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")In order to use the example data from the eunomia dataset, you need to set an environment variable. You can do it like this:
{r} usethis::edit_r_environ()- EUNOMIA_DATA_FOLDER = “/mypath/eunomiaData”
- save the file
- restart RStudio
library(DrugExposureDiagnostics)
library(CDMConnector)
library(dplyr)cdm <- getEunomiaCdm()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, 1125315, minCellCount = NULL)
#> population after earliestStartDate smaller than sample, ignoring date for sampling
#> Joining with `by = join_by(ingredient_concept_id)`
#> Joining with `by = join_by(ingredient_concept_id)`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] "drugSourceConceptsByConcept" "drugDaysSupply"
#> [13] "drugDaysSupplyByConcept" "drugVerbatimEndDate"
#> [15] "drugVerbatimEndDateByConcept" "drugDose"
#> [17] "drugDoseByConcept" "drugSig"
#> [19] "drugSigByConcept" "drugQuantity"
#> [21] "drugQuantityByConcept" "drugIngredientOverview"
#> [23] "drugIngredientPresence" "diagnostics_summary"The first item contains information on the concept ids that are used in the database for a given ingredient.
glimpse(all_checks$conceptSummary)
#> Rows: 2
#> Columns: 24
#> $ drug_concept_id <dbl> 40162522, 1127078
#> $ drug <chr> "Acetaminophen 325 MG / Hydrocodone Bitart…
#> $ ingredient_concept_id <dbl> 1125315, 1125315
#> $ ingredient <chr> "Acetaminophen", "Acetaminophen"
#> $ n_records <dbl> 312, 2158
#> $ domain_id <chr> "Drug", "Drug"
#> $ vocabulary_id <chr> "RxNorm", "RxNorm"
#> $ concept_class_id <chr> "Clinical Drug", "Clinical Drug"
#> $ standard_concept <chr> "S", "S"
#> $ concept_code <chr> "857005", "282464"
#> $ valid_start_date <date> 2009-08-02, 1970-01-01
#> $ valid_end_date <date> 2099-12-31, 2099-12-31
#> $ invalid_reason <lgl> NA, NA
#> $ amount_value <dbl> 1, 1
#> $ amount_unit_concept_id <dbl> 1, 1
#> $ numerator_value <dbl> 1, 1
#> $ numerator_unit_concept_id <dbl> 1, 1
#> $ numerator_unit <chr> NA, NA
#> $ denominator_value <dbl> 1, 1
#> $ denominator_unit_concept_id <dbl> 1, 1
#> $ denominator_unit <chr> NA, NA
#> $ box_size <int> NA, NA
#> $ amount_unit <chr> NA, NA
#> $ dose_form <chr> NA, NA
all_checks$conceptSummary %>%
select("drug_concept_id", "drug")
#> # A tibble: 2 × 2
#> drug_concept_id drug
#> <dbl> <chr>
#> 1 40162522 Acetaminophen 325 MG / Hydrocodone Bitartrate 7.5 MG Oral Tab…
#> 2 1127078 Acetaminophen 160 MG Oral TabletOther tibbles then contain information from the various checks performed.
For example, we can see a summary of missingness for the ingredient-related records in the drug exposure table, both overall and by concept.
all_checks$missingValuesOverall
#> # A tibble: 18 × 7
#> # Groups: ingredient_concept_id, ingredient [1]
#> ingredient_concept_id ingredient variable n_records n_records_not_missin…¹
#> <dbl> <chr> <chr> <dbl> <dbl>
#> 1 1125315 Acetaminophen n_missi… 2470 2470
#> 2 1125315 Acetaminophen n_missi… 2470 2470
#> 3 1125315 Acetaminophen n_missi… 2470 2158
#> 4 1125315 Acetaminophen n_missi… 2470 2470
#> 5 1125315 Acetaminophen n_missi… 2470 0
#> 6 1125315 Acetaminophen n_missi… 2470 2470
#> 7 1125315 Acetaminophen n_missi… 2470 2470
#> 8 1125315 Acetaminophen n_missi… 2470 2470
#> 9 1125315 Acetaminophen n_missi… 2470 0
#> 10 1125315 Acetaminophen n_missi… 2470 2470
#> 11 1125315 Acetaminophen n_missi… 2470 2470
#> 12 1125315 Acetaminophen n_missi… 2470 2470
#> 13 1125315 Acetaminophen n_missi… 2470 2470
#> 14 1125315 Acetaminophen n_missi… 2470 2470
#> 15 1125315 Acetaminophen n_missi… 2470 2470
#> 16 1125315 Acetaminophen n_missi… 2470 2470
#> 17 1125315 Acetaminophen n_missi… 2470 0
#> 18 1125315 Acetaminophen n_missi… 2470 0
#> # ℹ abbreviated name: ¹n_records_not_missing_value
#> # ℹ 2 more variables: n_records_missing_value <dbl>,
#> # proportion_records_missing_value <dbl>
all_checks$missingValuesByConcept
#> # A tibble: 36 × 9
#> # Groups: drug_concept_id, drug, ingredient_concept_id, ingredient [2]
#> drug_concept_id drug ingredient_concept_id ingredient variable n_records
#> <dbl> <chr> <dbl> <chr> <chr> <dbl>
#> 1 1127078 Acetamin… 1125315 Acetamino… n_missi… 2158
#> 2 1127078 Acetamin… 1125315 Acetamino… n_missi… 2158
#> 3 1127078 Acetamin… 1125315 Acetamino… n_missi… 2158
#> 4 1127078 Acetamin… 1125315 Acetamino… n_missi… 2158
#> 5 1127078 Acetamin… 1125315 Acetamino… n_missi… 2158
#> 6 1127078 Acetamin… 1125315 Acetamino… n_missi… 2158
#> 7 1127078 Acetamin… 1125315 Acetamino… n_missi… 2158
#> 8 1127078 Acetamin… 1125315 Acetamino… n_missi… 2158
#> 9 1127078 Acetamin… 1125315 Acetamino… n_missi… 2158
#> 10 1127078 Acetamin… 1125315 Acetamino… n_missi… 2158
#> # ℹ 26 more rows
#> # ℹ 3 more variables: n_records_not_missing_value <dbl>,
#> # n_records_missing_value <dbl>, proportion_records_missing_value <dbl>Or we can also see a summary of drug exposure duration (drug_exposure_end_date - drug_exposure_end_date + 1), again overall or by concept.
all_checks$drugExposureDurationOverall
#> # A tibble: 1 × 15
#> # Groups: ingredient_concept_id [1]
#> ingredient_concept_id ingredient n_records n_non_negative_days n_negative_days
#> <dbl> <chr> <int> <int> <int>
#> 1 1125315 Acetamino… 2470 2470 0
#> # ℹ 10 more variables: 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>
all_checks$drugExposureDurationByConcept
#> # A tibble: 2 × 17
#> # Groups: drug_concept_id, drug, ingredient_concept_id [2]
#> drug_concept_id drug ingredient_concept_id ingredient n_records
#> <dbl> <chr> <dbl> <chr> <int>
#> 1 1127078 Acetaminophen 160 … 1125315 Acetamino… 2158
#> 2 40162522 Acetaminophen 325 … 1125315 Acetamino… 312
#> # ℹ 12 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>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())