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Some Thoughts on the Use of Predictive Probability in Clinical Trials
Heng Li1, Manuela Buzoianu1, Adrijo Chakraborty1
1Division of Biostatistics, Center for Devices and Radiological Health, U.S. Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, MD, 20993, USA.
Therapeutic Innovation & Regulatory Science
|January 11, 2023
Summary
This viewpoint discusses using Bayesian predictive probability to determine if a study is successful. It offers a perspective on this statistical approach for clinical trial analysis.
Area of Science:
- Statistics in Medicine
- Clinical Trial Methodology
- Bayesian Inference
Background:
- Traditional study success metrics can be limiting.
- Bayesian predictive probability offers a probabilistic approach to evaluating study outcomes.
- Assessing study success is crucial for decision-making in research.
Purpose of the Study:
- To present a viewpoint on utilizing Bayesian predictive probability for declaring study success.
- To advocate for a nuanced interpretation of statistical evidence in clinical research.
- To highlight the potential benefits of Bayesian methods in trial evaluation.
Main Methods:
- Conceptual analysis and discussion of statistical principles.
- Review of the application of Bayesian predictive probability in study design.
- Exploration of the interpretation of Bayesian predictive probability for success criteria.
Main Results:
- The viewpoint emphasizes that Bayesian predictive probability provides a forward-looking assessment of study success.
- It suggests that this probability can inform decisions about continuing or stopping a study.
- The note argues for a clear definition of success based on these probabilities.
Conclusions:
- Bayesian predictive probability offers a valuable framework for declaring study success.
- Adopting this approach can lead to more informed and efficient research practices.
- Further discussion and adoption of this statistical viewpoint are encouraged in the scientific community.
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