Joint composite quantile regression modeling analysis of multivariate longitudinal data with its application to A liver cirrhosis dataset

In longitudinal data regression modeling, an individual measurements often include two or more responses. Especially in the field of clinical health research, there is usually some correlation between the response variables in longitudinal data. For joint modeling of multi-response longitudinal data, research is typically based on mean regression (MR) methods. However, for data with non-normal errors and the presence of outliers, mean regression (MR) methods often perform poorly. Composite quantile regression (CQR) methods not only provide robust estimates but also allow for the examination of the combined effects of a set of explanatory variables on multiple quantile points of the response variable. This paper proposes a joint composite quantile regression method for multi-response longitudinal data and investigates its application in a set of longitudinal medical datasets related to liver cirrhosis. First, a joint CQR method for multi-response longitudinal data is constructed based on the Pseudo Composite Asymmetric Laplace Distribution (PCALD) and latent variable models. Next, the MCMC algorithm is used to investigate the parameter estimation issues of the model. Finally, the effectiveness of the proposed model and methods is validated through Monte Carlo simulations and analysis of a set of longitudinal medical datasets related to liver cirrhosis. Copyright © 2025 Informa UK Limited, trading as Taylor & Francis Group.