Competing risks joint models using R-INLA. (February 2021)

• Record Type: Journal Article
• Authors: Niekerk, Janet van; Bakka, Haakon; Rue, Håvard
• Other Names: Armero Carmen guest-editor.; Gómez-Rubio Virgilio guest-editor.
• Is Part Of: Statistical modelling. Volume 21:Number 1/2(2021)
• Page Start: 56
• Page End: 71
• Publication Date: 2021-02
• Subjects: accelerated failure time model -- competing risks -- integrated nested Laplace approximation -- joint model -- non-Gaussian -- spline; Linear models (Statistics) -- Periodicals; Mathematical models -- Periodicals; Modèles linéaires (Statistique) -- Périodiques; Modèles mathématiques -- Périodiques; Modèle statistique; Modèle linéaire; Modélisation statistique; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 519.5011
• DOI: 10.1177/1471082X20913654
• Languages: English
• ISSNs: 1471-082X

Abstract

The methodological advancements made in the field of joint models are numerous. None the less, the case of competing risks joint models has largely been neglected, especially from a practitioner's point of view. In the relevant works on competing risks joint models, the assumptions of a Gaussian linear longitudinal series and proportional cause-specific hazard functions, amongst others, have remained unchallenged. In this article, 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, times-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.