In ohdsi-studies/LegendT2dm: Large-Scale Evidence Generation and Evaluation in a Network of Databases

Exposure Cohort Definitions

baseCohortJson <- SqlRender::readSql(system.file("cohorts", "class", "ID101100000.json", package = "LegendT2dm"))
baseCohort <- RJSONIO::fromJSON(baseCohortJson)

baseCohort$ConceptSets <- baseCohort$ConceptSets[-c(8, 9, 13, 14)] # remove CVD, renal and alternative targets
baseCohortJson <- RJSONIO::toJSON(baseCohort, digits = 50)

printCohortDefinitionFromNameAndJson(name = "Class-vs-Class Exposure (DPP4 New-User) Cohort / OT1 {#class-cohort}",
                                     json = baseCohortJson)

Metformin Use Modifier {#prior-metformin}

metCohortJson <- SqlRender::readSql(system.file("cohorts", "class", "ID101200000.json", package = "LegendT2dm"))
obj <- RJSONIO::fromJSON(metCohortJson)
obj$InclusionRules[[1]] <- NULL # alt 1
obj$InclusionRules[[1]] <- NULL # alt 2
obj$InclusionRules[[1]] <- NULL # alt 3
obj$InclusionRules[[1]] <- NULL # anti-diabetic

obj$InclusionRules[[2]] <- NULL
metCohortJson <- RJSONIO::toJSON(obj, digits = 50)

metCohort <- CirceR::cohortExpressionFromJson(metCohortJson)

printInclusionCriteria(metCohort, removeDescription = FALSE)

Escalation Exit Criteria {#escalation-cohort}

ot2CohortJson <- SqlRender::readSql(system.file("cohorts", "class","ID102100000.json", package = "LegendT2dm"))
ot2Cohort <- CirceR::cohortExpressionFromJson(ot2CohortJson)

# printCohortDefinitionFromNameAndJson(name = "Class-vs-Class Exposure (GLP1 New-User) Cohort / OT2",
#                                      json = ot2CohortJson, withConcepts = FALSE, withClosing = FALSE)

printExitCriteria(ot2Cohort)
printConceptSet(ot2Cohort$conceptSets[[14]])
printCohortClose()

Heterogenity Study Inclusion Criteria {#atlas-subgroups}

removeMetforminAndInsulin <- function(json) {
  obj <- RJSONIO::fromJSON(json)
  obj$InclusionRules[[1]] <- NULL # alt 1
  obj$InclusionRules[[1]] <- NULL # alt 2
  obj$InclusionRules[[1]] <- NULL # alt 3
  obj$InclusionRules[[1]] <- NULL # anti-diabetic

  obj$InclusionRules[[2]] <- NULL
  obj$InclusionRules[[2]] <- NULL
  RJSONIO::toJSON(obj, digits = 50)
}

# Age
youngerJson <- SqlRender::readSql(system.file("cohorts", "class", "ID101210000.json", package = "LegendT2dm"))
youngerCohort <- CirceR::cohortExpressionFromJson(removeMetforminAndInsulin(youngerJson))
printInclusionCriteria(youngerCohort)

middleJson <- SqlRender::readSql(system.file("cohorts", "class", "ID101220000.json", package = "LegendT2dm"))
middleCohort <- CirceR::cohortExpressionFromJson(removeMetforminAndInsulin(middleJson))
printInclusionCriteria(middleCohort)

olderJson <- SqlRender::readSql(system.file("cohorts", "class", "ID101230000.json", package = "LegendT2dm"))
olderCohort <- CirceR::cohortExpressionFromJson(removeMetforminAndInsulin(olderJson))
printInclusionCriteria(olderCohort)

# Sex
femaleJson <- SqlRender::readSql(system.file("cohorts", "class", "ID101201000.json", package = "LegendT2dm"))
femaleCohort <- CirceR::cohortExpressionFromJson(removeMetforminAndInsulin(femaleJson))
printInclusionCriteria(femaleCohort)

maleJson <- SqlRender::readSql(system.file("cohorts", "class", "ID101202000.json", package = "LegendT2dm"))
maleCohort <- CirceR::cohortExpressionFromJson(removeMetforminAndInsulin(maleJson))
printInclusionCriteria(maleCohort)

