Huang Yangxin, Chen Jiaqing, Xu Lan, Tang Nian-Sheng
College of Public Health, University of South Florida, Tampa, FL, United States.
Department of Statistics, College of Science, Wuhan University of Technology, Wuhan, China.
Front Big Data. 2022 Apr 27;5:812725. doi: 10.3389/fdata.2022.812725. eCollection 2022.
Joint models of longitudinal and time-to-event data have received a lot of attention in epidemiological and clinical research under a linear mixed-effects model with the normal assumption for a single longitudinal outcome and Cox proportional hazards model. However, those model-based analyses may not provide robust inference when longitudinal measurements exhibit skewness and/or heavy tails. In addition, the data collected are often featured by multivariate longitudinal outcomes which are significantly correlated, and ignoring their correlation may lead to biased estimation. Under the umbrella of Bayesian inference, this article introduces multivariate joint (MVJ) models with a skewed distribution for multiple longitudinal exposures in an attempt to cope with correlated multiple longitudinal outcomes, adjust departures from normality, and tailor linkage in specifying a time-to-event process. We develop a Bayesian joint modeling approach to MVJ models that couples a multivariate linear mixed-effects (MLME) model with the skew-normal (SN) distribution and a Cox proportional hazards model. Our proposed models and method are evaluated by simulation studies and are applied to a real example from a diabetes study.
在对单个纵向结局采用正态假设的线性混合效应模型以及Cox比例风险模型下，纵向数据和事件发生时间数据的联合模型在流行病学和临床研究中受到了广泛关注。然而，当纵向测量呈现偏态和/或厚尾时，那些基于模型的分析可能无法提供稳健的推断。此外，收集到的数据通常具有显著相关的多变量纵向结局特征，忽略它们之间的相关性可能会导致有偏估计。在贝叶斯推断的框架下，本文引入了具有偏态分布的多变量联合（MVJ）模型，用于多个纵向暴露，以试图应对相关的多个纵向结局、调整偏离正态的情况，并在指定事件发生时间过程时进行适当的关联。我们开发了一种针对MVJ模型的贝叶斯联合建模方法，该方法将具有偏态正态（SN）分布的多变量线性混合效应（MLME）模型与Cox比例风险模型相结合。我们提出的模型和方法通过模拟研究进行评估，并应用于一项糖尿病研究的实际例子。
