Multivariate Shared-Parameter Mixed-Effects Location Scale Model for Analysis of Intensive Longitudinal Data

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.