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The document discusses a strategy using regression models to estimate relative survival and analyze prognostic factors in cancer survival studies. It highlights the use of parametric continuous functions and cubic regression splines to model mortality hazards over a 10-year follow-up period, while addressing issues related to sparse data. The methodology includes maximum likelihood estimation and model selection via the Akaike Information Criterion to improve survival estimates and interpret clinical data e

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eecverite

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EN