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DrugUtilisation

CRANstatus codecov.io R-CMD-check Lifecycle:Experimental

Package overview

DrugUtilisation contains functions to instantiate and characterise drug cohorts in data mapped to the OMOP Common Data Model. The package supports:

Example usage

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)

Create a cohort of acetaminophen users

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
)
#> Warning: ! 1 casted column in dus_cohort (cohort_attrition) as do not match expected
#>   column type:
#> • `reason_id` from numeric to integer
cdm$dus_cohort |>
  requireIsFirstDrugEntry() |>
  requireObservationBeforeDrug(days = 30)
#> Warning: ! 1 casted column in dus_cohort (cohort_attrition) as do not match expected
#>   column type:
#> • `reason_id` from numeric to integer
#> ! 1 casted column in dus_cohort (cohort_attrition) as do not match expected
#>   column type:
#> • `reason_id` from numeric to integer
#> # Source:   table<main.dus_cohort> [?? x 4]
#> # Database: DuckDB v1.0.0 [root@Darwin 23.4.0:R 4.4.1/:memory:]
#>    cohort_definition_id subject_id cohort_start_date cohort_end_date
#>                   <int>      <int> <date>            <date>         
#>  1                    1         99 1996-03-26        1996-12-20     
#>  2                    1         31 2000-05-14        2005-08-28     
#>  3                    1         37 2015-01-22        2015-01-28     
#>  4                    1         49 2018-07-10        2018-12-19     
#>  5                    1         54 2015-08-27        2015-09-15     
#>  6                    1          4 2018-02-11        2022-05-24     
#>  7                    1         18 2020-08-05        2021-02-12     
#>  8                    1         25 2001-02-18        2003-06-26     
#>  9                    1         38 2018-06-04        2019-07-18     
#> 10                    1         39 2001-04-02        2004-11-23     
#> # ℹ more rows

Indications of acetaminophen users

Now 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"
)
#> Warning: ! 3 casted columns in indications_cohort (cohort_attrition) as do not match
#>   expected column type:
#> • `reason_id` from numeric to integer
#> • `excluded_records` from numeric to integer
#> • `excluded_subjects` from numeric to integer
#> Warning: ! 1 casted column in indications_cohort (cohort_codelist) as do not match
#>   expected column type:
#> • `concept_id` from numeric to integer

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))
  )
#> Getting specified indications
#> Creating indication summary variables
#> Getting unknown indications
#> Summarising indication results
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> "161_acetaminophen", "161_acetaminophen", "161_acetam…
#> $ strata_name      <chr> "overall", "overall", "overall", "overall", "overall"…
#> $ strata_level     <chr> "overall", "overall", "overall", "overall", "overall"…
#> $ variable_name    <chr> "number records", "number subjects", "Indication from…
#> $ variable_level   <chr> NA, NA, "headache", "headache", "influenza", "influen…
#> $ estimate_name    <chr> "count", "count", "count", "percentage", "count", "pe…
#> $ estimate_type    <chr> "integer", "integer", "integer", "percentage", "integ…
#> $ estimate_value   <chr> "43", "43", "2", "4.65116279069767", "0", "0", "0", "…
#> $ additional_name  <chr> "overall", "overall", "overall", "overall", "overall"…
#> $ additional_level <chr> "overall", "overall", "overall", "overall", "overall"…

Drug use

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: 58
#> 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> "161_acetaminophen", "161_acetaminophen", "161_acetam…
#> $ 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> "43", "43", "1", "1", "1", "1.11627906976744", "0.324…
#> $ 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 cumulative quantity        exposed time  initial daily dose 
#>                   7                   7                   7                   7 
#>    initial quantity         number eras    number exposures      number records 
#>                   7                   7                   7                   1 
#>     number subjects    time to exposure 
#>                   1                   7

Combine and share results

Now 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: 70
#> 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> "161_acetaminophen", "161_acetaminophen", "161_acetam…
#> $ strata_name      <chr> "overall", "overall", "overall", "overall", "overall"…
#> $ strata_level     <chr> "overall", "overall", "overall", "overall", "overall"…
#> $ variable_name    <chr> "number records", "number subjects", "Indication from…
#> $ variable_level   <chr> NA, NA, "headache", "headache", "influenza", "influen…
#> $ estimate_name    <chr> "count", "count", "count", "percentage", "count", "pe…
#> $ estimate_type    <chr> "integer", "integer", "integer", "percentage", "integ…
#> $ estimate_value   <chr> "43", "43", NA, NA, "0", "0", "0", "0", NA, NA, "40",…
#> $ additional_name  <chr> "overall", "overall", "overall", "overall", "overall"…
#> $ additional_level <chr> "overall", "overall", "overall", "overall", "overall"…

Further analyses

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.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.