C-mix: a high dimensional mixture model for censored durations, with applications to genetic data
This is an incremental improvement for biomedical researchers analyzing genetic data with censored time-to-event outcomes.
The authors tackled the problem of modeling censored durations in high-dimensional genetic data by introducing a mixture model (C-mix) with penalized maximum likelihood inference, showing that it outperforms state-of-the-art methods like CURE and Cox models in terms of C-index and AUC(t).
We introduce a mixture model for censored durations (C-mix), and develop maximum likelihood inference for the joint estimation of the time distributions and latent regression parameters of the model. We consider a high-dimensional setting, with datasets containing a large number of biomedical covariates. We therefore penalize the negative log-likelihood by the Elastic-Net, which leads to a sparse parameterization of the model. Inference is achieved using an efficient Quasi-Newton Expectation Maximization (QNEM) algorithm, for which we provide convergence properties. We then propose a score by assessing the patients risk of early adverse event. The statistical performance of the method is examined on an extensive Monte Carlo simulation study, and finally illustrated on three genetic datasets with high-dimensional covariates. We show that our approach outperforms the state-of-the-art, namely both the CURE and Cox proportional hazards models for this task, both in terms of C-index and AUC(t).