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Validation: cohorts, datasets, and methodology

This article documents how rwevalidate was validated before release: the cohorts and datasets used, the results, and the method to reproduce them. No patient-level data is included; MIMIC-IV is credentialed and is not distributed with the package. Only the procedure and the cohort-defining seed concepts are recorded, so the validation can be re-run by anyone with access to the same data.

Datasets

Dataset Source CDM Patients Role
mimic_cdm MIMIC-IV (real ICU) v5.3.1 100 domain/edge battery
mimiciv_omop MIMIC-IV (real) v5.4 5,000 gold-phenotype validation
Eunomia / GiBleed Synthea (synthetic) v5.3.1 2,694 independent cross-dataset

MIMIC-IV requires PhysioNet credentialing; both MIMIC builds are local. Eunomia is public and downloads on demand via the Eunomia package.

Tier 1 - domain battery (100-patient CDM)

Cohorts spanning every index domain plus an empty-cohort edge case, each run through validate_cohort() with a complement comparator. All ran without error; the empty cohort aborts with a clear message (the intended guard).

kbl(data.frame(
  cohort = c("Heart failure","Atrial fibrillation","Obesity","Acetaminophen",
             "Oxycodone","Mech ventilation","CBC lymphocytes","Empty (edge)"),
  domain = c("condition","condition","condition","drug","drug","procedure",
             "measurement","condition"),
  n = c(25,30,20,89,64,45,91,0),
  status = c(rep("ok",7),"handled-error")
))
cohort domain n status
Heart failure condition 25 ok
Atrial fibrillation condition 30 ok
Obesity condition 20 ok
Acetaminophen drug 89 ok
Oxycodone drug 64 ok
Mech ventilation procedure 45 ok
CBC lymphocytes measurement 91 ok
Empty (edge) condition 0 handled-error

Tier 2 - gold-standard OHDSI phenotypes (5,000-patient CDM)

Five cohorts from the OHDSI Phenotype Library (via the sibling omop-phenotype-pipeline project) instantiated on mimiciv_omop. All four modules ran on cohorts of 481-849 patients.

kbl(data.frame(
  phenotype = c("Type 2 diabetes","Cardiac valve + AF","Acute liver injury",
                "Drug-induced pancreatitis","Treatment-resistant depression"),
  cohort_id = c(288, 1103, 736, 253, 1009),
  n_target = c(645, 481, 849, 485, 775),
  status = "ok (4 checks)"
))
phenotype cohort_id n_target status
Type 2 diabetes 288 645 ok (4 checks)
Cardiac valve + AF 1103 481 ok (4 checks)
Acute liver injury 736 849 ok (4 checks)
Drug-induced pancreatitis 253 485 ok (4 checks)
Treatment-resistant depression 1009 775 ok (4 checks)

Tier 3 - independent synthetic source (Eunomia)

The same tool on Eunomia/GiBleed returns a data-appropriate traffic light: attrition passes (full longitudinal histories) where MIMIC fails (ICU-short windows), and power is adequate at large N. Evidence the checks reflect real data characteristics rather than fixed output.

Reproducing the validation

The harness scripts live in data-raw/ of the source repository (run_battery.R, run_phenotypes.R, run_eunomia.R). They are not part of the installed package. The steps below are the method.

1. Connect (split-schema)

Production CDMs usually keep the vocabulary in a separate schema. cdm_connect() validates clinical tables in cdm_schema and vocabulary tables in vocab_schema.

con <- cdm_connect(
  dbname = "FHIR", user = "...", password = "...",
  cdm_schema = "mimiciv_omop",   # clinical
  vocab_schema = "vocab"          # concept, concept_ancestor
)

2. Derive observation_period (if absent)

The mimiciv_omop build ships without observation_period. Derive one row per person spanning their first to last dated event across clinical domains:

DBI::dbExecute(con, "
  CREATE TABLE mimiciv_omop.observation_period AS
  SELECT ROW_NUMBER() OVER (ORDER BY person_id) AS observation_period_id,
         person_id,
         MIN(d) AS observation_period_start_date,
         MAX(d) AS observation_period_end_date,
         44814724 AS period_type_concept_id
  FROM (
    SELECT person_id, condition_start_date d FROM mimiciv_omop.condition_occurrence
    UNION ALL SELECT person_id, drug_exposure_start_date FROM mimiciv_omop.drug_exposure
    UNION ALL SELECT person_id, measurement_date FROM mimiciv_omop.measurement
    UNION ALL SELECT person_id, procedure_date FROM mimiciv_omop.procedure_occurrence
    UNION ALL SELECT person_id, visit_start_date FROM mimiciv_omop.visit_occurrence
  ) e WHERE d IS NOT NULL GROUP BY person_id")

3. Instantiate a cohort (OHDSI concept-set method)

For each phenotype, expand the gold seed concepts to descendants via concept_ancestor, remove the excluded descendants, and index on the first qualifying event across domains. subject_id is NUMERIC because mimiciv_omop person ids exceed bigint.

DBI::dbExecute(con, "
  INSERT INTO results.phenotype_cohorts
  SELECT person_id, 288 AS cohort_definition_id, MIN(d), MAX(d) FROM (
    SELECT person_id, condition_start_date d FROM mimiciv_omop.condition_occurrence
     WHERE condition_concept_id IN (
       SELECT descendant_concept_id FROM vocab.concept_ancestor
        WHERE ancestor_concept_id IN ( /* gold seeds */ )
          AND descendant_concept_id NOT IN (
            SELECT descendant_concept_id FROM vocab.concept_ancestor
             WHERE ancestor_concept_id IN ( /* excluded */ )))
    /* UNION ALL the drug/measurement/procedure domains likewise */
  ) e GROUP BY person_id")

4. Seeds used (replicable)

The exact seed and excluded concept ids for all five phenotypes ship with the package and can be read back:

seeds <- read.csv(system.file("extdata", "phenotype_seeds.csv",
                              package = "rwevalidate"))
kbl(seeds[, c("cohort_id", "phenotype", "n_seed", "n_excluded")])
cohort_id phenotype n_seed n_excluded
288 t2dm 17 6
1103 cardiac_valve_af 181 0
736 acute_liver_injury 30 9
253 drug_pancreatitis 31 2
1009 trd 69 15 
# Example: the Type 2 diabetes seed concept ids
strsplit(seeds$seed_concept_ids[seeds$phenotype == "t2dm"], ";")[[1]]
#>  [1] "443238"   "201820"   "442793"   "195771"   "37392407" "4184637" 
#>  [7] "40484649" "42689695" "765533"   "43531006" "765650"   "45770986"
#> [13] "201254"   "45768456" "40484648" "4128019"  "435216"

The authoritative cohort definitions are the OHDSI Phenotype Library entries for cohort ids 288, 1103, 736, 253, and 1009 (https://github.com/OHDSI/PhenotypeLibrary). Only the numeric seed ids are recorded here, for reproducibility; the definitions themselves are not copied.

5. Validate

validate_cohort(
  cdm_schema   = "mimiciv_omop",
  cohort_table = "results.phenotype_cohorts",
  cohort_id    = 288,
  comparator_id = 10288,           # complement
  concept_ids  = c(443238, 201820, 442793),  # seeds -> Module 1
  concept_domain = "condition",
  vocab_schema = "vocab",
  output_dir   = "./validation_report"
)

Machine-readable results

Summary tables ship under inst/extdata/:

list.files(system.file("extdata", package = "rwevalidate"))
#> [1] "phenotype_seeds.csv"    "phenotype_summary.csv"  "robustness_summary.csv"

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.
Health stats visible at Monitor.