Set up
Let’s again load required packages and connect to our Eunomia dataset in duckdb.
library(CDMConnector)
library(omopgenerics)
library(dplyr)
write_schema <- "main"
cdm_schema <- "main"
con <- DBI::dbConnect(duckdb::duckdb(),
dbdir = eunomia_dir())
cdm <- cdm_from_con(con,
cdm_name = "eunomia",
cdm_schema = cdm_schema,
write_schema = write_schema,
cdm_version = "5.3")CDM reference attributes
Our cdm reference has various attributes associated with it. These can be useful both when programming and when developing analytic packages on top of CDMConnector.
CDM name
It’s a requirement that every cdm reference has name associated with
it. This is particularly useful for network studies so that we can
associate results with a particular cdm. We can use cdmName
(or it’s snake case equivalent cdm_name) to get the cdm
name.
CDM version
The OMOP CDM has various versions. We also have an attribute giving the version of the cdm we have connected to.
cdmVersion(cdm)
#> [1] "5.3"Database connection
We also have an attribute identifying the database connection underlying the cdm reference.
cdmCon(cdm)
#> <duckdb_connection 35d30 driver=<duckdb_driver dbdir='/private/var/folders/2j/8z0yfn1j69q8sxjc7vj9yhz40000gp/T/RtmpgAfm82/file1079a2508fbad.duckdb' read_only=FALSE bigint=numeric>>This can be useful, for example, if we want to make use of DBI
functions to work with the database. For example we could use
dbListTables to list the names of remote tables accessible
through the connection, dbListFields to list the field
names of a specific remote table, and dbGetQuery to returns
the result of a query
DBI::dbListTables(cdmCon(cdm))
#> [1] "care_site" "cdm_source" "concept"
#> [4] "concept_ancestor" "concept_class" "concept_relationship"
#> [7] "concept_synonym" "condition_era" "condition_occurrence"
#> [10] "cost" "death" "device_exposure"
#> [13] "domain" "dose_era" "drug_era"
#> [16] "drug_exposure" "drug_strength" "fact_relationship"
#> [19] "location" "measurement" "metadata"
#> [22] "note" "note_nlp" "observation"
#> [25] "observation_period" "payer_plan_period" "person"
#> [28] "procedure_occurrence" "provider" "relationship"
#> [31] "source_to_concept_map" "specimen" "visit_detail"
#> [34] "visit_occurrence" "vocabulary"
DBI::dbListFields(cdmCon(cdm), "person")
#> [1] "person_id" "gender_concept_id"
#> [3] "year_of_birth" "month_of_birth"
#> [5] "day_of_birth" "birth_datetime"
#> [7] "race_concept_id" "ethnicity_concept_id"
#> [9] "location_id" "provider_id"
#> [11] "care_site_id" "person_source_value"
#> [13] "gender_source_value" "gender_source_concept_id"
#> [15] "race_source_value" "race_source_concept_id"
#> [17] "ethnicity_source_value" "ethnicity_source_concept_id"
DBI::dbGetQuery(cdmCon(cdm), "SELECT * FROM person LIMIT 5")
#> person_id gender_concept_id year_of_birth month_of_birth day_of_birth
#> 1 6 8532 1963 12 31
#> 2 123 8507 1950 4 12
#> 3 129 8507 1974 10 7
#> 4 16 8532 1971 10 13
#> 5 65 8532 1967 3 31
#> birth_datetime race_concept_id ethnicity_concept_id location_id provider_id
#> 1 1963-12-31 8516 0 NA NA
#> 2 1950-04-12 8527 0 NA NA
#> 3 1974-10-07 8527 0 NA NA
#> 4 1971-10-13 8527 0 NA NA
#> 5 1967-03-31 8516 0 NA NA
#> care_site_id person_source_value gender_source_value
#> 1 NA 001f4a87-70d0-435c-a4b9-1425f6928d33 F
#> 2 NA 052d9254-80e8-428f-b8b6-69518b0ef3f3 M
#> 3 NA 054d32d5-904f-4df4-846b-8c08d165b4e9 M
#> 4 NA 00444703-f2c9-45c9-a247-f6317a43a929 F
#> 5 NA 02a3dad9-f9d5-42fb-8074-c16d45b4f5c8 F
#> gender_source_concept_id race_source_value race_source_concept_id
#> 1 0 black 0
#> 2 0 white 0
#> 3 0 white 0
#> 4 0 white 0
#> 5 0 black 0
#> ethnicity_source_value ethnicity_source_concept_id
#> 1 west_indian 0
#> 2 italian 0
#> 3 polish 0
#> 4 american 0
#> 5 dominican 0Cohort attributes
Generated cohort set
When we generate a cohort in addition to the cohort table itself we also have various attributes that can be useful for subsequent analysis.
