Cohorts are a fundamental building block for observational health data analysis. A “cohort” is a set of persons satisfying a one or more inclusion criteria for a duration of time. If you are familiar with the idea of sets in math then a cohort can be nicely represented as a set of person-days. In the OMOP Common Data Model we represent cohorts using a table with four columns.
| cohort_definition_id | subject_id | cohort_start_date | cohort_end_date |
|---|---|---|---|
| 1 | 1000 | 2020-01-01 | 2020-05-01 |
| 1 | 1000 | 2021-06-01 | 2020-07-01 |
| 1 | 2000 | 2020-03-01 | 2020-09-01 |
| 2 | 1000 | 2020-02-01 | 2020-03-01 |
A cohort table can contain multiple cohorts and each cohort can have multiple persons. There can even be multiple records for the same person in a single cohort as long as the date ranges do not overlap. In the same way that an element is either in a set or not, a single person-day is either in a cohort or not. For a more comprehensive treatment of cohorts in OHDSI check out the Cohorts chapter in The Book of OHDSI.
Cohort Generation
The cohort table is created through the process of cohort generation. To generate a cohort on a specific CDM dataset means that we combine a cohort definition with CDM to produce a cohort table. The standardization provided by the OMOP CDM allows researchers to generate the same cohort definition on any OMOP CDM dataset.
A cohort definition is an expression of the rules goverining the inclusion/exclusion of person-days in the cohort. There are three common ways to create cohort definitions for the OMOP CDM.
The Atlas cohort builder
The Capr R package
Custom SQL and/or R code
Atlas is a web application that provides a graphical user interface for creating cohort definitions. . To get started with Atlas check out the free course on Ehden Academy and the demo at https://atlas-demo.ohdsi.org/.
Capr is an R package that provides a code-based interface for creating cohort definitions. The options available in Capr exactly match the options available in Atlas and the resulting cohort tables should be identical.
There are times when more customization is needed and it is possible
to use bespoke SQL or dplyr code to build a cohort. CDMConnector
provides the generate_concept_cohort_set function for
quickly building simple cohorts that can then be a starting point for
further subsetting.
Atlas cohorts are represented using json text files. To “generate”
one or more Atlas cohorts on a cdm object use the
read_cohort_set function to first read a folder of Atlas
cohort json files into R. Then create the cohort table with
generate_cohort_set. There can be an optional csv file
called “CohortsToCreate.csv” in the folder that specifies the cohort IDs
and names to use. If this file doesn’t exist IDs will be assigned
automatically using alphabetical order of the filenames.
path_to_cohort_json_files <- system.file("cohorts1", package = "CDMConnector")
list.files(path_to_cohort_json_files)
#> [1] "cerebral_venous_sinus_thrombosis_01.json"
#> [2] "CohortsToCreate.csv"
#> [3] "deep_vein_thrombosis_01.json"
readr::read_csv(file.path(path_to_cohort_json_files, "CohortsToCreate.csv"),
show_col_types = FALSE)
#> # A tibble: 2 × 3
#> cohortId cohortName jsonPath
#> <dbl> <chr> <chr>
#> 1 1 cerebral_venous_sinus_thrombosis_01 cerebral_venous_sinus_thrombosis…
#> 2 2 deep_vein_thrombosis_01 deep_vein_thrombosis_01.jsonAtlas cohort definitions
First we need to create our CDM object. Note that we will need to
specify a write_schema when creating the object. Cohort
tables will go into the CDM’s write_schema.
library(CDMConnector)
path_to_cohort_json_files <- system.file("example_cohorts",
package = "CDMConnector")
list.files(path_to_cohort_json_files)
#> [1] "GiBleed_default.json" "GIBleed_male.json"
con <- DBI::dbConnect(duckdb::duckdb(), eunomia_dir("GiBleed"))
cdm <- cdm_from_con(con, cdm_name = "eunomia", cdm_schema = "main", write_schema = "main")
#> Note: method with signature 'DBIConnection#Id' chosen for function 'dbExistsTable',
#> target signature 'duckdb_connection#Id'.
