Cohort Diagnostics

Nathan Hall

2024-10-22

Introduction

Asessubg and diagnosing the characteristics of cohorts & phenotypes is fundamental to ensuring the reliability and credibility of observational research for OMOP CDM-compliant data. This is also an essential step in phenotype development. The OHDSI community has developed an R package, CohortDiagnostics, which provides researchers with a systematic approach to examine various facets of cohorts, enabling them to identify potential biases, assess data completeness and compare characteristics of cohorts, and validate the suitability of cohorts for analysis. This tool is crucial for researchers working within the Observational Health Data Sciences and Informatics (OHDSI) ecosystem, enabling them to ensure the accuracy and reliability of cohort definitions through a detailed examination of incidence rates, cohort characteristics, and the specific codes triggering cohort inclusion criteria. CohortDiagnostics streamlines the process of cohort evaluation by:

  1. Generating a broad spectrum of diagnostics against a CDM database - see more details here: Features and Functionalities
  2. Providing an interactive R Shiny application within the package for an intuitive exploration and visualization of these diagnostics. For more information on R Shiny, see here.
  3. For more detailed information and documentation on CohortDiagnostics, visit the Github site for the package here.

Features and Functionalities

CohortDiagnostics offers a suite of features designed to deepen the understanding of cohort dynamics and the intricacies of cohort definitions, including:

  1. Cohort Definition: Facilitates the examination and validation of the logic behind cohort definitions, ensuring they accurately capture the intended population and allows for the asessment of cohort inclusion rule logic & attrition.
  2. Concepts in Data Source: Identifies the specific concepts present within the data source that are relevant to the cohort definitions, enabling a deeper understanding of standard and non-standard concepts present in the underlying patient population for each database.
  3. Orphan Concepts: Highlights concepts that, despite their relevance, are not captured within a cohort’s definition. This helps in refining concept sets and cohort criteria to ensure comprehensiveness and relevance.
  4. Cohort Counts: Provides counts of individuals and records within cohorts, offering a basic measure of cohort size and scope.
  5. Incidence Rate: Calculates the incidence rate of cohorts, stratified by various demographic and temporal factors such as age, sex, and calendar year, to assess the frequency of patients/records in the cohort and potential patterns over these strata.
  6. Time Distributions: Examines the distribution of time-related variables within cohorts, such as observation time before and after cohort index date as well as cohort duration, offering insights into cohort dynamics over time and available observation time.
  7. Index Event Breakdown: Summarizes the specific concepts (both standard and non-standard) that patients in the cohort are entering on across each database.
  8. Visit Context: Analyzes the healthcare context (e.g., inpatient, outpatient, laboratory visit) of the index events, highlighting the relationship between the cohort start date and the visits recorded in each database, both before, during, and after cohort entry.
  9. Cohort Overlap: Assesses the degree of patient overlap between cohorts, which can inform on potential biases, errors, or redundancies in cohort construction.
  10. Cohort Characterization: Characterizes cohorts by detailing prevalent conditions, medication use, procedures, and more, to understand the clinical profile of cohort members over various time periods relative to index.
  11. Compare Cohort Characterization: Enables the direct comparison of characteristics between cohorts, facilitating the identification of unique or shared features across different cohorts and across time points (both before and after index).
  12. Meta Data: Provides meta-information about the data and analyses conducted.

Together, these features equip researchers with the tools necessary for a thorough examination of cohort definitions, enhancing the quality and reliability of observational health research.

Utility and Application

CohortDiagnostics significantly contributes to the field of observational health research by providing a robust framework for the evaluation and validation of cohort definitions. Its utility spans several critical areas:

  1. Enhancing Cohort Definition Confidence: By offering detailed diagnostics, CohortDiagnostics helps researchers refine their cohort definitions, ensuring they accurately capture the intended population. This is a critical step in phenotype development, which is a cornerstone of modern observational health data research.
  2. Identifying Missing Concepts & Cohort Entry Events: Through the identification of orphan concepts and the detailed breakdown of index events, researchers can pinpoint gaps or misspecifications in cohort definitions. Iterating over multiple potential cohort definitions after analyzing these diagnostics is an encouraged and common practice.
  3. Facilitating the Ideas Behind Comparative Analyses: The package’s capabilities to characterize and compare cohorts, as well as to analyze cohort overlaps, are invaluable for researchers looking to understand the nuances and dynamics of their study populations. These diagnostics can help inform comparative studies in the future, after the cohorts and phenotypes are refined and finalized.
  4. Supporting Transparent Research: By enabling the listing of source codes, data source information, and providing a platform for detailed diagnostics exploration, CohortDiagnostics fosters a culture of transparency and reproducibility in observational research.