To add a new column with the exposed time. To add multiple columns use addDrugUtilisation() for efficiency.

To add a new column with the exposed time. To add multiple columns use addDrugUtilisation() for efficiency.

Usage

addExposedTime(
  cohort,
  conceptSet,
  gapEra,
  indexDate = "cohort_start_date",
  censorDate = "cohort_end_date",
  restrictIncident = TRUE,
  nameStyle = "exposed_time_{concept_name}",
  name = NULL
)

Arguments

cohort: Cohort in the cdm.
conceptSet: List of concepts to be included.
gapEra: Number of days between two continuous exposures to be considered in the same era.
indexDate: Name of a column that indicates the date to start the analysis.
censorDate: Name of a column that indicates the date to stop the analysis, if NULL end of individuals observation is used.
restrictIncident: Whether to include only incident prescriptions in the analysis. If FALSE all prescriptions that overlap with the study period will be included.
nameStyle: Character string to specify the nameStyle of the new columns.
name: Name of the new computed cohort table, if NULL a temporary tables is created.

Value

The same cohort with the added column.

Examples

# \donttest{
library(DrugUtilisation)

cdm <- mockDrugUtilisation()
#> Warning: ! 6 column in person do not match expected column type:
#> • `gender_concept_id` is numeric but expected integer
#> • `race_concept_id` is numeric but expected integer
#> • `ethnicity_concept_id` is numeric but expected integer
#> • `location_id` is numeric but expected integer
#> • `provider_id` is numeric but expected integer
#> • `care_site_id` is numeric but expected integer
#> Warning: ! 1 column in observation_period do not match expected column type:
#> • `period_type_concept_id` is numeric but expected integer
#> Warning: ! 2 column in visit_occurrence do not match expected column type:
#> • `visit_concept_id` is numeric but expected integer
#> • `visit_type_concept_id` is numeric but expected integer
#> Warning: ! 10 column in condition_occurrence do not match expected column type:
#> • `condition_concept_id` is numeric but expected integer
#> • `condition_type_concept_id` is numeric but expected integer
#> • `condition_status_concept_id` is numeric but expected integer
#> • `stop_reason` is logical but expected character
#> • `provider_id` is logical but expected integer
#> • `visit_occurrence_id` is logical but expected integer
#> • `visit_detail_id` is logical but expected integer
#> • `condition_source_value` is logical but expected character
#> • `condition_source_concept_id` is logical but expected integer
#> • `condition_status_source_value` is logical but expected character
#> Warning: ! 2 column in drug_exposure do not match expected column type:
#> • `drug_concept_id` is numeric but expected integer
#> • `drug_type_concept_id` is numeric but expected integer
#> Warning: ! 2 column in observation do not match expected column type:
#> • `observation_concept_id` is numeric but expected integer
#> • `observation_type_concept_id` is numeric but expected integer
#> Warning: ! 4 column in concept do not match expected column type:
#> • `concept_id` is numeric but expected integer
#> • `valid_start_date` is character but expected date
#> • `valid_end_date` is character but expected date
#> • `invalid_reason` is logical but expected character
#> Warning: ! 2 column in concept_relationship do not match expected column type:
#> • `concept_id_1` is numeric but expected integer
#> • `concept_id_2` is numeric but expected integer
#> Warning: ! 4 column in concept_ancestor do not match expected column type:
#> • `ancestor_concept_id` is numeric but expected integer
#> • `descendant_concept_id` is numeric but expected integer
#> • `min_levels_of_separation` is numeric but expected integer
#> • `max_levels_of_separation` is numeric but expected integer
#> Warning: ! 9 column in drug_strength do not match expected column type:
#> • `drug_concept_id` is numeric but expected integer
#> • `ingredient_concept_id` is numeric but expected integer
#> • `amount_unit_concept_id` is numeric but expected integer
#> • `numerator_unit_concept_id` is numeric but expected integer
#> • `denominator_unit_concept_id` is numeric but expected integer
#> • `box_size` is logical but expected integer
#> • `valid_start_date` is character but expected date
#> • `valid_end_date` is character but expected date
#> • `invalid_reason` is logical but expected character
#> Warning: ! 6 column in person do not match expected column type:
#> • `gender_concept_id` is numeric but expected integer
#> • `race_concept_id` is numeric but expected integer
#> • `ethnicity_concept_id` is numeric but expected integer
#> • `location_id` is numeric but expected integer
#> • `provider_id` is numeric but expected integer
#> • `care_site_id` is numeric but expected integer
#> Warning: ! 1 column in observation_period do not match expected column type:
#> • `period_type_concept_id` is numeric but expected integer
codelist <- CodelistGenerator::getDrugIngredientCodes(
  cdm,
  name = "acetaminophen"
)
#> Warning: ! `codelist` contains numeric values, they are casted to integers.
cdm <- generateDrugUtilisationCohortSet(
  cdm = cdm, name = "dus_cohort", conceptSet = codelist
)

cdm$dus_cohort |>
  addExposedTime(conceptSet = codelist, gapEra = 1)
#> # Source:   table<og_048_1730712135> [5 x 5]
#> # Database: DuckDB v1.1.2 [unknown@Linux 6.5.0-1025-azure:R 4.4.2/:memory:]
#>   cohort_definition_id subject_id cohort_start_date cohort_end_date
#>                  <int>      <int> <date>            <date>         
#> 1                    1          3 2017-10-05        2017-10-27     
#> 2                    1          8 2021-12-27        2022-08-07     
#> 3                    1          2 2003-06-09        2007-05-18     
#> 4                    1          3 2017-07-29        2017-08-24     
#> 5                    1          5 2014-04-28        2015-04-04     
#> # ℹ 1 more variable: exposed_time_161_acetaminophen <int>
# }