A new approach to estimate time-to-cure from cancer registries data

Abstract : BackgroundCure 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.MethodsCure 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.ResultsDepending 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.ConclusionsWe 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.
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https://hal-univ-bourgogne.archives-ouvertes.fr/hal-01707745
Contributeur : Lnc - Université de Bourgogne <>
Soumis le : mardi 13 février 2018 - 09:55:38
Dernière modification le : jeudi 16 mai 2019 - 13:48:50

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Olayidé Boussari, Gaëlle Romain, Laurent Remontet, Nadine Bossard, Morgane Mounier, et al.. A new approach to estimate time-to-cure from cancer registries data. Cancer Epidemiology, Biomarkers and Prevention, American Association for Cancer Research, 2018, 53, pp.72 - 80. ⟨https://www.sciencedirect.com/science/article/pii/S1877782118300298⟩. ⟨10.1016/j.canep.2018.01.013⟩. ⟨hal-01707745⟩

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