Coxmos: Interpretable survival models for high-dimensional and multi-omic data

Survival analysis in high-dimensional (HD) and multi-block (MB) settings, such as omic and multi-omic studies, poses major methodological challenges due to multicollinearity, low events-per-variable ratios, and limited model interpretability. While Cox Proportional Hazards models remain widely used, their applicability is restricted in HD scenarios. Machine learning approaches can handle such complexity but often lack interpretability. PLS-based survival models offer an attractive compromise, yet existing implementations provide limited support for model optimization, evaluation, and interpretation. To overcome these limitations, we introduce Coxmos, a CRAN R package that integrates adapted Cox regression models with HD-adapted variable selection, and optimized PLS-based approaches tailored for HD and MB data. Coxmos also provides validation, comparison, interpretation, and visualization tools. Benchmarking on clinical, single-omic, and multi-omic datasets demonstrated that Coxmos outperformed other state-of-the-art machine learning methods, while enhancing interpretability. With an ovarian cancer case study, we illustrated how Coxmos facilitates model selection, validation, and biological interpretation in a complex multi-omic survival analysis. Coxmos provides a robust, flexible, and interpretable solution for survival analysis in HD and MB data. By combining optimized PLS-based methodologies with rigorous evaluation and rich visualization tools, Coxmos enables reliable survival prediction and facilitates the identification of relevant risk and protective factors in complex biomedical studies. The package fills an important methodological gap and represents a valuable resource for reproducible and interpretable survival modeling in the big data scenario. Coxmos is available at CRAN repository: https://cran.r-project.org/web/packages/Coxmos.