The goal of DrugExposureDiagnostics is to summarise ingredient specific drug exposure data in the OMOP CDM.
You can install the DrugExposureDiagnostics from CRAN like this:
install.packages("DrugExposureDiagnostics")or install the development version:
install.packages("remotes")
remotes::install_github("darwin-eu/DrugExposureDiagnostics")citation("DrugExposureDiagnostics")
#> To cite package 'DrugExposureDiagnostics' in publications use:
#>
#> Inberg G, Burn E, Burkard T (2026). _DrugExposureDiagnostics:
#> Diagnostics for OMOP Common Data Model Drug Records_. R package
#> version 1.1.7, commit a2252b98a38603ab7c8342d3a91bd25a13ecf65b,
#> <https://github.com/darwin-eu/DrugExposureDiagnostics>.
#>
#> A BibTeX entry for LaTeX users is
#>
#> @Manual{,
#> title = {DrugExposureDiagnostics: Diagnostics for OMOP Common Data Model Drug Records},
#> author = {Ger Inberg and Edward Burn and Theresa Burkard},
#> year = {2026},
#> note = {R package version 1.1.7, commit a2252b98a38603ab7c8342d3a91bd25a13ecf65b},
#> url = {https://github.com/darwin-eu/DrugExposureDiagnostics},
#> }library(DrugExposureDiagnostics)
library(CDMConnector)
library(dplyr)First, connect to the database. The package is using the CDMConnector
object. You can create this object by passing a DBI connection and
schema names. When creating a DBIConnection using dbConnect, please
don’t forget to specify the bigint parameter (see below). If this is not
set, you could get a merge error when running DrugExposureDiagnostics.
More examples of how to connect your database using CDMConnector can be
found here: https://darwin-eu.github.io/CDMConnector/articles/a04_DBI_connection_examples.html
Here we use the internal mock database.
# conn <- DBI::dbConnect(
# RPostgres::Postgres(),
# dbname = dbname,
# port = port,
# host = host,
# user = user,
# password = password,
# bigint = c("numeric")
# )
# cdm <- CDMConnector::cdmFromCon(
# con = conn,
# cdmSchema = "cdm schema name"
# )
cdm <- mockDrugExposure()Let´s look at the ingredient acetaminophen (https://athena.ohdsi.org/search-terms/terms/1125315).
We can run all the checks available in ´DrugExposureDiagnostics´ using the ´executeChecks´ function.
all_checks <- executeChecks(
cdm = cdm,
ingredients = 1125315,
checks = c(
"missing", "exposureDuration", "type", "route", "sourceConcept", "daysSupply",
"verbatimEndDate", "dose", "sig", "quantity", "diagnosticsSummary"
)
)
#> population after earliestStartDate smaller than sample, sampling ignored
#> ℹ The following estimates will be calculated:
#> • daily_dose: count_missing, percentage_missing, mean, sd, q05, q25, median,
#> q75, q95, min, max
#> ! Table is collected to memory as not all requested estimates are supported on
#> the database side
#> → Start summary of data, at 2026-06-18 09:51:45.790348
#>
#> ✔ Summary finished, at 2026-06-18 09:51:45.995946The output is a list which contains the following set of tibbles:
names(all_checks)
#> [1] "conceptSummary" "missingValuesOverall"
#> [3] "missingValuesByConcept" "drugExposureDurationOverall"
#> [5] "drugExposureDurationByConcept" "drugTypesOverall"
#> [7] "drugTypesByConcept" "drugRoutesOverall"
#> [9] "drugRoutesByConcept" "drugSourceConceptsOverall"
#> [11] "drugDaysSupply" "drugDaysSupplyByConcept"
#> [13] "drugVerbatimEndDate" "drugVerbatimEndDateByConcept"
#> [15] "drugDose" "drugSig"
#> [17] "drugSigByConcept" "drugQuantity"
#> [19] "drugQuantityByConcept" "diagnosticsSummary"
#> [21] "metadata"The first item contains information on the concept ids that are used in the database for a given ingredient.
