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CohortCharacteristics contains functions for summarising characteristics of cohorts of patients identified in an OMOP CDM dataset. Once a cohort table has been created, CohortCharacteristics provides a number of functions to help provide a summary of the characteristics of the individuals within the cohort.
You can install the latest version of CohortCharacteristics from CRAN:
Or from github:
The CohortCharacteristics package is designed to work with data in the OMOP CDM format, so our first step is to create a reference to the data using the CDMConnector package. For this example we will work with the example Eunomia dataset.
We can see that in this example data we have a cohort table called cohort1.
cdm$cohort1
#> # Source: table<main.cohort1> [4 x 4]
#> # Database: DuckDB v0.10.0 [martics@Windows 10 x64:R 4.2.1/:memory:]
#> cohort_definition_id subject_id cohort_start_date cohort_end_date
#> <dbl> <dbl> <date> <date>
#> 1 1 1 2020-01-01 2020-04-01
#> 2 1 1 2020-06-01 2020-08-01
#> 3 1 2 2020-01-02 2020-02-02
#> 4 2 3 2020-01-01 2020-03-01With one line of code from CohortCharacteristics we can generate summary statistics on this cohort.
cohort1_characteristics <- summariseCharacteristics(cdm$cohort1)
cohort1_characteristics |>
glimpse()
#> Rows: 70
#> Columns: 13
#> $ result_id <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
#> $ cdm_name <chr> "PP_MOCK", "PP_MOCK", "PP_MOCK", "PP_MOCK", "PP_MOCK"…
#> $ group_name <chr> "cohort_name", "cohort_name", "cohort_name", "cohort_…
#> $ group_level <chr> "cohort_1", "cohort_2", "cohort_1", "cohort_2", "coho…
#> $ strata_name <chr> "overall", "overall", "overall", "overall", "overall"…
#> $ strata_level <chr> "overall", "overall", "overall", "overall", "overall"…
#> $ variable_name <chr> "Number records", "Number records", "Number subjects"…
#> $ variable_level <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ estimate_name <chr> "count", "count", "count", "count", "min", "min", "q2…
#> $ estimate_type <chr> "integer", "integer", "integer", "integer", "date", "…
#> $ estimate_value <chr> "3", "1", "2", "1", "2020-01-01", "2020-01-01", "2020…
#> $ additional_name <chr> "overall", "overall", "overall", "overall", "overall"…
#> $ additional_level <chr> "overall", "overall", "overall", "overall", "overall"…And with another line we can create a table of these results.
tableCharacteristics(cohort1_characteristics, type = "tibble")
#> # A tibble: 17 × 6
#> `CDM name` `Variable name` `Variable level` `Estimate name`
#> <chr> <chr> <chr> <chr>
#> 1 PP_MOCK Number records <NA> N
#> 2 PP_MOCK Number subjects <NA> N
#> 3 PP_MOCK Cohort start date <NA> Median [Q25 - Q75]
#> 4 PP_MOCK Cohort start date <NA> Range
#> 5 PP_MOCK Cohort end date <NA> Median [Q25 - Q75]
#> 6 PP_MOCK Cohort end date <NA> Range
#> 7 PP_MOCK Sex Female N (%)
#> 8 PP_MOCK Sex Male N (%)
#> 9 PP_MOCK Age <NA> Median [Q25 - Q75]
#> 10 PP_MOCK Age <NA> Mean (SD)
#> 11 PP_MOCK Age <NA> Range
#> 12 PP_MOCK Prior observation <NA> Median [Q25 - Q75]
#> 13 PP_MOCK Prior observation <NA> Mean (SD)
#> 14 PP_MOCK Prior observation <NA> Range
#> 15 PP_MOCK Future observation <NA> Median [Q25 - Q75]
#> 16 PP_MOCK Future observation <NA> Mean (SD)
#> 17 PP_MOCK Future observation <NA> Range
#> # ℹ 2 more variables: `[header]Cohort name\n[header_level]Cohort 1` <chr>,
#> # `[header]Cohort name\n[header_level]Cohort 2` <chr>CohortCharacteristics provides a number of other functions to help summarise cohort tables and present the results in publication-ready tables and figures. See the vignettes for more details.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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