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CohortCharacteristics: an R package for population characterisation in observational studies using the OMOP common

Mike Du1, Albert Prats-Uribe1, Núria Mercadé-Besora1

  • 1Pharmaco- and Device Epidemiology Group, Health Data Sciences, Botnar Research Centre, NDORMS, University of Oxford, Windmill Road, Oxford, OX3 7LD, UK.

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PubMed
Summary
This summary is machine-generated.

A new R package, CohortCharacteristics, standardizes cohort characterization for multi-database observational studies using the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). It ensures data comparability and reproducibility across diverse datasets.

Keywords:
CharacterisationCommon data modelEpidemiologyOMOP CDMObservational studiesR

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Area of Science:

  • Health Informatics
  • Observational Research
  • Data Science

Background:

  • Standardized cohort characterization is crucial for comparability and reproducibility in multi-database observational studies.
  • Existing methods lack standardization, hindering cross-database research.
  • The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) provides a framework for data standardization.

Purpose of the Study:

  • To introduce CohortCharacteristics, an open-source R package for standardized cohort characterization.
  • To explain the development and demonstrate the core functionality of the CohortCharacteristics package.
  • To showcase the package's application in generating descriptive statistics across multiple databases mapped to the OMOP CDM.

Main Methods:

  • Developed CohortCharacteristics, an open-source R package.
  • Applied the package to datasets from seven different databases mapped to the OMOP CDM.
  • Generated descriptive statistics on demographics, comorbidities, medication exposures, cohort overlap, and timing of cohort entries.

Main Results:

  • Cohort characteristics were largely consistent across databases, showing similar age and sex distributions.
  • Prescribing patterns for certain medication classes varied across databases.
  • Timing analyses supported existing literature, e.g., dementia diagnoses following insomnia.

Conclusions:

  • CohortCharacteristics facilitates consistent and transparent cohort characterization across networks of OMOP CDM data.
  • The package enhances reproducibility and comparability in multi-database observational research.
  • Demonstrated applicability of CohortCharacteristics in real-world observational studies.