ArticlePloS one2025
CohortDiagnostics: Phenotype evaluation across a network of observational data sources using population-level characterization.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Semaglutide and Neovascular Age-Related Macular Degeneration among Adults with Type 2 Diabetes: An Observational Health Data Sciences and Informatics Network Study.Ophthalmology · 2026Observational
- PDA (Privacy-Preserving Distributed Algorithms) in action: ten principles for high-quality multi-site clinical evidence generation.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Active Safety Surveillance Using Real-World Evidence (ASSURE) Implementation: Transparent and Reproducible Real-World Evidence Standardized Framework to Support Safety Signal Evaluation.Pharmacoepidemiology and drug safety · 2026Article
- Unlocking multi-institutional insights into disease progression with PEAL as a lossless, one-shot federated learning solution.NPJ digital medicine · 2026Article
- Beyond Missingness: Systematizing Methods for Comprehensive Data Fitness Assessment in Clinical Research.Journal of medical Internet research · 2026Article
- Identification of Adult Patients With Dermatomyositis Using Real-World Data Sources.Arthritis care & research · 2026Observational
- PhenoFit: a framework for determining computable phenotyping algorithm fitness for purpose and reuse.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Observational
- Multi-domain rule-based phenotyping algorithms enable improved GWAS signal.NPJ digital medicine · 2025Article
- Objective study validity diagnostics: a framework requiring pre-specified, empirical verification to increase trust in the reliability of real-world evidence.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- The necessity of validity diagnostics when drawing causal inferences from observational data: lessons from a multi-database evaluation of the risk of non-infectious uveitis among patients exposed to RemicadeBMC medical research methodology · 2024Article
- Variation in Phenotype Definitions in Observational Clinical Research: A Review of Three Conditions.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Review
Corrections and comments
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Authors and funding
20 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis paper introduces a novel framework for evaluating phenotype algorithms (PAs) using the open-source tool, Cohort Diagnostics. MATERIALS AND
methodsThe method is based on several diagnostic criteria to evaluate a patient cohort returned by a PA. Diagnostics include estimates of incidence rate, index date entry code breakdown, and prevalence of all observed clinical events prior to, on, and after index date. We test our framework by evaluating one PA for systemic lupus erythematosus (SLE) and two PAs for Alzheimer's disease (AD) across 10 different observational data sources.
resultsBy utilizing CohortDiagnostics, we found that the population-level characteristics of individuals in the cohort of SLE closely matched the disease's anticipated clinical profile. Specifically, the incidence rate of SLE was consistently higher in occurrence among females. Moreover, expected clinical events like laboratory tests, treatments, and repeated diagnoses were also observed. For AD, although one PA identified considerably fewer patients, absence of notable differences in clinical characteristics between the two cohorts suggested similar specificity. DISCUSSION: We provide a practical and data-driven approach to evaluate PAs, using two clinical diseases as examples, across a network of OMOP data sources. Cohort Diagnostics can ensure the subjects identified by a specific PA align with those intended for inclusion in a research study.
conclusionDiagnostics based on large-scale population-level characterization can offer insights into the misclassification errors of PAs.
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