2026-04-17T18:58:20Zhttps://recercat.cat/oai/request

oai:recercat.cat:10256/217602024-06-18T12:42:03Zcom_2072_452955com_2072_2054col_2072_453079

2024-06-18T12:42:03Z urn:hdl:10256/21760 No Excess Mortality up to 10 Years in Early Stages of Breast Cancer in Women Adherent to Oral Endocrine Therapy: A Probabilistic Graphical Modeling Approach Clèries, Ramon Buxó Pujolràs, Maria Vilardell, Mireia Ameijide, Alberto Martínez, José Miguel Font, Rebeca Marcos-Gragera, Rafael Puigdemont, Montserrat Viñas, Gemma Carulla, Marià Espinàs, Josep Alfons Galceran, Jaume Izquierdo i Font, Àngel Xavier Borràs, Josep M. Mama -- Càncer -- Tractament Breast -- Cancer -- Treatment Mama -- Càncer -- Hormonoteràpia Breast -- Cancer -- Hormone therapy Modelització gràfica (Estadística) Graphical modeling (Statistics) Mama -- Càncer -- Mortalitat Breast -- Cancer -- Mortality Breast cancer (BC) is globally the most frequent cancer in women. Adherence to endocrine therapy (ET) in hormone-receptor-positive BC patients is active and voluntary for the first five years after diagnosis. This study examines the impact of adherence to ET on 10-year excess mortality (EM) in patients diagnosed with Stages I to III BC (N = 2297). Since sample size is an issue for estimating age-and stage-specific survival indicators, we developed a method, ComSynSurData, for generating a large synthetic dataset (SynD) through probabilistic graphical modeling of the original cohort. We derived population-based survival indicators using a Bayesian relative survival model fitted to the SynD. Our modeling showed that hormone-receptor-positive BC patients diagnosed beyond 49 years of age at Stage I or beyond 59 years at Stage II do not have 10-year EM if they follow the prescribed ET regimen. This result calls for developing interventions to promote adherence to ET in patients with hormone receptor-positive BC and in turn improving cancer survival. The presented methodology here demonstrates the potential use of probabilistic graphical modeling for generating reliable synthetic datasets for validating population-based survival indicators when sample size is an issue This work was supported by Instituto de Salud Carlos III PI18/01836 funded by FEDER funds/European Regional Development Fund (ERDF)-a way to Build Europe-//FONDOS FEDER “una manera de hacer Europa” 2024-06-18T12:42:03Z 2024-06-18T12:42:03Z 2022-03-18 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion peer-reviewed http://hdl.handle.net/10256/21760 info:eu-repo/semantics/altIdentifier/doi/10.3390/ijerph19063605 info:eu-repo/semantics/altIdentifier/issn/1661-7827 info:eu-repo/semantics/altIdentifier/eissn/1660-4601 Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess MDPI (Multidisciplinary Digital Publishing Institute) International Journal of Environmental Research and Public Health, 2022, vol. 19, núm. 6, p. 3605 Articles publicats (IdIBGi)