A cohort is a set of people that fulfill a certain set of criteria for a period of time.
In omopgenerics we defined the cohort_table class that
allows us to represent individuals in a cohort.
A cohort_table is created using the
newCohortTable() function that is defined by:
A cohort table.
A cohort set.
A cohort attrition.
Let’s start by creating a cdm reference with just two people.
person <- tibble(
person_id = c(1,2),
gender_concept_id = 0, year_of_birth = 1990,
race_concept_id = 0, ethnicity_concept_id = 0
)
observation_period <- dplyr::tibble(
observation_period_id = c(1,2), person_id = c(1,2),
observation_period_start_date = as.Date("2000-01-01"),
observation_period_end_date = as.Date("2021-12-31"),
period_type_concept_id = 0
)
cdm <- cdmFromTables(
tables = list(
"person" = person,
"observation_period" = observation_period
),
cdmName = "example_cdm"
)
#> Warning: ! 5 column in person do not match expected column type:
#> • `person_id` is numeric but expected integer
#> • `gender_concept_id` is numeric but expected integer
#> • `year_of_birth` is numeric but expected integer
#> • `race_concept_id` is numeric but expected integer
#> • `ethnicity_concept_id` is numeric but expected integer
#> Warning: ! 3 column in observation_period do not match expected column type:
#> • `observation_period_id` is numeric but expected integer
#> • `person_id` is numeric but expected integer
#> • `period_type_concept_id` is numeric but expected integer
cdm
#>
#> ── # OMOP CDM reference (local) of example_cdm ─────────────────────────────────
#> • omop tables: person, observation_period
#> • cohort tables: -
#> • achilles tables: -
#> • other tables: -Now let’s say one of these people have a clinical event of interest, we can include them in a cohort table which can then be used as part of an analysis.
cohort <- tibble(
cohort_definition_id = 1, subject_id = 1,
cohort_start_date = as.Date("2020-01-01"),
cohort_end_date = as.Date("2020-01-10")
)
cdm <- insertTable(cdm = cdm, name = "cohort", table = cohort)
cdm$cohort <- newCohortTable(cdm$cohort)
#> Warning: ! 2 column in cohort do not match expected column type:
#> • `cohort_definition_id` is numeric but expected integer
#> • `subject_id` is numeric but expected integerThe cohort table will be associated with settings and attrition. As
we didn’t specify these in newCohortTable() above they will have been
automatically populated. You can access the cohort set of a cohort table
using the function settings()
settings(cdm$cohort)
#> # A tibble: 1 × 2
#> cohort_definition_id cohort_name
#> <int> <chr>
#> 1 1 cohort_1Meanwhile, you can access the cohort attrition of a cohort table
using the function attrition()
attrition(cdm$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>Cohort attrition table is also used to compute the number of counts
that each cohort (ie from the last row of the attrition). It can be seen
with the function cohortCount().
cohortCount(cdm$cohort)
#> # A tibble: 1 × 3
#> cohort_definition_id number_records number_subjects
#> <int> <int> <int>
#> 1 1 1 1Note that because the cohort count is taken from the last row of attrition, if we make changes to a cohort we should then update attrition as we go. We can do this
cdm$cohort <- cdm$cohort |>
filter(cohort_start_date == as.Date("2019-01-01")) |>
compute(name = "cohort", temporary = FALSE) |>
recordCohortAttrition("Require cohort start January 1st 2019")
#> Warning: ! 1 casted column in cohort (cohort_attrition) as do not match expected column
#> type:
#> • `cohort_definition_id` from numeric to integer
#> Warning: ! 2 column in cohort do not match expected column type:
#> • `cohort_definition_id` is numeric but expected integer
#> • `subject_id` is numeric but expected integer
attrition(cdm$cohort)
#> # A tibble: 2 × 7
#> cohort_definition_id number_records number_subjects reason_id reason
#> <int> <int> <int> <int> <chr>
#> 1 1 1 1 1 Initial qualify…
#> 2 1 0 0 2 Require cohort …
#> # ℹ 2 more variables: excluded_records <int>, excluded_subjects <int>
cohortCount(cdm$cohort)
#> # A tibble: 1 × 3
#> cohort_definition_id number_records number_subjects
#> <int> <int> <int>
#> 1 1 0 0An additional, optional, attribute keeps track of the concepts used to create the cohort. In this example we do not have a codelist associated with our cohort.
