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Title:	Frequentist and Bayesian approaches for a joint model for prostate cancer risk and longitudinal prostate-specific antigen data
Author:	Serrat Piè, Carles; Rué, Montserrat; Armero, Carmen; Piulachs, Xavier; Perpiñán Fabuel, Hèctor; Forte, Anabel; Páez, Álvaro; Gómez Melis, Guadalupe
Other authors:	Universitat Politècnica de Catalunya. Departament de Matemàtiques; Universitat Politècnica de Catalunya. Departament d'Estadística i Investigació Operativa; Universitat Politècnica de Catalunya. GRBIO - Grup de Recerca en Bioestadística i Bioinformàtica
Abstract:	The paper describes the use of frequentist and Bayesian shared-parameter joint models of longitudinal measurements of prostate-specific antigen (PSA) and the risk of prostate cancer (PCa). The motivating dataset corresponds to the screening arm of the Spanish branch of the European Randomized Screening for Prostate Cancer study. The results show that PSA is highly associated with the risk of being diagnosed with PCa and that there is an age-varying effect of PSA on PCa risk. Both the frequentist and Bayesian paradigms produced very close parameter estimates and subsequent 95% confidence and credibility intervals. Dynamic estimations of disease-free probabilities obtained using Bayesian inference highlight the potential of joint models to guide personalized risk-based screening strategies.
Abstract:	Peer Reviewed
Subject(s):	-Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica -Survival analysis (Biometry) -62P10 -62N01 -relative risk models -linear mixed models -shared-parameter models -prostate cancer screening -joint models -TO-EVENT DATA -LATENT CLASS MODELS -DISEASE PROGRESSION -FOLLOW-UP -TIME -MORTALITY -SURVIVAL -FAILURE -PSA -PREDICTION -Anàlisi de supervivència (Biometria)
Rights:	http://creativecommons.org/licenses/by-nc-nd/3.0/es/
Document type:	Article - Submitted version Article
Published by:	Taylor & Francis
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