Privacy-aware multi-institutional time-to-event studies

Clinical time-to-event studies are dependent on large sample sizes, often not available at a single institution. However, this is countered by the fact that, particularly in the medical field, individual institutions are often legally unable to share their data, as medical data is subject to strong privacy protection due to its particular sensitivity. But the collection, and especially aggregation into centralized datasets, is also fraught with substantial legal risks and often outright unlawful. Existing solutions using federated learning have already demonstrated considerable potential as an alternative for central data collection. Unfortunately, current approaches are incomplete or not easily applicable in clinical studies owing to the complexity of federated infrastructures. This work presents privacy-aware and federated implementations of the most used time-to-event algorithms (survival curve, cumulative hazard rate, log-rank test, and Cox proportional hazards model) in clinical trials, based on a hybrid approach of federated learning, additive secret sharing, and differential privacy. On several benchmark datasets, we show that all algorithms produce highly similar, or in some cases, even identical results compared to traditional centralized time-to-event algorithms. Furthermore, we were able to reproduce the results of a previous clinical time-to-event study in various federated scenarios. All algorithms are accessible through the intuitive web-app Partea (https://partea.zbh.uni-hamburg.de), offering a graphical user interface for clinicians and non-computational researchers without programming knowledge. Partea removes the high infrastructural hurdles derived from existing federated learning approaches and removes the complexity of execution. Therefore, it is an easy-to-use alternative to central data collection, reducing bureaucratic efforts but also the legal risks associated with the processing of personal data to a minimum.Author summary: Collecting data centrally from different sites in the clinical time-to-event analysis is still challenging due to the high bureaucratic effort and strict data protection laws such as the GDPR. However, huge datasets are needed to extract valuable insights from the data by applying statistical and machine learning approaches. Current approaches are still incomplete: they often do not address privacy issues in any depth, have inaccessible user interfaces, do not cover multiple algorithms, or are not open-source. In contrast, the approach we present in this work is an open-source tool for privacy-aware time-to-event analysis (Partea) that can be intuitively used by clinicians, statisticians, and other researchers. It allows the users to run state-of-the-art privacy-aware time-to-event analysis on data distributed between multiple sites through an easy-to-use interface and solves technical and legal issues for the underlying technologies.