Phenotype a cohort — phenotypeDiagnostics • PhenotypeR

This comprises all the diagnostics that are being offered in this package, this includes:

* A diagnostics on the database via `databaseDiagnostics`. * A diagnostics on the cohort_codelist attribute of the cohort via `codelistDiagnostics`. * A diagnostics on the cohort via `cohortDiagnostics`. * A diagnostics on the population via `populationDiagnostics`. * A diagnostics on the matched cohort via `matchedDiagnostics`.

Usage

phenotypeDiagnostics(
  cohort,
  databaseDiagnostics = TRUE,
  codelistDiagnostics = TRUE,
  cohortDiagnostics = TRUE,
  populationDiagnostics = TRUE,
  populationSample = 1e+06,
  matchedDiagnostics = TRUE,
  matchedSample = 1000
)

Arguments

cohort: Cohort table in a cdm reference
databaseDiagnostics: If TRUE, database diagnostics will be run.
codelistDiagnostics: If TRUE, codelist diagnostics will be run.
cohortDiagnostics: If TRUE, cohort diagnostics will be run.
populationDiagnostics: If TRUE, population diagnostics will be run.
populationSample: Number of people from the cdm to sample. If NULL no sampling will be performed
matchedDiagnostics: If TRUE, cohort to population diagnostics will be run.
matchedSample: The number of people to take a random sample for matching. If NULL, no sampling will be performed.

Value

A summarised result

Examples

# \donttest{
  cdm_local <- omock::mockCdmReference() |>
    omock::mockPerson(nPerson = 100) |>
    omock::mockObservationPeriod() |>
    omock::mockConditionOccurrence() |>
    omock::mockDrugExposure() |>
    omock::mockObservation() |>
    omock::mockMeasurement() |>
    omock::mockCohort(name = "my_cohort",
                      numberCohorts = 2)
  cdm_local$visit_occurrence <- dplyr::tibble(
    person_id = 1L,
    visit_occurrence_id = 1L,
    visit_concept_id = 1L,
    visit_start_date = as.Date("2000-01-01"),
    visit_end_date = as.Date("2000-01-01"),
    visit_type_concept_id = 1L)
  cdm_local$procedure_occurrence <- dplyr::tibble(
    person_id = 1L,
    procedure_occurrence_id = 1L,
    procedure_concept_id = 1L,
    procedure_date = as.Date("2000-01-01"),
    procedure_type_concept_id = 1L)

