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Bayesian Change-Point Joint Models for Multivariate Longitudinal and Time-to-Event Data
Statistics in Biopharmaceutical Research ( IF 1.8 ) Pub Date : 2020-11-25 , DOI: 10.1080/19466315.2020.1837234
Jiaqing Chen 1 , Yangxin Huang 2, 3 , Nian-Sheng Tang 3
Affiliation  

Abstract

Joint modeling of longitudinal and survival data is an active area of statistical research that has received much attention recently. Although it is a common practice to analyze complex longitudinal data using nonlinear mixed-effects (NLME) or nonparametric mixed-effects (NPME) models in literature, the following issues may standout: (i) In practice, the profile of each subject’s longitudinal response may follow a “broken-stick” like trajectory, indicating multiple phases of increase and/or decline in response trajectory. Such multiple phases (with random change-points) may be an important indicator to help quantify treatment effect and clinical diagnosis of a disease. To estimate random change-points, NLME or NPME models become a challenge. (ii) Many studies are often to collect data of multivariate longitudinal variables which may be significantly correlated, ignoring their correlation may lead to biased estimate and reduce efficiency. Moreover, the time-to-event may be dependent of the multivariate longitudinal measures and it is of importance to explore their association. (iii) Missing observations in the longitudinal responses are often encountered. The missing data are likely to be informative (nonignorable) and ignoring this phenomenon may result in inaccurate statistical inference. In this article, under a Bayesian framework we consider a piecewise joint model for multivariate longitudinal and time-to-event data to accurately estimate change rates of longitudinal trajectory patterns and timing of change-point which may be critical indicators to quantify the effect of longitudinal profile on the risk of an event. The proposed models and method are applied to analyze a longitudinal dataset arising from a diabetes study. Simulation studies are conducted to assess the performance of the proposed models under various scenarios.



中文翻译:

多变量纵向和事件时间数据的贝叶斯变点联合模型

摘要

纵向和生存数据的联合建模是统计研究的一个活跃领域,最近备受关注。尽管在文献中使用非线性混合效应 (NLME) 或非参数混合效应 (NPME) 模型分析复杂的纵向数据是一种常见的做法,但以下问题可能很突出:(i) 在实践中,每个受试者纵向响应的概况可能遵循类似“断棍”的轨迹,表明响应轨迹的增加和/或下降的多个阶段。这样的多个阶段(具有随机变化点)可能是帮助量化疾病的治疗效果和临床诊断的重要指标。为了估计随机变化点,NLME 或 NPME 模型成为一个挑战。(ii) 许多研究往往是收集可能显着相关的多元纵向变量的数据,忽略它们的相关性可能导致估计偏倚并降低效率。此外,事件发生时间可能取决于多变量纵向测量,探索它们的关联很重要。(iii) 经常会遇到纵向响应中的缺失观察。丢失的数据可能是信息丰富的(不可忽略的),忽略这种现象可能会导致不准确的统计推断。在本文中,在贝叶斯框架下,我们考虑了多元纵向和事件时间数据的分段联合模型,以准确估计纵向轨迹模式的变化率和变化点的时间,这可能是量化纵向剖面对风险的影响的关键指标的一个事件。所提出的模型和方法用于分析糖尿病研究产生的纵向数据集。进行模拟研究以评估所提出的模型在各种情况下的性能。

更新日期:2020-11-25
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