Randomized controlled trials provide essential evidence for the evaluation of new and existing medical treatments. Unfortunately, the statistical analysis is often complicated by the occurrence of protocol deviations, which mean we cannot always measure the intended outcomes for individuals who deviate, resulting in a missing-data problem. In such settings, however one approaches the analysis, an untestable assumption about the distribution of the unobserved data must be made. To understand how far the results depend on these assumptions, the primary analysis should be supplemented by a range of sensitivity analyses, which explore how the conclusions vary over a range of different credible assumptions for the missing data. In this article, we describe a new command, mimix, that can be used to perform reference-based sensitivity analyses for randomized controlled trials with longitudinal quantitative outcome data, using the approach proposed by Carpenter, Roger, and Kenward (2013, Journal of Biopharmaceutical Statistics 23: 1352-1371). Under this approach, we make qualitative assumptions about how individuals' missing outcomes relate to those observed in relevant groups in the trial, based on plausible clinical scenarios. Statistical analysis then proceeds using the method of multiple imputation.

中文翻译：

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通过多重插补进行基于参考的敏感性分析，用于具有协议偏差的纵向试验。

随机对照试验为评估新的和现有的药物治疗提供了必要的证据。不幸的是，统计分析通常会因协议偏差的发生而变得复杂，这意味着我们不能总是衡量偏差个体的预期结果，从而导致数据缺失问题。在这种情况下，无论采用何种方法进行分析，都必须对未观察到的数据的分布做出不可检验的假设。为了了解结果在多大程度上依赖于这些假设，主要分析应辅以一系列敏感性分析，这些分析探索结论如何在缺失数据的一系列不同可信假设下变化。在本文中，我们描述了一个新命令 mimix，可用于使用 Carpenter、Roger 和 Kenward (2013, Journal of Biopharmaceutical Statistics 23: 1352-1371) 提出的方法，对具有纵向定量结果数据的随机对照试验进行基于参考的敏感性分析。在这种方法下，我们根据似是而非的临床情景，对个体缺失的结果与试验相关组中观察到的结果之间的关系做出定性假设。然后使用多重插补方法进行统计分析。根据合理的临床情况，缺失的结果与试验中相关组观察到的结果相关。然后使用多重插补方法进行统计分析。根据合理的临床情况，缺失的结果与试验中相关组观察到的结果相关。然后使用多重插补方法进行统计分析。