Phenotype a cohort
phenotypeDiagnostics.RdThis 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`.
Arguments
- cohort
Cohort table in a cdm reference
- diagnostics
Vector indicating which diagnostics to perform. Options include: `databaseDiagnostics`, `codelistDiagnostics`, `cohortDiagnostics`, and `populationDiagnostics`.
- 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.
- populationSample
Number of people from the cdm to sample. If NULL no sampling will be performed. Sample will be within populationDateRange if specified.
- populationDateRange
Two dates. The first indicating the earliest cohort start date and the second indicating the latest possible cohort end date. If NULL or the first date is set as missing, the earliest observation_start_date in the observation_period table will be used for the former. If NULL or the second date is set as missing, the latest observation_end_date in the observation_period table will be used for the latter.
Examples
# \donttest{
library(PhenotypeR)
cdm <- mockPhenotypeR()
result <- phenotypeDiagnostics(cdm$my_cohort)
#>
#> Warning: Vocabulary version in cdm_source (NA) doesn't match the one in the vocabulary
#> table (mock)
#>
#> Warning: ! cohort_codelist attribute for cohort is empty
#> ℹ Returning an empty summarised result
#> ℹ You can add a codelist to a cohort with `addCodelistAttribute()`.
#>
#> • Starting Cohort Diagnostics
#> → Getting cohort attrition
#> → Getting cohort count
#> ℹ summarising data
#> ℹ summarising cohort cohort_1
#> ℹ summarising cohort cohort_2
#> ✔ summariseCharacteristics finished!
#> → Skipping cohort sampling as all cohorts have less than 20000 individuals.
#> → Getting cohort overlap
#> → Getting cohort timing
#> ℹ The following estimates will be computed:
#> • days_between_cohort_entries: median, q25, q75, min, max, density
#> ! Table is collected to memory as not all requested estimates are supported on
#> the database side
#> → Start summary of data, at 2025-08-20 13:15:14.188652
#> ✔ Summary finished, at 2025-08-20 13:15:14.312073
#> → Creating matching cohorts
#> → Sampling cohort `tmp_018_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 cohort_1
#> 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
#> → Sampling cohort `tmp_018_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 cohort_2
#> 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 cohort_1
#> ℹ summarising cohort cohort_2
#> ℹ summarising cohort cohort_1_sampled
#> ℹ summarising cohort cohort_1_matched
#> ℹ summarising cohort cohort_2_sampled
#> ℹ summarising cohort cohort_2_matched
#> ✔ summariseCharacteristics finished!
#> → Calculating age density
#> ℹ The following estimates will be computed:
#> • age: density
#> → Start summary of data, at 2025-08-20 13:15:38.773885
#> ✔ Summary finished, at 2025-08-20 13:15:39.104672
#> → Run large scale characteristics (including source and standard codes)
#> 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)
#> Formatting result
#> ✔ Summarising large scale characteristics
#> → Run large scale characteristics (including only standard codes)
#> 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)
#> Formatting result
#> ✔ Summarising large scale characteristics
#> `cohortSample` and `matchedSample` casted to character.
#>
#> • Creating denominator for incidence and prevalence
#> • Sampling person table to 1e+06
#> ℹ Creating denominator cohorts
#> ✔ Cohorts created in 0 min and 5 sec
#> • Estimating incidence
#> ℹ Getting incidence for analysis 1 of 14
#> ℹ Getting incidence for analysis 2 of 14
#> ℹ Getting incidence for analysis 3 of 14
#> ℹ Getting incidence for analysis 4 of 14
#> ℹ Getting incidence for analysis 5 of 14
#> ℹ Getting incidence for analysis 6 of 14
#> ℹ Getting incidence for analysis 7 of 14
#> ℹ Getting incidence for analysis 8 of 14
#> ℹ Getting incidence for analysis 9 of 14
#> ℹ Getting incidence for analysis 10 of 14
#> ℹ Getting incidence for analysis 11 of 14
#> ℹ Getting incidence for analysis 12 of 14
#> ℹ Getting incidence for analysis 13 of 14
#> ℹ Getting incidence for analysis 14 of 14
#> ✔ Overall time taken: 0 mins and 13 secs
#> • Estimating prevalence
#> ℹ Getting prevalence for analysis 1 of 14
#> ℹ Getting prevalence for analysis 2 of 14
#> ℹ Getting prevalence for analysis 3 of 14
#> ℹ Getting prevalence for analysis 4 of 14
#> ℹ Getting prevalence for analysis 5 of 14
#> ℹ Getting prevalence for analysis 6 of 14
#> ℹ Getting prevalence for analysis 7 of 14
#> ℹ Getting prevalence for analysis 8 of 14
#> ℹ Getting prevalence for analysis 9 of 14
#> ℹ Getting prevalence for analysis 10 of 14
#> ℹ Getting prevalence for analysis 11 of 14
#> ℹ Getting prevalence for analysis 12 of 14
#> ℹ Getting prevalence for analysis 13 of 14
#> ℹ Getting prevalence for analysis 14 of 14
#> ✔ Time taken: 0 mins and 7 secs
#> `populationDateStart`, `populationDateEnd`, and `populationSample` casted to
#> character.
#> `populationDateStart` and `populationDateEnd` eliminated from settings as all
#> elements are NA.
#>
CDMConnector::cdmDisconnect(cdm = cdm)
# }