  In epidemiological and clinical studies, identifying patients' phenotypes
based on longitudinal profiles is critical to understanding the disease's
developmental patterns. The current study was motivated by data from a Canadian
birth cohort study, the CHILD Cohort Study. Our goal was to use multiple
longitudinal respiratory traits to cluster the participants into subgroups with
similar longitudinal respiratory profiles in order to identify clinically
relevant disease phenotypes. To appropriately account for distinct structures
and types of these longitudinal markers, we proposed a novel joint model for
clustering mixed-type (continuous, discrete and categorical) multivariate
longitudinal data. We also developed a Markov Chain Monte Carlo algorithm to
estimate the posterior distribution of model parameters. Analysis of the CHILD
Cohort data and simulated data were presented and discussed. Our study
demonstrated that the proposed model serves as a useful analytical tool for
clustering multivariate mixed-type longitudinal data. We developed an R package
BCClong to implement the proposed model efficiently.
