Korn Edward L, Freidlin Boris
Biometric Research Program, National Cancer Institute, Bethesda, MD, USA
Biometric Research Program, National Cancer Institute, Bethesda, MD, USA.
Clin Trials. 2016 Dec;13(6):651-659. doi: 10.1177/1740774516659472. Epub 2016 Jul 19.
BACKGROUND/AIMS: Factorial analyses of 2 × 2 trial designs are known to be problematic unless one can be sure that there is no interaction between the treatments (A and B). Instead, we consider non-factorial analyses of a factorial trial design that addresses clinically relevant questions of interest without any assumptions on the interaction. Primary questions of interest are as follows: (1) is A better than the control treatment C, (2) is B better than C, (3) is the combination of A and B (AB) better than C, and (4) is AB better than A, B, and C.
A simple three-step procedure that tests the first three primary questions of interest using a Bonferroni adjustment at the first step is proposed. A Hochberg procedure on the four primary questions is also considered. The two procedures are evaluated and compared in limited simulations. Published results from three completed trials with factorial designs are re-evaluated using the two procedures.
Both suggested procedures (that answer multiple questions) require a 50%-60% increase in per arm sample size over a two-arm design asking a single question. The simulations suggest a slight advantage to the three-step procedure in terms of power (for the primary and secondary questions). The proposed procedures would have formally addressed the questions arising in the highlighted published trials arguably more simply than the pre-specified factorial analyses used.
Factorial trial designs are an efficient way to evaluate two treatments, alone and in combination. In situations where a statistical interaction between the treatment effects cannot be assumed to be 0, simple non-factorial analyses are possible that directly assess the questions of interest without the zero interaction assumption.
背景/目的：已知2×2试验设计的析因分析存在问题，除非能够确定各治疗方法（A和B）之间不存在交互作用。相反，我们考虑对析因试验设计进行非析因分析，该分析无需对交互作用做任何假设就能解决临床上相关的感兴趣问题。感兴趣的主要问题如下：（1）A是否优于对照治疗C，（2）B是否优于C，（3）A与B的联合治疗（AB）是否优于C，以及（4）AB是否优于A、B和C。
提出了一个简单的三步程序，第一步使用Bonferroni校正来检验前三个感兴趣的主要问题。还考虑了针对这四个主要问题的Hochberg程序。在有限的模拟中对这两个程序进行了评估和比较。使用这两个程序对三项已完成的析因设计试验的已发表结果进行了重新评估。
两种建议的程序（回答多个问题）与回答单个问题的双臂设计相比，每个治疗组的样本量都需要增加50%-60%。模拟结果表明，在检验效能方面（针对主要和次要问题），三步程序略有优势。所提出的程序可以比使用预先指定的析因分析更简单地正式解决突出显示的已发表试验中出现的问题。
析因试验设计是单独和联合评估两种治疗方法的有效方式。在不能假设治疗效果之间的统计交互作用为0的情况下，可以进行简单的非析因分析，直接评估感兴趣的问题，而无需零交互作用假设。