#> "duckdb_connection#ANY" would also be valid
cohort_details <- read_cohort_set(path_to_cohort_json_files) |>
mutate(cohort_name = snakecase::to_snake_case(cohort_name))
cohort_details
#> # A tibble: 2 × 5
#> cohort_definition_id cohort_name cohort json cohort_name_snakecase
#> <int> <chr> <list> <list> <chr>
#> 1 1 gibleed_default <named list> <chr> gibleed_default
#> 2 2 gibleed_male <named list> <chr> gibleed_male
cdm <- generate_cohort_set(
cdm = cdm,
cohort_set = cohort_details,
name = "study_cohorts"
)
#> ℹ Generating 2 cohorts
#> ℹ Generating cohort (1/2) - gibleed_default
#> ✔ Generating cohort (1/2) - gibleed_default [108ms]
#>
#> ℹ Generating cohort (2/2) - gibleed_male
#> ✔ Generating cohort (2/2) - gibleed_male [100ms]
#>
cdm$study_cohorts
#> # Source: table<main.study_cohorts> [?? x 4]
#> # Database: DuckDB v1.1.2 [root@Darwin 23.1.0:R 4.3.3//private/var/folders/2j/8z0yfn1j69q8sxjc7vj9yhz40000gp/T/RtmpfzTPaQ/file107447790203d.duckdb]
#> cohort_definition_id subject_id cohort_start_date cohort_end_date
#> <int> <int> <date> <date>
#> 1 1 2243 2005-10-16 2019-02-08
#> 2 1 2777 1999-12-04 2019-05-24
#> 3 1 4117 1996-08-08 2018-08-10
#> 4 1 4641 2004-12-29 2018-07-19
#> 5 1 1762 1979-12-16 2019-05-09
#> 6 1 2470 1992-12-29 2019-05-07
#> 7 1 4886 2002-05-01 2018-12-25
#> 8 1 4091 2009-06-12 2018-12-02
#> 9 1 4113 1992-06-03 2019-06-10
#> 10 1 1641 1997-11-10 2019-03-27
#> # ℹ more rowsThe generated cohort has associated metadata tables. We can access these with utility functions.
-
cohort_countcontains the person and record counts for each cohort in the cohort set -
settingstable contains the cohort id and cohort name -
attritiontable contains the attrition information (persons, and records dropped at each sequential inclusion rule)
cohort_count(cdm$study_cohorts)
#> # A tibble: 2 × 3
#> cohort_definition_id number_records number_subjects
#> <int> <int> <int>
#> 1 1 479 479
#> 2 2 237 237
cohort_set(cdm$study_cohorts)
#> Warning: `cohort_set()` was deprecated in CDMConnector 1.3.
#> ℹ Please use `settings()` instead.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
#> generated.
#> # A tibble: 2 × 2
#> cohort_definition_id cohort_name
#> <int> <chr>
#> 1 1 gibleed_default
#> 2 2 gibleed_male
attrition(cdm$study_cohorts)
#> # A tibble: 4 × 7
#> cohort_definition_id number_records number_subjects reason_id reason
#> <int> <int> <int> <int> <chr>
#> 1 1 479 479 1 Qualifying init…
#> 2 2 479 479 1 Qualifying init…
#> 3 2 237 237 2 Male
#> 4 2 237 237 3 30 days prior o…
#> # ℹ 2 more variables: excluded_records <int>, excluded_subjects <int>Note the this cohort table is still in the database so it can be quite large. We can also join it to other CDM table or subset the entire cdm to just the persons in the cohort.
cdm_gibleed <- cdm %>%
cdm_subset_cohort(cohort_table = "study_cohorts")Subset a cohort
Suppose you have a generated cohort and you would like to create a new cohort that is a subset of the first. This can be done using the
First we will generate an example cohort set and then create a new cohort based on filtering the Atlas cohort.