Here we create a cohort table with a single cohort.
cdm <- generateConceptCohortSet(cdm = cdm,
conceptSet = list("gi_bleed" = 192671,
"celecoxib" = 1118084),
name = "study_cohorts",
overwrite = TRUE)
cdm$study_cohorts %>%
glimpse()We have a cohort set attribute that gives details on the settings associated with the cohorts (along with utility functions to make it easier to access this attribute).
settings(cdm$study_cohorts)
cohort_set(cdm$study_cohorts) We have a cohort_count attribute with counts for each of the cohorts.
cohortCount(cdm$study_cohorts)
cohort_count(cdm$study_cohorts)And we also have an attribute, cohort attrition, with a summary of attrition when creating the cohorts.
cohortAttrition(cdm$study_cohorts)
cohort_attrition(cdm$study_cohorts)Creating a bespoke cohort
Say we create a custom GI bleed cohort with the standard cohort structure
cdm$gi_bleed <- cdm$condition_occurrence %>%
filter(condition_concept_id == 192671) %>%
mutate(cohort_definition_id = 1) %>%
select(
cohort_definition_id,
subject_id = person_id,
cohort_start_date = condition_start_date,
cohort_end_date = condition_start_date
) %>%
compute(name = "gi_bleed", temporary = FALSE, overwrite = TRUE)
cdm$gi_bleed %>%
glimpse()
#> Rows: ??
#> Columns: 4
#> Database: DuckDB v1.1.2 [root@Darwin 23.1.0:R 4.3.3//private/var/folders/2j/8z0yfn1j69q8sxjc7vj9yhz40000gp/T/RtmpgAfm82/file1079a2508fbad.duckdb]
#> $ cohort_definition_id <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
#> $ subject_id <int> 273, 61, 351, 579, 549, 116, 163, 304, 326, 285, …
#> $ cohort_start_date <date> 2011-10-10, 2005-09-15, 2018-06-28, 1999-11-06, …
#> $ cohort_end_date <date> 2011-10-10, 2005-09-15, 2018-06-28, 1999-11-06, …We can add the required attributes using the
newCohortTable function. The minimum requirement for this
is that we also define the cohort set to associate with our set of
custom cohorts.
GI_bleed_cohort_ref <- tibble(cohort_definition_id = 1, cohort_name = "custom_gi_bleed")
cdm$gi_bleed <- omopgenerics::newCohortTable(
table = cdm$gi_bleed, cohortSetRef = GI_bleed_cohort_ref
)
#> Warning: ! 1 casted column in gi_bleed (cohort_set) as do not match expected column
#> type:
#> • `cohort_definition_id` from numeric to integer
#> Warning: ! 1 column in gi_bleed do not match expected column type:
#> • `cohort_definition_id` is numeric but expected integerNow our custom cohort GI_bleed has the same attributes associated
with it as if it had been created by
generateConceptCohortSet. This will mean that it can be
used by analytic packages designed to work with cdm cohorts.
settings(cdm$gi_bleed)
#> # A tibble: 1 × 2
#> cohort_definition_id cohort_name
#> <int> <chr>
#> 1 1 custom_gi_bleed
cohortCount(cdm$gi_bleed)
#> # A tibble: 1 × 3
#> cohort_definition_id number_records number_subjects
#> <int> <int> <int>
#> 1 1 479 479
cohortAttrition(cdm$gi_bleed)
#> Warning: `cohortAttrition()` was deprecated in CDMConnector 1.3.
#> ℹ Please use `attrition()` instead.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
#> generated.
#> # A tibble: 1 × 7
#> cohort_definition_id number_records number_subjects reason_id reason
#> <int> <int> <int> <int> <chr>
#> 1 1 479 479 1 Initial qualify…
#> # ℹ 2 more variables: excluded_records <int>, excluded_subjects <int>