Literature DB >> 17066402

Bayesian evaluation of group sequential clinical trial designs.

Scott S Emerson1, John M Kittelson, Daniel L Gillen.   

Abstract

Clinical trial designs often incorporate a sequential stopping rule to serve as a guide in the early termination of a study. When choosing a particular stopping rule, it is most common to examine frequentist operating characteristics such as type I error, statistical power, and precision of confidence intervals (Statist. Med. 2005, in revision). Increasingly, however, clinical trials are designed and analysed in the Bayesian paradigm. In this paper, we describe how the Bayesian operating characteristics of a particular stopping rule might be evaluated and communicated to the scientific community. In particular, we consider a choice of probability models and a family of prior distributions that allows concise presentation of Bayesian properties for a specified sampling plan. Copyright (c) 2006 John Wiley & Sons, Ltd.

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Year:  2007        PMID: 17066402     DOI: 10.1002/sim.2640

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  6 in total

1.  A comparison of two worlds: How does Bayes hold up to the status quo for the analysis of clinical trials?

Authors:  Alice R Pressman; Andrew L Avins; Alan Hubbard; William A Satariano
Journal:  Contemp Clin Trials       Date:  2011-03-29       Impact factor: 2.226

2.  Exploring the benefits of adaptive sequential designs in time-to-event endpoint settings.

Authors:  Sarah C Emerson; Kyle D Rudser; Scott S Emerson
Journal:  Stat Med       Date:  2010-12-29       Impact factor: 2.373

3.  Using short-term response information to facilitate adaptive randomization for survival clinical trials.

Authors:  Xuelin Huang; Jing Ning; Yisheng Li; Elihu Estey; Jean-Pierre Issa; Donald A Berry
Journal:  Stat Med       Date:  2009-05-30       Impact factor: 2.373

4.  Bayesian sequential meta-analysis design in evaluating cardiovascular risk in a new antidiabetic drug development program.

Authors:  Ming-Hui Chen; Joseph G Ibrahim; H Amy Xia; Thomas Liu; Violeta Hennessey
Journal:  Stat Med       Date:  2013-12-16       Impact factor: 2.373

5.  Adaptive designs in clinical trials: why use them, and how to run and report them.

Authors:  Philip Pallmann; Alun W Bedding; Babak Choodari-Oskooei; Munyaradzi Dimairo; Laura Flight; Lisa V Hampson; Jane Holmes; Adrian P Mander; Lang'o Odondi; Matthew R Sydes; Sofía S Villar; James M S Wason; Christopher J Weir; Graham M Wheeler; Christina Yap; Thomas Jaki
Journal:  BMC Med       Date:  2018-02-28       Impact factor: 8.775

6.  Comparison of Bayesian and frequentist group-sequential clinical trial designs.

Authors:  Nigel Stallard; Susan Todd; Elizabeth G Ryan; Simon Gates
Journal:  BMC Med Res Methodol       Date:  2020-01-07       Impact factor: 4.615

  6 in total

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