Trim demographics

NAMESPACE

@@ -5,6 +5,7 @@ export(cohortCodelist)
 export(cohortCount)
 export(collapseCohort)
 export(conceptCohort)
+export(demographicsCohort)
 export(endDateColumn)
 export(getIdentifier)
 export(intersectCohort)
@@ -26,6 +27,7 @@ export(settings)
 export(splitOverlap)
 export(startDateColumn)
 export(tableName)
+export(trimDemographics)
 export(trimToDateRange)
 importFrom(PatientProfiles,endDateColumn)
 importFrom(PatientProfiles,startDateColumn)

R/demographicsCohort.R

@@ -0,0 +1,58 @@
+#' Create the observation period cohort
+#'
+#' @param cdm A cdm_reference.
+#' @param name Name of the new cohort_table object.
+#' @param ageRange A list of minimum and maximum age.
+#' @param sex Can be "Both", "Male" or "Female". If one of the latter, only
+#' those with that sex will be included.
+#' @param minPriorObservation A minimum number of prior observation days in
+#' the database.
+#' @param minFutureObservation A minimum number of future observation days in
+#' the database.
+#'
+#' @return The cohort with the observation period
+#'
+#' @export
+#'
+demographicsCohort <- function(cdm,
+                               name,
+                               ageRange = NULL,
+                               sex = NULL,
+                               minPriorObservation = NULL,
+                               minFutureObservation = NULL) {
+  # initial checks
+  cdm <- validateCdm(cdm)
+  name <- validateName(name)
+
+  cdm[[name]] <- cdm$observation_period |>
+    dplyr::inner_join(
+      cdm$person |> dplyr::select("person_id") |> dplyr::distinct(),
+      by = "person_id"
+    ) |>
+    dplyr::select(
+      "subject_id" = "person_id",
+      "cohort_start_date" = "observation_period_start_date",
+      "cohort_end_date" = "observation_period_end_date"
+    ) |>
+    dplyr::mutate("cohort_definition_id" = 1) |>
+    dplyr::compute(name = name, temporary = FALSE) |>
+    omopgenerics::newCohortTable(
+      cohortSetRef = dplyr::tibble(
+        "cohort_definition_id" = 1, "cohort_name" = "demographics"
+      ),
+      cohortAttritionRef = NULL,
+      cohortCodelistRef = NULL
+    )
+
+  cdm[[name]] <- trimDemographics(
+    cohort = cdm[[name]],
+    cohortId = NULL,
+    ageRange = ageRange,
+    sex = sex,
+    minPriorObservation = minPriorObservation,
+    minFutureObservation = minFutureObservation,
+    name = name
+  )
+
+  return(cdm[[name]])
+}

R/trimDemographics.R

@@ -0,0 +1,310 @@
+#' Restrict cohort on patient demographics
+#'
+#' @param cohort A cohort table in a cdm reference.
+#' @param cohortId Vector of cohort definition ids to include. If NULL, all
+#' cohort definition ids will be used.
+#' @param ageRange A list of minimum and maximum age.
+#' @param sex Can be "Both", "Male" or "Female". If one of the latter, only
+#' those with that sex will be included.
+#' @param minPriorObservation A minimum number of prior observation days in
+#' the database.
+#' @param minFutureObservation A minimum number of future observation days in
+#' the database.
+#' @param name Name of the new cohort with the demographic requirements.
