Model checking in multiple imputation: an overview and case study
Model checking in multiple imputation: an overview and case study
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DOI：
10.1186/s12982-017-0062-6
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发表时间：
2017-08-23
影响因子：
2.3
通讯作者：
Lee, Katherine J.
中科院分区：
文献类型：
--
作者：
Nguyen, Cattram D.;Carlin, John B.;Lee, Katherine J.
背景：多重插补作为一种处理缺失数据的通用方法已经变得非常流行。基于多重插补的分析的有效性依赖于使用适当的模型来插补缺失值。尽管广泛使用的多重插补，有几个准则可用于检查插补models.Analysis：在本文中，我们提供了一个概述目前可用的方法检查插补models.Analysis。这些包括图形检查和数值汇总，以及基于模拟的方法，如后验预测检查。这些模型检查技术说明使用的分析影响缺失的数据从纵向研究Australian Children.Conclusions：作为多重插补成为进一步建立作为一个标准的方法处理缺失的数据，它将变得越来越重要，研究人员采用适当的模型检查方法，以确保使用这种方法时，获得可靠的结果。
Background: 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.Analysis: 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.Conclusions: 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.