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RESEARCH PRODUCT

An Ordinal Joint Model for Breast Cancer

Hèctor PerpiñánHèctor PerpiñánCarmen ArmeroMontserrat RuéAnabel ForteCarles FornéMarisa BaréGuadalupe Gómez

subject

Oncologymedicine.medical_specialtyProportional hazards modelComputer scienceBayesian probabilityPosterior probabilityMarkov chain Monte CarloRandom effects modelmedicine.diseasesymbols.namesakeBreast cancerInternal medicineCovariateStatisticsmedicinesymbolsEvent (probability theory)

description

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.

https://doi.org/10.1007/978-3-319-55639-0_2