On the finite-sample and asymptotic error control of a randomization-probability test for response-adaptive clinical trials

Area of Science:

• Clinical trial methodology
• Statistical inference
• Adaptive experimental design

Background:

• Response-adaptive designs can optimize trial outcomes but often lack guaranteed statistical error control.
• Practical implementation in clinical trials is hindered by concerns over type-I error rates and power, especially with limited data.

Purpose of the Study:

• To address the inferential challenges of response-adaptive designs.
• To develop a novel statistical approach that guarantees frequentist error control while maintaining outcome optimization.
• To evaluate the performance of this approach in both simulated and real-world clinical settings.

Main Methods:

• Development of a novel test statistic based on randomization probabilities within adaptive designs.
• Derivation of finite-sample and asymptotic statistical guarantees for the proposed test statistic.
• Evaluation of theoretical properties for Thompson sampling, a common Bayesian adaptive design.
• Application and validation using a phase-II oncology clinical trial and simulation experiments.

Main Results:

• The proposed test statistic demonstrates finite-sample and asymptotic guarantees for type-I error control.
• The novel approach successfully preserves the outcome-optimizing potential of adaptive designs.
• Thompson sampling's properties were theoretically analyzed within the new framework.
• The method showed advantages in frequentist error control in a real-world oncology trial.

Conclusions:

• The developed statistical methodology provides a robust framework for response-adaptive designs, ensuring reliable inference.
• This approach bridges the gap between optimizing trial outcomes and maintaining rigorous statistical validity.
• The findings support the broader adoption of adaptive designs in clinical research and other fields.