (!is.null(imbalance)) {
    table <- table %>% inner_join(imbalance, by = c("targetName", "comparatorName", "analysisId"))

    toString <- function(x) {
      ifelse(sapply(x, is.numeric),
             ifelse(x > 10,
                    sprintf("%2.1f", x),
                    sprintf("%1.2f", x)),
             x)
    }

    annotate <- function(x) {
      str <- toString(x)
      ifelse(table$imbalanced > 0,
             paste0("{\\color{light-gray}", str, "}"),
             str)
    } 

    table <- table %>% mutate(rr = annotate(rr),
                              ci = annotate(ci),
                              p = annotate(p),
                              calibratedRr = annotate(calibratedRr),
                              calibratedCi = annotate(calibratedCi),
                              calibratedP = annotate(calibratedP)) %>% select(-databaseId, -imbalanced)
  }

  header <- readr::read_file(texTop)
  header <- sub(pattern = "OUTCOME", replacement = outcomeName, x = header)
  header <- sub(pattern = "DESIGN", replacement = designName, x = header)
  header <- sub(pattern = "DATASOURCE", replacement = databaseName, x = header)
  cat(header)
  print(xtable(table %>% select(-outcomeId, -analysisId), 
               format = "latex"),
        include.rownames = FALSE,
        include.colnames = FALSE,
        hline.after = NULL,
        only.contents = TRUE,
        sanitize.text.function = identity)
  footer <- readr::read_file(texBottom)
  cat(footer)
}

PS Stratification

databaseId <- "SIDIAP"

outcomesOfInterest <- Covid19SusceptibilityAlphaBlockers:::getOutcomesOfInterest()
cmResults <- readRDS(file.path(studyFolder, databaseId, "shinyData", sprintf("cohort_method_result_%s.rds", databaseId)))
colnames(cmResults) <- SqlRender::snakeCaseToCamelCase(colnames(cmResults))
cmResults <- cmResults %>% select(-i2) %>% na.omit()

sidiapTable <- do.call(
  "rbind",
  lapply(tcs, function(tc) {
    cmResults %>% filter(targetId == tc$targetId, comparatorId == tc$comparatorId) %>%
      left_join(prettyNames, by = c("targetId" = "cohortId")) %>% rename(targetName = cohortName) %>%
      left_join(prettyNames, by = c("comparatorId" = "cohortId")) %>% rename(comparatorName = cohortName) %>%
      mutate(ci = sprintf("(%1.2f - %1.2f)", ci95Lb, ci95Ub),
             calibratedCi = sprintf("(%1.2f - %1.2f)", calibratedCi95Lb, calibratedCi95Ub)) %>%
      select(targetName, comparatorName, outcomeId, analysisId,
             rr, ci, p, 
             calibratedRr, calibratedCi, calibratedP)
  }))

databaseId <- "VA-OMOP"

outcomesOfInterest <- Covid19SusceptibilityAlphaBlockers:::getOutcomesOfInterest()
cmResults <- readRDS(file.path(studyFolder, databaseId, "shinyData", sprintf("cohort_method_result_%s.rds", databaseId)))
colnames(cmResults) <- SqlRender::snakeCaseToCamelCase(colnames(cmResults))
cmResults <- cmResults %>% select(-i2) %>% na.omit()

vaTable <- do.call(
  "rbind",
  lapply(tcs, function(tc) {
    cmResults %>% filter(targetId == tc$targetId, comparatorId == tc$comparatorId) %>%
      left_join(prettyNames, by = c("targetId" = "cohortId")) %>% rename(targetName = cohortName) %>%
      left_join(prettyNames, by = c("comparatorId" = "cohortId")) %>% rename(comparatorName = cohortName) %>%
      mutate(ci = sprintf("(%1.2f - %1.2f)", ci95Lb, ci95Ub),
             calibratedCi = sprintf("(%1.2f - %1.2f)", calibratedCi95Lb, calibratedCi95Ub)) %>%
      select(targetName, comparatorName, outcomeId, analysisId,
             rr, ci, p, 
             calibratedRr, calibratedCi, calibratedP)
  }))

databaseId <- "CUIMC"

outcomesOfInterest <- Covid19SusceptibilityAlphaBlockers:::getOutcomesOfInterest()
cmResults <- readRDS(file.path(studyFolder, databaseId, "shinyData", sprintf("cohort_method_result_%s.rds", databaseId))) 
colnames(cmResults) <- SqlRender::snakeCaseToCamelCase(colnames(cmResults))
cmResults <- cmResults %>% select(-i2) %>% na.omit()

cuimcTable <- do.call(
  "rbind",
  lapply(tcs, function(tc) {
    cmResults %>% filter(targetId == tc$targetId, comparatorId == tc$comparatorId) %>%
      left_join(prettyNames, by = c("targetId" = "cohortId")) %>% rename(targetName = cohortName) %>%
      left_join(prettyNames, by = c("comparatorId" = "cohortId")) %>% rename(comparatorName = cohortName) %>%
      mutate(ci = sprintf("(%1.2f - %1.2f)", ci95Lb, ci95Ub),
             calibratedCi = sprintf("(%1.2f - %1.2f)", calibratedCi95Lb, calibratedCi95Ub)) %>%
      select(targetName, comparatorName, outcomeId, analysisId,
             rr, ci, p, 
             calibratedRr, calibratedCi, calibratedP)
  }))

