DrugUtilisation contains functions to instantiate and characterise drug cohorts in data mapped to the OMOP Common Data Model. The package supports:
Creation of drug cohorts
Identification of indications for those in a drug cohort
Summarising drug utilisation among a cohort in terms of duration, quantity, and dose
Description of treatment adherence based on proportion of patients covered
Detailing treatment restart and switching after an initial treatment discontinuation
First, we need to create a cdm reference for the data we´ll be using. Here we generate an example with simulated data, but to see how you would set this up for your database please consult the CDMConnector package connection examples.
library(DrugUtilisation)
library(CDMConnector)
library(omopgenerics)
library(dplyr)
cdm <- mockDrugUtilisation(numberIndividual = 100)To generate the cohort of acetaminophen users we will use
generateIngredientCohortSet, concatenating any records with
fewer than 7 days between them. We then filter our cohort records to
only include the first record per person and require that they have at
least 30 days observation in the database prior to their drug start
date.
cdm <- generateIngredientCohortSet(
cdm = cdm,
name = "dus_cohort",
ingredient = "acetaminophen",
gapEra = 7
)
cdm$dus_cohort |>
requireIsFirstDrugEntry() |>
requireObservationBeforeDrug(days = 30)
#> # Source: table<dus_cohort> [?? x 4]
#> # Database: DuckDB v1.2.0 [root@Darwin 24.4.0:R 4.4.1/:memory:]
#> cohort_definition_id subject_id cohort_start_date cohort_end_date
#> <int> <int> <date> <date>
#> 1 1 19 2015-03-30 2015-08-24
#> 2 1 24 2004-05-12 2005-10-17
#> 3 1 27 2008-05-25 2010-11-30
#> 4 1 3 2011-10-10 2014-04-19
#> 5 1 47 2009-05-20 2013-10-09
#> 6 1 51 2022-07-23 2022-07-24
#> 7 1 73 2008-11-23 2012-08-16
#> 8 1 31 2003-11-05 2005-03-24
#> 9 1 17 2019-10-26 2020-12-19
#> 10 1 28 1966-11-26 1967-03-31
#> # ℹ more rowsNow we´ve created our cohort we could first summarise the indications of the cohort. These indications will always be cohorts, so we first need to create them. Here we create two indication cohorts, one for headache and the other for influenza.
indications <- list(headache = 378253, influenza = 4266367)
cdm <- generateConceptCohortSet(cdm,
conceptSet = indications,
name = "indications_cohort"
)We can summarise the indication results using the
summariseIndication function:
indication_summary <- cdm$dus_cohort |>
summariseIndication(
indicationCohortName = "indications_cohort",
unknownIndicationTable = "condition_occurrence",
indicationWindow = list(c(-30, 0))
)
#> ℹ Intersect with indications table (indications_cohort)
#> ℹ Summarising indications.
indication_summary |> glimpse()
#> Rows: 12
#> Columns: 13
#> $ result_id <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1
#> $ cdm_name <chr> "DUS MOCK", "DUS MOCK", "DUS MOCK", "DUS MOCK", "DUS …
#> $ group_name <chr> "cohort_name", "cohort_name", "cohort_name", "cohort_…
#> $ group_level <chr> "acetaminophen", "acetaminophen", "acetaminophen", "a…
#> $ strata_name <chr> "overall", "overall", "overall", "overall", "overall"…
#> $ strata_level <chr> "overall", "overall", "overall", "overall", "overall"…
#> $ variable_name <chr> "Indication from 30 days before to the index date", "…
#> $ variable_level <chr> "headache", "headache", "influenza", "influenza", "he…
#> $ estimate_name <chr> "count", "percentage", "count", "percentage", "count"…
#> $ estimate_type <chr> "integer", "percentage", "integer", "percentage", "in…
#> $ estimate_value <chr> "9", "16.0714285714286", "7", "12.5", "6", "10.714285…
#> $ additional_name <chr> "window_name", "window_name", "window_name", "window_…
#> $ additional_level <chr> "-30 to 0", "-30 to 0", "-30 to 0", "-30 to 0", "-30 …We can quickly obtain a summary of drug utilisation among our cohort, with various measures calculated for a provided ingredient concept (in this case the concept for acetaminophen).
drug_utilisation_summary <- cdm$dus_cohort |>
summariseDrugUtilisation(
ingredientConceptId = 1125315,
gapEra = 7
)
drug_utilisation_summary |> glimpse()
#> Rows: 72
#> Columns: 13
#> $ result_id <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
#> $ cdm_name <chr> "DUS MOCK", "DUS MOCK", "DUS MOCK", "DUS MOCK", "DUS …
#> $ group_name <chr> "cohort_name", "cohort_name", "cohort_name", "cohort_…
#> $ group_level <chr> "acetaminophen", "acetaminophen", "acetaminophen", "a…
#> $ strata_name <chr> "overall", "overall", "overall", "overall", "overall"…
#> $ strata_level <chr> "overall", "overall", "overall", "overall", "overall"…
#> $ variable_name <chr> "number records", "number subjects", "number exposure…
#> $ variable_level <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ estimate_name <chr> "count", "count", "q25", "median", "q75", "mean", "sd…
#> $ estimate_type <chr> "integer", "integer", "integer", "integer", "integer"…
#> $ estimate_value <chr> "56", "56", "1", "1", "1", "1.21428571428571", "0.494…
#> $ additional_name <chr> "overall", "overall", "concept_set", "concept_set", "…
#> $ additional_level <chr> "overall", "overall", "ingredient_1125315_descendants…
table(drug_utilisation_summary$variable_name)
#>
#> cumulative dose milligram cumulative quantity
#> 7 7
#> days exposed days prescribed
#> 7 7
#> initial daily dose milligram initial exposure duration
#> 7 7
#> initial quantity number eras
#> 7 7
#> number exposures number records
#> 7 1
#> number subjects time to exposure
#> 1 7Now we can combine our results and suppress any counts less than 5 so that they are ready to be shared.
results <- bind(
indication_summary,
drug_utilisation_summary
) |>
suppress(minCellCount = 5)
results |> glimpse()
#> Rows: 84
#> Columns: 13
#> $ result_id <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2,…
#> $ cdm_name <chr> "DUS MOCK", "DUS MOCK", "DUS MOCK", "DUS MOCK", "DUS …
#> $ group_name <chr> "cohort_name", "cohort_name", "cohort_name", "cohort_…
#> $ group_level <chr> "acetaminophen", "acetaminophen", "acetaminophen", "a…
#> $ strata_name <chr> "overall", "overall", "overall", "overall", "overall"…
#> $ strata_level <chr> "overall", "overall", "overall", "overall", "overall"…
#> $ variable_name <chr> "Indication from 30 days before to the index date", "…
#> $ variable_level <chr> "headache", "headache", "influenza", "influenza", "he…
#> $ estimate_name <chr> "count", "percentage", "count", "percentage", "count"…
#> $ estimate_type <chr> "integer", "percentage", "integer", "percentage", "in…
#> $ estimate_value <chr> "9", "16.0714285714286", "7", "12.5", "6", "10.714285…
#> $ additional_name <chr> "window_name", "window_name", "window_name", "window_…
#> $ additional_level <chr> "-30 to 0", "-30 to 0", "-30 to 0", "-30 to 0", "-30 …There are many more drug-related analyses that we could have done with this acetaminophen cohort using the DrugUtilisation package. Please see the package website for more details.