Faculty, Staff and Student Publications
Language
English
Publication Date
7-1-2025
Journal
npj Digital Medicine
DOI
10.1038/s41746-025-01803-y
PMID
40593099
PMCID
PMC12214564
PubMedCentral® Posted Date
7-1-2025
PubMedCentral® Full Text Version
Post-print
Abstract
Target trial emulation (TTE) aims to estimate treatment effects by simulating randomized controlled trials using real-world observational data. Applying TTE across distributed datasets shows great promise in improving generalizability and power but is always infeasible due to privacy and data-sharing constraints. Here we propose a Federated Learning-based TTE framework, FL-TTE, that enables TTE across multiple sites without sharing patient-level data. FL-TTE incorporates federated protocol design, federated inverse probability of treatment weighting, and a federated Cox proportional hazards model to estimate time-to-event outcomes across heterogeneous data. We validated FL-TTE by emulating Sepsis trials using eICU and MIMIC-IV data from 192 hospitals, and Alzheimer's trials using INSIGHT Network across five New York City health systems. FL-TTE produced less biased estimates than traditional meta-analysis methods when compared to pooled results and is theoretically supported. Our FL-TTE enables federated treatment effect estimation across distributed and heterogeneous data in a privacy-preserved way.
Keywords
Drug development, Clinical trials
Published Open-Access
yes
Recommended Citation
Li, Haoyang; Zang, Chengxi; Xu, Zhenxing; et al., "Federated Target Trial Emulation Using Distributed Observational Data for Treatment Effect Estimation" (2025). Faculty, Staff and Student Publications. 973.
https://digitalcommons.library.tmc.edu/uthshis_docs/973