Bayesian mixed-effects location and scale models for multivariate longitudinal outcomes: an application to ecological momentary assessment data.

In the statistical literature, the methods to understand the relationship of explanatory variables on each individual outcome variable are well developed and widely applied. However, in most health-related studies given the technological advancement and sophisticated methods of obtaining and storing data, a need to perform joint analysis of multivariate outcomes while explaining the impact of predictors simultaneously and accounting for all the correlations is in high demand. In this manuscript, we propose a generalized approach within a Bayesian framework that models the changes in the variation in terms of explanatory variables and captures the correlations between the multivariate continuous outcomes by the inclusion of random effects at both the location and scale levels. We describe the use of a spherical transformation for the correlations between the random location and scale effects in order to apply separation strategy for prior elicitation while ensuring positive semi-definiteness of the covariance matrix. We present the details of our approach using an example from an ecological momentary assessment study on adolescents.