Abstract This paper considers screening of adaptive interventions or adaptive treatment strategies embedded in a sequential multiple assignment randomized trial (SMART). As a SMART typically consists of numerous adaptive interventions, inferential procedures based on pairwise comparisons of all interventions may suffer substantial loss in efficiency after accounting for multiplicity. We propose simultaneous confidence intervals that compare the values of interventions of interest to that of the unknown best intervention by generalizing the method in Edwards and Hsu (1983). The multiple comparison with the best (MCB) intervals are applied as screening tool: an intervention with MCB interval excluding zero will be declared as inferior to the true best at a pre-specified confidence level, and hence excluded from further exploration. Simulation studies show that the proposed method outperforms the multiple comparison procedures based on Bonferroni’s correction in terms of width of confidence intervals for estimation. The method is applied to analyze data from the CODIACS trial in patients with depression.

中文翻译：

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通过与最佳结果的多重比较，比较一般顺序多重分配随机试验设计下的适应性干预措施

摘要 本文考虑筛选嵌入在顺序多重分配随机试验 (SMART) 中的适应性干预或适应性治疗策略。由于 SMART 通常由许多自适应干预组成，因此基于所有干预的成对比较的推理程序在考虑到多样性后可能会在效率上遭受重大损失。我们提出了同步置信区间，通过推广 Edwards 和 Hsu（1983）中的方法，将感兴趣的干预措施的值与未知的最佳干预措施的值进行比较。与最佳 (MCB) 区间的多重比较被用作筛选工具：在预先指定的置信水平下，MCB 区间不包括零的干预将被宣布为低于真正的最佳，因此被排除在进一步探索之外。模拟研究表明，所提出的方法在估计置信区间的宽度方面优于基于 Bonferroni 校正的多重比较程序。该方法用于分析来自抑郁症患者的 CODIACS 试验的数据。