A conditional error function approach for subgroup selection in adaptive clinical trials
A conditional error function approach for subgroup selection in adaptive clinical trials
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DOI：
10.1002/sim.5541
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发表时间：
2012-12-30
影响因子：
2
通讯作者：
Stallard, N.
中科院分区：
文献类型：
--
作者：
Friede, T.;Parsons, N.;Stallard, N.
人们对个性化医学和靶向治疗的兴趣与日俱增，这导致了亚组分析的重要性增加。如果计划将预定义亚组和整个总体的治疗比较视为共同的主要分析，则统计分析控制家庭I型错误率是很重要的。Spiessens和Debois(续)克莱恩。Trials，2010，31,647656)最近提出了一种专门针对这一设置的方法，该方法结合了基于不同组的已知大小的相关性的假设，并表明这比一般的多重比较程序(如Bonferroni校正)更有效。如果相对于观察结果的时间长度，招聘速度较慢，进行中期分析可能是有效的。在这篇文章中，我们提出了一种基于条件误差函数原理的自适应临床试验的新方法，该方法在预定义的子组和全总体中进行共同初级分析。这种方法是通用的，因为我们假设测试统计数据可以被认为是正态分布的，而不是对个别患者数据做出任何特定的分布假设。在仿真研究中，我们证明了新方法比以前提出的分析策略更有效。此外，我们还展示了如何将该方法扩展到选择不是基于最终结果而是基于早期结果的情况。我们使用肿瘤学靶向治疗中的一个案例研究来说明所建议的方法在非正常结果下的使用。版权所有(C)2012 John Wiley&Sons，Ltd.
Growing interest in personalised medicine and targeted therapies is leading to an increase in the importance of subgroup analyses. If it is planned to view treatment comparisons in both a predefined subgroup and the full population as co-primary analyses, it is important that the statistical analysis controls the familywise type I error rate. Spiessens and Debois (Cont. Clin. Trials, 2010, 31, 647656) recently proposed an approach specific for this setting, which incorporates an assumption about the correlation based on the known sizes of the different groups, and showed that this is more powerful than generic multiple comparisons procedures such as the Bonferroni correction. If recruitment is slow relative to the length of time taken to observe the outcome, it may be efficient to conduct an interim analysis. In this paper, we propose a new method for an adaptive clinical trial with co-primary analyses in a predefined subgroup and the full population based on the conditional error function principle. The methodology is generic in that we assume test statistics can be taken to be normally distributed rather than making any specific distributional assumptions about individual patient data. In a simulation study, we demonstrate that the new method is more powerful than previously suggested analysis strategies. Furthermore, we show how the method can be extended to situations when the selection is not based on the final but on an early outcome. We use a case study in a targeted therapy in oncology to illustrate the use of the proposed methodology with non-normal outcomes. Copyright (c) 2012 John Wiley & Sons, Ltd.