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Privacy-enhancing sequential learning under heterogeneous selection bias in multi-site electronic health records

Ritoban Kundu¹, Maxwell Salvatore^1,2, Kumar Kshitij Patel³

¹Department of Biostatistics, Epidemiology & Informatics, University of Pennsylvania, Philadelphia, PA 19104, United States.

Journal of the American Medical Informatics Association : JAMIA

|June 15, 2026

Summary

New statistical methods, Sequential Pseudo-Likelihood (SPL) and Sequential Augmented Inverse Probability Weighting (SAIPW), enable privacy-preserving disease risk estimation across electronic health record sites. These methods accurately adjust for selection bias without sharing patient data.

Keywords:

decentralized learning electronic health records multiple robustness selection bias sequential learning

Related Experiment Videos

Area of Science:

Biostatistics
Epidemiology
Health Informatics

Background:

Distributed health platforms face challenges in centralized analysis due to patient privacy concerns.
Heterogeneous selection mechanisms across electronic health record (EHR) sites complicate disease risk parameter estimation.
Sharing individual-level patient data is often infeasible in multi-site health research.

Purpose of the Study:

To develop and validate privacy-enhancing statistical methods for disease risk estimation across multiple EHR sites.
To address challenges posed by heterogeneous selection mechanisms and avoid individual-level data sharing.
To illustrate the utility of proposed methods in a cross-biobank analysis of smoking and cancer subtypes.

Main Methods:

Proposed Sequential Pseudo-Likelihood (SPL) and Sequential Augmented Inverse Probability Weighting (SAIPW) methods.
Utilized summary statistics shared across sites and external population information to adjust for selection bias.
Compared SPL and SAIPW against unweighted and centralized/meta-learning benchmarks using simulated and real-world data from the NIH All of Us (AOU) and Michigan Genomics Initiative (MGI) biobanks.

Main Results:

Unweighted estimators showed significant bias, while SPL and SAIPW produced unbiased estimates with valid coverage.
SAIPW demonstrated robustness to selection model misspecification.
Both SPL and SAIPW exhibited minimal efficiency loss compared to centralized methods, outperforming unstable meta-learning approaches for rare outcomes.
Analyses identified strong associations between smoking and lung, bladder, and larynx cancers.

Conclusions:

Site-specific selection biases must be accounted for in distributed health networks.
SPL and SAIPW provide practical, scalable, privacy-enhancing solutions for harmonizing diverse biobanks.
The developed framework facilitates valid inference across EHR sites, enabling scalable, distributed research with real-world data.