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Epistemic uncertainty in Bayesian predictive probabilities.

Charles C Liu¹, Ron Xiaolong Yu¹

¹Department of Biostatistics, Gilead Sciences, Foster City, CA, USA.

Journal of Biopharmaceutical Statistics

|May 9, 2023

Summary

This summary is machine-generated.

Bayesian predictive probabilities in clinical trials are often averaged. This study proposes using intervals or quantiles instead, offering a more nuanced view of uncertainty and improving trial design and monitoring.

Keywords:

Assurance conditional power predictive power probability of success promising zone

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Area of Science:

Clinical Trials Methodology
Biostatistics
Statistical Inference

Background:

Bayesian predictive probabilities are widely used for clinical trial design and monitoring.
Current methods typically involve averaging these probabilities over prior or posterior distributions.
This averaging approach may oversimplify the interpretation of trial uncertainty.

Purpose of the Study:

To highlight the limitations of solely averaging Bayesian predictive probabilities.
To propose reporting predictive probabilities as intervals or quantiles.
To demonstrate a more informative approach to quantifying uncertainty in clinical trials.

Main Methods:

The study proposes using intervals and quantiles of Bayesian predictive probabilities.
It contrasts this with the traditional method of averaging probabilities.
Four distinct clinical trial applications are used for demonstration.

Main Results:

Reporting predictive probabilities as intervals or quantiles provides a more formal representation of decreasing uncertainty.
This approach enhances the understanding of information gain during a trial.
The proposed method is shown to be practical and broadly applicable across different trial phases and objectives.

Conclusions:

Averaging Bayesian predictive probabilities has limitations in fully capturing trial uncertainty.
Reporting predictive probability intervals or quantiles offers a more robust and informative approach.
The proposed method improves the design and monitoring of clinical trials, including dose escalation, futility stopping, sample size re-estimation, and probability of success calculations.