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Bayesian approaches to benefit-risk assessment for diagnostic tests
Tianyu Bai1, Huang Lan1, Ram Tiwari1
1Food and Drug Administration, Center for Device and Radiological Health, Silver Spring, Maryland, USA.
Journal of Biopharmaceutical Statistics
|June 7, 2021
Summary
This study introduces novel Bayesian models for benefit-risk (BR) assessment in diagnostic tests. These methods quantify diagnostic utility and uncertainty, aiding medical device evaluation.
Area of Science:
- Biostatistics
- Medical Device Evaluation
- Diagnostic Test Analysis
Background:
- Benefit-risk (BR) assessment is crucial for medical devices, particularly diagnostic tests influencing patient management indirectly.
- Diagnostic tests with binary outcomes require robust methods to integrate benefits (true positives/negatives) and risks (false positives/negatives).
Discussion:
- Two Bayesian models, Multinomial with Dirichlet prior and Binomial with Beta priors, are developed for BR score construction and credible intervals.
- A Bayesian power prior model is proposed for incorporating historical or real-world data (RWD) using fixed or random power prior parameters for Bayesian borrowing.
Key Insights:
- The developed models provide a quantitative framework for evaluating diagnostic test utility and uncertainty.
- Bayesian power prior approach facilitates data borrowing, enhancing model robustness with external information.
Outlook:
- Further validation of these Bayesian models with diverse real-world diagnostic data is warranted.
- These methods can inform regulatory decisions and clinical adoption of novel diagnostic technologies.
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