A Bayesian comparative effectiveness trial in action: developing a platform for multisite study adaptive randomization.
A Bayesian comparative effectiveness trial in action: developing a platform for multisite study adaptive randomization.
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DOI：
10.1186/s13063-016-1544-5
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发表时间：
2016-08-31
期刊：
影响因子：
2.5
通讯作者：
Barohn RJ
中科院分区：
文献类型：
--
作者：
Brown AR;Gajewski BJ;Aaronson LS;Mudaranthakam DP;Hunt SL;Berry SM;Quintana M;Pasnoor M;Dimachkie MM;Jawdat O;Herbelin L;Barohn RJ
在过去的几十年中，使用贝叶斯方法的试验数量迅速增长。 1990 年之前的出版物仅包括 3 项使用贝叶斯方法的临床试验，但这一数字在 1990 年代迅速跃升至 19 项，从 2000 年到 2012 年跃升至 99 项。虽然这些文献提供了贝叶斯自适应设计 (BAD) 的许多示例，但没有一篇可用的论文能够引导读者完成进行 BAD 的详细过程。本文通过描述用于一项比较有效性试验（神经病患者辅助干预：现实生活情况下的治疗比较）的 BAD 过程来填补这一空白，该试验可以推广供其他人使用。选择 BAD 时考虑到了效率。响应自适应随机化可以实现更小的样本量，并且可以更快地得出关于哪种或多种治疗最有效的结论。基于互联网的电子数据采集工具具有随机化模块，有助于跨研究站点的数据采集，并开发了内部计算软件程序来实现响应自适应随机化。开发了一种在尽量减少对研究地点的干扰的情况下调整随机化的过程。新的随机化表可以快速生成，并且可以无缝集成到数据捕获工具中，同时最大限度地减少对研究站点的干扰。这份手稿是第一篇详细介绍使用自适应随机化评估多站点比较有效性试验的技术流程。应用贝叶斯试验的一个重要机会是比较有效性试验。本文提出的具体案例研究可以用作未来结合统计软件和基于网络的应用程序进行临床试验的模型。 ClinicalTrials.gov 标识符：NCT02260388，注册于 2014 年 10 月 6 日
In the last few decades, the number of trials using Bayesian methods has grown rapidly. Publications prior to 1990 included only three clinical trials that used Bayesian methods, but that number quickly jumped to 19 in the 1990s and to 99 from 2000 to 2012. While this literature provides many examples of Bayesian Adaptive Designs (BAD), none of the papers that are available walks the reader through the detailed process of conducting a BAD. This paper fills that gap by describing the BAD process used for one comparative effectiveness trial (Patient Assisted Intervention for Neuropathy: Comparison of Treatment in Real Life Situations) that can be generalized for use by others. A BAD was chosen with efficiency in mind. Response-adaptive randomization allows the potential for substantially smaller sample sizes, and can provide faster conclusions about which treatment or treatments are most effective. An Internet-based electronic data capture tool, which features a randomization module, facilitated data capture across study sites and an in-house computation software program was developed to implement the response-adaptive randomization. A process for adapting randomization with minimal interruption to study sites was developed. A new randomization table can be generated quickly and can be seamlessly integrated in the data capture tool with minimal interruption to study sites. This manuscript is the first to detail the technical process used to evaluate a multisite comparative effectiveness trial using adaptive randomization. An important opportunity for the application of Bayesian trials is in comparative effectiveness trials. The specific case study presented in this paper can be used as a model for conducting future clinical trials using a combination of statistical software and a web-based application. ClinicalTrials.gov Identifier: NCT02260388, registered on 6 October 2014