Computational strategies for multivariate linear mixed-effects models with missing values
Computational strategies for multivariate linear mixed-effects models with missing values
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DOI：
10.1198/106186002760180608
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发表时间：
2002-06-01
影响因子：
2.4
通讯作者：
Yucel, RM
中科院分区：
文献类型：
--
作者：
Schafer, JL;Yucel, RM
本文介绍了新的计算技术，多变量纵向或集群数据的缺失值。目前的线性混合效应模型方法可以处理单个响应变量的不平衡或缺失数据，但不能处理多个响应或额外协变量的缺失值。应用一个流行的线性混合效应模型的多变量扩展，我们创建一个简单有效的马尔可夫链蒙特卡罗程序的后续分析的缺失值的多重插补。我们还推导并实现了一个新的EM算法的参数估计，收敛速度比传统的EM算法，因为它不把随机效应作为“缺失数据”，但将它们整合出来的似然函数分析。这些技术说明了青少年酒精使用的模型在一个大型的学校为基础的预防试验。
This article presents new computational techniques for multivariate longitudinal or clustered data with missing values. Cur-rent methodology for linear mixed-effects models can accommodate imbalance or missing data in a single response variable, but it cannot handle missing values in multiple responses or additional covariates. Applying a multivariate extension of a popular linear mixed-effects model, we create multiple imputations of missing values for subsequent analyses by a straightforward and effective Markov chain Monte Carlo procedure. We also derive and implement a new EM algorithm for parameter estimation which converges more rapidly than traditional EM algorithms because it does not treat the random effects as "missing data," but integrates them out of the likelihood function analytically. These techniques are illustrated on models for adolescent alcohol use in a large school-based prevention trial.