Competing risks joint models using R-INLA

Janet van Niekerk, Haakon Bakka, Haavard Rue

Research output: Contribution to journalArticlepeer-review

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.
Original languageEnglish (US)
Pages (from-to)1471082X1991365
JournalStatistical Modelling
DOIs
StatePublished - May 25 2020

Fingerprint

Dive into the research topics of 'Competing risks joint models using R-INLA'. Together they form a unique fingerprint.

Cite this