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When Can We Trust Real-World Data To Evaluate New Medical Treatments?
Gregory E Simon1, Andrew B Bindman2, Nancy A Dreyer3
1Kaiser Permanente Washington Health Research Institute, Seattle, Washington, USA.
Clinical Pharmacology and Therapeutics
|May 1, 2021
Summary
Real-world evidence (RWE) requires careful assessment of real-world data (RWD) sources. Evaluating each step from clinical phenomenon to research database minimizes bias and ensures RWD reliability for treatment evaluation.
Area of Science:
- Health Informatics
- Clinical Epidemiology
- Data Science
Background:
- Traditional randomized trials face limitations in generalizability and speed.
- Increasing demand for real-world evidence (RWE) in treatment and product evaluation.
- Real-world data (RWD) from diverse sources (e.g., healthcare records, consumer devices) are crucial for RWE.
Purpose of the Study:
- To propose a framework for assessing the fitness of specific real-world data (RWD) elements for particular research purposes.
- To highlight the need for granular evaluation of RWD sources rather than global assessments.
- To ensure the reliability and validity of RWE studies.
Main Methods:
- Focus on the specific health state or clinical phenomenon of interest.
- Analyze each step between the clinical phenomenon and its representation in a research database.
- Develop specific questions to identify potential errors or biases at each data collection and processing stage.
- Consider clinical, organizational, and technical expertise for data harmonization and phenotyping.
Main Results:
- Global assessments of RWD sources are insufficient for determining fitness for purpose.
- A step-by-step evaluation framework is proposed to address potential errors and biases.
- Key questions focus on patient presentation, clinical recognition, recording environment, data harmonization (technical and semantic), and phenotyping.
- Transparency in RWD creation is essential for RWE study credibility.
Conclusions:
- Rigorous, step-by-step evaluation of RWD is necessary for reliable real-world evidence (RWE).
- Addressing potential biases in data collection and processing is critical for valid research outcomes.
- Collaboration across clinical, organizational, and technical domains is required for robust RWD utilization.
- Transparency throughout the RWD lifecycle builds trust in RWE findings.
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