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 \(n*4\) 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] "cerebralVenousSinusThrombosis01.json"
#> [2] "CohortsToCreate.csv"
#> [3] "deepVeinThrombosis01.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 cerebralVenousSinusThrombosis01 ./cerebralVenousSinusThrombosis01.js…
#> 2 2 deepVeinThrombosis01 ./deepVeinThrombosis01.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_schema = "main", write_schema = "main")
cohort_set <- read_cohort_set(path_to_cohort_json_files)
cohort_set
#> # 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> gi_bleed_default
#> 2 2 GIBleed_male <named list> <chr> gi_bleed_male
cdm <- generate_cohort_set(cdm,
cohort_set,
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) [143ms]
#>
cdm$study_cohorts
#> # Source: table<study_cohorts> [?? x 4]
#> # Database: DuckDB v0.9.2 [root@Darwin 23.0.0:R 4.3.1//var/folders/xx/01v98b6546ldnm1rg1_bvk000000gn/T//RtmpBa6aon/file9acb6e1c3c18.duckdb]
#> cohort_definition_id subject_id cohort_start_date cohort_end_date
#> <int> <dbl> <date> <date>
#> 1 1 188 2000-02-09 2019-05-26
#> 2 1 616 2001-03-05 2019-04-21
#> 3 1 774 2008-06-22 2019-06-10
#> 4 1 4376 1961-02-23 2019-02-12
#> 5 1 1695 2010-07-26 2018-11-22
#> 6 1 2266 2008-07-01 2018-12-16
#> 7 1 3572 1993-11-20 2014-04-16
#> 8 1 4485 1994-12-14 2019-04-28
#> 9 1 4639 2004-09-05 2018-03-16
#> 10 1 393 1997-10-01 2019-01-09
#> # ℹ more rowsThe generated cohort has associated metadata tables.
-
cohort_countcontains the person and record counts for each cohort in the cohort set - The
cohort_attritiontable contains the attrition information (persons, and records dropped at each sequential inclusion rule) - The
cohort_settable contains the cohort id and cohort name
cohort_count(cdm$study_cohorts)
#> # A tibble: 2 × 3
#> cohort_definition_id number_records number_subjects
#> <int> <dbl> <dbl>
#> 1 2 237 237
#> 2 1 479 479
cohort_attrition(cdm$study_cohorts)
#> # A tibble: 4 × 7
#> cohort_definition_id number_records number_subjects reason_id reason
#> <int> <dbl> <dbl> <dbl> <chr>
#> 1 1 479 479 1 Qualifying init…
#> 2 2 479 479 1 Qualifying init…
#> 3 2 237 237 2 Male
#> 4 2 237 237 3 30 days prior o…
#> # ℹ 2 more variables: excluded_records <dbl>, excluded_subjects <dbl>
cohort_set(cdm$study_cohorts)
#> # A tibble: 2 × 2
#> cohort_definition_id cohort_name
#> <int> <chr>
#> 1 1 GiBleed_default
#> 2 2 GIBleed_maleNote 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")Capr cohort definitions
Capr allows us to use R code to create the same cohorts that can be created in Atlas. This is helpful when you need to create a large number of similar cohort definitions. Below we create a single Cohort definition with one inclusion criteria
generate_cohort_set will accept a named list of Capr
library(Capr)
#> Loading required package: CirceR
gibleed_concept_set <- cs(192671, name = "gibleed")
gibleed_definition <- cohort(
entry = conditionOccurrence(gibleed_concept_set)
)
gibleed_male_definition <- cohort(
entry = conditionOccurrence(gibleed_concept_set, male())
)
# create a named list of Capr cohort definitions
cohort_set = list(gibleed = gibleed_definition,
gibleed_male = gibleed_male_definition)
# generate cohorts
cdm <- generate_cohort_set(
cdm,
cohort_set = cohort_set,
name = "gibleed" # name for the cohort table in the cdm
)
#> ℹ Generating 2 cohorts
#> ℹ Generating cohort (1/2) - gibleed)
#> ✔ Generating cohort (1/2) - gibleed) [43ms]
#>
#> ℹ Generating cohort (2/2) - gibleed_male)
#> ✔ Generating cohort (2/2) - gibleed_male) [44ms]
#>
cdm$gibleed
#> # Source: table<gibleed> [?? x 4]
#> # Database: DuckDB v0.9.2 [root@Darwin 23.0.0:R 4.3.1//var/folders/xx/01v98b6546ldnm1rg1_bvk000000gn/T//RtmpBa6aon/file9acb6e1c3c18.duckdb]
#> cohort_definition_id subject_id cohort_start_date cohort_end_date
#> <int> <dbl> <date> <date>
#> 1 1 1934 2000-09-08 2019-03-09
#> 2 1 4988 1991-06-05 2018-07-18
#> 3 1 5207 1995-05-04 2018-10-24
#> 4 1 3556 2013-10-15 2018-07-23
#> 5 1 1902 1985-11-17 2019-03-17
#> 6 1 1860 1996-12-24 1997-07-15
#> 7 1 3263 1985-04-21 2019-04-09
#> 8 1 2470 1992-12-29 2019-05-07
#> 9 1 4756 1985-04-21 2018-12-01
#> 10 1 549 1987-12-28 2018-10-04
#> # ℹ more rowsWe should get the exact same result from Capr and Atlas if the definitions are equivalent.