glimpse(all_checks$conceptSummary)
#> Rows: 6
#> Columns: 26
#> Rowwise:
#> $ drug_concept_id <int> 40162522, 1127078, 40229134, 1127433, 4023…
#> $ drug <chr> "acetaminophen 325 MG Oral Tablet", "aceta…
#> $ ingredient_concept_id <int> 1125315, 1125315, 1125315, 1125315, 112531…
#> $ ingredient <chr> "acetaminophen", "acetaminophen", "acetami…
#> $ n_records <int> 18, 19, 12, 13, 10, 14
#> $ n_patients <int> 15, 13, 11, 11, 9, 13
#> $ domain_id <chr> "Drug", "Drug", "Drug", "Drug", "Drug", "D…
#> $ vocabulary_id <chr> "RxNorm", "RxNorm", "RxNorm", "RxNorm", "R…
#> $ concept_class_id <chr> "Clinical Drug", "Clinical Drug", "Clinica…
#> $ standard_concept <chr> "S", "S", "S", "S", "S", "S"
#> $ concept_code <chr> "313782", "833036", "1043400", "1049221", …
#> $ valid_start_date <date> 1970-01-01, 1970-01-01, 1970-01-01, 1970-0…
#> $ valid_end_date <date> 2099-12-31, 2099-12-31, 2099-12-31, 2099-1…
#> $ invalid_reason <chr> NA, NA, NA, NA, NA, NA
#> $ amount_value <dbl> 300, NA, 300, 100, 200, 100
#> $ amount_unit_concept_id <int> 8576, NA, 8576, 8576, 8576, 8576
#> $ numerator_value <dbl> NA, 3, NA, NA, NA, NA
#> $ numerator_unit_concept_id <int> NA, 8576, NA, NA, NA, NA
#> $ numerator_unit <chr> NA, NA, NA, NA, NA, NA
#> $ denominator_value <dbl> NA, 1, NA, NA, NA, NA
#> $ denominator_unit_concept_id <int> NA, 8576, NA, NA, NA, NA
#> $ denominator_unit <chr> NA, NA, NA, NA, NA, NA
#> $ box_size <dbl> 0, 0, 0, 0, 0, 0
#> $ amount_unit <chr> NA, NA, NA, NA, NA, NA
#> $ dose_form <chr> "Oral Tablet", "Oral Tablet", "Oral Tablet…
#> $ result_obscured <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE
all_checks$conceptSummary %>%
select("drug_concept_id", "drug")
#> # A tibble: 6 × 2
#> # Rowwise:
#> drug_concept_id drug
#> <int> <chr>
#> 1 40162522 acetaminophen 325 MG Oral Tablet
#> 2 1127078 acetaminophen 750 MG / Hydrocodone Bitartrate
#> 3 40229134 acetaminophen 21.7 MG/ML / Dextromethorphan
#> 4 1127433 acetaminophen 325 MG / Oxycodone Hydrochloride
#> 5 40231925 acetaminophen 325 MG / Hydrocodone Bitartrate
#> 6 19133768 acetaminophen 160 MG Oral TabletOther tibbles then contain information from the various checks performed.
For example, we can see a summary of missingness for the ingredient-related records in the drug exposure table, both overall and by concept.
all_checks$missingValuesOverall
#> # A tibble: 15 × 10
#> # Rowwise: ingredient_concept_id, ingredient
#> ingredient_concept_id ingredient variable n_records n_sample n_person
#> <int> <chr> <chr> <int> <dbl> <dbl>
#> 1 1125315 acetaminophen n_missing_dr… 44 10000 25
#> 2 1125315 acetaminophen n_missing_pe… 44 10000 25
#> 3 1125315 acetaminophen n_missing_dr… 44 10000 25
#> 4 1125315 acetaminophen n_missing_dr… 44 10000 25
#> 5 1125315 acetaminophen n_missing_dr… 44 10000 25
#> 6 1125315 acetaminophen n_missing_ve… 44 10000 25
#> 7 1125315 acetaminophen n_missing_dr… 44 10000 25
#> 8 1125315 acetaminophen n_missing_qu… 44 10000 25
#> 9 1125315 acetaminophen n_missing_da… 44 10000 25
#> 10 1125315 acetaminophen n_missing_sig 44 10000 25
#> 11 1125315 acetaminophen n_missing_ro… 44 10000 25
#> 12 1125315 acetaminophen n_missing_dr… 44 10000 25
#> 13 1125315 acetaminophen n_missing_dr… 44 10000 25
#> 14 1125315 acetaminophen n_missing_ro… 44 10000 25
