Abstract
We propose a Bayesian joint model to analyze the association between longitudinal measurements of an ordinal marker and time to a relevant event. The longitudinal process is defined in terms of a proportional-odds cumulative logit model and the time-to-event process through a left-truncated Cox proportional hazards model with information of the longitudinal marker and baseline covariates. Both longitudinal and survival processes are connected by a common vector of random effects.
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References
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Acknowledgements
This paper was partially supported by the research grants MTM2013-42323-P, MTM2012-38067-C02-1, PI14/00113 from the Spanish Ministry of Economy and Competitiveness, ACOMP/2015/202 from the Generalitat Valenciana, and GRBIO-2014-SGR464 and GRAES-2014-SGR978 from the Generalitat de Catalunya.
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Armero, C. et al. (2017). An Ordinal Joint Model for Breast Cancer. In: Ainsbury, E., Calle, M., Cardis, E., Einbeck, J., Gómez, G., Puig, P. (eds) Extended Abstracts Fall 2015. Trends in Mathematics(), vol 7. Birkhäuser, Cham. https://doi.org/10.1007/978-3-319-55639-0_2
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DOI: https://doi.org/10.1007/978-3-319-55639-0_2
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