# Race
blackJson <- SqlRender::readSql(system.file("cohorts", "class", "ID101200100.json", package = "LegendT2dm"))
blackCohort <- CirceR::cohortExpressionFromJson(removeMetforminAndInsulin(blackJson))
printInclusionCriteria(blackCohort)

# CVD
lowCvdJson <- SqlRender::readSql(system.file("cohorts", "class", "ID101200010.json", package = "LegendT2dm"))
lowCvdCohort <- CirceR::cohortExpressionFromJson(removeMetforminAndInsulin(lowCvdJson))

printInclusionCriteria(lowCvdCohort)

highCvdJson <- SqlRender::readSql(system.file("cohorts", "class", "ID101200020.json", package = "LegendT2dm"))
highCvdCohort <- CirceR::cohortExpressionFromJson(removeMetforminAndInsulin(highCvdJson))

printInclusionCriteria(highCvdCohort)

printConceptSet(lowCvdCohort$conceptSets[[8]])
printConceptSet(lowCvdCohort$conceptSets[[9]])
printCohortClose()

# Renal
noRenalJson <- SqlRender::readSql(system.file("cohorts", "class", "ID101200001.json", package = "LegendT2dm"))
noRenalCohort <- CirceR::cohortExpressionFromJson(removeMetforminAndInsulin(noRenalJson))

printInclusionCriteria(noRenalCohort)

withRenalJson <- SqlRender::readSql(system.file("cohorts", "class", "ID101200002.json", package = "LegendT2dm"))

cohort <- RJSONIO::fromJSON(withRenalJson)

withRenalCohort <- CirceR::cohortExpressionFromJson(removeMetforminAndInsulin(withRenalJson))

printInclusionCriteria(withRenalCohort)

printConceptSet(withRenalCohort$conceptSets[[13]])
printCohortClose()

drugCohortJson <- SqlRender::readSql(system.file("cohorts", "drug", "ID111100000.json", package = "LegendT2dm"))
drugCohort <- CirceR::cohortExpressionFromJson(drugCohortJson)

printCohortDefinitionFromNameAndJson(name = "Drug-vs-Drug Exposure (Alogliptin New-User) Cohort / OT1 {#drug-cohort}",
                                     json = drugCohortJson, withConcepts = FALSE)

printConceptSet(drugCohort$conceptSets[[1]])
printConceptSet(drugCohort$conceptSets[[15]])
printCohortClose()

Outcome Cohort Definitions

invisible(readr::read_csv(system.file("settings/OutcomesOfInterest.csv", package = "LegendT2dm")) %>%
  mutate(text = atlasName, fileName = name) %>%
  select(text, fileName) %>%
  rowwise() %>%
  mutate(
    output = printCohortDefinition(
      list(name = text,
           jsonFileName = system.file("cohorts", paste0(fileName, ".json"), package = "LegendT2dm")
      )
    )))

Negative Control Concepts {#negative-controls}

\captionsetup{labelformat=empty}

negative_controls <- readr::read_csv(system.file("settings/NegativeControls.csv", package = "LegendT2dm")) %>%
  select(name, conceptId) %>% arrange(name)

colnames(negative_controls) <- c("", "Concept ID")

tab <- kable(negative_controls, booktabs = TRUE, linesep = "", longtable = TRUE,
      caption = "Negative outcome controls specified through condition occurrences that map to (a descendent of) the indicated concept ID") %>%
  kable_styling(bootstrap_options = "striped", latex_options = "striped")

if (knitr::is_latex_output()) {
  tab %>%
    column_spec(1, width = "40em") %>%
    column_spec(2, width = "15em") %>%
    kable_styling(font_size = latex_table_font_size,
                  latex_options = c("repeat_header"),
                  repeat_header_continued = "\\textit{(Continued on Next Page...)}")  

} else {
  tab
}

ohdsi-studies/LegendT2dm documentation built on July 4, 2025, 8:25 p.m.