Analysis of Serially Dependent Multivariate Longitudinal Non-Gaussian Continuous Data

Abstract

Serially dependent multivariate longitudinal non-Gaussian outcome data are commonly encountered in many fields of study, especially in biomedical sciences, finance, and so on. However, flexible methodologies for joint analysis of these outcomes are not well developed. Recently, Wu and de Leon (2014) and Withanage and de Leon (2015) introduced the class of Gaussian copula mixed models (GCMMs) for joint analysis of non-Gaussian outcomes. We adapt and extend the GCMM to settings that involve conditional as well as serial dependencies among longitudinal observations on the same or on different outcomes. We investigate the impact of failing to account for these dependencies via simulations. We illustrate our methodology on two datasets: one on data obtained from primary biliary cirrhosis patients, and the other on data from the Iowa Youth and Families Project.