ABSTRACT

In multivariate longitudinal studies, several outcomes are repeatedly measured for each subject over time. The data structure of these studies creates two types of associations which should take into account by the model: association of outcomes at a given time point and association among repeated measurements over time for a specific outcome. In our approach, because of some advantageous arisen from features like flexibility of marginal distributions, a copula-based approach is used for joint modeling of multivariate outcomes at each time points, also a transition model is used for considering the association of longitudinal measurements over time. For the problem of incomplete data, missingness mechanism is assumed to be ignorable. Some simulation results are reported in different scenarios using the Gaussian, t and several commonly used copulas of the family of Archimedean copulas. Akaike information criterion (AIC) is used to select the best copula function. The proposed approach is also used for analyzing a real obesity data set.

摘要

在多变量纵向研究中，随着时间的推移重复测量每个受试者的几个结果。这些研究的数据结构创建了模型应考虑的两种类型的关联：给定时间点的结果关联和特定结果随时间重复测量之间的关联。在我们的方法中，由于边缘分布的灵活性等特征产生了一些优势，基于 copula 的方法用于在每个时间点对多变量结果进行联合建模，还使用过渡模型来考虑纵向测量随时间的关联. 对于数据不完整的问题，假设缺失机制是可忽略的。使用高斯在不同场景下报告了一些模拟结果，t以及阿基米德系的几个常用系词。Akaike 信息准则 (AIC) 用于选择最佳 copula 函数。所提出的方法也用于分析真实的肥胖数据集。