Run cohort-level diagnostics
cohortDiagnostics.RdRuns phenotypeR diagnostics on the cohort. The diganostics include: * Age groups and sex summarised. * A summary of visits of everyone in the cohort using visit_occurrence table. * A summary of age and sex density of the cohort. * Attritions of the cohorts. * Overlap between cohorts (if more than one cohort is being used).
Arguments
- cohort
Cohort table in a cdm reference
- survival
Boolean variable. Whether to conduct survival analysis (TRUE) or not (FALSE).
- cohortSample
The number of people to take a random sample for cohortDiagnostics. If `cohortSample = NULL`, no sampling will be performed,
- matchedSample
The number of people to take a random sample for matching. If `matchedSample = NULL`, no sampling will be performed. If `matchedSample = 0`, no matched cohorts will be created.
Examples
# \donttest{
library(omock)
library(CohortConstructor)
library(PhenotypeR)
cdm <- mockCdmFromDataset(source = "duckdb")
#> ℹ Reading GiBleed tables.
#> ℹ Adding drug_strength table.
#> ℹ Creating local <cdm_reference> object.
#> ℹ Inserting <cdm_reference> into duckdb.
cdm$warfarin <- conceptCohort(cdm,
conceptSet = list(warfarin = c(1310149L,
40163554L)),
name = "warfarin")
#> ℹ Subsetting table drug_exposure using 2 concepts with domain: drug.
#> ℹ Combining tables.
#> ℹ Creating cohort attributes.
#> ℹ Applying cohort requirements.
#> ℹ Merging overlapping records.
#> ✔ Cohort warfarin created.
result <- cohortDiagnostics(cdm$warfarin)
#> • Starting Cohort Diagnostics
#> → Getting cohort attrition
#> → Getting cohort count
#> ℹ summarising data
#> ℹ summarising cohort warfarin
#> ✔ summariseCharacteristics finished!
#> → Skipping cohort sampling as all cohorts have less than 20000 individuals.
#> → Creating matching cohorts
#> → Sampling cohort `tmp_004_sampled`
#> Returning entry cohort as the size of the cohorts to be sampled is equal or
#> smaller than `n`.
#> • Generating an age and sex matched cohort for warfarin
#> Starting matching
#> ℹ Creating copy of target cohort.
#> • 1 cohort 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
#> → Getting cohorts and indexes
#> → Summarising cohort characteristics
#> ℹ adding demographics columns
#> ℹ adding tableIntersectCount 1/1
#> window names casted to snake_case:
#> • `-365 to -1` -> `365_to_1`
#> ℹ summarising data
#> ℹ summarising cohort warfarin
#> ℹ summarising cohort warfarin_sampled
#> ℹ summarising cohort warfarin_matched
#> ✔ summariseCharacteristics finished!
#> → Calculating age density
#> ℹ The following estimates will be computed:
#> • age: density
#> ! Table is collected to memory as not all requested estimates are supported on
#> the database side
#> → Start summary of data, at 2025-12-03 15:32:23.310636
#> ✔ Summary finished, at 2025-12-03 15:32:23.454847
#> → Run large scale characteristics
#> ℹ Summarising large scale characteristics
#> - getting characteristics from table condition_occurrence (1 of 8)
#> - getting characteristics from table condition_occurrence (1 of 8) for time wi…
#> - getting characteristics from table condition_occurrence (1 of 8) for time wi…
#> - getting characteristics from table condition_occurrence (1 of 8) for time wi…
#> - getting characteristics from table condition_occurrence (1 of 8) for time wi…
#> - getting characteristics from table condition_occurrence (1 of 8) for time wi…
#> - getting characteristics from table condition_occurrence (1 of 8) for time wi…
#> - getting characteristics from table condition_occurrence (1 of 8) for time wi…
#> - getting characteristics from table visit_occurrence (2 of 8)
#> - getting characteristics from table visit_occurrence (2 of 8) for time window…
#> - getting characteristics from table visit_occurrence (2 of 8) for time window…
#> - getting characteristics from table visit_occurrence (2 of 8) for time window…
#> - getting characteristics from table visit_occurrence (2 of 8) for time window…
#> - getting characteristics from table visit_occurrence (2 of 8) for time window…
#> - getting characteristics from table visit_occurrence (2 of 8) for time window…
