feat: 🚧 skeleton of the core diabetes classification

R/classify-diabetes.R

@@ -0,0 +1,105 @@
+#' Classify diabetes status using Danish registers.
+#'
+#' @return The same object type as the input data, which would be a
+#'   [tibble::tibble()] type object.
+#' @export
+#' @seealso See the [vignette("function-flow", package = "osdc")] for a detailed
+#'   description of the internal implementation of this classification function.
+#'
+#' @examples
+#' classify_diabetes(
+#'   kontakter = register_data$kontakter,
+#'   diagnoser = register_data$diagnoser,
+#'   lpr_diag = register_data$lpr_diag,
+#'   lpr_adm = register_data$lpr_adm,
+#'   sysi = register_data$sysi,
+#'   sssy = register_data$sssy,
+#'   lab_forsker = register_data$lab_forsker,
+#'   bef = register_data$bef,
+#'   lmdb = register_data$lmdb
+#' )
+classify_diabetes <- function(kontakter, diagnoser, lpr_diag, lpr_adm, sysi, sssy, lab_forsker, bef, lmdb) {
+  # Verification step -----
+  verify_required_variables(kontakter, "kontakter")
+  verify_required_variables(diagnoser, "diagnoser")
+  verify_required_variables(lpr_diag, "lpr_diag")
+  verify_required_variables(lpr_adm, "lpr_adm")
+  verify_required_variables(sysi, "sysi")
+  verify_required_variables(sssy, "sssy")
+  verify_required_variables(lab_forsker, "lab_forsker")
+  verify_required_variables(bef, "bef")
+  verify_required_variables(lmdb, "lmdb")
+
+  # Initially processing -----
+  lpr2 <- join_lpr2(
+    lpr_diag = lpr_diag,
+    lpr_adm = lpr_adm
+  )
+
+  lpr3 <- join_lpr3(
+    kontakter = kontakter,
+    diagnoser = diagnoser
+  )
+
+  # pregnancy_dates <-  get_pregrancy_dates(
+  #   lpr2 = lpr2,
+  #   lpr3 = lpr3
+  # )
+
+  # Inclusion steps -----
+  # diabetes_diagnosis <-  include_diabetes_diagnosis(
+  #   lpr2 = lpr2,
+  #   lpr3 = lpr3
+  # )
+
+  # podiatrist_services <- include_podiatrist_services(
+  #   sysi = sysi,
+  #   sssy = sssy
+  # )
+
+  # gld_purchases <- include_gld_purchases(
+  #   lmdb = lmdb
+  # )
+
+  included_hba1c <- include_hba1c(
+    lab_forsker = lab_forsker
+  )
+
+  # Exclusion steps -----
+  exclusions <- gld_purchases |>
+    exclude_potential_pcos(bef = bef) |>
+    exclude_wld_purchases(lmdb = lmdb) |>
+    exclude_pregnancy(
+      # TODO: Need to think about arg naming here..
+      hba1c = included_hba1c,
+      pregnancy_dates = pregnancy_dates
+    )
+
+  # Joining into an initial dataset -----
+  # inclusions <- join_inclusions(
+  #   included_diabetes_diagnosis,
+  #   included_podiatrist_services,
+  #   exclusions
+  # )
+
+  # inclusions |>
+  #   get_diagnosis_dates() |>
+  #   classify_t1d()
+}
+
+#' After inclusion and exclusion, classify those with type 1 diabetes.
+#'
+#' @param data Joined data output from the inclusion and exclusion steps.
+#'
+#' @return The same object type as the input data, which would be a
+#'   [tibble::tibble()] type object.
+#' @keywords internal
+#'
+classify_t1d <- function(data) {
+  # data |>
+  #   get_has_t1d_primary_diagnosis() |>
+  #   get_only_insulin_purchases() |>
+  #   get_majority_of_t1d_primary_diagnosis() |>
+  #   get_insulin_purchases_within_180_days() |>
+  #   get_insulin_is_two_thirds_of_gld_purchases()
+}

R/include-hba1c.R

@@ -3,7 +3,7 @@
 #' In the `lab_forsker` register, NPU27300 is HbA1c in the modern units (IFCC)
 #' while NPU03835 is HbA1c in old units (DCCT).
 #'
-#' @param data The `lab_forsker` register.
+#' @param lab_forsker The `lab_forsker` register.
 #'
 #' @return An object of the same input type, default as a [tibble::tibble()],
 #'   with two columns: `pnr` and `included_hba1c`.
@@ -13,12 +13,12 @@
 #' \dontrun{
 #' register_data$lab_forsker |> include_hba1c()
 #' }
-include_hba1c <- function(data) {
-  verify_required_variables(data, "lab_forsker")
+include_hba1c <- function(lab_forsker) {
+  verify_required_variables(lab_forsker, "lab_forsker")
   hba1c_criteria <- get_algorithm_logic("hba1c") |>
     # To convert the string into an R expression.
     rlang::parse_expr()
-  data |>
+  lab_forsker |>
     column_names_to_lower() |>
     # Use !! to inject the expression into filter.
     dplyr::filter(!!hba1c_criteria) |>