cohortCodelist(cdm$cohort, cohortId = 1, type = "index event")
#> Warning: No codelists found for the specified cohorts
#>
#> ── 0 codelists ─────────────────────────────────────────────────────────────────We could though associate our cohort with a codelist
cdm$cohort <- newCohortTable(cdm$cohort,
cohortCodelistRef = dplyr::tibble(
cohort_definition_id = c(1,1),
codelist_name =c("disease X", "disease X"),
concept_id = c(101,102),
type = "index event"
))
#> Warning: ! 2 casted column in cohort (cohort_codelist) as do not match expected column
#> type:
#> • `cohort_definition_id` from numeric to integer
#> • `concept_id` from numeric to integer
#> Warning: ! 2 column in cohort do not match expected column type:
#> • `cohort_definition_id` is numeric but expected integer
#> • `subject_id` is numeric but expected integer
cohortCodelist(cdm$cohort, cohortId = 1, type = "index event")
#>
#> ── 1 codelist ──────────────────────────────────────────────────────────────────
#>
#> - disease X (2 codes)Each one of the elements that define a cohort table have to fulfill certain criteria.
A cohort set must be a table with:
Lower case column names.
At least cohort_definition_id, cohort_name columns
(cohortColumns("cohort_set")).
cohort_name it must contain unique cohort names
(currently they are cased to snake case).
cohort_definition_id it must contain unique cohort
ids, all the ids present in table must be present in the cohort set and
the same ids must be present in cohort attrition.
A cohort attrition must be a table with:
Lower case column names.
At least cohort_definition_id, number_records, number_subjects,
reason_id, reason, excluded_records, excluded_subjects columns
(cohortColumns("cohort_attrition")).
cohort_definition_id it must contain cohort ids, all
the ids present in table must be present in the cohort attrition and the
same ids must be present in cohort set.
There must exist unique pairs of
cohort_definition_id and reason_id.
A cohort codelist must be a table with:
Lower case column names.
At least cohort_definition_id, codelist_name, concept_id, type
columns (cohortColumns("cohort_codelist")).
cohort_definition_id it must contain cohort ids, all
the ids present in table must be present in the cohort attrition and the
same ids must be present in cohort set.
type must be one of “index event”, “inclusion
criteria”, and “exit criteria”
A cohort table must be a table with:
It comes from a cdm_reference (extracted via
cdm$cohort).
It has the same source than this cdm_reference.
Lower case column names.
At least cohort_definition_id, subject_id, cohort_start_date,
cohort_end_date columns (cohortColumns("cohort")).
There is no record with NA value in the required
columns.
There is no record with cohort_start_date after
cohort_end_date.
There is no overlap between records. A person can be in a cohort several times (several records with the same subject_id). But it can’t enter (cohort_start_date) the cohort again before leaving it (cohort_end_date). So an individual can’t be simultaneously more than once in the same cohort. This rule is applied at the cohort_definition_id level, so records with different cohort_definition_id can overlap.
All the time between cohort_start_date and cohort_end_date (both included) the individual must be in observation.
You can bind two cohort tables using the method bind().