+#'
+#' @return The cohort table with only records for individuals satisfying the
+#' demographic requirements
+#'
+#' @export
+#'
+trimDemographics <- function(cohort,
+                             cohortId = NULL,
+                             ageRange = NULL,
+                             sex = NULL,
+                             minPriorObservation = NULL,
+                             minFutureObservation = NULL,
+                             name = tableName(cohort)) {
+  # initial validation
+  cohort <- validateCohortTable(cohort, TRUE)
+  cohortId <- validateCohortId(cohortId, settings(cohort)$cohort_definition_id)
+  ageRange <- validateAgeRange(ageRange)
+  sex <- validateSex(sex)
+  minPriorObservation <- validateMinPriorObservation(minPriorObservation)
+  minFutureObservation <- validateMinFutureObservation(minFutureObservation)
+  name <- validateName(name)
+
+  cdm <- omopgenerics::cdmReference(cohort)
+  tablePrefix <- omopgenerics::tmpPrefix()
+
+  cli::cli_inform(c("i" = "Building new trimmed cohort"))
+
+  cohort <- cohort |>
+    dplyr::filter(.data$cohort_definition_id %in% .env$cohortId)
+
+  if (!is.null(ageRange)) {
+    cli::cli_inform(c("Adding birth date"))
+    cohort <- cohort |>
+      PatientProfiles::addDateOfBirth(name = "date_0") %>%
+      dplyr::mutate(!!!datesAgeRange(ageRange))
+  }
+  if (!is.null(minPriorObservation) |
+      !is.null(minFutureObservation) |
+      !is.null(sex)) {
+    cli::cli_inform(c("Adding demographics information"))
+    cohort <- cohort |>
+      PatientProfiles::addDemographics(
+        age = FALSE,
+        sex = !is.null(sex),
+        priorObservation = !is.null(minPriorObservation),
+        priorObservationType = "date",
+        futureObservation = !is.null(minFutureObservation),
+        futureObservationType = "date"
+      )
+  }
+
+  newSettings <- settings(cohort) |>
+    getNewSettings(
+      cohortId, ageRange, sex, minPriorObservation, minFutureObservation
+    )
+
+  # insert settings
+  nm <- omopgenerics::uniqueTableName(tablePrefix)
+  cdm <- omopgenerics::insertTable(
+    cdm = cdm,
+    name = nm,
+    table = newSettings |> dplyr::select(dplyr::any_of(c(
+      "cohort_definition_id", "require_min_age", "require_max_age",
+      "require_sex", "require_min_prior_observation",
+      "require_min_future_observation", "new_cohort_definition_id"
+    )))
+  )
+
+  cli::cli_inform(c("Creating initial cohort"))
+  cohort <- cohort |>
+    dplyr::inner_join(cdm[[nm]], by = "cohort_definition_id") |>
+    dplyr::select(-"cohort_definition_id") |>
+    dplyr::rename("cohort_definition_id" = "new_cohort_definition_id") |>
+    dplyr::compute(name = name, temporary = FALSE) |>
+    omopgenerics::newCohortTable(
+      cohortSetRef = newSettings |>
+        dplyr::select(-"cohort_definition_id") |>
+        dplyr::rename("cohort_definition_id" = "new_cohort_definition_id"),
+      cohortAttritionRef = attrition(cohort) |>
+        dplyr::inner_join(
+          newSettings |>
+            dplyr::select("cohort_definition_id", "new_cohort_definition_id"),
+          by = "cohort_definition_id"
+        ) |>
+        dplyr::select(-"cohort_definition_id") |>
+        dplyr::rename("cohort_definition_id" = "new_cohort_definition_id"),
+      cohortCodelistRef = attr(cohort, "cohort_codelist") |>
+        dplyr::collect() |>
+        dplyr::inner_join(
+          newSettings |>
+            dplyr::select("cohort_definition_id", "new_cohort_definition_id"),
+          by = "cohort_definition_id"
