C-mix: a high dimensional mixture model for censored durations, with applications to genetic data

Abstract : We introduce a supervised learning mixture model for censored durations (C-mix) to simultaneously detect subgroups of patients with different prognosis and order them based on their risk. Our method is applicable in a high-dimensional setting, i.e. with a large number of biomedical covariates. Indeed, we penalize the negative log-likelihood by the Elastic-Net, which leads to a sparse parameterization of the model and automatically pinpoints the relevant covariates for the survival prediction. Inference is achieved using an efficient Quasi-Newton Expectation Maximization (QNEM) algorithm, for which we provide convergence properties. The statistical performance of the method is examined on an extensive Monte Carlo simulation study, and finally illustrated on three publicly available genetic cancer datasets with high-dimensional co-variates. We show that our approach outperforms the state-of-the-art survival models in this context, namely both the CURE and Cox proportional hazards models penalized by the Elastic-Net, in terms of C-index, AUC(t) and survival prediction. Thus, we propose a powerfull tool for personalized medicine in cancerology.
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Pré-publication, Document de travail
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Contributeur : Simon Bussy <>
Soumis le : samedi 25 novembre 2017 - 18:50:13
Dernière modification le : mercredi 23 janvier 2019 - 10:29:27


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  • HAL Id : hal-01648389, version 1


Simon Bussy, Agathe Guilloux, Stéphane Gaïffas, Anne-Sophie Jannot. C-mix: a high dimensional mixture model for censored durations, with applications to genetic data. 2017. 〈hal-01648389〉



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