R/mockVocabRef.R

In oxford-pharmacoepi/CodelistGenerator: Identify Relevant Clinical Codes and Evaluate Their Use

# Copyright 2023 DARWIN EU®
#
# This file is part of IncidencePrevalence
#
# 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.


#' Generate example vocabulary database
#'
#' @param backend 'database' (duckdb) or 'data_frame'
#' @return cdm reference with mock vocabulary
#' @export
#'
#' @examples
#' \dontrun{
#' cdm <- mockVocabRef()
#' cdm
#' CDMConnector::cdmDisconnect(cdm)
#' }
mockVocabRef <- function(backend = "database") {
  errorMessage <- checkmate::makeAssertCollection()
  checkmate::assertTRUE(backend %in% c("database", "data_frame"))
  checkmate::assertTRUE(length(backend) == 1)
  checkmate::reportAssertions(collection = errorMessage)

  # compulsory tables
  person <- dplyr::tibble(
    person_id = 1, gender_concept_id = 0, year_of_birth = 1990,
    race_concept_id = 0, ethnicity_concept_id = 0
  )
  observationPeriod <- dplyr::tibble(
    observation_period_id = 1, person_id = 1,
    observation_period_start_date = as.Date("2000-01-01"),
    observation_period_end_date = as.Date("2025-12-31"),
    period_type_concept_id = 0
  )

  # vocab tables
  concept <- data.frame(
    concept_id = 1:19,
    concept_name = c(
      "Musculoskeletal disorder",
      "Osteoarthrosis",
      "Arthritis",
      "Osteoarthritis of knee",
      "Osteoarthritis of hip",
      "Osteonecrosis",
      "Degenerative arthropathy",
      "Knee osteoarthritis",
      "H/O osteoarthritis",
      "Adalimumab",
      "Injection",
      "ALIMENTARY TRACT AND METABOLISM",
      "Descendant drug",
      "Injectable",
      "Diseases of the musculoskeletal system and connective tissue",
      "Arthropathies",
      "Arthritis",
      "OA",
      "Other ingredient"
    ),
    domain_id = c(rep("Condition", 8), "Observation", rep("Drug", 5),
                  rep("Condition", 4), "Drug"),
    vocabulary_id = c(
      rep("SNOMED", 6),
      rep("Read", 2),
      "LOINC", "RxNorm", "OMOP",
      "ATC",
      "RxNorm", "OMOP",
      "ICD10", "ICD10", "ICD10", "ICD10", "RxNorm"
    ),
    standard_concept = c(
      rep("S", 6),
      rep(NA, 2),
      "S", "S", NA,
      NA, "S", NA, NA, NA, NA, NA, "S"
    ),
    concept_class_id = c(
      rep("Clinical Finding", 6),
      rep("Diagnosis", 2),
      "Observation", "Ingredient", "Dose Form",
      "ATC 1st", "Drug", "Dose Form",
      "ICD10 Chapter", "ICD10 SubChapter",
      "ICD Code","ICD Code", "Ingredient"
    ),
    concept_code = "1234",
    valid_start_date = NA,
    valid_end_date = NA,
    invalid_reason =
      NA
  )
  conceptAncestor <- dplyr::bind_rows(
    data.frame(
      ancestor_concept_id = 1L,
      descendant_concept_id = 1L,
      min_levels_of_separation = 1,
      max_levels_of_separation = 1
    ),
    data.frame(
      ancestor_concept_id = 3L,
      descendant_concept_id = 3L,
      min_levels_of_separation = 1,
      max_levels_of_separation = 1
    ),
    data.frame(
      ancestor_concept_id = 1L,
      descendant_concept_id = 2L,
      min_levels_of_separation = 1,
      max_levels_of_separation = 1
    ),
    data.frame(
      ancestor_concept_id = 1L,
      descendant_concept_id = 3L,
      min_levels_of_separation = 1,
      max_levels_of_separation = 1
    ),
    data.frame(
      ancestor_concept_id = 1L,
      descendant_concept_id = 4L,
      min_levels_of_separation = 2,
      max_levels_of_separation = 2
    ),
    data.frame(
      ancestor_concept_id = 1L,
      descendant_concept_id = 5L,
      min_levels_of_separation = 2,
      max_levels_of_separation = 2
    ),
    data.frame(
      ancestor_concept_id = 3L,
      descendant_concept_id = 4L,
      min_levels_of_separation = 1,
      max_levels_of_separation = 1
    ),
    data.frame(
      ancestor_concept_id = 3L,
      descendant_concept_id = 5L,
      min_levels_of_separation = 1,
      max_levels_of_separation = 1
    ),
    data.frame(
      ancestor_concept_id = 10L,
      descendant_concept_id = 10L,
      min_levels_of_separation = 1,
      max_levels_of_separation = 1
    ),
    data.frame(
      ancestor_concept_id = 10L,
      descendant_concept_id = 13L,
      min_levels_of_separation = 1,
      max_levels_of_separation = 1
    ),
    data.frame(
      ancestor_concept_id = 12L,
      descendant_concept_id = 12L,
      min_levels_of_separation = 1,
      max_levels_of_separation = 1
    ),
    data.frame(
      ancestor_concept_id = 12L,
      descendant_concept_id = 13L,
      min_levels_of_separation = 1,
      max_levels_of_separation = 1
    ),
    data.frame(
      ancestor_concept_id = 19L,
      descendant_concept_id = 13L,
      min_levels_of_separation = 1,
      max_levels_of_separation = 1
    )
  )
  conceptSynonym <- dplyr::bind_rows(
    data.frame(
      concept_id = 2L,
      concept_synonym_name = "Arthritis"
    ),
    data.frame(
      concept_id = 3L,
      concept_synonym_name = "Osteoarthrosis"
    )
  )%>%
    dplyr::mutate(language_concept_id  = NA)
  conceptRelationship <- dplyr::bind_rows(
    data.frame(
      concept_id_1 = 2L,
      concept_id_2 = 7L,
      relationship_id = "Mapped from"
    ),
    data.frame(
      concept_id_1 = 4L,
      concept_id_2 = 8L,
      relationship_id = "Mapped from"
    ),
    data.frame(
      concept_id_1 = 10L,
      concept_id_2 = 11L,
      relationship_id = "RxNorm has dose form"
    ),
    data.frame(
      concept_id_1 = 3L,
      concept_id_2 = 6L,
      relationship_id = "Due to of"
    ),
    data.frame(
      concept_id_1 = 13L,
      concept_id_2 = 14L,
      relationship_id = "RxNorm has dose form"
    ),
    data.frame(
      concept_id_1 = 15L,
      concept_id_2 = 16L,
      relationship_id = "Subsumes"
    ),
    data.frame(
      concept_id_1 = 16L,
      concept_id_2 = 17L,
      relationship_id = "Subsumes"
    ),
    data.frame(
      concept_id_1 = 17L,
      concept_id_2 = 18L,
      relationship_id = "Subsumes"
    ),
    data.frame(
      concept_id_1 = 18L,
      concept_id_2 = 3L,
      relationship_id = "Maps to"
    )
  ) %>%
    dplyr::mutate(valid_start_date = NA,
                  valid_end_date = NA,
                  invalid_reason = NA)
  vocabulary <- dplyr::bind_rows(
    data.frame(
      vocabulary_id = "SNOMED",
      vocabulary_name = "SNOMED",
      vocabulary_reference = "1",
      vocabulary_version = "1",
      vocabulary_concept_id = 1
    ),
    data.frame(
      vocabulary_id = "None",
      vocabulary_name = "OMOP Standardized Vocabularies",
      vocabulary_reference = "Omop generated",
      vocabulary_version = "v5.0 22-JUN-22",
      vocabulary_concept_id = 44819096
    )
  )