Learn more about Capr at the package website https://ohdsi.github.io/Capr/.
DBI::dbDisconnect(con, shutdown = TRUE)Subset a cohort
Suppose you have a generated cohort and you would like to create a new cohort that is a subset of the first. This can be done using the
First we will generate an example cohort set and then create a new cohort based on filtering the Atlas cohort.
library(CDMConnector)
con <- DBI::dbConnect(duckdb::duckdb(), eunomia_dir())
cdm <- cdm_from_con(con, cdm_schema = "main", write_schema = "main")
cohort_set <- read_cohort_set(system.file("cohorts3", package = "CDMConnector"))
cdm <- generate_cohort_set(cdm, cohort_set, name = "cohort")
#> ℹ Generating 5 cohorts
#> ℹ Generating cohort (1/5) - GiBleed_all_end10)
#> ✔ Generating cohort (1/5) - GiBleed_all_end10) [43ms]
#>
#> ℹ Generating cohort (2/5) - gibleed_all)
#> ✔ Generating cohort (2/5) - gibleed_all) [41ms]
#>
#> ℹ Generating cohort (3/5) - gibleed_default_with_descendants)
#> ✔ Generating cohort (3/5) - gibleed_default_with_descendants) [46ms]
#>
#> ℹ Generating cohort (4/5) - gibleed_default)
#> ✔ Generating cohort (4/5) - gibleed_default) [44ms]
#>
#> ℹ Generating cohort (5/5) - GiBleed_end10)
#> ✔ Generating cohort (5/5) - GiBleed_end10) [46ms]
#>
cdm$cohort
#> # Source: table<cohort> [?? x 4]
#> # Database: DuckDB v0.9.2 [root@Darwin 23.0.0:R 4.3.1//var/folders/xx/01v98b6546ldnm1rg1_bvk000000gn/T//RtmpBa6aon/file9acb31e7756c.duckdb]
#> cohort_definition_id subject_id cohort_start_date cohort_end_date
#> <int> <dbl> <date> <date>
#> 1 1 613 1977-02-09 1977-02-19
#> 2 1 2682 2016-06-01 2016-06-11
#> 3 1 5267 2006-04-03 2006-04-13
#> 4 1 1606 1989-06-03 1989-06-13
#> 5 1 5152 2012-06-20 2012-06-30
#> 6 1 1902 1985-11-17 1985-11-27
#> 7 1 807 1994-11-04 1994-11-14
#> 8 1 366 1944-10-06 1944-10-16
#> 9 1 5245 2016-01-31 2016-02-10
#> 10 1 2677 2007-01-26 2007-02-05
#> # ℹ more rows
cohort_count(cdm$cohort)
#> # A tibble: 5 × 3
#> cohort_definition_id number_records number_subjects
#> <int> <dbl> <dbl>
#> 1 1 479 479
#> 2 4 479 479
#> 3 3 479 479
#> 4 2 479 479
#> 5 5 479 479As an example we will take only people in the cohort that have a cohort duration that is longer than 4 weeks. Using dplyr we can write this query and save the result in a new table in the cdm.
library(dplyr)
cdm$cohort_subset <- cdm$cohort %>%
# only keep persons who are in the cohort at least 28 days
filter(!!datediff("cohort_start_date", "cohort_end_date") >= 28) %>%
# optionally you can modify the cohort_id
mutate(cohort_definition_id = 100 + cohort_definition_id) %>%
compute_query(name = "cohort_subset", temporary = FALSE, schema = attr(cdm, "write_schema"), overwrite = TRUE) %>%
new_generated_cohort_set()
cdm$cohort2
#> NULL
cohort_count(cdm$cohort_subset)
#> # A tibble: 3 × 3
#> cohort_definition_id number_records number_subjects
#> <dbl> <dbl> <dbl>
#> 1 103 466 466
#> 2 102 466 466
#> 3 104 466 466In this case we can see that cohorts 1 and 5 were dropped completely and some patients were dropped from cohorts 2, 3, and 4.
Let’s confirm that everyone in cohorts 1 and 5 were in the cohort for less than 28 days.
days_in_cohort <- cdm$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 5 1 10
#> 5 1 9 1
#> 6 5 10 467
#> 7 5 2 1
#> 8 1 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_query(name = "cohort_subset",
temporary = FALSE,
schema = attr(cdm, "write_schema"),
overwrite = TRUE) %>%
new_generated_cohort_set() # this function creates the cohort object and metadata
cdm$cohort_subset %>%
mutate(days_in_cohort = !!datediff("cohort_start_date", "cohort_end_date")) %>%
group_by(cohort_definition_id) %>%
summarize(mean_days_in_cohort = mean(days_in_cohort, na.rm = TRUE)) %>%
collect() %>%
arrange(mean_days_in_cohort)
#> # A tibble: 9 × 2
#> cohort_definition_id mean_days_in_cohort
#> <dbl> <dbl>
#> 1 13 7586.
