An alternative parameterization of the general linear mixture model for longitudinal data with non-ignorable drop-outs
An alternative parameterization of the general linear mixture model for longitudinal data with non-ignorable drop-outs
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DOI：
10.1002/sim.718
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发表时间：
2001-04-15
影响因子：
2
通讯作者：
Shneyer, L
中科院分区：
文献类型：
--
作者：
Fitzmaurice, GM;Laird, NM;Shneyer, L
本文考虑了在具有连续结果的纵向研究中处理不可忽略的退出的混合模型方法。最近，Hogan 和 Laird 开发了一种用于不可忽略的退出的混合模型，这是一个标准的线性混合效应模型，不同之处在于表征随时间变化的参数也取决于退出时间。也就是说，平均响应在时间、其他协变量和退出时间以及它们的交互作用上是线性的。针对退出问题的混合建模方法的主要吸引力之一是，探索结果对模型规范的敏感性相对容易。然而，混合模型的主要缺点是通常感兴趣的参数不能立即获得，而是需要在退出时间内对结果分布进行边缘化。此外，尽管对于给定退出时间的结果向量的条件均值假设了线性模型，但在边缘化之后，结果向量的无条件均值通常在回归参数中不是线性的。因此，不可能用回归系数来简单地描述协变量对结果边际分布的影响。混合建模方法的两个不吸引人的特征是需要对退出时间的分布进行显式平均，并且缺乏描述协变量对结果影响的回归系数。在本文中，我们描述了规避这两个问题的一般线性混合模型的特定参数化。版权所有 (C) 2001 John Wiley & Sons, Ltd.
This paper considers the mixture model methodology for handling non-ignorable drop-outs in longitudinal studies with continuous outcomes. Recently, Hogan and Laird have developed a mixture model for non-ignorable drop-outs which is a standard linear mixed effects model except that the parameters which characterize change over time depend also upon time of drop-out. That is, the mean response is linear in time, other covariates and drop-out time, and their interactions. One of the key attractions of the mixture modelling approach to drop-outs is that it is relatively easy to explore the sensitivity of results to model specification. However, the main drawback of mixture models is that the parameters that are ordinarily of interest are not immediately available, but require marginalization of the distribution of outcome over drop-out times. Furthermore, although a linear model is assumed for the conditional mean of the outcome vector given time of drop out, after marginalization, the unconditional mean of the outcome vector is not, in general, linear in the regression parameters. As a result, it is not possible to parsimoniously describe the effects of covariates on the marginal distribution of the outcome in terms of regression coefficients. The need to explicitly average over the distribution of the drop-out times and the absence of regression coefficients that describe the effects of covariates on the outcome are two unappealing features of the mixture modelling approach. In this paper we describe a particular parameterization of the general linear mixture model that circumvents both of these problems. Copyright (C) 2001 John Wiley & Sons, Ltd.