#> - getting characteristics from table visit_occurrence (2 of 8) for time window…
#> - getting characteristics from table measurement (3 of 8)
#> - getting characteristics from table measurement (3 of 8) for time window -Inf…
#> - getting characteristics from table measurement (3 of 8) for time window -365…
#> - getting characteristics from table measurement (3 of 8) for time window -30 …
#> - getting characteristics from table measurement (3 of 8) for time window 0 an…
#> - getting characteristics from table measurement (3 of 8) for time window 1 an…
#> - getting characteristics from table measurement (3 of 8) for time window 31 a…
#> - getting characteristics from table measurement (3 of 8) for time window 366 …
#> - getting characteristics from table procedure_occurrence (4 of 8)
#> - getting characteristics from table procedure_occurrence (4 of 8) for time wi…
#> - getting characteristics from table procedure_occurrence (4 of 8) for time wi…
#> - getting characteristics from table procedure_occurrence (4 of 8) for time wi…
#> - getting characteristics from table procedure_occurrence (4 of 8) for time wi…
#> - getting characteristics from table procedure_occurrence (4 of 8) for time wi…
#> - getting characteristics from table procedure_occurrence (4 of 8) for time wi…
#> - getting characteristics from table procedure_occurrence (4 of 8) for time wi…
#> - getting characteristics from table device_exposure (5 of 8)
#> - getting characteristics from table device_exposure (5 of 8) for time window …
#> - getting characteristics from table device_exposure (5 of 8) for time window …
#> - getting characteristics from table device_exposure (5 of 8) for time window …
#> - getting characteristics from table device_exposure (5 of 8) for time window …
#> - getting characteristics from table device_exposure (5 of 8) for time window …
#> - getting characteristics from table device_exposure (5 of 8) for time window …
#> - getting characteristics from table device_exposure (5 of 8) for time window …
#> - getting characteristics from table observation (6 of 8)
#> - getting characteristics from table observation (6 of 8) for time window -Inf…
#> - getting characteristics from table observation (6 of 8) for time window -365…
#> - getting characteristics from table observation (6 of 8) for time window -30 …
#> - getting characteristics from table observation (6 of 8) for time window 0 an…
#> - getting characteristics from table observation (6 of 8) for time window 1 an…
#> - getting characteristics from table observation (6 of 8) for time window 31 a…
#> - getting characteristics from table observation (6 of 8) for time window 366 …
#> - getting characteristics from table drug_exposure (7 of 8)
#> - getting characteristics from table drug_exposure (7 of 8) for time window -I…
#> - getting characteristics from table drug_exposure (7 of 8) for time window -3…
#> - getting characteristics from table drug_exposure (7 of 8) for time window -3…
#> - getting characteristics from table drug_exposure (7 of 8) for time window 0 …
#> - getting characteristics from table drug_exposure (7 of 8) for time window 1 …
#> - getting characteristics from table drug_exposure (7 of 8) for time window 31…
#> - getting characteristics from table drug_exposure (7 of 8) for time window 36…
#> - getting characteristics from table drug_era (8 of 8)
#> - getting characteristics from table drug_era (8 of 8) for time window -Inf an…
#> - getting characteristics from table drug_era (8 of 8) for time window -365 an…
#> - getting characteristics from table drug_era (8 of 8) for time window -30 and…
#> - getting characteristics from table drug_era (8 of 8) for time window 0 and 0
#> - getting characteristics from table drug_era (8 of 8) for time window 1 and 30
#> - getting characteristics from table drug_era (8 of 8) for time window 31 and …
#> - getting characteristics from table drug_era (8 of 8) for time window 366 and…
#> Formatting result
#> 886 estimates dropped as frequency less than 1%
#> ✔ Summarising large scale characteristics
#> `cohort_sample` and `matched_sample` casted to character.
# }