KAUST DepartmentCEMSE Division, King Abdullah University of Science and Technology, Saudi Arabia
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Preprint Posting Date2019-09-04
Permanent link to this recordhttp://hdl.handle.net/10754/660709
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AbstractThe 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.
CitationNiekerk, J. van, Bakka, H., & Rue, H. (2020). Competing risks joint models using R-INLA. Statistical Modelling, 1471082X1991365. doi:10.1177/1471082x19913654