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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()
)

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Version

Install

install.packages('DrugExposureDiagnostics')

Monthly Downloads

608

Version

1.1.4

License

Apache License (>= 2)

Issues

1

Pull Requests

0

Stars

4

Forks

2

Maintainer

Ger Inberg

Last Published

June 16th, 2025

Functions in DrugExposureDiagnostics (1.1.4)

DrugExposureDiagnostics-package

DrugExposureDiagnostics: Diagnostics for OMOP Common Data Model Drug Records
dataPlotPanel

dataPlotPanel
executeChecks

Execute given checks on Drug Exposure.
%>%

Pipe operator
printDurationAndMessage

Print duration from start to now and print it as well as new status message
getDrugRoutes

Get drug exposure route types
getDrugSourceConcepts

Check drug exposure source types
validateChecks

Validate the "checks" parameter
summariseTimeBetween

Check time in between drug records per person and report the summary
mockDrugExposure

Mock Drug exposure tables for ingredients of interest
writeResultToDisk

Write diagnostics results to a zip file on disk in given output folder.
obscureCounts

Obscure the small number of counts
executeChecksSingleIngredient

Execute given checks on Drug Exposure for a single ingredient.
writeIngredientResultToDisk

Write (ingredient) diagnostics results on disk in given output folder.
getAllCheckOptions

Get all options that can be passed to the "checks" parameter
checkDrugSig

Check the drug sig field; this is the verbatim instruction for the drug as written by the provider.
checkIngredientInTable

Check ingredient is present in given table
ShinyModule

Module Decorator Class
checkSampleMinCellCount

Check that the sample is bigger than the mincellcount
checkDrugDose

Get a summary of the daily drug dose
getDrugMissings

Check missings in drug exposure records
checkDaysSupply

Check if Days_supply is the same as datediff(drug_exp_start_date,drug_exp_end_date)
getDuration

Compute the difference in days between 2 variables in a database table.
checkDbType

Check the database type.
getDrugRecords

Drug exposure records for ingredients of interest
getDrugTypes

Get drug exposure record types
ShinyApp

DrugExposureDiagnostics ShinyApp
checkTableExists

Check if given table exists in cdm.
ingredientDescendantsInDb

Get the descendants for the given ingredients
summariseChecks

Create a summary about the diagnostics results
runBenchmarkExecuteSingleIngredient

Run benchmark for ExecuteSingleIngredient
checkVerbatimEndDate

Check the verbatim_end_date field
computeDBQuery

Store the given input in a remote database table. It will be stored either in a permanent table or a temporary table depending on tablePrefix.
metaDataPanel

metaDataPanel
writeZipToDisk

Write (ingredient) diagnostics results on disk in given output folder.
writeFile

Write a result to a file on disk.
viewResults

View the results in the Shiny app
checkIsIngredient

Check is an ingredient
checkLogical

Check if given object is a boolean.
summariseDrugExposureDuration

Summarise drug exposure record durations
summariseQuantity

Summarise the quantity column of the drug_exposure table