Zhang Hongbin, Manandhar Binod
Department of Epidemiology and Biostatistics, Graduate School of Public Health and Health Policy, City University of New York, 55 West 125th Street, New York, United States.
Institute of Implementation Science for Population Health, City University of New York, 55 West 125th Street, New York, United States.
Proc Int Conf Stat Theory Appl ICSTA. 2021 Jul;2021. doi: 10.11159/icsta21.119.
Random effect change-point models are commonly used to infer individual-specific time of event that induces trend change of longitudinal data. Linear models are often employed before and after the change point. However, in applications such as HIV studies, a mechanistic nonlinear model can be derived for the process based on the underlying data-generation mechanisms and such nonlinear model may provide better ``predictions". In this article, we propose a random change-point model in which we model the longitudinal data by segmented nonlinear mixed effect models. Inference wise, we propose a maximum likelihood solution where we use the Stochastic Expectation-Maximization (StEM) algorithm coupled with independent multivariate rejection sampling through Gibbs's sampler. We evaluate the method with simulations to gain insights.
随机效应变点模型通常用于推断引起纵向数据趋势变化的个体特定事件时间。在变点前后通常采用线性模型。然而，在诸如艾滋病研究等应用中，可以基于潜在的数据生成机制为该过程推导一个机械非线性模型，并且这种非线性模型可能会提供更好的“预测”。在本文中，我们提出了一种随机变点模型，其中我们通过分段非线性混合效应模型对纵向数据进行建模。在推断方面，我们提出了一种最大似然解，其中我们使用随机期望最大化（StEM）算法，并通过吉布斯采样器进行独立多元拒绝采样。我们通过模拟评估该方法以获得深入了解。