library(CDMConnector)
con <- DBI::dbConnect(duckdb::duckdb(), eunomia_dir())
cdm <- cdm_from_con(con, cdm_schema = "main", write_schema = "main")
cohort_set <- read_cohort_set(system.file("cohorts3", package = "CDMConnector"))
cdm <- generate_cohort_set(cdm, cohort_set, name = "cohort")
#> ℹ Generating 5 cohorts
#> ℹ Generating cohort (1/5) - gibleed_all_end_10
#> ✔ Generating cohort (1/5) - gibleed_all_end_10 [55ms]
#>
#> ℹ Generating cohort (2/5) - gibleed_all
#> ✔ Generating cohort (2/5) - gibleed_all [44ms]
#>
#> ℹ Generating cohort (3/5) - gibleed_default_with_descendants
#> ✔ Generating cohort (3/5) - gibleed_default_with_descendants [45ms]
#>
#> ℹ Generating cohort (4/5) - gibleed_default
#> ✔ Generating cohort (4/5) - gibleed_default [49ms]
#>
#> ℹ Generating cohort (5/5) - gibleed_end_10
#> ✔ Generating cohort (5/5) - gibleed_end_10 [49ms]
#>
cdm$cohort
#> # Source: table<main.cohort> [?? x 4]
#> # Database: DuckDB v1.1.2 [root@Darwin 23.1.0:R 4.3.3//private/var/folders/2j/8z0yfn1j69q8sxjc7vj9yhz40000gp/T/RtmpfzTPaQ/file107442ba90fd3.duckdb]
#> cohort_definition_id subject_id cohort_start_date cohort_end_date
#> <int> <int> <date> <date>
#> 1 1 1846 1987-12-07 1987-12-17
#> 2 1 2356 2017-02-27 2017-03-09
#> 3 1 2556 2004-08-16 2004-08-26
#> 4 1 3820 2005-08-10 2005-08-20
#> 5 1 1789 2017-04-02 2017-04-12
#> 6 1 3196 2008-07-06 2008-07-16
#> 7 1 4883 1986-04-18 1986-04-28
#> 8 1 4380 2001-10-25 2001-11-04
#> 9 1 5252 1995-12-19 1995-12-29
#> 10 1 299 2000-10-07 2000-10-17
#> # ℹ more rows
cohort_count(cdm$cohort)
#> # A tibble: 5 × 3
#> cohort_definition_id number_records number_subjects
#> <int> <int> <int>
#> 1 1 479 479
#> 2 2 479 479
#> 3 3 479 479
#> 4 4 479 479
#> 5 5 479 479As an example we will take only people in the cohort that have a cohort duration that is longer than 4 weeks. Using dplyr we can write this query and save the result in a new table in the cdm.
library(dplyr)
cdm$cohort_subset <- cdm$cohort %>%
# only keep persons who are in the cohort at least 28 days
filter(!!datediff("cohort_start_date", "cohort_end_date") >= 28) %>%
# optionally you can modify the cohort_id
mutate(cohort_definition_id = 100 + cohort_definition_id) %>%
compute(name = "cohort_subset", temporary = FALSE, overwrite = TRUE) %>%
new_generated_cohort_set()
#> Warning: `new_generated_cohort_set()` was deprecated in CDMConnector 1.3.
#> ℹ Please use `newCohortTable()` instead.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
#> generated.
#> Warning: ! 1 column in cohort_subset do not match expected column type:
#> • `cohort_definition_id` is numeric but expected integer
cohort_count(cdm$cohort_subset)
#> # A tibble: 3 × 3
#> cohort_definition_id number_records number_subjects
#> <int> <int> <int>
#> 1 102 466 466
#> 2 103 466 466
#> 3 104 466 466In this case we can see that cohorts 1 and 5 were dropped completely and some patients were dropped from cohorts 2, 3, and 4.
