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dc.contributor.authorNiekerk, Janet van
dc.contributor.authorBakka, Haakon
dc.contributor.authorRue, Haavard
dc.date.accessioned2019-12-19T13:14:14Z
dc.date.available2019-12-19T13:14:14Z
dc.date.issued2019-09-04
dc.identifier.urihttp://hdl.handle.net/10754/660709.1
dc.description.abstractThe 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.
dc.publisherarXiv
dc.relation.urlhttps://arxiv.org/pdf/1909.01637
dc.rightsArchived with thanks to arXiv
dc.titleCompeting risks joint models using R-INLA
dc.typePreprint
dc.contributor.departmentCEMSE Division, King Abdullah University of Science and Technology, Saudi Arabia
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentStatistics Program
dc.eprint.versionPre-print
dc.identifier.arxivid1909.01637
kaust.personNiekerk, Janet van
kaust.personBakka, Haakon
kaust.personRue, Haavard
refterms.dateFOA2019-12-19T13:14:32Z


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