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Frequentist and Bayesian approaches for a joint model for prostate cancer risk and longitudinal prostate-specific antigen data |
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| Authors: | Carles Serrat Montserrat Rué Carmen Armero Xavier Piulachs Hèctor Perpiñán Anabel Forte |
| Institution: | 1. Department of Applied Mathematics I, Universitat Politècnica de Catalunya-BarcelonaTECH, Avgda. Dr. Mara?ón, 44–50, 08028 Barcelona, Spaincarles.serrat@upc.edu;3. Department of Basic Medical Sciences, Institut de Recerca Biomèdica de Lleida, Universitat de Lleida, Avgda. Rovira Roure, 80, 25198 Lleida, Spain;4. Health Services Research Network in Chronic Diseases (REDISSEC);5. Department of Statistics and OR, Universitat de València, Doctor Moliner, 50, 46100 Burjassot, Spain;6. Department of Econometrics. Riskcenter-IREA, Universitat de Barcelona, Avgda. Diagonal, 690, 08034 Barcelona, Spain |
| 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. |
| Keywords: | joint models linear mixed models prostate cancer screening relative risk models shared-parameter models |