Statistical Modelling 21 (1-2) (2021), 56–71

Competing risks joint models using R-INLA

Janet van Niekerk,
CEMSE Division,
King Abdullah University of Science and Technology,
Saudi Arabia
e-mail: Janet.vanNiekerk@kaust.edu.sa

Haakon Bakka,
CEMSE Division,
King Abdullah University of Science and Technology,
Saudi Arabia


Håvard Rue,
CEMSE Division,
King Abdullah University of Science and Technology,
Saudi Arabia


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.

Keywords:

accelerated failure time model; competing risks; integrated nested Laplace approximation; joint model; non-Gaussian; spline.

Downloads:

Code can be found in the zipped archive. Data for the application are available in R as mentioned in the paper.
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