Summarise treatments • DrugUtilisation

Introduction

After creating a study cohort, for example of some specific condition of interest, we may be interested in describing the treatments received by the individuals within it. Here we show how such a summary can be obtained.

Create mock table

We will use mock data contained in the package throughout the vignette. Let’s modify cohort tables cohort1 and cohort2 in our mock dataset, so the first table includes 3 cohorts of health conditions (our study cohorts), while the second contains three are of treatments they could receive.

library(DrugUtilisation)

cdm <- mockDrugUtilisation(numberIndividual = 200)

new_cohort_set <- settings(cdm$cohort1) |>
  dplyr::arrange(cohort_definition_id) |>
  dplyr::mutate(cohort_name = c("asthma", "bronchitis", "pneumonia"))

cdm$cohort1 <- cdm$cohort1 |>
  omopgenerics::newCohortTable(cohortSetRef = new_cohort_set)

new_cohort_set <- settings(cdm$cohort2) |>
  dplyr::arrange(cohort_definition_id) |>
  dplyr::mutate(cohort_name = c("albuterol", "fluticasone", "montelukast"))

cdm$cohort2 <- cdm$cohort2 |>
  omopgenerics::newCohortTable(cohortSetRef = new_cohort_set)

Notice that cohort1 is a cohort table with three cohorts representing three different conditions:

settings(cdm$cohort1)
#> # A tibble: 3 × 2
#>   cohort_definition_id cohort_name
#>                  <int> <chr>      
#> 1                    1 asthma     
#> 2                    2 bronchitis 
#> 3                    3 pneumonia

And cohort2 is a cohort table with three different treatment cohorts:

settings(cdm$cohort2)
#> # A tibble: 3 × 2
#>   cohort_definition_id cohort_name
#>                  <int> <chr>      
#> 1                    1 albuterol  
#> 2                    2 fluticasone
#> 3                    3 montelukast

Summarise treatment

The summariseTreatment() function produces a summary of the treatment received by our study cohorts. There are three mandatory arguments:

cohort: cohort from the cdm object.
treatmentCohortName: name of the treatment cohort’s table.
window: list of the windows where to summarise the treatments.

See an example of its usage below, where we use summariseTreatment() to summarise treatments defined in cohort2 in the cohorts defined in cohort1.

summariseTreatment(
  cohort = cdm$cohort1,
  treatmentCohortName = c("cohort2"),
  window = list(c(0, 0), c(1, 30))
)
#> # A tibble: 48 × 13
#>    result_id cdm_name group_name  group_level strata_name strata_level
#>        <int> <chr>    <chr>       <chr>       <chr>       <chr>       
#>  1         1 DUS MOCK cohort_name asthma      overall     overall     
#>  2         1 DUS MOCK cohort_name asthma      overall     overall     
#>  3         1 DUS MOCK cohort_name asthma      overall     overall     
#>  4         1 DUS MOCK cohort_name asthma      overall     overall     
#>  5         1 DUS MOCK cohort_name asthma      overall     overall     
#>  6         1 DUS MOCK cohort_name asthma      overall     overall     
#>  7         1 DUS MOCK cohort_name asthma      overall     overall     
#>  8         1 DUS MOCK cohort_name asthma      overall     overall     
#>  9         1 DUS MOCK cohort_name asthma      overall     overall     
#> 10         1 DUS MOCK cohort_name asthma      overall     overall     
#> # ℹ 38 more rows
#> # 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>

strata parameter

We can also stratify our cohort and calculate the estimates within each strata group by using the strata parameter.

cdm[["cohort1"]] <- cdm[["cohort1"]] |>
  PatientProfiles::addSex() |>
  PatientProfiles::addAge(ageGroup = list("<40" = c(0, 39), ">=40" = c(40, 150)))

results <- summariseTreatment(
  cohort = cdm$cohort1,
  treatmentCohortName = c("cohort2"),
  window = list(c(0, 0)),
  treatmentCohortId = 1,
  strata = list("sex", "age_group")
)

Notice that we have also used the treatmentCohortId parameter to specify that we only want to explore albuterol across the cohorts defined in cohort1.

Visualise results

The package includes table and plot functions to help visualise the results from summariseTreatment().

Tables

The tableTreatment() function generates a table in gt, flextable, or tibble format from the summarised_result produced by summariseTreatment(). This function has customisation options to format the table according to user preferences.

tableTreatment(
  result = results,
  header = c("strata"),
  splitStrata = FALSE,
  cdmName = FALSE,
  groupColumn = c("cohort_name"),
  type = "gt",
  formatEstimateName = c("N (%)" = "<count> (<percentage> %)"),
  .options = list()
)
#> Warning: The `split` argument of `visOmopTable()` is deprecated as of visOmopResults
#> 0.4.0.
#> ℹ The deprecated feature was likely used in the DrugUtilisation package.
#>   Please report the issue at
#>   <https://github.com/darwin-eu/DrugUtilisation/issues>.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
#> generated.
#> ! Results have not been suppressed.

			Sex
			overall	Female	Male	overall
Treatment	Estimate name	Window name	Age group
Treatment	Estimate name	Window name	overall	overall	overall	<40	>=40
asthma
albuterol	N (%)	0 to 0	0 (0.00 %)	0 (0.00 %)	0 (0.00 %)	0 (0.00 %)	0 (0.00 %)
untreated	N (%)	0 to 0	66 (100.00 %)	29 (100.00 %)	37 (100.00 %)	51 (100.00 %)	15 (100.00 %)
bronchitis
albuterol	N (%)	0 to 0	0 (0.00 %)	0 (0.00 %)	0 (0.00 %)	0 (0.00 %)	0 (0.00 %)
untreated	N (%)	0 to 0	51 (100.00 %)	27 (100.00 %)	24 (100.00 %)	44 (100.00 %)	7 (100.00 %)
pneumonia
albuterol	N (%)	0 to 0	0 (0.00 %)	0 (0.00 %)	0 (0.00 %)	0 (0.00 %)	0 (0.00 %)
untreated	N (%)	0 to 0	83 (100.00 %)	39 (100.00 %)	44 (100.00 %)	60 (100.00 %)	23 (100.00 %)

Plots

The plotTreatment() function creates a bar plot showing the percentage of treated and untreated in each cohort, stratum, and time-window. This function offers customization options for colors, faceting, and handling of strata.

plotTreatment(
  result = results,
  facetX = c("cohort_name", "window_name"),
  facetY = c("strata"),
  splitStrata = TRUE,
  colour = "treatment"
)