Joint modelling of longitudinal measurements and survival times via a multivariate copula approach.

ABSTRACT: Joint modelling of longitudinal and time-to-event data is usually described by a joint model which uses shared or correlated latent effects to capture associations between the two processes. Under this framework, the joint distribution of the two processes can be derived straightforwardly by assuming conditional independence given the random effects. Alternative approaches to induce interdependency into sub-models have also been considered in the literature and one such approach is using copulas to introduce non-linear correlation between the marginal distributions of the longitudinal and time-to-event processes. The multivariate Gaussian copula joint model has been proposed in the literature to fit joint data by applying a Monte Carlo expectation-maximisation algorithm. In this paper, we

SUBMITTER: Zhang Z 

PROVIDER: S-EPMC10503460 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature