R/checkDaysSupply.R

In DrugExposureDiagnostics: Diagnostics for OMOP Common Data Model Drug Records

# Copyright 2024 DARWIN EU®
#
# This file is part of DrugExposureDiagnostics
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


#' Check if Days_supply is the same as datediff(drug_exp_start_date,drug_exp_end_date)
#'
#' @param cdm CDMConnector reference object
#' @param drugRecordsTable a modified version of the drug exposure table, default "ingredient_drug_records"
#' @param byConcept whether to get result by drug concept
#' @param sampleSize the sample size given in execute checks
#'
#' @return a table with the stats of days supply compared to start and end date
checkDaysSupply <- function(cdm,
                            drugRecordsTable = "ingredient_drug_records",
                            byConcept = TRUE,
                            sampleSize = 10000) {
  errorMessage <- checkmate::makeAssertCollection()
  checkDbType(cdm = cdm, messageStore = errorMessage)
  checkTableExists(
    cdm = cdm, tableName = drugRecordsTable,
    messageStore = errorMessage
  )
  checkmate::reportAssertions(collection = errorMessage)

  records <- cdm[[drugRecordsTable]] %>%
    dplyr::select(
      "drug_concept_id",
      "drug",
      "ingredient_concept_id",
      "ingredient",
      "drug_exposure_start_date",
      "drug_exposure_end_date",
      "days_supply",
      "person_id"
    )

  records <- records %>%
    dplyr::mutate(
      n = dplyr::if_else(days_supply == as.integer(
        !!CDMConnector::datediff(
          start = "drug_exposure_start_date",
          end = "drug_exposure_end_date",
          interval = "day"
        )
      ), 0, 1)
    ) %>%
    dplyr::collect()

  if (isTRUE(byConcept)) {
    grouping <- c(
      "drug_concept_id", "drug",
      "ingredient_concept_id", "ingredient"
    )
  } else {
    grouping <- c("ingredient_concept_id", "ingredient")
  }

  records <- records %>%
    dplyr::group_by(dplyr::across(dplyr::all_of(grouping))) %>%
    dplyr::summarise(
      n_records = as.integer(dplyr::n()),
      n_sample = .env$sampleSize,
      n_different_days_supply_and_drug_dates = sum(.data$n, na.rm = T),
      n_days_supply_match_drug_dates = sum(.data$n == 0, na.rm = T),
      n_missing_days_supply = sum(is.na(.data$n)),
      proportion_different_days_supply_and_drug_dates = .data$n_different_days_supply_and_drug_dates / dplyr::n(),
      proportion_days_supply_match_drug_dates = .data$n_days_supply_match_drug_dates / dplyr::n(),
      proportion_missing_days_supply = .data$n_missing_days_supply / dplyr::n(),
      minimum_drug_exposure_days_supply = min(.data$days_supply, na.rm = T),
      maximum_drug_exposure_days_supply = max(.data$days_supply, na.rm = T),
      n_person = dplyr::n_distinct(.data$person_id),
      q05_drug_exposure_days_supply = stats::quantile(
        .data$days_supply,
        0.05,
        na.rm = T
      ),
      q10_drug_exposure_days_supply = stats::quantile(
        .data$days_supply,
        0.10,
        na.rm = T
      ),
      q25_drug_exposure_days_supply = stats::quantile(
        .data$days_supply,
        0.25,
        na.rm = T
      ),
      median_drug_exposure_days_supply = stats::median(.data$days_supply, na.rm = T),
      q75_drug_exposure_days_supply = stats::quantile(
        .data$days_supply,
        0.75,
        na.rm = T
      ),
      q90_drug_exposure_days_supply = stats::quantile(
        .data$days_supply,
        0.90,
        na.rm = T
      ),
      q95_drug_exposure_days_supply = stats::quantile(
        .data$days_supply,
        0.95,
        na.rm = T
      ),
    ) %>%
    dplyr::select(dplyr::all_of(c(
      grouping, "n_records", "n_sample", "n_person", "minimum_drug_exposure_days_supply",
      "q05_drug_exposure_days_supply", "q10_drug_exposure_days_supply", "q25_drug_exposure_days_supply", "median_drug_exposure_days_supply",
      "q75_drug_exposure_days_supply", "q90_drug_exposure_days_supply", "q95_drug_exposure_days_supply", "maximum_drug_exposure_days_supply",
      "n_different_days_supply_and_drug_dates", "n_days_supply_match_drug_dates", "n_missing_days_supply",
      "proportion_different_days_supply_and_drug_dates", "proportion_days_supply_match_drug_dates", "proportion_missing_days_supply"
    )))
  return(records)
}