Huang Yangxin, Tang Nian-Sheng, Chen Jiaqing
College of Public Health, University of South Florida, Tampa, FL, USA.
Department of Statistics, College of Science, Yunnan University, Kunming, People's Republic of China.
J Appl Stat. 2021 Jun 4;49(12):3063-3089. doi: 10.1080/02664763.2021.1935797. eCollection 2022.
Methodological development and application of joint models for longitudinal and time-to-event data have mostly coupled a single longitudinal outcome-based linear mixed-effects model with normal distribution and Cox proportional hazards model. In practice, however, (i) profile of subject's longitudinal response may follow a `broken-stick nonlinear' (piecewise) trajectory. Such multiple phases are an important indicator to help quantify treatment effect, disease diagnosis and clinical decision-making. (ii) Normality in longitudinal models is a routine assumption, but it may be unrealistically obscuring important features of subject variations. (iii) Data collected are often featured by multivariate longitudinal outcomes which are significantly correlated, ignoring their correlation may lead to biased estimation. (iv) It is of importance to investigate how multivariate longitudinal outcomes are associated with event time of interest. In the article, driven by a motivating example, we propose Bayesian multivariate piecewise joint models with a skewed distribution and random change-points for longitudinal measures with an attempt to cope with correlated multivariate longitudinal data, adjust departures from normality, mediate accuracy from longitudinal trajectories with random change-point and tailor linkage in specifying a time-to-event process. A real example is analyzed to demonstrate methodology and simulation studies are conducted to evaluate performance of the proposed models and method.
纵向数据和事件发生时间数据联合模型的方法学发展与应用，大多是将基于单个纵向结局的正态分布线性混合效应模型与Cox比例风险模型相结合。然而在实际中，（i）受试者纵向反应的轨迹可能呈“折断式非线性”（分段）轨迹。这种多阶段情况是帮助量化治疗效果、疾病诊断和临床决策的重要指标。（ii）纵向模型中的正态性是一个常规假设，但它可能会不切实际地掩盖受试者变异的重要特征。（iii）收集到的数据通常具有显著相关的多变量纵向结局特征，忽略它们的相关性可能导致有偏估计。（iv）研究多变量纵向结局如何与感兴趣的事件时间相关联很重要。在本文中，受一个激励性实例的驱动，我们提出具有偏态分布和随机变化点的贝叶斯多变量分段联合模型，用于纵向测量，旨在处理相关的多变量纵向数据，调整与正态性的偏差，通过随机变化点从纵向轨迹中调节准确性，并在指定事件发生时间过程中调整联系。通过一个实际例子进行分析以展示方法，并进行模拟研究以评估所提出模型和方法的性能。
