Olayidé Boussari1, Gaëlle Romain2, Laurent Remontet3, Nadine Bossard3, Morgane Mounier4, Anne-Marie Bouvier2, Christine Binquet5, Marc Colonna6, Valérie Jooste7. 1. Dijon-Bourgogne University Hospital, Registre Bourguignon des Cancers Digestifs, Dijon F-21000, France; INSERM, U1231, EPICAD team, Univ Bourgogne-Franche-Comté, UMR 1231, Dijon F-21000, France; LabEX LipSTIC, ANR-11-LABX-0021, Dijon F-21000, France. 2. Dijon-Bourgogne University Hospital, Registre Bourguignon des Cancers Digestifs, Dijon F-21000, France; INSERM, U1231, EPICAD team, Univ Bourgogne-Franche-Comté, UMR 1231, Dijon F-21000, France. 3. Service de Biostatistique-Bioinformatique, Hospices Civils de Lyon, Lyon F-69003, France; Université de Lyon, Lyon F-69000, France; Université Lyon 1, Villeurbanne F-69100, France; CNRS UMR 5558, Laboratoire de Biométrie et Biologie Évolutive, Équipe Biostatistique Santé, Pierre-Bénite F-69310, France. 4. Dijon-Bourgogne University Hospital, Univ Bourgogne-Franche-Comté, Registre des Hémopathies Malignes de Côte d'Or, Dijon, France. 5. INSERM, U1231, EPICAD team, Univ Bourgogne-Franche-Comté, UMR 1231, Dijon F-21000, France; INSERM, CIC1432, Clinical Epidemiology Unit, Dijon F-21000, France; Dijon-Bourgogne University Hospital, Clinical Investigation Centre, Clinical Epidemiology/Clinical Trials Unit, Dijon F-21000, France. 6. Registre du Cancer de l'Isère, Grenoble University Hospital, Grenoble F-38000, France. 7. Dijon-Bourgogne University Hospital, Registre Bourguignon des Cancers Digestifs, Dijon F-21000, France; INSERM, U1231, EPICAD team, Univ Bourgogne-Franche-Comté, UMR 1231, Dijon F-21000, France. Electronic address: valerie.jooste@u-bourgogne.fr.
Abstract
BACKGROUND: Cure models have been adapted to net survival context to provide important indicators from population-based cancer data, such as the cure fraction and the time-to-cure. However existing methods for computing time-to-cure suffer from some limitations. METHODS: Cure models in net survival framework were briefly overviewed and a new definition of time-to-cure was introduced as the time TTC at which P(t), the estimated covariate-specific probability of being cured at a given time t after diagnosis, reaches 0.95. We applied flexible parametric cure models to data of four cancer sites provided by the French network of cancer registries (FRANCIM). Then estimates of the time-to-cure by TTC and by two existing methods were derived and compared. Cure fractions and probabilities P(t) were also computed. RESULTS: Depending on the age group, TTC ranged from to 8 to 10 years for colorectal and pancreatic cancer and was nearly 12 years for breast cancer. In thyroid cancer patients under 55 years at diagnosis, TTC was strikingly 0: the probability of being cured was >0.95 just after diagnosis. This is an interesting result regarding the health insurance premiums of these patients. The estimated values of time-to-cure from the three approaches were close for colorectal cancer only. CONCLUSIONS: We propose a new approach, based on estimated covariate-specific probability of being cured, to estimate time-to-cure. Compared to two existing methods, the new approach seems to be more intuitive and natural and less sensitive to the survival time distribution.
BACKGROUND: Cure models have been adapted to net survival context to provide important indicators from population-based cancer data, such as the cure fraction and the time-to-cure. However existing methods for computing time-to-cure suffer from some limitations. METHODS: Cure models in net survival framework were briefly overviewed and a new definition of time-to-cure was introduced as the time TTC at which P(t), the estimated covariate-specific probability of being cured at a given time t after diagnosis, reaches 0.95. We applied flexible parametric cure models to data of four cancer sites provided by the French network of cancer registries (FRANCIM). Then estimates of the time-to-cure by TTC and by two existing methods were derived and compared. Cure fractions and probabilities P(t) were also computed. RESULTS: Depending on the age group, TTC ranged from to 8 to 10 years for colorectal and pancreatic cancer and was nearly 12 years for breast cancer. In thyroid cancerpatients under 55 years at diagnosis, TTC was strikingly 0: the probability of being cured was >0.95 just after diagnosis. This is an interesting result regarding the health insurance premiums of these patients. The estimated values of time-to-cure from the three approaches were close for colorectal cancer only. CONCLUSIONS: We propose a new approach, based on estimated covariate-specific probability of being cured, to estimate time-to-cure. Compared to two existing methods, the new approach seems to be more intuitive and natural and less sensitive to the survival time distribution.
Authors: Luigino Dal Maso; Chiara Panato; Stefano Guzzinati; Diego Serraino; Silvia Francisci; Laura Botta; Riccardo Capocaccia; Andrea Tavilla; Anna Gigli; Emanuele Crocetti; Massimo Rugge; Giovanna Tagliabue; Rosa Angela Filiberti; Giuliano Carrozzi; Maria Michiara; Stefano Ferretti; Rosaria Cesaraccio; Rosario Tumino; Fabio Falcini; Fabrizio Stracci; Antonietta Torrisi; Guido Mazzoleni; Mario Fusco; Stefano Rosso; Francesco Tisano; Anna Clara Fanetti; Giovanna Maria Sini; Carlotta Buzzoni; Roberta De Angelis Journal: Cancer Med Date: 2019-06-17 Impact factor: 4.452
Authors: Lasse H Jakobsen; Therese M-L Andersson; Jorne L Biccler; Laurids Ø Poulsen; Marianne T Severinsen; Tarec C El-Galaly; Martin Bøgsted Journal: BMC Med Res Methodol Date: 2020-03-26 Impact factor: 4.615