Proust Cécile, Jacqmin-Gadda Hélène
Institut National de la Santé et de la Recherche Médicale, Equipe de biostatistique E0338, Université de Bordeaux 2, 146 rue Léo Saignat, 33076 Bordeaux Cedex, France.
Comput Methods Programs Biomed. 2005 May;78(2):165-73. doi: 10.1016/j.cmpb.2004.12.004.
The aim of this paper is to propose an algorithm to estimate linear mixed model when random effect distribution is a mixture of Gaussians. This heterogeneous linear mixed model relaxes the classical Gaussian assumption for the random effects and, when used for longitudinal data, can highlight distinct patterns of evolution. The observed likelihood is maximized using a Marquardt algorithm instead of the EM algorithm which is frequently used for mixture models. Indeed, the EM algorithm is computationally expensive and does not provide good convergence criteria nor direct estimates of the variance of the parameters. The proposed method also allows to classify subjects according to the estimated profiles by computing posterior probabilities of belonging to each component. The use of heterogeneous linear mixed model is illustrated through a study of the different patterns of cognitive evolution in the elderly. HETMIXLIN is a free Fortran90 program available on the web site: http://www.isped.u-bordeaux2.fr.
本文旨在提出一种算法，用于在随机效应分布为高斯混合分布时估计线性混合模型。这种异质性线性混合模型放宽了对随机效应的经典高斯假设，并且在用于纵向数据时，可以突出不同的演变模式。使用Marquardt算法而不是常用于混合模型的EM算法来最大化观察到的似然度。实际上，EM算法计算成本高昂，且没有提供良好的收敛标准，也无法直接估计参数的方差。所提出的方法还允许通过计算属于每个成分的后验概率，根据估计的概况对受试者进行分类。通过对老年人认知演变的不同模式的研究，说明了异质性线性混合模型的使用。HETMIXLIN是一个免费的Fortran90程序，可在网站：http://www.isped.u-bordeaux2.fr上获取。
