Binacox: automatic cut-point detection in high-dimensional Cox model with applications in genetics

TL;DR

Binacox is a new high-dimensional Cox model method that automatically detects multiple cut-points in features, improving risk prediction and interpretability in genetic survival analysis.

Contribution

It introduces the binacox method combining one-hot encoding with total-variation regularization for automatic cut-point detection and feature selection in high-dimensional Cox models.

Findings

Outperforms state-of-the-art models in C-index for genetic datasets

Provides faster computation times by orders of magnitude

Offers interpretable cut-points for clinical insights

Abstract

We introduce the binacox, a prognostic method to deal with the problem of detecting multiple cut-points per features in a multivariate setting where a large number of continuous features are available. The method is based on the Cox model and combines one-hot encoding with the binarsity penalty, which uses total-variation regularization together with an extra linear constraint, and enables feature selection. Original nonasymptotic oracle inequalities for prediction (in terms of Kullback-Leibler divergence) and estimation with a fast rate of convergence are established. The statistical performance of the method is examined in an extensive Monte Carlo simulation study, and then illustrated on three publicly available genetic cancer datasets. On these high-dimensional datasets, our proposed method significantly outperforms state-of-the-art survival models regarding risk prediction in terms of the C-index, with a computing time orders of magnitude faster. In addition, it provides powerful interpretability from a clinical perspective by automatically pinpointing significant cut-points in relevant variables.