Format a drug_utilisation object into a visual table.
tableDrugUtilisation.Rd![[Experimental]](figures/lifecycle-experimental.svg)
Usage
tableDrugUtilisation(
result,
header = c("group", "strata"),
splitStrata = TRUE,
cohortName = TRUE,
cdmName = TRUE,
conceptSet = TRUE,
ingredient = TRUE,
groupColumn = NULL,
type = "gt",
formatEstimateName = c(`N (%)` = "<count_missing> (<percentage_missing> %)", N =
"<count>", `Mean (SD)` = "<mean> (<sd>)", `Median (Q25 - Q75)` =
"<median> (<q25> - <q75>)"),
.options = list()
)Arguments
- result
A summarised_result object with results from summariseDrugUtilisation().
- header
A vector containing which elements should go into the header in order. Allowed are:
cdm_name,group,strata,variable,estimate.- splitStrata
If TRUE strata columns will be split.
- cohortName
If TRUE cohort names will be displayed.
- cdmName
If TRUE database names will be displayed.
- conceptSet
If TRUE concept sets name will be displayed.
- ingredient
If TRUE ingredients names will be displayed for dose calculation.
- groupColumn
Column to use as group labels, these can be: "cdm_name", "cohort_name", "concept_set", "variable_name", and/or "ingredient". If strata is split, any of the levels can be used, otherwise "strata_name" and "strata_level" can be used for table group format.
- type
Type of desired formatted table, possibilities: "gt", "flextable", "tibble".
- formatEstimateName
Named list of estimate name's to join, sorted by computation order. Indicate estimate_name's between <...>.
- .options
Named list with additional formatting options. DrugUtilisation::defaultTableOptions() shows allowed arguments and their default values.
Examples
# \donttest{
library(DrugUtilisation)
library(CodelistGenerator)
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, "acetaminophen")
#> Warning: ! `codelist` contains numeric values, they are casted to integers.
cdm <- generateDrugUtilisationCohortSet(cdm, "dus_cohort", codelist)
cdm[["dus_cohort"]] %>%
summariseDrugUtilisation(ingredientConceptId = 1125315) |>
tableDrugUtilisation()
#> Warning: ! `codelist` contains numeric values, they are casted to integers.
#> ! Results have not been suppressed.
# }