Multiple imputation by chained equations for systematically and sporadically missing multilevel data.
Multiple imputation by chained equations for systematically and sporadically missing multilevel data.
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DOI：
10.1177/0962280216666564
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发表时间：
2018-06
影响因子：
2.3
通讯作者：
White IR
中科院分区：
文献类型：
--
作者：
Resche-Rigon M;White IR
在多层次的设置，如个人参与者的数据荟萃分析，一个变量是“系统性失踪”，如果它是完全失踪的一些集群和“零星失踪”，如果它是部分失踪的一些集群。以前提出的方法来填补不完整的多层次数据处理系统或零星缺失的数据，但经常观察到这两种模式。我们描述了一个新的多重插补链式方程（MICE）算法的多层次数据的任意模式的系统性和偶发性缺失的变量。该算法描述了多级正常数据，但可以很容易地扩展到其他变量类型。首先，我们提出了两种方法来填补一个单一的不完整的变量：现有的方法和一个新的两阶段的方法，方便地允许异方差数据的扩展。然后，我们讨论了在多层次数据中使用MICE的几个变量中的缺失值的估算的困难，并表明，即使是最简单的联合多层次模型意味着条件模型，其中涉及聚类均值和异方差。然而，模拟研究发现，所提出的方法可以成功地结合在一个多层次的MICE过程中，即使集群的意思是不包括在插补模型。
In multilevel settings such as individual participant data meta-analysis, a variable is ‘systematically missing’ if it is wholly missing in some clusters and ‘sporadically missing’ if it is partly missing in some clusters. Previously proposed methods to impute incomplete multilevel data handle either systematically or sporadically missing data, but frequently both patterns are observed. We describe a new multiple imputation by chained equations (MICE) algorithm for multilevel data with arbitrary patterns of systematically and sporadically missing variables. The algorithm is described for multilevel normal data but can easily be extended for other variable types. We first propose two methods for imputing a single incomplete variable: an extension of an existing method and a new two-stage method which conveniently allows for heteroscedastic data. We then discuss the difficulties of imputing missing values in several variables in multilevel data using MICE, and show that even the simplest joint multilevel model implies conditional models which involve cluster means and heteroscedasticity. However, a simulation study finds that the proposed methods can be successfully combined in a multilevel MICE procedure, even when cluster means are not included in the imputation models.