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We introduce a mixture model for censored durations (C-mix), and developmaximum likelihood inference for the joint estimation of the time distributionsand latent regression parameters of the model. We consider a high-dimensionalsetting, with datasets containing a large number of biomedical covariates. Wetherefore penalize the negative log-likelihood by the Elastic-Net, which leadsto a sparse parameterization of the model. Inference is achieved using anefficient Quasi-Newton Expectation Maximization (QNEM) algorithm, for which weprovide convergence properties. We then propose a score by assessing thepatients risk of early adverse event. The statistical performance of the methodis examined on an extensive Monte Carlo simulation study, and finallyillustrated on three genetic datasets with high-dimensional covariates. We showthat our approach outperforms the state-of-the-art, namely both the CURE andCox proportional hazards models for this task, both in terms of C-index andAUC(t).

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