Let’s confirm that everyone in cohorts 1 and 5 were in the cohort for less than 28 days.
days_in_cohort <- cdm$cohort %>%
filter(cohort_definition_id %in% c(1,5)) %>%
mutate(days_in_cohort = !!datediff("cohort_start_date", "cohort_end_date")) %>%
count(cohort_definition_id, days_in_cohort) %>%
collect()
days_in_cohort
#> # A tibble: 8 × 3
#> cohort_definition_id days_in_cohort n
#> <int> <dbl> <dbl>
#> 1 1 10 467
#> 2 1 2 1
#> 3 5 9 1
#> 4 1 9 1
#> 5 5 10 467
#> 6 5 2 1
#> 7 1 1 10
#> 8 5 1 10We have confirmed that everyone in cohorts 1 and 5 were in the cohort less than 10 days.
Now suppose we would like to create a new cohort table with three
different versions of the cohorts in the original cohort table. We will
keep persons who are in the cohort at 2 weeks, 3 weeks, and 4 weeks. We
can simply write some custom dplyr to create the table and then call
new_generated_cohort_set just like in the previous
example.
cdm$cohort_subset <- cdm$cohort %>%
filter(!!datediff("cohort_start_date", "cohort_end_date") >= 14) %>%
mutate(cohort_definition_id = 10 + cohort_definition_id) %>%
union_all(
cdm$cohort %>%
filter(!!datediff("cohort_start_date", "cohort_end_date") >= 21) %>%
mutate(cohort_definition_id = 100 + cohort_definition_id)
) %>%
union_all(
cdm$cohort %>%
filter(!!datediff("cohort_start_date", "cohort_end_date") >= 28) %>%
mutate(cohort_definition_id = 1000 + cohort_definition_id)
) %>%
compute(name = "cohort_subset", temporary = FALSE, overwrite = TRUE) %>%
new_generated_cohort_set() # this function creates the cohort object and metadata
#> Warning: ! 1 column in cohort_subset do not match expected column type:
#> • `cohort_definition_id` is numeric but expected integer
cdm$cohort_subset %>%
mutate(days_in_cohort = !!datediff("cohort_start_date", "cohort_end_date")) %>%
group_by(cohort_definition_id) %>%
summarize(mean_days_in_cohort = mean(days_in_cohort, na.rm = TRUE)) %>%
collect() %>%
arrange(mean_days_in_cohort)
#> # A tibble: 9 × 2
#> cohort_definition_id mean_days_in_cohort
#> <dbl> <dbl>
#> 1 12 7586.
#> 2 14 7586.
#> 3 13 7586.
#> 4 102 7602.
#> 5 1002 7602.
#> 6 103 7602.
#> 7 1004 7602.
#> 8 104 7602.
#> 9 1003 7602.This is an example of creating new cohorts from existing cohorts using CDMConnector. There is a lot of flexibility with this approach. Next we will look at completely custom cohort creation which is quite similar.
Custom Cohort Creation
Sometimes you may want to create cohorts that cannot be easily
expressed using Atlas or Capr. In these situations you can create
implement cohort creation using SQL or R. See the chapter in The
Book of OHDSI for details on using SQL to create cohorts.
CDMConnector provides a helper function to build simple cohorts from a
list of OMOP concepts. generate_concept_cohort_set accepts
a named list of concept sets and will create cohorts based on those
concept sets. While this function does not allow for inclusion/exclusion
criteria in the initial definition, additional criteria can be applied
“manually” after the initial generation.
library(dplyr, warn.conflicts = FALSE)
cdm <- generate_concept_cohort_set(
cdm,
concept_set = list(gibleed = 192671),
name = "gibleed2", # name of the cohort table
limit = "all", # use all occurrences of the concept instead of just the first
end = 10 # set explicit cohort end date 10 days after start
)
cdm$gibleed2 <- cdm$gibleed2 %>%
semi_join(
filter(cdm$person, gender_concept_id == 8507),
by = c("subject_id" = "person_id")
) %>%
record_cohort_attrition(reason = "Male")
attrition(cdm$gibleed2)
#> # A tibble: 2 × 7
#> cohort_definition_id number_records number_subjects reason_id reason
#> <int> <int> <int> <int> <chr>
#> 1 1 479 479 1 Initial qualify…
#> 2 1 237 237 2 Male
#> # ℹ 2 more variables: excluded_records <int>, excluded_subjects <int>In the above example we built a cohort table from a concept set. The cohort essentially captures patient-time based off of the presence or absence of OMOP standard concept IDs. We then manually applied an inclusion criteria and recorded a new attrition record in the cohort. To learn more about this approach to building cohorts check out the PatientProfiles R package.
