When distributed learning meets heterogeneity: choice of methods for federated survival analysis

Music City Center 

Survival analysis is widely utilized for analyzing risk factors and predicting disease progression. In multi-center studies, it is beneficial to integrate data from multiple sites to enhance the power of analyses, such as Cox proportional hazards regression. Federated learning algorithms have been employed to integrate multi-site clinical data, especially when individual patient data (IPD) cannot be shared across sites. While heterogeneity can significantly impact the development of federated algorithms, the performance of commonly used federated learning methods under various heterogeneity scenarios has not been thoroughly evaluated. In this paper, we compare three distributed learning algorithms: the meta-estimator, the One-shot Distributed Algorithm for Cox regression (ODAC), and the heterogeneous version of ODAC (ODACH). These comparisons are conducted through both a simulation study and a real-world application within a research network. We offer recommendations for their use in survival analysis practice.

Survival analysis

Federated learning

Cox pregression

Heterogeneity

Privacy-preserving 

Biometrics Section