This paper focuses on developing latent class models for longitudinal data with zero-inflated count response variables. The goals are to model discrete longitudinal patterns of rare events counts (for instance, health-risky behavior), and to identify individual-specific covariates associated with latent class probabilities. Two discrete latent structures are present in this type of model: a latent categorical variable that classifies subgroups with distinct developmental trajectories and a latent binary variable that identifies whether an observation is from a zero-inflation process or a regular count process. Within each class, two sets of covariates are used to separately model the probability of structural zeros and the mean trajectories of the count process. The estimation of the latent variables and regression parameters are carried jointly in a hierarchical Bayesian framework. Our methods are validated through a simulation study and then applied to cigarette smoking data, obtained from the National Longitudinal Study of Adolescent Health.

中文翻译：

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贝叶斯零膨胀增长混合模型及其对健康风险行为数据的应用

本文着重于开发具有零膨胀计数响应变量的纵向数据的潜在类模型。目标是对稀有事件计数（例如，健康风险行为）的离散纵向模式进行建模，并识别与潜在类别概率相关的特定于个体的协变量。在这种类型的模型中存在两个离散的潜在结构：一个潜在的分类变量，对具有不同发展轨迹的子组进行分类；一个潜在的二进制变量，用于标识观察结果是来自零通货膨胀过程还是常规计数过程。在每个类别内，使用两组协变量分别对结构零的概率和计数过程的平均轨迹建模。潜在变量和回归参数的估计在分层贝叶斯框架中共同进行。我们的方法通过模拟研究得到验证，然后应用于从国家青少年健康纵向研究中获得的吸烟数据。