Federated Cox Proportional Hazards Model with multicentric privacy-preserving LASSO feature selection for survival analysis from the perspective of personalized medicine

The Cox Proportional Hazards regression is among the most widely used models in clinical and epidemiological research for investigating the association between time-to-event outcomes and multiple predictors, that, in the modern perspective of personalized medicine, tend to belong to ever wider spheres relating to the patient and his medical condition. When the goal is to include a large number of variables in a prediction model, feature selection techniques are often required to ensure a certain level of interpretability of the results and federated learning is necessary to recruit in the study the sufficient number of patients for reliable model outcomes, overcoming the main problems of data privacy and ownership. In this regard, we here propose an adaptation for federated learning of the optimization algorithm of the Cox Proportional Hazards regression model with LASSO regularization as feature selector and we demonstrate the efficacy of our algorithm on real and simulated data sets in a simulated distributed environment with no patient-level data sharing by comparing its model parameter estimation performances with its centralised version.