Run benchmark of drug utilisation cohort generation

Usage

benchmarkDrugUtilisation(
  cdm,
  ingredient = "acetaminophen",
  alternativeIngredient = c("ibuprofen", "aspirin", "diclofenac"),
  indicationCohort = NULL,
  personSample = 1e+05
)

Arguments

cdm: A cdm_reference object.
ingredient: Name of ingredient to benchmark.
alternativeIngredient: Name of ingredients to use as alternative treatments.
indicationCohort: Name of a cohort in the cdm_reference object to use as indicatiomn.
personSample: Number of individuals to subset the cdm for the benchmark. If NULL no sample is applied.

Value

A summarise_result object.

Examples

# \donttest{
library(DrugUtilisation)
library(omock)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")
#> ℹ Loading bundled GiBleed tables from package data.
#> ℹ Adding drug_strength table.
#> ℹ Creating local <cdm_reference> object.
#> ℹ Inserting <cdm_reference> into duckdb.
#> duckdb keeps downloaded extensions and secrets in a temporary directory:
#> ℹ /tmp/Rtmp8PRavt/duckdb
#> This is removed when the R session ends.
#> • Extensions are re-downloaded each session.
#> • Secrets are lost.
#> ℹ Run duckdb(shared_home = TRUE) (or create ~/.duckdb) to keep them (suitable for most users).
#> ℹ Run duckdb(shared_home = FALSE) to accept the temporary directory (and silence this message).
#> ℹ See ?duckdb_storage for details and alternatives.

timings <- benchmarkDrugUtilisation(cdm)
#> 24-08-2026 09:19:03 Benchmark get necessary concepts
#> 24-08-2026 09:19:03 Benchmark generateDrugUtilisation
#> 24-08-2026 09:19:06 Benchmark generateDrugUtilisation with numberExposures and
#> daysPrescribed
#> 24-08-2026 09:19:08 Benchmark require
#> 24-08-2026 09:19:10 Benchmark generateIngredientCohortSet
#> 24-08-2026 09:19:13 Benchmark summariseDrugUtilisation
#> 24-08-2026 09:19:17 Benchmark summariseDrugRestart
#> 24-08-2026 09:19:19 Benchmark summariseProportionOfPatientsCovered
#> 24-08-2026 09:19:19 Benchmark summariseTreatment
#> 24-08-2026 09:19:22 Benchmark drop created tables

timings
#> # A tibble: 10 × 13
#>    result_id cdm_name group_name group_level            strata_name strata_level
#>        <int> <chr>    <chr>      <chr>                  <chr>       <chr>       
#>  1         1 GiBleed  task       get necessary concepts overall     overall     
#>  2         1 GiBleed  task       generateDrugUtilisati… overall     overall     
#>  3         1 GiBleed  task       generateDrugUtilisati… overall     overall     
#>  4         1 GiBleed  task       require                overall     overall     
#>  5         1 GiBleed  task       generateIngredientCoh… overall     overall     
#>  6         1 GiBleed  task       summariseDrugUtilisat… overall     overall     
#>  7         1 GiBleed  task       summariseDrugRestart   overall     overall     
#>  8         1 GiBleed  task       summariseProportionOf… overall     overall     
#>  9         1 GiBleed  task       summariseTreatment     overall     overall     
#> 10         1 GiBleed  task       drop created tables    overall     overall     
#> # ℹ 7 more variables: variable_name <chr>, variable_level <chr>,
#> #   estimate_name <chr>, estimate_type <chr>, estimate_value <chr>,
#> #   additional_name <chr>, additional_level <chr>
# }