Frequentist and Bayesian approaches for a joint model for prostate cancer risk and longitudinal prostate-specific antigen data

AbstractThe 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 modelslinear mixed modelsprostate cancer screeningrelative risk modelsshared-parameter modelsAMS Subject Classification: 62N0162P10 AcknowledgementsAuthors are grateful to the TRUEJM group, specially to professor Dimitris Rizopoulos from the Erasmus Medical Center, for the fruitful discussions on joint modeling issues. We are particularly indebted to Dr Marcos Luján from the Hospital Universitario Infanta Cristina for providing the Spanish ERSPC database and for his generous collaboration on data interpretation and review of the manuscript. We also thank the reviewers of this paper for their valuable comments on a preliminary version of the manuscript and JP Glutting for review and editing.Disclosure statementNo potential conflict of interest was reported by the author(s).FundingThis paper has been partially supported by research grants MTM2012-38067-C02-01 and MTM2013-42323-P from the Spanish Ministry of Economy and Competitiveness and 2014 SGR 464 from the Departament d'Economia i Coneixement de la Generalitat de Catalunya.ORCIDCarles Serrat http://orcid.org/0000-0002-1504-5354