You can also create a generated cohort set using any method you choose. As long as the table is in the CDM database and has the four required columns it can be added to the CDM object as a generated cohort set.
Suppose for example our cohort table is
cohort <- dplyr::tibble(
cohort_definition_id = 1L,
subject_id = 1L,
cohort_start_date = as.Date("1999-01-01"),
cohort_end_date = as.Date("2001-01-01")
)
cohort
#> # A tibble: 1 × 4
#> cohort_definition_id subject_id cohort_start_date cohort_end_date
#> <int> <int> <date> <date>
#> 1 1 1 1999-01-01 2001-01-01First make sure the table is in the database and create a dplyr table reference to it and add it to the CDM object.
library(omopgenerics)
#>
#> Attaching package: 'omopgenerics'
#> The following objects are masked from 'package:CDMConnector':
#>
#> cdmName, recordCohortAttrition, uniqueTableName
#> The following object is masked from 'package:stats':
#>
#> filter
cdm <- insertTable(cdm = cdm, name = "cohort", table = cohort, overwrite = TRUE)
cdm$cohort
#> # Source: table<main.cohort> [1 x 4]
#> # Database: DuckDB v1.1.2 [root@Darwin 23.1.0:R 4.3.3//private/var/folders/2j/8z0yfn1j69q8sxjc7vj9yhz40000gp/T/RtmpfzTPaQ/file107442ba90fd3.duckdb]
#> cohort_definition_id subject_id cohort_start_date cohort_end_date
#> <int> <int> <date> <date>
#> 1 1 1 1999-01-01 2001-01-01To make this a true generated cohort object use the
cohort_table
cdm$cohort <- newCohortTable(cdm$cohort)We can see that this cohort is now has the class “cohort_table” as well as the various metadata tables.
cohort_count(cdm$cohort)
#> # A tibble: 1 × 3
#> cohort_definition_id number_records number_subjects
#> <int> <int> <int>
#> 1 1 1 1
cohort_set(cdm$cohort)
#> # A tibble: 1 × 2
#> cohort_definition_id cohort_name
#> <int> <chr>
#> 1 1 cohort_1
attrition(cdm$cohort)
#> # A tibble: 1 × 7
#> cohort_definition_id number_records number_subjects reason_id reason
#> <int> <int> <int> <int> <chr>
#> 1 1 1 1 1 Initial qualify…
#> # ℹ 2 more variables: excluded_records <int>, excluded_subjects <int>If you would like to override the attribute tables then pass additional dataframes to cohortTable
cdm <- insertTable(cdm = cdm, name = "cohort2", table = cohort, overwrite = TRUE)
cdm$cohort2 <- newCohortTable(cdm$cohort2)
settings(cdm$cohort2)
#> # A tibble: 1 × 2
#> cohort_definition_id cohort_name
#> <int> <chr>
#> 1 1 cohort_1
cohort_set <- data.frame(cohort_definition_id = 1L,
cohort_name = "made_up_cohort")
cdm$cohort2 <- newCohortTable(cdm$cohort2, cohortSetRef = cohort_set)
settings(cdm$cohort2)
#> # A tibble: 1 × 2
#> cohort_definition_id cohort_name
#> <int> <chr>
#> 1 1 made_up_cohort
DBI::dbDisconnect(con, shutdown = TRUE)Cohort building is a fundamental building block of observational
health analysis and CDMConnector supports different ways of creating
cohorts. As long as your cohort table is has the required structure and
columns you can add it to the cdm with the
new_generated_cohort_set function and use it in any
downstream OHDSI analytic packages.