Introduction

Obtaining information on drug restart or switching to another drug after discontinuation of the original treatment is often of interest in drug utilisation studies. In this vignette, we show how to assess drug switching and restart with this package.

Data

library(DrugUtilisation)

cdm <- mockDrugUtilisation(numberIndividual = 200)

# codelists
metformin <- CodelistGenerator::getDrugIngredientCodes(cdm = cdm, name = "metformin")
insulin <- CodelistGenerator::getDrugIngredientCodes(cdm = cdm, name = "insulin detemir")

cdm <- generateDrugUtilisationCohortSet(
  cdm = cdm, name = "metformin", conceptSet = metformin
)
cdm$metformin |>
  cohortCount()
#> # A tibble: 1 × 3
#>   cohort_definition_id number_records number_subjects
#>                  <int>          <int>           <int>
#> 1                    1            117             101

cdm <- generateDrugUtilisationCohortSet(
  cdm = cdm, name = "insulin", conceptSet = insulin
)
cdm$insulin |>
  cohortCount()
#> # A tibble: 1 × 3
#>   cohort_definition_id number_records number_subjects
#>                  <int>          <int>           <int>
#> 1                    1            101              88

Assess drug restart

The summariseDrugRestart() function analyses the outcomes within a treatment cohort following the first exposure to a specific drug. It categorises the events into four distinct groups:

• Restarting the same treatment.

• Switching to a different treatment.

• Restarting the same treatment while also switching to another.

• Discontinuing treatment altogether (neither the original treatment nor any potential switch).

The figure below illustrates the analysis, focusing on the outcomes after the initial exposure to a particular drug (in blue), with consideration of a specific switch drug (in orange). This study examines what occurs within 100, 180, and 365 days following first treatment discontinuation in the cohort.

Now, let’s use the function to assess metformin restart and switch to insulin after the first metformin treatment.

results <- cdm$metformin |>
  summariseDrugRestart(
    switchCohortTable = "insulin",
    switchCohortId = NULL,
    strata = list(),
    followUpDays = Inf,
    censorDate = NULL,
    restrictToFirstDiscontinuation = TRUE
  )

results |>
  dplyr::glimpse()
#> Rows: 8
#> Columns: 13
#> $ result_id        <int> 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> "6809_metformin", "6809_metformin", "6809_metformin",…
#> $ strata_name      <chr> "overall", "overall", "overall", "overall", "overall"…
#> $ strata_level     <chr> "overall", "overall", "overall", "overall", "overall"…
#> $ variable_name    <chr> "Drug restart till end of observation", "Drug restart…
#> $ variable_level   <chr> "restart", "restart", "switch", "switch", "restart an…
#> $ estimate_name    <chr> "count", "percentage", "count", "percentage", "count"…
#> $ estimate_type    <chr> "integer", "percentage", "integer", "percentage", "in…
#> $ estimate_value   <chr> "11", "10.8910891089109", "10", "9.9009900990099", "4…
#> $ additional_name  <chr> "follow_up_days", "follow_up_days", "follow_up_days",…
#> $ additional_level <chr> "inf days", "inf days", "inf days", "inf days", "inf …

We could be interested in getting these results in different follow-up periods since the first metformin exposure ended. For instance, next we get the results in the first 180 days, the first year, and until the end of observation.

results <- cdm$metformin |>
  summariseDrugRestart(
    switchCohortTable = "insulin",
    switchCohortId = NULL,
    strata = list(),
    followUpDays = c(180, 365, Inf),
    censorDate = NULL,
    restrictToFirstDiscontinuation = TRUE
  )

Other options that this function allows are:

• restrictToFirstDiscontinuation

By default this argument is set to TRUE, which means that we only consider the firsts exposure of the subject. If FALSE, the analysis is conducted on a record level, considering all exposures in the cohort, as the following image illustrates:

• censorEndDate

This argument allows to stop considering restart and switch events after a certain date, which must specified as a column in the cohort.

• incident

This argument is by default TRUE, which means we will only consider switch treatments starting after discontinuation. If set to FALSE, we will allow switch treatments starting before the discontinuation of the treatment and ending afterwards.

• followUpDays

The follow-up of the individuals will be set to Inf by default, i.e. we will follow them up for as long as possible. However, we can restrict the follow-up period to any other time interval as seen in the previous example.

• strata

This argument must be a list pointing to columns or combinations of columns in the cohort to use as strata. It will produce stratified estimates as well as for the overall cohort.

For instance, we reproduce the last calculation but this time straifying by sex. We first use PatientProfiles to add a column indicating the sex, which later we use in strata.

results <- cdm$cohort1 |>
  PatientProfiles::addSex(name = "cohort1") |>
  summariseDrugRestart(
    switchCohortTable = "insulin",
    switchCohortId = NULL,
    strata = list("sex"),
    followUpDays = c(180, 365, Inf),
    censorDate = NULL,
    restrictToFirstDiscontinuation = TRUE
  )

Visualise drug restart

The package has table and plot functions to help visualising the results from summariseDrugRestart().

results |>
  tableDrugRestart()
#> Warning: cdm_name, cohort_name, variable_name, follow_up_days, censor_date,
#> cohort_table_name, incident, restrict_to_first_discontinuation, and
#> switch_cohort_table are missing in `columnOrder`, will be added last.

results |>
  plotDrugRestart(facet = cohort_name + sex ~ follow_up_days)

cdm$metformin |>
  addDrugRestart(
    switchCohortTable = "insulin",
    switchCohortId = NULL,
    followUpDays = c(180, 365, Inf),
    censorDate = NULL
  ) |>
  gt::gt()