You can combine several cohort tables using this method. The only
constrain is that cohort names must be unique across the different
cohort tables. You have to provide a name for the new cohort table.
asthma <- tibble(
cohort_definition_id = 1, subject_id = 1,
cohort_start_date = as.Date("2020-01-01"),
cohort_end_date = as.Date("2020-01-10")
)
cdm <- insertTable(cdm, name = "asthma", table = asthma)
cdm$asthma <- newCohortTable(cdm$asthma,
cohortSetRef = tibble(cohort_definition_id = 1,
cohort_name = "asthma"))
#> Warning: ! 1 casted column in asthma (cohort_set) as do not match expected column type:
#> • `cohort_definition_id` from numeric to integer
#> Warning: ! 2 column in asthma do not match expected column type:
#> • `cohort_definition_id` is numeric but expected integer
#> • `subject_id` is numeric but expected integer
copd <- tibble(
cohort_definition_id = 1, subject_id = 2,
cohort_start_date = as.Date("2020-01-01"),
cohort_end_date = as.Date("2020-01-10")
)
cdm <- insertTable(cdm, name = "copd", table = copd)
cdm$copd <- newCohortTable(cdm$copd,
cohortSetRef = tibble(cohort_definition_id = 1,
cohort_name = "copd"))
#> Warning: ! 1 casted column in copd (cohort_set) as do not match expected column type:
#> • `cohort_definition_id` from numeric to integer
#> Warning: ! 2 column in copd do not match expected column type:
#> • `cohort_definition_id` is numeric but expected integer
#> • `subject_id` is numeric but expected integer
cdm <- bind(cdm$asthma,
cdm$copd,
name = "exposures")
#> Warning: ! 1 column in exposures do not match expected column type:
#> • `subject_id` is numeric but expected integer
cdm$exposures
#> # A tibble: 2 × 4
#> cohort_definition_id subject_id cohort_start_date cohort_end_date
#> * <int> <dbl> <date> <date>
#> 1 1 1 2020-01-01 2020-01-10
#> 2 2 2 2020-01-01 2020-01-10
settings(cdm$exposures)
#> # A tibble: 2 × 2
#> cohort_definition_id cohort_name
#> <int> <chr>
#> 1 1 asthma
#> 2 2 copd
attrition(cdm$exposures)
#> # A tibble: 2 × 7
#> cohort_definition_id number_records number_subjects reason_id reason
#> <int> <int> <int> <int> <chr>
#> 1 1 1 1 1 Initial qualify…
#> 2 2 1 1 1 Initial qualify…
#> # ℹ 2 more variables: excluded_records <int>, excluded_subjects <int>
cohortCount(cdm$exposures)
#> # A tibble: 2 × 3
#> cohort_definition_id number_records number_subjects
#> <int> <int> <int>
#> 1 1 1 1
#> 2 2 1 1You can export the metadata of a cohort_table using the
function: summary():
summary(cdm$exposures) |>
glimpse()
#> Rows: 12
#> Columns: 13
#> $ result_id <int> 1, 1, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4
#> $ cdm_name <chr> "example_cdm", "example_cdm", "example_cdm", "example…
#> $ group_name <chr> "cohort_name", "cohort_name", "cohort_name", "cohort_…
#> $ group_level <chr> "asthma", "asthma", "copd", "copd", "asthma", "asthma…
#> $ strata_name <chr> "overall", "overall", "overall", "overall", "reason",…
#> $ strata_level <chr> "overall", "overall", "overall", "overall", "Initial …
#> $ variable_name <chr> "number_records", "number_subjects", "number_records"…
#> $ variable_level <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA
#> $ estimate_name <chr> "count", "count", "count", "count", "count", "count",…
#> $ estimate_type <chr> "integer", "integer", "integer", "integer", "integer"…
#> $ estimate_value <chr> "1", "1", "1", "1", "1", "1", "0", "0", "1", "1", "0"…
#> $ additional_name <chr> "overall", "overall", "overall", "overall", "reason_i…
#> $ additional_level <chr> "overall", "overall", "overall", "overall", "1", "1",…This will provide a summarised_result object with the
metadata of the cohort (cohort set, cohort counts and cohort
attrition).