Frequentist and Bayesian approaches for a joint model for prostate cancer risk and longitudinal PSA data

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 (ERSPC) 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.

This paper has been partially supported by research grants MTM2012-38067-C02-01 and MTM2010-19528 from the Spanish Ministry of Economy and Competitiveness and the Spanish Ministry of Education and Science, respectively.