Department of Biostatistics, University of Florida, Gainesville, Florida 32610, USA.
Stat Med. 2011 Oct 15;30(23):2804-14. doi: 10.1002/sim.4308. Epub 2011 Aug 8.
In a two-stage, drop-the-losers clinical trial, researchers choose the 'best' among a number of treatments at an interim analysis after the first stage. The selected treatment continues to the second stage for confirmation of efficacy, and the remaining treatments (the 'losers') are dropped from the study. Wu et al. (Biometrika 2010; 97:405-418) showed how to construct confidence limits for the mean difference between the selected treatment and the control when the treatment is chosen after the first stage based on the highest efficacy in the primary clinical endpoint. In this article, we show how to construct a lower confidence limit for the mean difference when the treatment is chosen based on first-stage safety data, early endpoint efficacy data, a combination of safety and efficacy data or any other prespecified selection rule. The result extends the applicability of drop-the-losers designs, for in practice, the 'best' treatment often is not chosen for efficacy alone.
在两阶段、淘汰失败者的临床试验中，研究人员在第一阶段后进行中期分析时，从多种治疗方法中选择“最佳”方法。选定的治疗方法继续进入第二阶段以确认疗效，而其余的治疗方法（即“失败者”）则从研究中淘汰。Wu 等人（Biometrika 2010；97:405-418）展示了当根据主要临床终点的最高疗效在第一阶段后选择治疗方法时，如何为选定治疗方法与对照之间的均数差值构建置信限。在本文中，我们展示了当基于第一阶段安全性数据、早期终点疗效数据、安全性和疗效数据的组合或任何其他预设选择规则选择治疗方法时，如何构建均数差值的下限置信限。该结果扩展了淘汰失败者设计的适用性，因为在实践中，“最佳”治疗方法往往不仅仅因为疗效而被选择。
