Longitudinal semicontinuous data, characterized by repeated measures of a large portion of zeros and continuous positive values, are frequently encountered in many applications including biomedical, epidemiological, and social science studies. Two‐part random effects models (TPREM) have been used to investigate the association between such longitudinal semicontinuous data and covariates accounting for the within‐subject correlation. The existing TPREM is, however, limited to incorporate a functional covariate, which is often available in a longitudinal study. Moreover, the existing TPREM typically assumes the normality of subject‐specific random effects, which can be easily violated when there exists a subgroup structure. In this article, we propose a nonparametric Bayesian functional TPREM to assess the relationship between the longitudinal semicontinuous outcome and various types of covariates including a functional covariate. The proposed model also relaxes the normality assumption for the random effects through a Dirichlet process mixture of normals, which allows for identifying an underlying subgroup structure. The methodology is illustrated through an application to social insurance expenditure data collected by the Korean Welfare Panel Study and a simulation study.

中文翻译：

---

用于纵向半连续数据分析的非参数贝叶斯函数两部分随机效应模型

纵向半连续数据的特点是重复测量大部分零和连续正值，在许多应用中经常遇到，包括生物医学、流行病学和社会科学研究。两部分随机效应模型 (TPREM) 已被用于研究此类纵向半连续数据与解释受试者内相关性的协变量之间的关联。然而，现有的 TPREM 仅限于纳入功能协变量，这通常在纵向研究中可用。此外，现有的 TPREM 通常假设特定主题随机效应的正态性，当存在子组结构时很容易违反这一点。在本文中，我们提出了一个非参数贝叶斯函数 TPREM 来评估纵向半连续结果与包括函数协变量在内的各种类型的协变量之间的关系。所提出的模型还通过正态的 Dirichlet 过程混合放宽了随机效应的正态性假设，这允许识别潜在的子组结构。该方法通过对韩国福利小组研究和模拟研究收集的社会保险支出数据的应用来说明。