Add new columns with drug use related information
addDrugUtilisation.RdAdd new columns with drug use related information
Usage
addDrugUtilisation(
cohort,
gapEra,
conceptSet = NULL,
ingredientConceptId = NULL,
indexDate = "cohort_start_date",
censorDate = "cohort_end_date",
restrictIncident = TRUE,
numberExposures = TRUE,
numberEras = TRUE,
exposedTime = TRUE,
timeToExposure = TRUE,
initialQuantity = TRUE,
cumulativeQuantity = TRUE,
initialDailyDose = TRUE,
cumulativeDose = TRUE,
nameStyle = "{value}_{concept_name}_{ingredient}",
name = NULL
)Arguments
- cohort
Cohort in the cdm
- gapEra
Number of days between two continuous exposures to be considered in the same era.
- conceptSet
List of concepts to be included. If NULL all the descendants of ingredient concept id will be used.
- ingredientConceptId
Ingredient OMOP concept that we are interested for the study. It is a compulsory input, no default value is provided.
- 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.
- numberExposures
Whether to add a column with the number of exposures.
- numberEras
Whether to add a column with the number of eras.
- exposedTime
Whether to add a column with the number of exposed days.
- timeToExposure
Whether to add a column with the number of days between indexDate and start of the first exposure.
- initialQuantity
Whether to add a column with the initial quantity.
- cumulativeQuantity
Whether to add a column with the cumulative quantity of the identified prescription.
- initialDailyDose
Whether to add a column with the initial daily dose.
- cumulativeDose
Whether to add a column with the cumulative dose.
- 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.
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, "dus_cohort", codelist)
cdm[["dus_cohort"]] |>
addDrugUtilisation(ingredientConceptId = 1125315, gapEra = 30)
#> Warning: ! `codelist` contains numeric values, they are casted to integers.
#> # Source: table<og_041_1725980405> [7 x 12]
#> # Database: DuckDB v1.0.0 [unknown@Linux 6.5.0-1025-azure:R 4.4.1/:memory:]
#> cohort_definition_id subject_id cohort_start_date cohort_end_date
#> <int> <int> <date> <date>
#> 1 1 10 2012-03-26 2012-10-03
#> 2 1 1 1994-12-28 1998-01-18
#> 3 1 5 2019-12-27 2019-12-30
#> 4 1 10 2012-12-21 2013-10-18
#> 5 1 5 2020-01-03 2020-01-07
#> 6 1 3 2019-12-01 2020-01-15
#> 7 1 2 2015-10-12 2017-02-27
#> # ℹ 8 more variables: number_exposures_ingredient_1125315_descendants <int>,
#> # time_to_exposure_ingredient_1125315_descendants <int>,
#> # cumulative_quantity_ingredient_1125315_descendants <dbl>,
#> # initial_quantity_ingredient_1125315_descendants <dbl>,
#> # number_eras_ingredient_1125315_descendants <int>,
#> # exposed_time_ingredient_1125315_descendants <int>,
#> # cumulative_dose_milligram_ingredient_1125315_descendants_1125315 <dbl>, …
# }