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Overview of the OMOP Research Program

A primary goal of the Observational Medical Outcomes Partnership will assess the feasibility and utility of using observational data to identify and evaluate associations between drugs and health-related conditions.

The specific aims supporting this goal are:

  • Define the pool of potential analytical methods to be used to identify drug-condition associations in observational data
  • Assess the feasibility and utility of observational data for identifying associations between drugs and non-specified conditions
  • Assess the feasibility and utility of observational data for monitoring Health Outcomes of Interest and evaluating the associations between drugs and Health Outcomes of Interest
  • Assess the utility of natural history information generated from observational data in contributing to interpretation of observational analyses
  • Assess how decision-makers can use observational analyses to support active monitoring of the effects of medicines post-approval

OVERVIEW OF PHASES OF RESEARCH PROGRAM

To address these aims, we will answer the following research questions, which are separated into phases to denote key project milestones:

Phase 1: Feasibility of data infrastructure

    Research questions:
  • Can we establish a consistent framework to use across disparate observational data sources?
  • Does normalizing conditions in observational data improve identification of non-specified conditions?

    • Deliverables

  • Common data model, and data transformation applications
  • Common drug and condition vocabularies, and mapping tools
  • Report: Comparison of condition vocabularies for observational screening
  • Health Outcomes of Interest library
  • Simulated dataset
  • Report: Systems integration design and lessons learned

Phase 2: Feasibility of analyses

    Research questions:
  • Which identification methods are feasible within the current systems infrastructure?
  • Can we establish standard data quality and characterization procedures to assess the viability of data sources for observational analyses?

    • Deliverables

  • Report: Research Core Data quality summary
  • Data quality assessment procedure
  • Report: Feasibility of Identification methods
  • Open-source library of method implementations
  • Reference set of drug labeled events for screening studies
  • Report: Health Outcomes of Interest natural history

Phase 3: Performance of Analyses

    Research questions:
  • What are the performance characteristics of each identification method in simulated data?
  • What is the performance and consistency of each identification method for non-specified conditions across observational data sources and over time?
  • What is the performance of each observational data source in identifying associations between drugs and non-specified conditions?
  • What is the performance and consistency of each method in monitoring Health Outcomes of Interest?
  • What is the performance of each observational data source in monitoring Health Outcomes of Interest?
  • What is the performance of each observational data source in evaluating associations between drugs and Health Outcomes of Interest?

    • Deliverables:

  • Open-source library of applications to conduct methodological research against common data model
  • Report: Point-performance of identification methods in a simulated dataset
  • Report: Point-performance of identification methods on non-specified conditions
  • Report: Performance over time of identification methods on non-specified conditions
  • Report: Consistency of identification methods on non-specified conditions
  • Report: Concordance of identification methods and observational evaluation of Health Outcomes of Interest

Phase 4: Utility of Analyses and Process

    Research questions:
  • How does the performance of identifying associations in observational data differ from other surveillance approaches?
  • How does natural history information from observational data contribute to a decision regarding the results of observational analysis?
  • How do decision-makers interpret observational database analyses?

    • Deliverables

  • Report: Utility of Natural History information
  • Report: Efficiency of Identification: Comparison of observational data and other surveillance systems
  • Report: Utility of observational screening and observational evaluation of Health Outcomes of Interest over time
  • Report: Systems integration design and lessons learned
  • Report: Partnership governance design and lessons learned

Additional detail is available about the following topics: