Competing risks joint models using R-INLA

TL;DR

This paper introduces a flexible R-INLA framework for competing risks joint models, accommodating non-Gaussian data, spatial structures, and complex associations, addressing gaps in existing methods.

Contribution

It provides a unifying approach for competing risks joint models that handles non-Gaussian, spatial, and complex longitudinal data, expanding applicability beyond traditional assumptions.

Findings

Developed a framework using R-INLA for diverse competing risks joint models.

Applied the method to the SANAD trial with non-linear trajectories.

Presented specific models for count data and spatial structures.

Abstract

The methodological advancements made in the field of joint models are numerous. None the less, the case of competing risks joint models have largely been neglected, especially from a practitioner's point of view. In the relevant works on competing risks joint models, the assumptions of Gaussian linear longitudinal series and proportional cause-specific hazard functions, amongst others, have remained unchallenged. In this paper, we provide a framework based on R-INLA to apply competing risks joint models in a unifying way such that non-Gaussian longitudinal data, spatial structures, time dependent splines and various latent association structures, to mention a few, are all embraced in our approach. Our motivation stems from the SANAD trial which exhibits non-linear longitudinal trajectories and competing risks for failure of treatment. We also present a discrete competing risks joint model for longitudinal count data as well as a spatial competing risks joint model, as specific examples.