  drugStrength <- dplyr::bind_rows(
    data.frame(
      drug_concept_id = 10L,
      ingredient_concept_id = 10L,
      amount_value = NA,
      amount_unit_concept_id = 8576,
      numerator_value = 0.010,
      numerator_unit_concept_id = 8576,
      denominator_value = 0.5,
      denominator_unit_concept_id = 8587,
      box_size = NA,
      valid_start_date = NA,
      valid_end_date = NA
    )
  )

  cdmSource <- dplyr::as_tibble(
    data.frame(
      cdm_source_name  = "mock",
      cdm_source_abbreviation = NA,
      cdm_holder = NA,
      source_description = NA,
      source_documentation_reference = NA,
      cdm_etl_reference = NA,
      source_release_date = NA,
      cdm_release_date = NA,
      cdm_version = "5.3",
      vocabulary_version  = NA
    )
  )

  # achilles tables
  # count of 400 records for knee osteoarthritis
  # count of 200 records for hip osteoarthritis
  achillesAnalysis <- dplyr::tibble(analysis_id = 1,
                                    analysis_name = 1,
                                    stratum_1_name = NA,
                                    stratum_2_name = NA,
                                    stratum_3_name = NA,
                                    stratum_4_name = NA,
                                    stratum_5_name = NA,
                                    is_default = NA,
                                    category = NA )
  achillesResults <- dplyr::tibble(analysis_id = c(401, 401),
                             stratum_1 = c(4, 5),
                             stratum_2 = NA,
                             stratum_3 = NA,
                             stratum_4 = NA,
                             stratum_5 = NA,
                             count_value = c(400, 200))
  achillesResultsDist <- dplyr::tibble(analysis_id = 1,
                                       stratum_1 = NA,
                                       stratum_2 = NA,
                                       stratum_3 = NA,
                                       stratum_4 = NA,
                                       stratum_5 = NA,
                                       min_value = NA,
                                       max_value = NA,
                                       avg_value = NA,
                                       stdev_value = NA,
                                       median_value = NA,
                                       p10_value = NA,
                                       p25_value = NA,
                                       p75_value = NA,
                                       p90_value = NA,
                                  count_value = 5)

  cdm_df <- omopgenerics::cdmFromTables(tables = list(person = person,
                                                      concept = concept,
                                                      concept_ancestor = conceptAncestor,
                                                      concept_synonym = conceptSynonym,
                                                      concept_relationship = conceptRelationship,
                                                      vocabulary = vocabulary,
                                                      drug_strength = drugStrength,
                                                      observation_period = observationPeriod,
                                                      cdm_source = cdmSource,
                                                      achilles_analysis = achillesAnalysis,
                                            achilles_results = achillesResults,
                                            achilles_results_dist = achillesResultsDist),
                              cdmName = "mock")

  if (backend == "data_frame") {
    return(cdm_df)
  }

  # into db
  db <- DBI::dbConnect(duckdb::duckdb(), ":memory:")

  cdm <- CDMConnector::copyCdmTo(con = db,
                          cdm = cdm_df,
                          schema = "main",
                          overwrite = TRUE)

  cdm
}