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

TL;DR

This paper introduces an exact likelihood estimation method for joint modelling of longitudinal and survival data using multivariate copulas, improving computational efficiency and providing dynamic survival predictions.

Contribution

It proposes an exact likelihood approach for copula-based joint models, replacing the computationally intensive Monte Carlo method, and compares Gaussian and t copulas for better modeling.

Findings

Exact likelihood estimation reduces computational cost.

Model achieves comparable prediction accuracy to existing methods.

Both Gaussian and t copulas effectively capture dependencies.

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 propose an exact likelihood estimation approach to replace the more computationally expensive Monte Carlo expectation-maximisation algorithm and we consider results based on using both the multivariate Gaussian and $t$ copula functions. We also provide a straightforward way to compute dynamic predictions of survival probabilities, showing that our proposed model is comparable in prediction performance to the shared random effects joint model.