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The Uncertainty Principle

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Optimal adaptive single-arm phase II trials under quantified uncertainty.

Kevin Kunzmann¹, Meinhard Kieser¹

¹Institute of Biometry and Medical Informatics, University of Heidelberg, Heidelberg, Germany.

Journal of Biopharmaceutical Statistics

|April 27, 2019

Summary

This study introduces a Bayesian framework for evaluating two-stage clinical trial designs, offering robust methods for early stopping under uncertainty. The findings guide practitioners in selecting optimal adaptive designs for binary endpoints in oncology trials.

Keywords:

Bayesian Phase II binary endpoint optimal design

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Area of Science:

Clinical Trial Design
Bayesian Statistics
Oncology Research

Background:

Two-stage designs are common in single-arm trials with binary endpoints, particularly in early clinical oncology.
Existing designs often rely on strong assumptions about response rates, limiting their robustness to deviations.
Adaptive designs allow early trial termination based on observed data, but require careful planning.

Purpose of the Study:

To develop a Bayesian framework for scoring and optimizing two-stage clinical trial designs under uncertainty.
To compare designs optimizing a common performance score with a utility-based approach using expected power and sample size.
To provide guidance for practitioners selecting adaptive designs for oncology trials.

Main Methods:

Defined a Bayesian framework for scoring two-stage designs under uncertainty.
Investigated characteristics of designs optimizing a performance score (Liu et al.).
Compared optimal designs with a utility-based approach incorporating expected power and sample size.

Main Results:

The proposed Bayesian framework offers a robust method for evaluating two-stage designs.
Optimal designs derived from the Bayesian framework were compared to those based on expected power.
Insights were provided into the assumptions underlying expected power and conditional power in adaptive designs.

Conclusions:

The study offers guidance for selecting appropriate adaptive two-stage designs in clinical practice.
A software implementation of the proposed methods is available, facilitating practical application.
The Bayesian approach enhances the reliability of early stopping rules in clinical trials.