Summarise Database Characteristics for OMOP CDM

Usage

databaseCharacteristics(
  cdm,
  omopTableName = c("person", "observation_period", "visit_occurrence",
    "condition_occurrence", "drug_exposure", "procedure_occurrence", "device_exposure",
    "measurement", "observation", "death"),
  sex = FALSE,
  ageGroup = NULL,
  dateRange = NULL,
  interval = "overall",
  conceptIdCounts = FALSE,
  ...
)

Arguments

cdm: A cdm_reference object representing the Common Data Model (CDM) reference.
omopTableName: A character vector specifying the OMOP tables from the CDM to include in the analysis. If "person" is present, it will only be used for missing value summarisation.
sex: Logical; whether to stratify results by sex (TRUE) or not (FALSE).
ageGroup: A list of age groups to stratify the results by. Each element represents a specific age range.
dateRange: A vector of two dates defining the desired study period. Only the start_date column of the OMOP table is checked to ensure it falls within this range. If dateRange is NULL, no restriction is applied.
interval: Time interval to stratify by. It can either be "years", "quarters", "months" or "overall".
conceptIdCounts: Logical; whether to summarise concept ID counts (TRUE) or not (FALSE).
...: additional arguments passed to the OmopSketch functions that are used internally.

Value

A summarised_result object containing the results of the characterisation.

Examples

# \donttest{

cdm <- mockOmopSketch(numberIndividuals = 100)

result <- databaseCharacteristics(cdm = cdm,
omopTableNam = c("drug_exposure", "condition_occurrence"),
sex = TRUE, ageGroup = list(c(0,50), c(51,100)), interval = "years", conceptIdCounts = FALSE)
#> The characterisation will focus on the following OMOP tables: drug_exposure and
#> condition_occurrence
#> → Getting cdm snapshot
#> Warning: Vocabulary version in cdm_source (NA) doesn't match the one in the vocabulary
#> table (v5.0 18-JAN-19)
#> → Getting population characteristics
#> ℹ Building new trimmed cohort
#> Adding demographics information
#> Creating initial cohort
#> Trim sex
#> ✔ Cohort trimmed
#> ℹ Building new trimmed cohort
#> Adding demographics information
#> Creating initial cohort
#> Trim sex
#> Trim age
#> ✔ Cohort trimmed
#> ℹ adding demographics columns
#> ℹ summarising data
#> ℹ summarising cohort general_population
#> ℹ summarising cohort age_group_0_50
#> ℹ summarising cohort age_group_51_100
#> ✔ summariseCharacteristics finished!
#> → Summarising missing data
#> → Summarising table quality
#> → Summarising clinical records
#> ℹ Adding variables of interest to drug_exposure.
#> ℹ Summarising records per person in drug_exposure.
#> ℹ Summarising drug_exposure: `in_observation`, `standard_concept`,
#>   `source_vocabulary`, `domain_id`, and `type_concept`.
#> ℹ Adding variables of interest to condition_occurrence.
#> ℹ Summarising records per person in condition_occurrence.
#> ℹ Summarising condition_occurrence: `in_observation`, `standard_concept`,
#>   `source_vocabulary`, `domain_id`, and `type_concept`.
#> → Summarising observation period
#> → Summarising trends: records, subjects, person-days, age and sex
#> → The number of person-days is not computed for event tables
#> ☺ Database characterisation finished. Code ran in 1 min and 3 sec
#> ℹ 1 table created: "og_008_1755858381".

PatientProfiles::mockDisconnect(cdm)
# }