databaseId <- "Meta-analysis"

outcomesOfInterest <- Covid19SusceptibilityAlphaBlockers:::getOutcomesOfInterest()
cmResults <- readRDS(file.path(studyFolder, databaseId, "shinyData", sprintf("cohort_method_result_%s.rds", databaseId))) 
colnames(cmResults) <- SqlRender::snakeCaseToCamelCase(colnames(cmResults))
cmResults <- cmResults %>% na.omit()

maTable <- do.call(
  "rbind",
  lapply(tcs, function(tc) {
    cmResults %>% filter(targetId == tc$targetId, comparatorId == tc$comparatorId) %>%
      left_join(prettyNames, by = c("targetId" = "cohortId")) %>% rename(targetName = cohortName) %>%
      left_join(prettyNames, by = c("comparatorId" = "cohortId")) %>% rename(comparatorName = cohortName) %>%
      mutate(ci = sprintf("(%1.2f - %1.2f)", ci95Lb, ci95Ub),
             calibratedCi = sprintf("(%1.2f - %1.2f)", calibratedCi95Lb, calibratedCi95Ub)) %>%
      select(targetName, comparatorName, outcomeId, analysisId,
             rr, ci, p, 
             calibratedRr, calibratedCi, calibratedP, i2) %>% mutate(i2 = sprintf("%1.2f", i2))
  }))

SIDIAP

\definecolor{light-gray}{gray}{0.50}

makeEffectTable("COVID-19 diagnosis", "stratified", "SIDIAP", 
                sidiapTable %>% filter(outcomeId == covidNarrowId, analysisId == primaryAnalysisId), imbalance = imbalance %>% filter(databaseId == "SIDIAP"))

makeEffectTable("COVID-19 hospitalization", "stratifed", "SIDIAP", 
                sidiapTable %>% filter(outcomeId == covidBroadId, analysisId == primaryAnalysisId), imbalance = imbalance %>% filter(databaseId == "SIDIAP"))

makeEffectTable("pneumonia", "stratifed", "SIDIAP", 
                sidiapTable %>% filter(outcomeId == pnaId, analysisId == primaryAnalysisId), imbalance = imbalance %>% filter(databaseId == "SIDIAP"))

makeEffectTable("PAAS", "stratifed", "SIDIAP", 
                sidiapTable %>% filter(outcomeId == adverseId, analysisId == primaryAnalysisId), imbalance = imbalance %>% filter(databaseId == "SIDIAP"))

\clearpage

VA-OMOP

makeEffectTable("COVID-19 diagnosis", "stratified", "VA-OMOP", 
                vaTable %>% filter(outcomeId == covidNarrowId, analysisId == primaryAnalysisId), imbalance = imbalance %>% filter(databaseId == "VA-OMOP"))

makeEffectTable("COVID-19 hospitalization", "stratifed", "VA-OMOP", 
                vaTable %>% filter(outcomeId == covidBroadId, analysisId == primaryAnalysisId), imbalance = imbalance %>% filter(databaseId == "VA-OMOP"))

makeEffectTable("pneumonia", "stratifed", "VA-OMOP", 
                vaTable %>% filter(outcomeId == pnaId, analysisId == primaryAnalysisId), imbalance = imbalance %>% filter(databaseId == "VA-OMOP"))

makeEffectTable("PAAS", "stratifed", "VA-OMOP", 
                vaTable %>% filter(outcomeId == adverseId, analysisId == primaryAnalysisId), imbalance = imbalance %>% filter(databaseId == "VA-OMOP"))

\clearpage

CUIMC

makeEffectTable("COVID-19 diagnosis", "stratified", "CUIMC", 
                cuimcTable %>% filter(outcomeId == covidNarrowId, analysisId == primaryAnalysisId), imbalance = imbalance %>% filter(databaseId == "CUIMC"))

makeEffectTable("COVID-19 hospitalization", "stratifed", "CUIMC", 
                cuimcTable %>% filter(outcomeId == covidBroadId, analysisId == primaryAnalysisId), imbalance = imbalance %>% filter(databaseId == "CUIMC"))

makeEffectTable("pneumonia", "stratifed", "CUIMC", 
                cuimcTable %>% filter(outcomeId == pnaId, analysisId == primaryAnalysisId), imbalance = imbalance %>% filter(databaseId == "CUIMC"))

makeEffectTable("PAAS", "stratifed", "CUIMC", 
                cuimcTable %>% filter(outcomeId == adverseId, analysisId == primaryAnalysisId), imbalance = imbalance %>% filter(databaseId == "CUIMC"))

