SummaryAnalysis of longitudinal randomized clinical trials is frequently complicated because patients deviate from the protocol. Where such deviations are relevant for the estimand, we are typically required to make an untestable assumption about post-deviation behaviour to perform our primary analysis and to estimate the treatment effect. In such settings, it is now widely recognized that we should follow this with sensitivity analyses to explore the robustness of our inferences to alternative assumptions about post-deviation behaviour. Although there has been much work on how to conduct such sensitivity analyses, little attention has been given to the appropriate loss of information due to missing data within sensitivity analysis. We argue that more attention needs to be given to this issue, showing that it is quite possible for sensitivity analysis to decrease and increase the information about the treatment effect. To address this critical issue, we introduce the concept of information-anchored sensitivity analysis. By this we mean sensitivity analyses in which the proportion of information about the treatment estimate lost because of missing data is the same as the proportion of information about the treatment estimate lost because of missing data in the primary analysis. We argue that this forms a transparent, practical starting point for interpretation of sensitivity analysis. We then derive results showing that, for longitudinal continuous data, a broad class of controlled and reference-based sensitivity analyses performed by multiple imputation are information anchored. We illustrate the theory with simulations and an analysis of a peer review trial and then discuss our work in the context of other recent work in this area. Our results give a theoretical basis for the use of controlled multiple-imputation procedures for sensitivity analysis.

中文翻译：

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信息锚定敏感性分析：理论与应用

概括纵向随机临床试验的分析通常很复杂，因为患者偏离了方案。当此类偏差与被估计值相关时，我们通常需要对偏差后行为做出无法检验的假设，以执行我们的初步分析并估计治疗效果。在这种情况下，现在人们普遍认识到，我们应该遵循这一点进行敏感性分析，以探索我们对偏离后行为的替代假设的推论的稳健性。尽管在如何进行此类敏感性分析方面已经做了很多工作，但很少有人关注由于敏感性分析中数据缺失而导致的适当信息丢失。我们认为需要更多地关注这个问题，这表明敏感性分析很有可能减少和增加有关治疗效果的信息。为了解决这个关键问题，我们引入了信息锚定敏感性分析的概念。我们指的是敏感性分析，其中由于缺失数据而丢失的治疗估计信息的比例与主要分析中由于丢失数据而丢失的治疗估计信息的比例相同。我们认为，这为解释敏感性分析形成了一个透明、实用的起点。然后我们得出的结果表明，对于纵向连续数据，通过多重插补进行的一大类受控和基于参考的敏感性分析是信息锚定的。我们通过模拟和同行评审试验的分析来说明该理论，然后在该领域其他近期工作的背景下讨论我们的工作。我们的结果为使用受控多重插补程序进行敏感性分析提供了理论基础。