+        ) |>
+        dplyr::select(-"cohort_definition_id") |>
+        dplyr::rename("cohort_definition_id" = "new_cohort_definition_id")
+    )
+
+  if (!is.null(sex)) {
+    cli::cli_inform(c("Trim sex"))
+    cohort <- cohort |>
+      dplyr::filter(
+        tolower(.data$sex) == .data$require_sex |
+          tolower(.data$require_sex) == "both"
+      ) |>
+      dplyr::select(-c("sex", "require_sex")) |>
+      dplyr::compute(name = name, temporary = FALSE) |>
+      omopgenerics::recordCohortAttrition("Restrict sex")
+  }
+  if (!is.null(ageRange)) {
+    cli::cli_inform(c("Trim age"))
+    cohort <- cohort %>%
+      dplyr::mutate(
+        !!!caseAge(ageRange),
+        "cohort_start_date" = dplyr::if_else(
+          .data$cohort_start_date <= .data$new_cohort_start_date,
+          .data$new_cohort_start_date,
+          .data$cohort_start_date
+        ),
+        "cohort_end_date" = dplyr::if_else(
+          .data$cohort_end_date <= .data$new_cohort_end_date,
+          .data$cohort_end_date,
+          .data$new_cohort_end_date
+        )
+      ) |>
+      dplyr::select(-c(
+        dplyr::starts_with("date_"), "require_min_age", "require_max_age",
+        "new_cohort_start_date", "new_cohort_end_date"
+      )) |>
+      dplyr::filter(.data$cohort_start_date <= .data$cohort_end_date) |>
+      dplyr::compute(name = name, temporary = FALSE) |>
+      omopgenerics::recordCohortAttrition("Trim age_group")
+  }
+  if (!is.null(minPriorObservation)) {
+    cli::cli_inform(c("Trim prior observation"))
+    cohort <- cohort %>%
+      dplyr::mutate(
+        "new_cohort_start_date" = as.Date(!!CDMConnector::dateadd(
+          date = "prior_observation",
+          number = "require_min_prior_observation",
+          interval = "day"
+        )),
+        "cohort_start_date" = dplyr::if_else(
+          .data$new_cohort_start_date >= .data$cohort_start_date,
+          .data$new_cohort_start_date,
+          .data$cohort_start_date
+        )
+      ) |>
+      dplyr::filter(.data$cohort_start_date <= .data$cohort_end_date) |>
+      dplyr::select(-c("require_min_prior_observation", "prior_observation", "new_cohort_start_date")) |>
+      dplyr::compute(name = name, temporary = FALSE) |>
+      omopgenerics::recordCohortAttrition("Trim prior_observation")
+  }
+  if (!is.null(minFutureObservation)) {
+    cli::cli_inform(c("Trim future observation"))
+    cohort <- cohort %>%
+      dplyr::filter(
+        !!CDMConnector::datediff(
+          start = "cohort_start_date",
+          end = "future_observation",
+          interval = "day"
+        ) >=
+          .data$require_min_future_observation
+      ) |>
+      dplyr::select(-c("require_min_future_observation", "future_observation")) |>
+      dplyr::compute(name = name, temporary = FALSE) |>
+      omopgenerics::recordCohortAttrition("Require future_observation")
+  }
+
+  # TODO update attrition names to be more coherent with the age groups, sex and so
+
+  cli::cli_inform(c("v" = "Cohort trimmed"))
+  return(cohort)
+}
+
+datesAgeRange <- function(ageRange) {
+  qA <- list()
+  values <- lapply(ageRange, function(x) {
+    x[2] <- x[2] + 1
+    return(x)
+  }) |>
+    unlist() |>
+    unique()
+  values <- values[!is.infinite(values)]
+  values <- values[values != 0]
+  glue::glue("as.Date(local(CDMConnector::dateadd('date_0', {values}, interval = 'year')))") |>
+    rlang::parse_exprs() |>
+    rlang::set_names(glue::glue("date_{values}"))
+}
