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dc.contributor.authorVan Niekerk, Janet
dc.contributor.authorBakka, Haakon
dc.contributor.authorRue, Haavard
dc.date.accessioned2020-03-04T11:09:58Z
dc.date.available2019-12-19T13:14:14Z
dc.date.available2020-03-04T11:09:58Z
dc.date.issued2020-05-25
dc.identifier.citationNiekerk, J. van, Bakka, H., & Rue, H. (2020). Competing risks joint models using R-INLA. Statistical Modelling, 1471082X1991365. doi:10.1177/1471082x19913654
dc.identifier.issn1477-0342
dc.identifier.issn1471-082X
dc.identifier.doi10.1177/1471082X19913654
dc.identifier.urihttp://hdl.handle.net/10754/660709
dc.description.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.
dc.publisherSAGE Publications
dc.relation.urlhttp://journals.sagepub.com/doi/10.1177/1471082X19913654
dc.rightsArchived with thanks to Statistical Modelling
dc.titleCompeting risks joint models using R-INLA
dc.typeArticle
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.identifier.journalStatistical Modelling
dc.eprint.versionPost-print
dc.identifier.pages1471082X1991365
pubs.publication-statusAccepted
dc.identifier.arxivid1909.01637
kaust.personNiekerk, Janet van
kaust.personBakka, Haakon
kaust.personRue, Haavard
dc.identifier.eid2-s2.0-85085365715
refterms.dateFOA2019-12-19T13:14:32Z
dc.date.posted2019-09-04


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