Lee Katherine J, Simpson Julie A
Clinical Epidemiology and Biostatistics Unit, Murdoch Childrens Research Institute, Melbourne, Victoria, Australia.
Department of Paediatrics, The University of Melbourne, Melbourne, Victoria, Australia.
Respirology. 2014 Feb;19(2):162-167. doi: 10.1111/resp.12226. Epub 2013 Dec 23.
Missing data are common in both observational and experimental studies. Multiple imputation (MI) is a two-stage approach where missing values are imputed a number of times using a statistical model based on the available data and then inference is combined across the completed datasets. This approach is becoming increasingly popular for handling missing data. In this paper, we introduce the method of MI, as well as a discussion surrounding when MI can be a useful method for handling missing data and the drawbacks of this approach. We illustrate MI when exploring the association between current asthma status and forced expiratory volume in 1 s after adjustment for potential confounders using data from a population-based longitudinal cohort study.
缺失数据在观察性研究和实验性研究中都很常见。多重填补（MI）是一种两阶段方法，即使用基于现有数据的统计模型对缺失值进行多次填补，然后在完整的数据集中进行推断合并。这种方法在处理缺失数据方面越来越受欢迎。在本文中，我们介绍了多重填补方法，以及关于何时多重填补可成为处理缺失数据的有用方法的讨论和该方法的缺点。我们使用基于人群的纵向队列研究的数据，在调整潜在混杂因素后，探索当前哮喘状态与一秒用力呼气量之间的关联时，展示了多重填补方法。
