存在和不存在治疗因素间似是而非的统计交互作用时的析因临床试验：方法学文献的历史回顾
Factorial clinical trials in the presence and absence of plausible statistical interactions between treatment factors. A historical review of the methodological literature
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中文总结 AI 辅助
回顾存在或不存在治疗因素统计交互作用时析因临床试验的文献，整合看似冲突的指导，通过示例和实证说明相关争论及不同分析方法影响，强调试验者明确目标的重要性。
中文摘要 AI 辅助
析因试验可在不预期两个或更多治疗因素间存在统计交互作用时进行，也可在预期存在时进行。若无交互作用，k-in-1析因试验用与一个平行组试验相同数量的单位回答k个单因素问题。英国关于k-in-1析因试验的文献主导了试验者对析因试验的理解。然而，析因实验起源于实验设计领域以能稳健估计交互作用。这些文献中看似冲突的指导给试验者带来困惑和误解。我们整合这些文献以阐明在有和无交互作用情况下推荐使用析因试验的论据。概述了激励性示例，总结了两种思想流派使用析因试验的基本原理、感兴趣的治疗对比及其估计量的属性，描述了可追溯到1935年的争论并用一个实证例子说明不同分析方法的影响。我们得出结论，试验者仔细且清晰地明确其目标至关重要，感兴趣的估计量和治疗对比随之而来，估计量的属性由该选择决定。
英文摘要
Factorial trials can be conducted when statistical interactions between two or more treatment factors are not anticipated, but also when they are. A k-in-1 factorial trial answers k single-factor questions with the same number of units as one parallel-group trial if there are no interactions. Literature on k-in-1 factorial trials has dominated trialists understanding of factorial trials in the UK. However, factorial experiments originated from the Design of Experiments field to enable interactions to be robustly estimated. Seemingly conflicting guidance from these literatures poses a source of confusion and misunderstanding for trialists. We bring these literatures together to provide clarity on the arguments that have been used to recommend use of factorial trials in the presence and absence of interactions. We outline motivating examples. We summarise the rationales for using factorial trials, the treatment contrasts of interest, and the properties of their estimators, for the two schools of thought. We describe the debate, going back to 1935, and use an empirical example to illustrate the impact of different analysis approaches. We conclude that it is vital that trialists carefully and clearly specify their objectives. Estimands of interest and treatment contrasts follow, with properties of estimators dictated by this choice.
