Multivariate bounded discrete data arises in many fields. In the setting of longitudinal dementia studies, such data is collected when individuals complete neuropsychological tests. We outline a modeling and inference procedure that can model the joint distribution conditional on baseline covariates, leveraging previous work on mixtures of experts and latent class models. Furthermore, we illustrate how the work can be extended when the outcome data is missing at random using a nested EM algorithm. The proposed model can incorporate covariate information, perform imputation and clustering, and infer latent trajectories. We apply our model on simulated data and an Alzheimer's disease data set.

翻译：多变量有界离散数据出现在许多领域中。在纵向痴呆研究的背景下，这种数据是在个体完成神经心理学测试时收集的。我们概述了一种建模和推理程序，该程序可以基于基线协变量对联合分布进行建模，利用了先前在专家混合模型和潜在类别模型方面的研究成果。此外，我们通过嵌套期望最大化算法展示了当结果数据随机缺失时如何扩展该工作。所提出的模型可以整合协变量信息、执行插补和聚类，并推断潜在轨迹。我们将模型应用于模拟数据和一个阿尔茨海默病数据集。