36: Federated learning methods for estimating heterogeneous treatment effect using multiple data sources

Music City Center 

Estimation of heterogeneous treatment effects (HTE) is critical for evidence-based medicine and individualized clinical decision-making. While combining data from multiple real-world studies and randomized trials allows for larger sample sizes and greater power to estimate HTE, it is statistically challenging due to factors such as cross-study heterogeneity, confounding in observational studies, and possibly inconsistent measurements across data sources. Importantly, sharing data across research sites raises concerns about data privacy. Recently, many studies have proposed methods to estimate HTE using federated learning (FL), which enables the use of data from multiple studies without sharing individual patient information across research sites. In this poster, we will compare several FL-based approaches for estimating HTE (including parametric and non-parametric machine learning approaches) and assess their performance under different realistic scenarios through simulation studies based on real-world data, providing practical recommendations.

Treatment effect heterogeneity

Federated learning

Personalized medicine

Combining data 

Biopharmaceutical Section