Missing data due to loss to follow‐up or intercurrent events are unintended, but unfortunately inevitable in clinical trials. Since the true values of missing data are never known, it is necessary to assess the impact of untestable and unavoidable assumptions about any unobserved data in sensitivity analysis. This tutorial provides an overview of controlled multiple imputation (MI) techniques and a practical guide to their use for sensitivity analysis of trials with missing continuous outcome data. These include δ ‐ and reference‐based MI procedures. In δ ‐based imputation, an offset term, δ , is typically added to the expected value of the missing data to assess the impact of unobserved participants having a worse or better response than those observed. Reference‐based imputation draws imputed values with some reference to observed data in other groups of the trial, typically in other treatment arms. We illustrate the accessibility of these methods using data from a pediatric eczema trial and a chronic headache trial and provide Stata code to facilitate adoption. We discuss issues surrounding the choice of δ in δ ‐based sensitivity analysis. We also review the debate on variance estimation within reference‐based analysis and justify the use of Rubin's variance estimator in this setting, since as we further elaborate on within, it provides information anchored inference.

中文翻译：

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缺少连续结果数据的临床试验的敏感性分析，使用受控多重插补：实用指南。

由于丢失随访或并发事件而导致的数据丢失是意料之外的，但不幸的是在临床试验中不可避免。由于永远不会知道丢失数据的真实值，因此有必要评估敏感性分析中关于任何未观察到的数据的无法测试和不可避免的假设的影响。本教程概述了可控多重插补（MI）技术，以及在缺少连续结果数据的试验中用于敏感性分析的实用指南。这些包括基于δ和基于参考的MI程序。在基于δ的估算中，偏移项δ通常将，添加到缺失数据的期望值中，以评估未观察到的参与者的反应比观察到的参与者差或更好的影响。基于参考的推算得出的推算值参考了其他试验组（通常是其他治疗组）中观察到的数据。我们使用儿童湿疹试验和慢性头痛试验的数据说明了这些方法的可及性，并提供了Stata代码以方便采用。我们讨论周围的选择问题δ以δ基于敏感性分析。我们还将回顾基于参考的分析中有关方差估计的争论，并证明在这种情况下使用鲁宾方差估计器是合理的，因为随着我们在内部进行进一步的阐述，它提供了信息锚定推理。