Nguyen Cattram D, Carlin John B, Lee Katherine J
Clinical Epidemiology and Biostatistics Unit, Murdoch Childrens Research Institute, The Royal Children's Hospital, Flemington Road, Parkville, VIC 3052 Australia.
Department of Paediatrics (RCH Academic Centre), Faculty of Medicine, Dentistry and Health Sciences, The Royal Children's Hospital, University of Melbourne, Flemington Road, Parkville, VIC 3052 Australia.
Emerg Themes Epidemiol. 2017 Aug 23;14:8. doi: 10.1186/s12982-017-0062-6. eCollection 2017.
Multiple imputation has become very popular as a general-purpose method for handling missing data. The validity of multiple-imputation-based analyses relies on the use of an appropriate model to impute the missing values. Despite the widespread use of multiple imputation, there are few guidelines available for checking imputation models.
In this paper, we provide an overview of currently available methods for checking imputation models. These include graphical checks and numerical summaries, as well as simulation-based methods such as posterior predictive checking. These model checking techniques are illustrated using an analysis affected by missing data from the Longitudinal Study of Australian Children.
As multiple imputation becomes further established as a standard approach for handling missing data, it will become increasingly important that researchers employ appropriate model checking approaches to ensure that reliable results are obtained when using this method.
多重填补作为处理缺失数据的通用方法已变得非常流行。基于多重填补的分析的有效性依赖于使用适当的模型来填补缺失值。尽管多重填补被广泛使用，但用于检查填补模型的指南却很少。
在本文中，我们概述了当前可用于检查填补模型的方法。这些方法包括图形检查和数值总结，以及基于模拟的方法，如后验预测检查。使用来自澳大利亚儿童纵向研究的受缺失数据影响的分析来说明这些模型检查技术。
随着多重填补作为处理缺失数据的标准方法得到进一步确立，研究人员采用适当的模型检查方法以确保在使用该方法时获得可靠结果将变得越来越重要。
