Create a new cohort table from stratifying an existing one
Source:R/stratifyCohorts.R
stratifyCohorts.RdstratifyCohorts() creates new cohorts, splitting an existing cohort based
on specified columns on which to stratify on.
Usage
stratifyCohorts(
cohort,
strata,
cohortId = NULL,
removeStrata = TRUE,
name = tableName(cohort),
.softValidation = TRUE
)Arguments
- cohort
A cohort table in a cdm reference.
- strata
A strata list that point to columns in cohort table.
- cohortId
Vector identifying which cohorts to include (cohort_definition_id or cohort_name). Cohorts not included will be removed from the cohort set.
- removeStrata
Whether to remove strata columns from final cohort table.
- name
Name of the new cohort table created in the cdm object.
- .softValidation
Whether to perform a soft validation of consistency. If set to FALSE four additional checks will be performed: 1) a check that cohort end date is not before cohort start date, 2) a check that there are no missing values in required columns, 3) a check that cohort duration is all within observation period, and 4) that there are no overlapping cohort entries
Examples
# \donttest{
library(CohortConstructor)
library(PatientProfiles)
cdm <- mockCohortConstructor()
cdm$my_cohort <- cdm$cohort1 |>
addAge(ageGroup = list("child" = c(0, 17), "adult" = c(18, Inf))) |>
addSex(name = "my_cohort") |>
stratifyCohorts(
strata = list("sex", c("sex", "age_group")), name = "my_cohort"
)
cdm$my_cohort
#> # Source: table<my_cohort> [?? x 5]
#> # Database: DuckDB v1.3.2 [unknown@Linux 6.11.0-1018-azure:R 4.5.1/:memory:]
#> cohort_definition_id subject_id cohort_start_date cohort_end_date age
#> <int> <int> <date> <date> <int>
#> 1 1 2 2010-03-30 2010-04-20 55
#> 2 1 3 2005-09-25 2007-04-24 46
#> 3 1 3 2007-04-25 2007-07-09 48
#> 4 1 3 2007-07-10 2007-11-24 48
#> 5 1 4 2017-05-07 2017-07-06 35
#> 6 1 5 2016-08-28 2016-10-13 53
#> 7 1 5 2016-11-01 2016-12-06 53
#> 8 2 6 1994-11-23 2001-04-26 26
#> 9 2 6 2014-10-27 2014-12-11 46
#> 10 1 9 2007-03-19 2007-09-15 22
#> 11 3 2 2010-03-30 2010-04-20 55
#> 12 3 3 2005-09-25 2007-04-24 46
#> 13 3 3 2007-04-25 2007-07-09 48
#> 14 3 3 2007-07-10 2007-11-24 48
#> 15 3 4 2017-05-07 2017-07-06 35
#> 16 3 5 2016-08-28 2016-10-13 53
#> 17 3 5 2016-11-01 2016-12-06 53
#> 18 4 6 1994-11-23 2001-04-26 26
#> 19 4 6 2014-10-27 2014-12-11 46
#> 20 3 9 2007-03-19 2007-09-15 22
settings(cdm$my_cohort)
#> # A tibble: 4 × 8
#> cohort_definition_id cohort_name target_cohort_id target_cohort_name
#> <int> <chr> <int> <chr>
#> 1 1 cohort_1_female 1 cohort_1
#> 2 2 cohort_1_male 1 cohort_1
#> 3 3 cohort_1_female_adult 1 cohort_1
#> 4 4 cohort_1_male_adult 1 cohort_1
#> # ℹ 4 more variables: target_cohort_table_name <chr>, strata_columns <chr>,
#> # sex <chr>, age_group <chr>
attrition(cdm$my_cohort)
#> # A tibble: 10 × 7
#> cohort_definition_id number_records number_subjects reason_id reason
#> <int> <int> <int> <int> <chr>
#> 1 1 10 6 1 Initial qualif…
#> 2 1 8 5 2 filter strata:…
#> 3 2 10 6 1 Initial qualif…
#> 4 2 2 1 2 filter strata:…
#> 5 3 10 6 1 Initial qualif…
#> 6 3 8 5 2 filter strata:…
#> 7 3 8 5 3 filter strata:…
#> 8 4 10 6 1 Initial qualif…
#> 9 4 2 1 2 filter strata:…
#> 10 4 2 1 3 filter strata:…
#> # ℹ 2 more variables: excluded_records <int>, excluded_subjects <int>
# }