Missing data are ubiquitous in medical research, yet there is still uncertainty over when restricting to the complete records is likely to be acceptable, when more complex methods (e.g. maximum likelihood, multiple imputation and Bayesian methods) should be used, how they relate to each other and the role of sensitivity analysis. This article seeks to address both applied practitioners and researchers interested in a more formal explanation of some of the results. For practitioners, the framework, illustrative examples and code should equip them with a practical approach to address the issues raised by missing data (particularly using multiple imputation), alongside an overview of how the various approaches in the literature relate. In particular, we describe how multiple imputation can be readily used for sensitivity analyses, which are still infrequently performed. For those interested in more formal derivations, we give outline arguments for key results, use simple examples to show how methods relate, and references for full details. The ideas are illustrated with a cohort study, a multi-centre case control study and a randomised clinical trial.

中文翻译：

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缺失数据：实践统计框架

缺失数据在医学研究中普遍存在，但仍然存在不确定性：何时限制完整记录可能是可以接受的、何时应使用更复杂的方法（例如最大似然法、多重插补和贝叶斯方法）、它们与每种方法的关系如何其他还有敏感性分析的作用。本文旨在针对对某些结果进行更正式解释感兴趣的应用从业者和研究人员。对于从业者来说，框架、说明性示例和代码应该为他们提供一种实用的方法来解决缺失数据（特别是使用多重插补）引起的问题，同时概述文献中的各种方法如何相互关联。特别是，我们描述了如何轻松地将多重插补用于敏感性分析，而这种分析仍然很少进行。对于那些对更正式的推导感兴趣的人，我们给出了关键结果的概要论证，使用简单的示例来展示方法之间的关系，并提供了完整细节的参考资料。这些想法通过队列研究、多中心病例对照研究和随机临床试验得到阐述。