#> 15 1125315 acetaminophen n_missing_do… 44 10000 25
#> # ℹ 4 more variables: n_records_not_missing_value <dbl>,
#> # n_records_missing_value <dbl>, proportion_records_missing_value <dbl>,
#> # result_obscured <lgl>
all_checks$missingValuesByConcept
#> # A tibble: 90 × 12
#> # Rowwise: drug_concept_id, drug, ingredient_concept_id, ingredient
#> drug_concept_id drug ingredient_concept_id ingredient variable n_records
#> <int> <chr> <int> <chr> <chr> <int>
#> 1 19133768 acetamin… 1125315 acetamino… n_missi… 8
#> 2 19133768 acetamin… 1125315 acetamino… n_missi… 8
#> 3 19133768 acetamin… 1125315 acetamino… n_missi… 8
#> 4 19133768 acetamin… 1125315 acetamino… n_missi… 8
#> 5 19133768 acetamin… 1125315 acetamino… n_missi… 8
#> 6 19133768 acetamin… 1125315 acetamino… n_missi… 8
#> 7 19133768 acetamin… 1125315 acetamino… n_missi… 8
#> 8 19133768 acetamin… 1125315 acetamino… n_missi… 8
#> 9 19133768 acetamin… 1125315 acetamino… n_missi… 8
#> 10 19133768 acetamin… 1125315 acetamino… n_missi… 8
#> # ℹ 80 more rows
#> # ℹ 6 more variables: n_sample <dbl>, n_person <dbl>,
#> # n_records_not_missing_value <dbl>, n_records_missing_value <dbl>,
#> # proportion_records_missing_value <dbl>, result_obscured <lgl>Or we can also see a summary of drug exposure duration (drug_exposure_end_date - drug_exposure_end_date + 1), again overall or by concept.
all_checks$drugExposureDurationOverall
#> # A tibble: 1 × 18
#> # Rowwise: ingredient_concept_id
#> ingredient_concept_id ingredient n_records n_sample n_person
#> <int> <chr> <int> <dbl> <int>
#> 1 1125315 acetaminophen 44 10000 25
#> # ℹ 13 more variables: n_non_negative_days <int>, n_negative_days <int>,
#> # proportion_negative_days <dbl>, minimum_drug_exposure_days <dbl>,
#> # q05_drug_exposure_days <dbl>, q10_drug_exposure_days <dbl>,
#> # q25_drug_exposure_days <dbl>, median_drug_exposure_days <dbl>,
#> # q75_drug_exposure_days <dbl>, q90_drug_exposure_days <dbl>,
#> # q95_drug_exposure_days <dbl>, maximum_drug_exposure_days <dbl>,
#> # result_obscured <lgl>
all_checks$drugExposureDurationByConcept
#> # A tibble: 6 × 20
#> # Rowwise: drug_concept_id, drug, ingredient_concept_id
#> drug_concept_id drug ingredient_concept_id ingredient n_records n_sample
#> <int> <chr> <int> <chr> <int> <dbl>
#> 1 1127078 acetamino… 1125315 acetamino… 8 10000
#> 2 1127433 acetamino… 1125315 acetamino… 8 10000
#> 3 19133768 acetamino… 1125315 acetamino… 8 10000
#> 4 40162522 acetamino… 1125315 acetamino… 12 10000
#> 5 40229134 acetamino… 1125315 acetamino… 6 10000
#> 6 40231925 acetamino… 1125315 acetamino… NA NA
#> # ℹ 14 more variables: n_person <int>, n_non_negative_days <int>,
#> # n_negative_days <int>, proportion_negative_days <dbl>,
#> # minimum_drug_exposure_days <dbl>, q05_drug_exposure_days <dbl>,
#> # q10_drug_exposure_days <dbl>, q25_drug_exposure_days <dbl>,
#> # median_drug_exposure_days <dbl>, q75_drug_exposure_days <dbl>,
#> # q90_drug_exposure_days <dbl>, q95_drug_exposure_days <dbl>,
#> # maximum_drug_exposure_days <dbl>, result_obscured <lgl>For further information on the checks performed please see the package vignettes.
After running the checks we can write the CSVs to disk using the
writeResultToDisk function.
writeResultToDisk(all_checks,
databaseId = "Synthea",
outputFolder = tempdir()
)