A multivariate functional joint model framework is proposed which enables the repeatedly measured functional outcomes, scalar outcomes, and survival process to be modeled simultaneously while accounting for association among the multiple (functional and scalar) longitudinal and survival processes. This data structure is increasingly common across medical studies of neurodegenerative diseases and is exemplified by the motivating Alzheimer's Disease Neuroimaging Initiative (ADNI) study, in which serial brain imaging, clinical and neuropsychological assessments are collected to measure the progression of Alzheimer's disease (AD). The proposed functional joint model consists of a longitudinal function-on-scalar submodel, a regular longitudinal submodel, and a survival submodel which allows time-dependent functional and scalar covariates. A Bayesian approach is adopted for parameter estimation and a dynamic prediction framework is introduced for predicting the subjects' future health outcomes and risk of AD conversion. The proposed model is evaluated by a simulation study and is applied to the motivating ADNI study.

中文翻译：

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多变量纵向和事件时间数据的贝叶斯函数联合模型

提出了一种多元功能联合模型框架，它能够同时对重复测量的功能结果、标量结果和生存过程进行建模，同时考虑到多个（功能和标量）纵向和生存过程之间的关联。这种数据结构在神经退行性疾病的医学研究中越来越普遍，并以激励阿尔茨海默病神经影像学倡议 (ADNI) 研究为例，其中收集了一系列脑成像、临床和神经心理学评估，以衡量阿尔茨海默病 (AD) 的进展。所提出的功能联合模型由一个纵向函数对标量子模型、一个规则纵向子模型和一个允许时间相关函数和标量协变量的生存子模型组成。采用贝叶斯方法进行参数估计，并引入动态预测框架来预测受试者未来的健康结果和 AD 转换风险。所提出的模型通过模拟研究进行评估，并应用于激励 ADNI 研究。