A0349
Title: Federated Bayesian inference for time-to-event data
Authors: Hassan Pazira - Radboud University Medical Center (Netherlands) [presenting]
Marianne Jonker - Radboud university medical center (Netherlands)
Ton Coolen - Kings College London (United Kingdom)
Abstract: Due to the limited size of the available survival data sets, especially in rare diseases, it is sometimes challenging to identify the most relevant predictive features using multivariable statistical analysis. This issue may be resolved by combining data from multiple centers into one centralized location without sharing their data with each other, but doing so is difficult in reality because of privacy and security concerns. To address these challenges, we develop and implement a Federated Bayesian Inference (FBI) framework for multi-center data. It aims to leverage the statistical power of larger (combined) data sets without requiring all the data to be aggregated in one location. The FBI framework allows each center to use its own local data to infer the optimal parameter values as well as additional features of the posterior parameter distribution to be able to gather more information which is not captured by alternative techniques. The benefit of FBI over alternative approaches is that, only one inference cycle across the centers is required in FBI. For both simulated and real data, we evaluate how well the suggested technique performs.