  db <- DBI::dbConnect(duckdb::duckdb())
  cdm <- CDMConnector::copyCdmTo(con = db,
                                 cdm = cdm_local,
                                 schema ="main",
                                 overwrite = TRUE)
  phenotypeDiagnostics(cdm$my_cohort)
#> 
#> 
#> • Getting codelists from cohorts
#> Warning: No codelists found for the specified cohorts
#> Warning: No codelists found for the specified cohorts
#> Warning: Empty cohort_codelist attribute for cohort
#> ℹ Returning an empty summarised result
#> 
#> • Getting cohort summary
#> ℹ adding demographics columns
#> ℹ adding tableIntersectCount 1/1
#> ℹ summarising data
#> ✔ summariseCharacteristics finished!
#> • Getting age density
#> • Getting cohort attrition
#> • Getting cohort overlap
#> • Getting cohort timing
#> ℹ The following estimates will be computed:
#> • days_between_cohort_entries: density
#> → Start summary of data, at 2024-11-21 11:01:21.093467
#> ✔ Summary finished, at 2024-11-21 11:01:21.167939
#> 
#> • Creating denominator for incidence and prevalence
#> • Sampling person table to 1e+06
#> ℹ Creating denominator cohorts
#> ✔ Cohorts created in 0 min and 7 sec
#> • Estimating incidence
#> Getting incidence for analysis 1 of 24
#> Getting incidence for analysis 2 of 24
#> Getting incidence for analysis 3 of 24
#> Getting incidence for analysis 4 of 24
#> Getting incidence for analysis 5 of 24
#> Getting incidence for analysis 6 of 24
#> Getting incidence for analysis 7 of 24
#> Getting incidence for analysis 8 of 24
#> Getting incidence for analysis 9 of 24
#> Getting incidence for analysis 10 of 24
#> Getting incidence for analysis 11 of 24
#> Getting incidence for analysis 12 of 24
#> Getting incidence for analysis 13 of 24
#> Getting incidence for analysis 14 of 24
#> Getting incidence for analysis 15 of 24
#> Getting incidence for analysis 16 of 24
#> Getting incidence for analysis 17 of 24
#> Getting incidence for analysis 18 of 24
#> Getting incidence for analysis 19 of 24
#> Getting incidence for analysis 20 of 24
#> Getting incidence for analysis 21 of 24
#> Getting incidence for analysis 22 of 24
#> Getting incidence for analysis 23 of 24
#> Getting incidence for analysis 24 of 24
#> Overall time taken: 0 mins and 18 secs
#> • Estimating prevalence
#> Getting prevalence for analysis 1 of 12
#> Getting prevalence for analysis 2 of 12
#> Getting prevalence for analysis 3 of 12
#> Getting prevalence for analysis 4 of 12
#> Getting prevalence for analysis 5 of 12
#> Getting prevalence for analysis 6 of 12
#> Getting prevalence for analysis 7 of 12
#> Getting prevalence for analysis 8 of 12
#> Getting prevalence for analysis 9 of 12
#> Getting prevalence for analysis 10 of 12
#> Getting prevalence for analysis 11 of 12
#> Getting prevalence for analysis 12 of 12
#> Time taken: 0 mins and 7 secs
#> 
#> • Taking 1000 person sample of cohorts
#> • Generating a age and sex matched cohorts
#> Starting matching
#> Warning: Multiple records per person detected. The matchCohorts() function is designed
#> to operate under the assumption that there is only one record per person within
#> each cohort. If this assumption is not met, each record will be treated
#> independently. As a result, the same individual may be matched multiple times,
#> leading to inconsistent and potentially misleading results.
#> ℹ Creating copy of target cohort.
#> • 2 cohorts to be matched.
#> ℹ Creating controls cohorts.
#> ℹ Excluding cases from controls
#> • Matching by gender_concept_id and year_of_birth
#> • Removing controls that were not in observation at index date
#> • Excluding target records whose pair is not in observation
#> • Adjusting ratio
#> Binding cohorts
#> ✔ Done
#> ℹ adding demographics columns
#> ℹ adding tableIntersectCount 1/1
#> ℹ summarising data
#> ✔ summariseCharacteristics finished!
#> • Running large scale characterisation
#> ℹ Summarising large scale characteristics 
#> 
#>  - getting characteristics from table condition_occurrence (1 of 6)
#>  - getting characteristics from table visit_occurrence (2 of 6)
#>  - getting characteristics from table measurement (3 of 6)
#>  - getting characteristics from table procedure_occurrence (4 of 6)
#>  - getting characteristics from table observation (5 of 6)
#>  - getting characteristics from table drug_exposure (6 of 6)
#> 
#> # A tibble: 21,598 × 13
#>    result_id cdm_name      group_name group_level strata_name strata_level
#>        <int> <chr>         <chr>      <chr>       <chr>       <chr>       
#>  1         1 mock database overall    overall     overall     overall     
#>  2         1 mock database overall    overall     overall     overall     
#>  3         1 mock database overall    overall     overall     overall     
#>  4         1 mock database overall    overall     overall     overall     
#>  5         1 mock database overall    overall     overall     overall     
#>  6         1 mock database overall    overall     overall     overall     
#>  7         1 mock database overall    overall     overall     overall     
#>  8         1 mock database overall    overall     overall     overall     
#>  9         1 mock database overall    overall     overall     overall     
#> 10         1 mock database overall    overall     overall     overall     
#> # ℹ 21,588 more rows
#> # ℹ 7 more variables: variable_name <chr>, variable_level <chr>,
#> #   estimate_name <chr>, estimate_type <chr>, estimate_value <chr>,
#> #   additional_name <chr>, additional_level <chr>
  CDMConnector::cdm_disconnect(cdm)
# }