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Efficient confidence limits for adaptive one-arm two-stage clinical trials with binary endpoints.

Guogen Shan¹, Hua Zhang², Tao Jiang³

¹Epidemiology and Biostatistics Program, Department of Environmental and Occupational Health, School of Community Health Sciences, University of Nevada Las Vegas, Las Vegas, 89154, NV, USA.

BMC Medical Research Methodology

|February 8, 2017

Summary

This study introduces new methods for calculating exact confidence limits in adaptive clinical trials. Two novel approaches guarantee coverage probability and are recommended for precise sample size determination in trial design.

Keywords:

Adaptive design Clopper-Pearson approach Exact one-sided interval Response rate Two-stage design

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Area of Science:

Biostatistics
Clinical Trial Design

Background:

Adaptive one-arm two-stage designs aim to reduce sample size in clinical trials with binary outcomes.
Accurate computation of confidence limits is crucial for these adaptive designs.

Purpose of the Study:

To propose and evaluate new methods for calculating exact one-sided confidence limits in adaptive clinical trials.
To compare the performance of proposed methods against existing approaches.

Main Methods:

Three new one-sided confidence limit approaches were developed, based on p-value, average response rate, and asymptotic lower limit.
Two of the proposed methods provide exact confidence limits with guaranteed coverage probability.
Performance was evaluated using simple average length and expected length criteria.

Main Results:

Existing average response rate approaches showed similar performance.
Proposed exact methods had slightly longer expected lengths but shorter simple average lengths compared to existing methods.
One proposed method based on average response rate was not exact.

Conclusions:

Recommend exact approaches based on p-value and asymptotic lower limit for simple average length criterion.
Recommend the average response rate approach for the expected length criterion.
Highlights the importance of choosing the appropriate confidence limit method based on desired criteria for adaptive trial design.