Multiple imputation and posterior simulation for multivariate missing data in longitudinal studies
Multiple imputation and posterior simulation for multivariate missing data in longitudinal studies
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DOI：
10.1111/j.0006-341x.2000.01157.x
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发表时间：
2000-12-01
期刊：
影响因子：
1.9
通讯作者：
Belin, TR
中科院分区：
文献类型：
--
作者：
Liu, MZ;Taylor, JMG;Belin, TR
本文概述了一种在设计的纵向研究中处理缺失数据的多重补偿方法。为了适应不完全的多变量连续纵向数据，建立了一个随机系数模型。多变量重复测量被联合建模；具体地说，是I.I.D.。在以时间自变量和时间为条件的回归模型中，假设时间自变量为正态模型，时间自变量为分层随机系数模型，且变量和时间点的误差方差不同。Gibbs抽样用于提取模型参数和对丢失的观测值进行推算。对惊吓反应研究数据的应用说明了该模型。一项模拟研究将多重补偿过程与Robins、Rotnitzky和赵(1995，Journal of the American Statistics Association 90,106-121)的加权方法进行了比较，后者可用于处理类似的数据结构。
This paper outlines a multiple imputation method for handling missing data in designed longitudinal studies. A random coefficients model is developed to accommodate incomplete multivariate continuous longitudinal data. Multivariate repeated measures are jointly modeled; specifically, an i.i.d. normal model is assumed for time-independent variables and a hierarchical random coefficients model is assumed for time-dependent variables in a regression model conditional on the time-independent variables and time, with heterogeneous error variances across variables and time points. Gibbs sampling is used to draw model parameters and for imputations of missing observations. An application to data from a study of startle reactions illustrates the model. A simulation study compares the multiple imputation procedure to the weighting approach of Robins, Rotnitzky, and Zhao (1995, Journal of the American Statistical Association 90, 106-121) that can be used to address similar data structures.