Factorial trials offer an efficient method to evaluate multiple interventions in a single trial, however the use of additional treatments can obscure research objectives, leading to inappropriate analytical methods and interpretation of results. We define a set of estimands for factorial trials, and describe a framework for applying these estimands, with the aim of clarifying trial objectives and ensuring appropriate primary and sensitivity analyses are chosen. This framework is intended for use in factorial trials where the intent is to conduct “two-trials-in-one” (ie, to separately evaluate the effects of treatments A and B), and is comprised of four steps: (i) specifying how additional treatment(s) (eg, treatment B) will be handled in the estimand, and how intercurrent events affecting the additional treatment(s) will be handled; (ii) designating the appropriate factorial estimator as the primary analysis strategy; (iii) evaluating the interaction to assess the plausibility of the assumptions underpinning the factorial estimator; and (iv) performing a sensitivity analysis using an appropriate multiarm estimator to evaluate to what extent departures from the underlying assumption of no interaction may affect results. We show that adjustment for other factors is necessary for noncollapsible effect measures (such as odds ratio), and through a trial re-analysis we find that failure to consider the estimand could lead to inappropriate interpretation of results. We conclude that careful use of the estimands framework clarifies research objectives and reduces the risk of misinterpretation of trial results, and should become a standard part of both the protocol and reporting of factorial trials.

中文翻译：

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析因试验的估计值

因子试验提供了一种在单个试验中评估多种干预措施的有效方法，但是使用额外的治疗可能会掩盖研究目标，从而导致不适当的分析方法和结果解释。我们为因子试验定义了一组估计值，并描述了应用这些估计值的框架，目的是阐明试验目标并确保选择适当的主要分析和敏感性分析。该框架旨在用于旨在进行“二合一试验”（即分别评估治疗 A 和 B 的效果）的析因试验，由四个步骤组成：(i) 指定如何在估计中处理额外的治疗（例如，治疗 B），以及如何处理影响额外治疗的并发事件；(ii) 指定适当的因子估计作为主要分析策略；(iii) 评估相互作用以评估支撑因子估计量的假设的合理性；(iv) 使用适当的多臂估计器进行敏感性分析，以评估偏离无相互作用的基本假设在多大程度上可能影响结果。我们表明，对于不可折叠的效应测量（例如比值比），需要对其他因素进行调整，并且通过试验重新分析，我们发现不考虑估计量可能会导致对结果的不恰当解释。我们的结论是，谨慎使用估计框架可以阐明研究目标并降低误解试验结果的风险，