Department of Mathematical Sciences, Korea Advanced Institute of Science and Technology, Daejeon, Korea.
Department of Statistics, Korea University, Seoul, Korea.
Biom J. 2021 Apr;63(4):787-805. doi: 10.1002/bimj.201900280. Epub 2021 Feb 8.
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.
纵向半连续数据的特点是对大量零值和连续正值进行重复测量，这种数据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）已被用于研究这种纵向半连续数据与协变量之间的关系，这些协变量考虑了个体内相关性。然而，现有的 TPREM 仅限于包含功能协变量，而功能协变量通常在纵向研究中可用。此外，现有的 TPREM 通常假设个体随机效应的正态性，当存在亚组结构时，这种正态性很容易被违反。在本文中，我们提出了一种非参数贝叶斯功能 TPREM，用于评估纵向半连续结果与各种类型的协变量之间的关系，包括功能协变量。所提出的模型还通过正态混合的狄利克雷过程放宽了对随机效应的正态性假设，从而可以识别潜在的亚组结构。该方法通过对韩国福利面板研究收集的社会保险支出数据的应用和模拟研究进行了说明。
