Studying Competing Events with Federated Cumulative Incidence Curves

2026-07-28

AI Summary

This study addresses the challenge of analyzing competing risks in post-marketing drug safety surveillance, which is hindered by privacy constraints limiting data sharing across multiple healthcare centers. The authors propose the first nonparametric federated learning algorithm capable of handling competing risks, enabling collaborative estimation of cumulative incidence functions without exchanging individual-level data. By integrating inverse probability weighting, the method adjusts for covariates and facilitates causal effect estimation under a federated framework. This approach uniquely enables nonparametric comparison of competing risks across multiple centers without direct data sharing and introduces the restricted mean time lost (RMTL) to quantify clinical impact. Applied to data from 10 centers involving 10,281 patients, the analysis revealed that patients with a history of non-endocrine autoimmune disease experienced significantly greater time lost due to endocrine immune-related adverse events within 18 months of immune checkpoint inhibitor initiation (4.8 vs. 3.2 months).

Abstract

Combining electronic health record (EHR) data from multiple institutions is a valuable strategy for conducting post-market safety surveillance of medical products, but privacy concerns limit sharing individual-level data. We develop a novel federated learning (FL) method for multi-site post-market safety surveillance of medical products using competing risks data. We apply this method to study immune-related adverse events (irAEs) following treatment with immune checkpoint inhibitors (ICIs) in patients with auto-immune disease (AID). We provide an algorithm for constructing non-parametric cumulative incidence curves for competing event types, which can be used to compare exposure groups (e.g. treated and untreated) with no sharing of patient-level data across institutions. We incorporate covariate adjustment via inverse propensity weighting, and informative causal comparison using the area under cumulative incidence curves, known as restricted mean time lost. We apply our method to $N=10,281$ cancer patients with no pre-existing endocrine-related AID receiving ICIs across $K=10$ sites from the OneFlorida+ network, comparing patients with a pre-existing non-endocrine AID to those with no pre-existing AID. After covariate adjustment, we found that patients with a pre-existing non-endocrine AID lost 4.8 [95% CI: 4.3,5.2] months of event-free survival time to endocrine irAEs in the first 18 months following treatment, compared to 3.2 [95% CI: 3.1,3.3] months in the group without prior AID. As patients with prior AID were initially excluded from clinical trials of ICIs, our findings provide important new information to clinicians and patients receiving or considering ICI treatment. Our proposed non-parametric federated algorithm is the first to allow investigators to use some of the most crucial non-parametric tools for conducting postmarket safety surveillance across multiple institutions.

Problem

• federated learning
• competing risks
• post-market safety surveillance
• cumulative incidence curves
• electronic health records

Innovation

• federated learning
• competing risks
• cumulative incidence curve
• non-parametric algorithm
• post-market safety surveillance