A Bayesian sequential design using alpha spending function to control type I error. (October 2017)

• Record Type: Journal Article
• Authors: Zhu, Han; Yu, Qingzhao
• Other Names: Balakrishnan N. guest-editor.
• Is Part Of: Statistical methods in medical research. Volume 26:Number 5(2017)
• Page Start: 2184
• Page End: 2196
• Publication Date: 2017-10
• Subjects: Alpha spending function -- Bayesian clinical trial -- sequential design -- type I error; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/0962280215595058
• Languages: English
• ISSNs: 0962-2802

Abstract

We propose in this article a Bayesian sequential design using alpha spending functions to control the overall type I error in phase III clinical trials. We provide algorithms to calculate critical values, power, and sample sizes for the proposed design. Sensitivity analysis is implemented to check the effects from different prior distributions, and conservative priors are recommended. We compare the power and actual sample sizes of the proposed Bayesian sequential design with different alpha spending functions through simulations. We also compare the power of the proposed method with frequentist sequential design using the same alpha spending function. Simulations show that, at the same sample size, the proposed method provides larger power than the corresponding frequentist sequential design. It also has larger power than traditional Bayesian sequential design which sets equal critical values for all interim analyses. When compared with other alpha spending functions, O'Brien-Fleming alpha spending function has the largest power and is the most conservative in terms that at the same sample size, the null hypothesis is the least likely to be rejected at early stage of clinical trials. And finally, we show that adding a step of stop for futility in the Bayesian sequential design can reduce the overall type I error and reduce the actual sample sizes.