A0717
Title: A two-level copula joint model for joint analysis of longitudinal and competing risks data
Authors: Thierry Chekouo - University of Minnesota (United States) [presenting]
Abstract: A two-level copula joint model is proposed to analyze clinical data with multiple disparate continuous longitudinal outcomes and multiple event times in the presence of competing risks. At the first level, a copula to model the dependence between competing latent event times, in the process of constructing the submodel for the observed event time is used, and the Gaussian copula is employed to construct the submodel for the longitudinal outcomes that account for their conditional dependence; these submodels are glued together at the second level via the Gaussian copula to construct a joint model that incorporates conditional dependence between the observed event-time and the longitudinal outcomes. To have the flexibility to accommodate skewed data and examine possibly different covariate effects on quantiles of a non-Gaussian outcome, linear quantile mixed models are proposed for the continuous longitudinal data. A Bayesian framework is adopted for model estimation and inference via Markov Chain Monte Carlo sampling. The performance of the Copula joint model is examined through a simulation study, and it is shown that the proposed method outperforms the conventional approach assuming conditional independence with smaller biases and better coverage probabilities of the Bayesian credible intervals. Finally, an analysis of clinical data is carried out on renal transplantation for illustration.
