Sample size determination in clinical trials with multiple co-primary binary endpoints
Sample size determination in clinical trials with multiple co-primary binary endpoints
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DOI：
10.1002/sim.3972
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发表时间：
2010-09-20
影响因子：
2
通讯作者：
Hamasaki, Toshimitsu
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文献类型：
--
作者：
Sozu, Takashi;Sugimoto, Tomoyuki;Hamasaki, Toshimitsu
临床试验通常采用两个或多个主要疗效终点。此类试验的主要问题之一是如何确定适用于多个共同主要相关终点的样本量。我们提供了计算功效和样本量的基本公式，以使所有多个主要终点（作为二元变量）具有统计学显著性。基于主要终点之间的三种相关性度量，我们讨论了五种计算功效和样本量的方法：有和无连续性校正的渐近正态方法、有和无连续性校正的反正弦方法和Fisher精确方法。对于所有五种方法，当终点之间的效应量近似相等时，实现的样本量随着关联度量值的增加而减少。特别是，高正相关对样本量的减少有更大的影响。另一方面，当效应量不同时，这种关系不是很强。版权所有（C）2010约翰威利父子有限公司
Clinical trials often employ two or more primary efficacy endpoints. One of the major problems in such trials is how to determine a sample size suitable for multiple co-primary correlated endpoints. We provide fundamental formulae for the calculation of power and sample size in order to achieve statistical significance for all the multiple primary endpoints given as binary variables. On the basis of three association measures among primary endpoints, we discuss five methods of power and sample size calculation: the asymptotic normal method with and without continuity correction, the arcsine method with and without continuity correction, and Fisher's exact method. For all five methods, the achieved sample size decreases as the value of association measure increases when the effect sizes among endpoints are approximately equal. In particular, a high positive association has a greater effect on the decrease in the sample size. On the other hand, such a relationship is not very strong when the effect sizes are different. Copyright (C) 2010 John Wiley & Sons, Ltd.