Multiple imputation: current perspectives
Multiple imputation: current perspectives
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DOI：
10.1177/0962280206075304
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发表时间：
2007-01-01
影响因子：
2.3
通讯作者：
Carpenter, James
中科院分区：
文献类型：
--
作者：
Kenward, Michael G.;Carpenter, James
本文就多重归因及其在医学研究中的应用现状作一综述。我们从一般处理缺失数据的问题开始简要回顾，并在此背景下放置多重归因，强调其与具有缺失协变量的纵向临床试验和观察性研究的相关性。我们概述了多重归责在实践中是如何进行的，然后概述了其基本原理。我们较详细地探讨了获得适当补偿的问题，并区分了两类主要的方法：基于完全多变量模型的方法和迭代条件单变量模型的方法。我们展示了如何使用所谓的不合适的归罪模型是特别有价值的敏感性分析，也是在临床试验环境中的某些分析。我们还讨论了使用多重归因的其他形式的敏感度分析。最后，我们给出了多重归因使用的日益增多所带来的一些有待解决的问题，我们相信这些问题对未来的研究是有用的。
This paper provides an overview of multiple imputation and current perspectives on its use in medical research. We begin with a brief review of the problem of handling missing data in general and place multiple imputation in this context, emphasizing its relevance for longitudinal clinical trials and observational studies with missing covariates. We outline how multiple imputation proceeds in practice and then sketch its rationale. We explore the problem of obtaining proper imputations in some detail and distinguish two main classes of approach, methods based on fully multivariate models, and those that iterate conditional univariate models. We show how the use of so-called uncongenial imputation models are particularly valuable for sensitivity analyses and also for certain analyses in clinical trial settings. We also touch upon other forms of sensitivity analysis that use multiple imputation. Finally, we give some open questions that the increasing use of multiple imputation has thrown up, which we believe are useful directions for future research.