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
Suraj Rajendran, Yong Chen, and Fei Wang, "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