+getNewSettings <- function(set, cohortId, age, sex, prior, future) {
+  if (length(age) == 0) {
+    ageId <- NULL
+  } else {
+    ageId <- seq_along(age)
+  }
+  if (length(prior) == 0) {
+    prior <- NULL
+  } else {
+    prior <- as.integer(prior)
+  }
+  if (length(future) == 0) {
+    future <- NULL
+  } else {
+    future <- as.integer(future)
+  }
+  sets <- tidyr::expand_grid(
+    "cohort_definition_id" = cohortId,
+    "require_age" = ageId,
+    "require_sex" = sex,
+    "require_min_prior_observation" = prior,
+    "require_min_future_observation" = future
+  )
+  if (!is.null(ageId)) {
+    ageMin <- lapply(age, function(x) {x[1]}) |> unlist()
+    ageMax <- lapply(age, function(x) {x[2]}) |> unlist()
+    sets <- sets |>
+      dplyr::inner_join(
+        dplyr::tibble(
+          "require_age" = ageId,
+          "require_min_age" = ageMin,
+          "require_max_age" = ageMax
+        ),
+        by = "require_age"
+      ) |>
+      dplyr::select(-"require_age")
+  }
+  sets <- sets |>
+    dplyr::select(dplyr::any_of(c(
+      "cohort_definition_id", "require_min_age", "require_max_age",
+      "require_sex", "require_min_prior_observation",
+      "require_min_future_observation"
+    )))
+  sets <- set |>
+    dplyr::inner_join(
+      sets, by = "cohort_definition_id", suffix = c(".original", "")
+    ) |>
+    dplyr::mutate("new_cohort_definition_id" = dplyr::row_number())
+  if (!is.null(age)) {
+    sets <- sets |>
+      dplyr::mutate("cohort_name" = paste0(
+        .data$cohort_name, "_", .data$require_min_age, "_",
+        .data$require_max_age
+      ))
+  }
+  if (!is.null(sex)) {
+    sets <- sets |>
+      dplyr::mutate("cohort_name" = paste0(
+        .data$cohort_name, "_", .data$require_sex
+      ))
+  }
+  if (!is.null(prior)) {
+    sets <- sets |>
+      dplyr::mutate("cohort_name" = paste0(
+        .data$cohort_name, "_", .data$require_min_prior_observation
+      ))
+  }
+  if (!is.null(future)) {
+    sets <- sets |>
+      dplyr::mutate("cohort_name" = paste0(
+        .data$cohort_name, "_", .data$require_min_future_observation
+      ))
+  }
+  sets <- sets |>
+    # we will need a new release of omopgenerics so we can change ::: -> ::
+    dplyr::mutate("cohort_name" = omopgenerics:::toSnakeCase(.data$cohort_name))
+  return(sets)
+}
+caseAge <- function(age) {
+  prepareColStart <- function(x, col) {
+    num <- x |> unlist() |> unique() |> as.character() |> tolower()
+    x <- paste0("date_", num)
+    x <- paste0(".data$", col, " == ", num, " ~ .data$", x) |>
+      paste0(collapse = ",")
+    x <- paste0("dplyr::case_when(", x, ")") |>
+      rlang::parse_exprs() |>
+      rlang::set_names(c("new_cohort_start_date"))
+    return(x)
+  }
+  prepareColEnd <- function(x, col) {
+    num <- unique(unlist(x))
+    infFlag <- any(is.infinite(num))
+    num <- num[!is.infinite(num)]
+    x <- paste0(".data$", col, " == ", as.character(num), " ~ as.Date(local(CDMConnector::dateadd(date = 'date_", as.character(num+1) ,"', number = -1, interval = 'day')))")
+    if (infFlag) {
+      x <- c(x, paste0("is.infinite(.data$", col, ") ~ .data$cohort_end_date"))
+    }
+    x <- paste0(x, collapse = ", ")
+    x <- paste0("dplyr::case_when(", x, ")") |>
+      rlang::parse_exprs() |>
+      rlang::set_names("new_cohort_end_date")
+    return(x)
+  }
+  ageMin <- lapply(age, function(x){x[1]}) |>
+    prepareColStart("require_min_age")
+  ageMax <- lapply(age, function(x){x[2]}) |>
+    prepareColEnd("require_max_age")
+  c(ageMin, ageMax)
+}