#> 2 12 7586.
#> 3 14 7586.
#> 4 103 7602.
#> 5 1004 7602.
#> 6 102 7602.
#> 7 1002 7602.
#> 8 104 7602.
#> 9 1003 7602.This is an example of creating new cohorts from existing cohorts using CDMConnector. There is a lot of flexibility with this approach. Next we will look at completely custom cohort creation which is quite similar.
Custom Cohort Creation
Sometimes you may want to create cohorts that cannot be easily
expressed using Atlas or Capr. In these situations you can create
implement cohort creation using SQL or R. See the chapter in The
Book of OHDSI for details on using SQL to create cohorts.
CDMConnector provides a helper function to build simple cohorts from a
list of OMOP concepts. generate_concept_cohort_set accepts
a named list of concept sets and will create cohorts based on those
concept sets. While this function does not allow for inclusion/exclusion
criteria in the initial definition, additional criteria can be applied
“manually” after the initial generation.
library(dplyr, warn.conflicts = FALSE)
cdm <- generate_concept_cohort_set(
cdm,
concept_set = list(gibleed = 192671),
name = "gibleed2", # name of the cohort table
limit = "all", # use all occurrences of the concept instead of just the first
end = 10 # set explicit cohort end date 10 days after start
)
cdm$gibleed2 <- cdm$gibleed2 %>%
semi_join(
filter(cdm$person, gender_concept_id == 8507),
by = c("subject_id" = "person_id")
) %>%
record_cohort_attrition(reason = "Male")
cohort_attrition(cdm$gibleed2)
#> # A tibble: 2 × 7
#> cohort_definition_id number_records number_subjects reason_id reason
#> <int> <dbl> <dbl> <dbl> <chr>
#> 1 1 479 479 1 Qualifying init…
#> 2 1 237 237 2 Male
#> # ℹ 2 more variables: excluded_records <dbl>, excluded_subjects <dbl>In the above example we built a cohort table from a concept set. The cohort essentially captures patient-time based off of the presence or absence of OMOP standard concept IDs. We then manually applied an inclusion criteria and recorded a new attrition record in the cohort. To learn more about this approach to building cohorts check out the PatientProfiles R package.
You can also create a generated cohort set using any method you choose. As long as the table is in the CDM database and has the four required columns it can be added to the CDM object as a generated cohort set.
Suppose for example our cohort table is
cohort <- dplyr::tibble(
cohort_definition_id = 1L,
subject_id = 1L,
cohort_start_date = as.Date("1999-01-01"),
cohort_end_date = as.Date("2001-01-01")
)
cohort
#> # A tibble: 1 × 4
#> cohort_definition_id subject_id cohort_start_date cohort_end_date
#> <int> <int> <date> <date>
#> 1 1 1 1999-01-01 2001-01-01First make sure the table is in the database and create a dplyr table reference to it and add it to the CDM object.
DBI::dbWriteTable(con, inSchema("main", "cohort", dbms = dbms(con)), value = cohort, overwrite = TRUE)
cdm$cohort <- tbl(con, inSchema("main", "cohort", dbms = dbms(con))) To make this a true generated cohort object use the
new_generated_cohort_set
cdm$cohort <- new_generated_cohort_set(cdm$cohort)We can see that this cohort is now has the class “GeneratedCohortSet” as well as the various metadata tables.
class(cdm$cohort)
#> [1] "GeneratedCohortSet" "tbl_duckdb_connection" "tbl_dbi"
#> [4] "tbl_sql" "tbl_lazy" "tbl"
cohort_count(cdm$cohort)
#> # A tibble: 1 × 3
#> cohort_definition_id number_records number_subjects
#> <int> <dbl> <dbl>
#> 1 1 1 1
cohort_set(cdm$cohort)
#> # A tibble: 1 × 2
#> cohort_definition_id cohort_name
#> <int> <chr>
#> 1 1 cohort_1
cohort_attrition(cdm$cohort)
#> # A tibble: 1 × 7
#> cohort_definition_id number_records number_subjects reason_id reason
#> <int> <dbl> <dbl> <dbl> <chr>
#> 1 1 1 1 1 Qualifying init…
#> # ℹ 2 more variables: excluded_records <dbl>, excluded_subjects <dbl>If you would like to override the attribute tables then pass additional dataframes into new_generated_cohort_set.
DBI::dbWriteTable(con, inSchema("main", "cohort2", dbms = dbms(con)), value = cohort)
cdm$cohort2 <- tbl(con, inSchema("main", "cohort2", dbms = dbms(con)))
cohort_set <- data.frame(cohort_definition_id = 1L,
cohort_name = "made up cohort")
cdm$cohort2 <- new_generated_cohort_set(cdm$cohort2,
cohort_set_ref = cohort_set)
cohort_set(cdm$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.