Discriminant analysis using a multivariate linear mixed model with a normal mixture in the random effects distribution
Discriminant analysis using a multivariate linear mixed model with a normal mixture in the random effects distribution
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DOI：
10.1002/sim.3849
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发表时间：
2010-12-30
影响因子：
2
通讯作者：
Lesaffre, Emmanuel
中科院分区：
文献类型：
--
作者：
Komarek, Arnost;Hansen, Bettina E.;Lesaffre, Emmanuel
我们已经开发出一种方法，使用与预后相关的标记物的纵向观察值将受试者纵向分类为两个或更多个预后组。我们假设训练数据集的可用性，其中受试者分配到预后组是已知的。所提出的方法进行两个步骤，如文献中前面所述。首先，在来自训练数据集的每个预后组中拟合多变量线性混合模型，以模拟标记物对时间和可能的其他协变量的依赖性。其次，拟合的混合模型被用来开发未来的主题的歧视规则。我们的方法改进了现有的方法，放松了正态性假设的随机效应的基础混合模型。也就是说，我们假设一个异方差的多元正态混合随机效应。使用马尔可夫链蒙特卡罗方法在贝叶斯框架中进行推理。已经为所提出的方法编写了软件，并且可以免费获得。该方法适用于荷兰原发性胆汁性肝硬化研究的数据。版权所有（C）2010约翰威利父子有限公司
We have developed a method to longitudinally classify subjects into two or more prognostic groups using longitudinally observed values of markers related to the prognosis. We assume the availability of a training data set where the subjects' allocation into the prognostic group is known. The proposed method proceeds in two steps as described earlier in the literature. First, multivariate linear mixed models are fitted in each prognostic group from the training data set to model the dependence of markers on time and on possibly other covariates. Second, fitted mixed models are used to develop a discrimination rule for future subjects. Our method improves upon existing approaches by relaxing the normality assumption of random effects in the underlying mixed models. Namely, we assume a heteroscedastic multivariate normal mixture for random effects. Inference is performed in the Bayesian framework using the Markov chain Monte Carlo methodology. Software has been written for the proposed method and it is freely available. The methodology is applied to data from the Dutch Primary Biliary Cirrhosis Study. Copyright (C) 2010 John Wiley & Sons, Ltd.