2026-04-17T11:41:51Zhttps://recercat.cat/oai/request

oai:recercat.cat:10459.1/569542024-12-05T22:28:13Zcom_2072_3622col_2072_479130

Frequentist and Bayesian approaches for a joint model for prostate cancer risk and longitudinal PSA data Serrat, Carles Rué i Monné, Montserrat Armero, Carmen Piulachs, Xavier Perpiñán, Hèctor Forte, Anabel Páez, Álvaro Gómez, Guadalupe Joint models Linear mixed models Prostate cancer screening 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. 2024-12-05T22:28:13Z 2024-12-05T22:28:13Z 2024-12-05T22:28:13Z 2016-05-05T08:50:04Z 2025-01-01 2014 article publishedVersion http://hdl.handle.net/10459.1/56954 MICINN/PN2008-2011/MTM2012-38067-C02-01 MICINN/PN2008-2011/MTM2010-19528 Reproducció del document publicat a https://doi.org/10.1080/02664763.2014.999032 Journal of Applied Statics, 2015, vol. 42, núm. 6 (c) Taylor & Francis, 2015 info:eu-repo/semantics/restrictedAccess Taylor & Francis