\clearpage

Meta-analysis

These meta-analytic estimates are based on all data source estimates, not just those that pass diagnostics

makeEffectTable("COVID-19 diagnosis", "stratified", "Meta-analysis", 
                maTable %>% filter(outcomeId == covidNarrowId, analysisId == primaryAnalysisId),
                texTop = "SupTabEffectMaTop.tex")

makeEffectTable("COVID-19 hospitalization", "stratifed", "Meta-analysis", 
                maTable %>% filter(outcomeId == covidBroadId, analysisId == primaryAnalysisId),
                texTop = "SupTabEffectMaTop.tex")

makeEffectTable("pneumonia", "stratifed", "Meta-analysis", 
                maTable %>% filter(outcomeId == pnaId, analysisId == primaryAnalysisId),
                texTop = "SupTabEffectMaTop.tex")

makeEffectTable("PAAS", "stratifed", "Meta-analysis", 
                maTable %>% filter(outcomeId == adverseId, analysisId == primaryAnalysisId),
                texTop = "SupTabEffectMaTop.tex")

PS Matching

SIDIAP

makeEffectTable("COVID-19 diagnosis", "matched", "SIDIAP", 
                sidiapTable %>% filter(outcomeId == covidNarrowId, analysisId == 6), imbalance = imbalance %>% filter(databaseId == "SIDIAP"))

makeEffectTable("COVID-19 hospitalization", "matched", "SIDIAP", 
                sidiapTable %>% filter(outcomeId == covidBroadId, analysisId == 6), imbalance = imbalance %>% filter(databaseId == "SIDIAP"))

makeEffectTable("pneumonia", "matched", "SIDIAP", 
                sidiapTable %>% filter(outcomeId == pnaId, analysisId == 6), imbalance = imbalance %>% filter(databaseId == "SIDIAP"))

makeEffectTable("PAAS", "matched", "SIDIAP", 
                sidiapTable %>% filter(outcomeId == adverseId, analysisId == 6), imbalance = imbalance %>% filter(databaseId == "SIDIAP"))

\clearpage

VA-OMOP

makeEffectTable("COVID-19 diagnosis", "matched", "VA-OMOP", 
                vaTable %>% filter(outcomeId == covidNarrowId, analysisId == 6), imbalance = imbalance %>% filter(databaseId == "VA-OMOP"))

makeEffectTable("COVID-19 hospitalization", "matched", "VA-OMOP", 
                vaTable %>% filter(outcomeId == covidBroadId, analysisId == 6), imbalance = imbalance %>% filter(databaseId == "VA-OMOP"))

makeEffectTable("pneumonia", "matched", "VA-OMOP", 
                vaTable %>% filter(outcomeId == pnaId, analysisId == 6), imbalance = imbalance %>% filter(databaseId == "VA-OMOP"))

makeEffectTable("PAAS", "matched", "VA-OMOP", 
                vaTable %>% filter(outcomeId == adverseId, analysisId == 6), imbalance = imbalance %>% filter(databaseId == "VA-OMOP"))

\clearpage

CUIMC

makeEffectTable("COVID-19 diagnosis", "matched", "CUIMC", 
                cuimcTable %>% filter(outcomeId == covidNarrowId, analysisId == 6), imbalance = imbalance %>% filter(databaseId == "CUIMC"))

makeEffectTable("COVID-19 hospitalization", "matched", "CUIMC", 
                cuimcTable %>% filter(outcomeId == covidBroadId, analysisId == 6), imbalance = imbalance %>% filter(databaseId == "CUIMC"))

makeEffectTable("pneumonia", "matched", "CUIMC", 
                cuimcTable %>% filter(outcomeId == pnaId, analysisId == 6), imbalance = imbalance %>% filter(databaseId == "CUIMC"))

makeEffectTable("PAAS", "matched", "CUIMC", 
                cuimcTable %>% filter(outcomeId == adverseId, analysisId == 6), imbalance = imbalance %>% filter(databaseId == "CUIMC"))

\clearpage

Meta-analysis

These meta-analytic estimates are based on all data source estimates, not just those that pass diagnostics

makeEffectTable("COVID-19 diagnosis", "matched", "Meta-analysis", 
                maTable %>% filter(outcomeId == covidNarrowId, analysisId == 6),
                texTop = "SupTabEffectMaTop.tex")

makeEffectTable("COVID-19 hospitalization", "matched", "Meta-analysis", 
                maTable %>% filter(outcomeId == covidBroadId, analysisId == 6),
                texTop = "SupTabEffectMaTop.tex")

makeEffectTable("pneumonia", "matched", "Meta-analysis", 
                maTable %>% filter(outcomeId == pnaId, analysisId == 6),
                texTop = "SupTabEffectMaTop.tex")

makeEffectTable("PAAS", "matched", "Meta-analysis", 
                maTable %>% filter(outcomeId == adverseId, analysisId == 6),
                texTop = "SupTabEffectMaTop.tex")

ohdsi-studies/Covid19SusceptibilityAlphaBlockers documentation built on Dec. 25, 2021, 4:45 p.m.