Department of Medical Statistics, London School of Hygiene & Tropical Medicine, London, UK.
MRC Clinical Trials Unit at UCL, London, UK.
Biom J. 2021 Jun;63(5):915-947. doi: 10.1002/bimj.202000196. Epub 2021 Feb 24.
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.
在医学研究中，缺失数据是普遍存在的，但仍不确定何时限制使用完整记录是可以接受的，何时应该使用更复杂的方法（例如最大似然法、多重插补法和贝叶斯方法），以及它们之间的关系和敏感性分析的作用。本文旨在为对缺失数据（特别是使用多重插补）的一些结果进行更正式解释感兴趣的应用实践者和研究人员提供帮助。对于实践者来说，该框架、说明性示例和代码应该使他们能够采用一种实用的方法来解决缺失数据引起的问题，同时还概述了文献中各种方法的关系。特别是，我们描述了如何使用多重插补进行敏感性分析，而敏感性分析仍然很少进行。对于那些对更正式推导感兴趣的人，我们给出了关键结果的概要论证，使用简单的示例来说明方法之间的关系，并提供了详细信息的参考文献。这些想法通过一项队列研究、一项多中心病例对照研究和一项随机临床试验得到了说明。
