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A Bayesian predictive sample size selection design for single-arm exploratory clinical trials.

Satoshi Teramukai¹, Takashi Daimon, Sarah Zohar

¹Department of Clinical Trial Design and Management, Translational Research Center, Kyoto University Hospital, Kyoto, Japan. steramu@kuhp.kyoto-u.ac.jp

Statistics in Medicine

|July 19, 2012

Summary

This summary is machine-generated.

This study introduces a Bayesian adaptive design for clinical trials, enabling flexible sample size adjustments during interim analyses. This predictive sample size selection design (PSSD) optimizes trial efficiency and resource allocation.

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Area of Science:

Clinical Trials
Biostatistics
Bayesian Statistics

Background:

Exploratory clinical trials often use single-arm designs with binary outcomes.
Early stopping for futility or efficacy is common in these trials.
Adaptive designs can improve trial efficiency and resource allocation.

Purpose of the Study:

To propose a novel Bayesian adaptive design for exploratory clinical trials.
To introduce the predictive sample size selection design (PSSD) for flexible sample size adjustments.
To enhance early stopping rules and optimize trial resource management.

Main Methods:

The PSSD utilizes a Bayesian approach with two prior distributions: 'analysis prior' and 'design prior'.
Sample size determination involves calculating two sizes: N and N(max).
Interim analyses use predictive probabilities to adjust sample size or stop the trial early.

Main Results:

The PSSD allows for dynamic sample size selection at interim analyses based on predictive probabilities.
Simulation studies investigated the operating characteristics of the PSSD.
The design was retrospectively applied to a lung cancer clinical trial, demonstrating its practical utility.

Conclusions:

The PSSD offers a flexible and efficient approach to exploratory clinical trial design.
This adaptive design can improve decision-making regarding sample size and early stopping.
The PSSD provides a robust framework for optimizing clinical trial conduct and resource utilization.