Muthén B, Shedden K
Graduate School of Education and Information Studies and Department of Statistics, University of California, Los Angeles, California 90095-1521, USA.
Biometrics. 1999 Jun;55(2):463-9. doi: 10.1111/j.0006-341x.1999.00463.x.
This paper discusses the analysis of an extended finite mixture model where the latent classes corresponding to the mixture components for one set of observed variables influence a second set of observed variables. The research is motivated by a repeated measurement study using a random coefficient model to assess the influence of latent growth trajectory class membership on the probability of a binary disease outcome. More generally, this model can be seen as a combination of latent class modeling and conventional mixture modeling. The EM algorithm is used for estimation. As an illustration, a random-coefficient growth model for the prediction of alcohol dependence from three latent classes of heavy alcohol use trajectories among young adults is analyzed.
本文讨论了一种扩展有限混合模型的分析方法，其中一组观测变量的混合成分对应的潜在类别会影响第二组观测变量。该研究的动机来自于一项重复测量研究，该研究使用随机系数模型来评估潜在生长轨迹类别成员资格对二元疾病结局概率的影响。更一般地说，该模型可以看作是潜在类别建模和传统混合建模的结合。使用期望最大化（EM）算法进行估计。作为一个示例，分析了一个随机系数增长模型，用于从年轻成年人中重度饮酒轨迹的三个潜在类别预测酒精依赖。
