Description
Diagnostics for OMOP Common Data Model Drug Records.
Description
Ingredient specific diagnostics for drug exposure records in the Observational Medical Outcomes Partnership (OMOP) common data model.
README.md
DrugExposureDiagnostics 
The goal of DrugExposureDiagnostics is to summarise ingredient specific drug exposure data in the OMOP CDM.
Installation
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
citation("DrugExposureDiagnostics")
#> Warning in citation("DrugExposureDiagnostics"): could not determine year for
#> 'DrugExposureDiagnostics' from package DESCRIPTION file
#> To cite package 'DrugExposureDiagnostics' in publications use:
#>
#> Inberg G, Burn E, Burkard T (????). _DrugExposureDiagnostics:
#> Diagnostics for OMOP Common Data Model Drug Records_. R package
#> version 1.1.0, https://github.com/darwin-eu/DrugExposureDiagnostics,
#> <https://darwin-eu.github.io/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},
#> note = {R package version 1.1.0, https://github.com/darwin-eu/DrugExposureDiagnostics},
#> url = {https://darwin-eu.github.io/DrugExposureDiagnostics/},
#> }
Example use
library(DrugExposureDiagnostics)
library(CDMConnector)
library(dplyr)
cdm <- mockDrugExposure()
#> Note: method with signature 'DBIConnection#Id' chosen for function 'dbExistsTable',
#> target signature 'duckdb_connection#Id'.
#> "duckdb_connection#ANY" would also be valid
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 computed:
#> • 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 2024-12-19 18:52:57.147139
#>
#> Registered S3 method overwritten by 'visOmopResults':
#> method from
#> tidy.summarised_result omopgenerics
#>
#> ✔ Summary finished, at 2024-12-19 18:52:57.302844
#> `sample_size` casted to character.
#> `sample_size` eliminated from settings as all elements are NA.
The 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> 1127078, 40162522, 40229134, 40231925, 191…
#> $ drug <chr> "acetaminophen 750 MG / Hydrocodone Bitart…
#> $ ingredient_concept_id <int> 1125315, 1125315, 1125315, 1125315, 112531…
#> $ ingredient <chr> "acetaminophen", "acetaminophen", "acetami…
#> $ n_records <int> 19, 18, 12, 10, 14, 13
#> $ n_patients <int> 13, 15, 11, 9, 13, 11
#> $ 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> "833036", "313782", "1043400", "857005", "…
#> $ 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> 200, NA, NA, 300, 200, 200
#> $ amount_unit_concept_id <int> 9655, NA, NA, 9655, 9655, 9655
#> $ numerator_value <dbl> NA, 1, 1, NA, NA, NA
#> $ numerator_unit_concept_id <int> NA, 8576, 8576, NA, NA, NA
#> $ numerator_unit <chr> NA, NA, NA, NA, NA, NA
#> $ denominator_value <dbl> NA, 10, 10, NA, NA, NA
#> $ denominator_unit_concept_id <int> NA, 8576, 8576, 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$conceptSummary %>%
select("drug_concept_id", "drug")
#> # A tibble: 6 × 2
#> # Rowwise:
#> drug_concept_id drug
#> <int> <chr>
#> 1 1127078 acetaminophen 750 MG / Hydrocodone Bitartrate
#> 2 40162522 acetaminophen 325 MG Oral Tablet
#> 3 40229134 acetaminophen 21.7 MG/ML / Dextromethorphan
#> 4 40231925 acetaminophen 325 MG / Hydrocodone Bitartrate
#> 5 19133768 acetaminophen 160 MG Oral Tablet
#> 6 1127433 acetaminophen 325 MG / Oxycodone Hydrochloride
Other tibbles then contain information from the various checks performed.
For example, we can see a summary of missingness for the ingredient-related records in the drug exposure table, both overall and by concept.
all_checks$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$missingValuesByConcept
#> # A tibble: 90 × 12
#> # Rowwise: drug_concept_id, drug, ingredient_concept_id, ingredient
#> drug_concept_id drug ingredient_concept_id ingredient variable n_records
#> <int> <chr> <int> <chr> <chr> <int>
#> 1 40229134 acetamin… 1125315 acetamino… n_missi… 6
#> 2 40229134 acetamin… 1125315 acetamino… n_missi… 6
#> 3 40229134 acetamin… 1125315 acetamino… n_missi… 6
#> 4 40229134 acetamin… 1125315 acetamino… n_missi… 6
#> 5 40229134 acetamin… 1125315 acetamino… n_missi… 6
#> 6 40229134 acetamin… 1125315 acetamino… n_missi… 6
#> 7 40229134 acetamin… 1125315 acetamino… n_missi… 6
#> 8 40229134 acetamin… 1125315 acetamino… n_missi… 6
#> 9 40229134 acetamin… 1125315 acetamino… n_missi… 6
#> 10 40229134 acetamin… 1125315 acetamino… n_missi… 6
#> # ℹ 80 more rows
#> # ℹ 6 more variables: n_sample <dbl>, n_person <dbl>,
#> # n_records_not_missing_value <dbl>, n_records_missing_value <dbl>,
#> # proportion_records_missing_value <dbl>, result_obscured <lgl>
Or we can also see a summary of drug exposure duration (drug_exposure_end_date - drug_exposure_end_date + 1), again overall or by concept.
all_checks$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()
)