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March 2017 Bayesian Two-Stage Design for Phase II Clinical Trials with Switching Hypothesis Tests
Haolun Shi, Guosheng Yin
Bayesian Anal. 12(1): 31-51 (March 2017). DOI: 10.1214/15-BA988

Abstract

Conventional phase II clinical trials use either a single-arm or a double-arm scheme to examine the treatment effect of an investigational drug. The hypotheses tests under these two schemes are different, as a single-arm study usually tests the response rate of the new drug against a set of fixed reference rates and a double-arm randomized trial compares the new drug with the standard treatment or placebo. To bridge the single- and double-arm schemes in one phase II clinical trial, we propose a Bayesian two-stage design with changing hypothesis tests. Stage 1 enrolls patients solely to the experimental arm to make a comparison with the reference rates, and stage 2 imposes a double-arm comparison of the experimental arm with the control arm. The design is calibrated with respect to error rates from both the frequentist and Bayesian perspectives. Moreover, we control the “type III error rate”, defined as the probability of prematurely stopping the trial at stage 1 when the trial is supposed to move on to stage 2. We conduct extensive simulations on the calculations of these error rates to examine the operational characteristics of our proposed method, and illustrate it with a non-small cell lung cancer trial.

Citation

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Haolun Shi. Guosheng Yin. "Bayesian Two-Stage Design for Phase II Clinical Trials with Switching Hypothesis Tests." Bayesian Anal. 12 (1) 31 - 51, March 2017. https://doi.org/10.1214/15-BA988

Information

Published: March 2017
First available in Project Euclid: 18 December 2015

zbMATH: 1384.62311
MathSciNet: MR3597566
Digital Object Identifier: 10.1214/15-BA988

Subjects:
Primary: 62C10
Secondary: 62P10

Keywords: Bayesian error rates , expected sample size , phase II clinical trial , single-to-double arm design , two-stage procedure , Type I error , type II error

Rights: Copyright © 2017 International Society for Bayesian Analysis

Vol.12 • No. 1 • March 2017
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