Abstract

With the advancement of modern technologies, intensive longitudinal studies, where subjects get intensively measured in a relatively short period of time, have become popular in psychological and biomedical area. Challenges arise in better analyzing the rich amount of data and at the same time avoiding biased statistical inference introduced by potential informative missingness. Building upon previous studies by Lin et al. and Kapura et al., we aim to address the problem of informative missingness in the context of multivariate intensive longitudinal outcomes. In this article, we present a multivariate shared-parameter model, where the multivariate intensive longitudinal outcomes are modeled by a mixed-effects locations scale model and further linked with the corresponding missing mechanism by sharing the subject random effects. The proposed model is then estimated using Bayesian sampling approach. An adolescent mood study example is illustrated and results show that joint modeling of the mood assessments not only provide more accurate effect estimates, but also valuable information on the associations between multiple outcomes.

摘要

随着现代技术的发展，在心理学和生物医学领域中，对纵向学科进行深入研究的密集型纵向研究已在相当短的时间内得到了广泛的测量。更好地分析丰富的数据，同时避免潜在的信息缺失带来的偏倚的统计推断，带来了挑战。在Lin等人以前的研究的基础上。和Kapura等人，我们的目标是在多元密集纵向结果的背景下解决信息缺失的问题。在本文中，我们提出了一个多元共享参数模型，其中，多元密集纵向结果是通过混合效应位置尺度模型来建模的，并通过共享主题随机效应进一步与相应的缺失机制联系起来。然后使用贝叶斯采样方法估计提出的模型。给出了一个青少年情绪研究示例，结果表明，情绪评估的联合建模不仅可以提供更准确的效果估计，而且还可以提供有关多个结果之间关联的有价值的信息。