{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T15:27:04Z","timestamp":1789658824560,"version":"4.0.1"},"reference-count":24,"publisher":"Oxford University Press (OUP)","issue":"9","license":[{"start":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T00:00:00Z","timestamp":1781568000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science Foundation and MI-CARES","award":["DMS1712933"],"award-info":[{"award-number":["DMS1712933"]}],"id":[{"id":"https:\/\/ror.org\/021nxhr62","id-type":"ROR","asserted-by":"crossref"}]},{"name":"National Science Foundation and MI-CARES","award":["1UG3CA267907"],"award-info":[{"award-number":["1UG3CA267907"]}],"id":[{"id":"https:\/\/ror.org\/021nxhr62","id-type":"ROR","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000054","name":"National Cancer Institute","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,9,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>To develop privacy-enhancing statistical methods for estimating disease risk parameters across multiple electronic health record (EHR) sites with heterogeneous selection mechanisms, avoiding individual-level data sharing. We illustrate their utility via a cross-biobank analysis of smoking and 97 cancer subtypes using NIH All of Us (AOU) and Michigan Genomics Initiative (MGI) data sites.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>Distributed health platforms often render centralized algorithms infeasible due to patient privacy protection. We propose Sequential Pseudo-Likelihood (SPL) and Sequential Augmented Inverse Probability Weighting (SAIPW) to adjust for selection bias using summary statistics shared across sites and external population information. SAIPW employs flexible auxiliary models for multiple robustness. We compared SPL and SAIPW against unweighted and centralized\/meta-learning benchmarks in simulations, applying them to harmonized MGI (n\u2009=\u200950\u00a0935) and AOU (n\u2009=\u2009241\u00a0563) data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Unweighted estimators exhibited substantial bias. SPL and SAIPW yielded unbiased estimates with valid coverage, with SAIPW remaining robust to selection model misspecification. Both approaches showed negligible efficiency loss relative to centralized methods. Meta-learning methods proved unstable for rare outcomes. Real-data analyses consistently identified strong associations between smoking and lung, bladder, and larynx cancers.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>These findings highlight the necessity of adjusting for site-specific selection biases in distributed health networks. SPL and SAIPW offer practical, scalable solutions that bypass the instability of meta-analysis for rare events, successfully harmonizing diverse biobanks while strictly enhancing patient privacy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Our framework enables valid, privacy-enhancing inference across EHR sites subject to heterogeneous selection, facilitating scalable, distributed research using real-world data.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/jamia\/ocag083","type":"journal-article","created":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T11:38:48Z","timestamp":1778672328000},"page":"1679-1692","source":"Crossref","is-referenced-by-count":0,"title":["Privacy-enhancing sequential learning under heterogeneous selection bias in multi-site electronic health records data"],"prefix":"10.1093","volume":"33","author":[{"given":"Ritoban","family":"Kundu","sequence":"first","affiliation":[{"name":"Department of Biostatistics, Epidemiology & Informatics, University of Pennsylvania , Philadelphia, PA 19104,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maxwell","family":"Salvatore","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Epidemiology & Informatics, University of Pennsylvania , Philadelphia, PA 19104,","place":["United States"]},{"name":"Department of Genetics, University of Pennsylvania , Philadelphia, PA 19104,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kumar Kshitij","family":"Patel","sequence":"additional","affiliation":[{"name":"Foundations of Data Science, Yale University , New Haven, CT 06511,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8005-7327","authenticated-orcid":false,"given":"Lucila","family":"Ohno-Machado","sequence":"additional","affiliation":[{"name":"Biomedical Informatics & Data Science, Yale University , New Haven, CT 06510,","place":["United States"]},{"name":"School of Medicine, Yale University , New Haven, CT 06510,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hyunghoon","family":"Cho","sequence":"additional","affiliation":[{"name":"Biomedical Informatics & Data Science, Yale University , New Haven, CT 06510,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8566-9552","authenticated-orcid":false,"given":"Xu","family":"Shi","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, University of Michigan , Ann Arbor, MI 48109,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0118-4561","authenticated-orcid":false,"given":"Bhramar","family":"Mukherjee","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Yale University , New Haven, CT 06510,","place":["United States"]},{"name":"Department of Chronic Disease Epidemiology, Yale University , New Haven, CT 06510,","place":["United States"]},{"name":"Department of Statistics, Yale University , New Haven, CT 06511,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,6,16]]},"reference":[{"key":"2026091614321287200_ocag083-B1","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1056\/NEJMp1006114","article-title":"The \u201cmeaningful use\u201d regulation for electronic health records","volume":"363","author":"Blumenthal","year":"2010","journal-title":"N Engl J Med"},{"key":"2026091614321287200_ocag083-B2","doi-asserted-by":"crossref","first-page":"2323","DOI":"10.1056\/NEJMsr1110507","article-title":"Wiring the health system\u2014origins and provisions of a new federal program","volume":"365","author":"Blumenthal","year":"2011","journal-title":"N Engl J Med"},{"key":"2026091614321287200_ocag083-B3","doi-asserted-by":"crossref","first-page":"1416","DOI":"10.1377\/hlthaff.2016.1651","article-title":"HITECH Act drove large gains in hospital electronic health record adoption","volume":"36","author":"Adler-Milstein","year":"2017","journal-title":"Health Aff (Millwood)"},{"key":"2026091614321287200_ocag083-B4","doi-asserted-by":"crossref","first-page":"100257","DOI":"10.1016\/j.xgen.2023.100257","article-title":"The Michigan Genomics Initiative: a biobank linking genotypes and electronic clinical records in Michigan Medicine patients","volume":"3","author":"Zawistowski","year":"2023","journal-title":"Cell Genom"},{"key":"2026091614321287200_ocag083-B5","doi-asserted-by":"crossref","first-page":"668","DOI":"10.1056\/NEJMsr1809937","article-title":"The \u201cAll of Us\u201d research program","volume":"381","author":"Denny","year":"2019","journal-title":"N Engl J Med"},{"key":"2026091614321287200_ocag083-B6","doi-asserted-by":"crossref","first-page":"ocae098","DOI":"10.1093\/jamia\/ocae098","article-title":"To weight or not to weight? The effect of selection bias in 3 large electronic health record-linked biobanks and recommendations for practice","volume":"31","author":"Salvatore","year":"2024","journal-title":"J Am Med Inform Assoc"},{"key":"2026091614321287200_ocag083-B7","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1111\/biom.13400","article-title":"Statistical inference for association studies using electronic health records: handling both selection bias and outcome misclassification","volume":"78","author":"Beesley","year":"2022","journal-title":"Biometrics"},{"key":"2026091614321287200_ocag083-B8","doi-asserted-by":"crossref","first-page":"606","DOI":"10.1093\/jrsssa\/qnae039","article-title":"A framework for understanding selection bias in real-world healthcare data","volume":"187","author":"Kundu","year":"2024","journal-title":"J R Stat Soc Ser A Stat Soc"},{"key":"2026091614321287200_ocag083-B9","doi-asserted-by":"crossref","first-page":"100192","DOI":"10.1016\/j.xgen.2022.100192","article-title":"Global biobank meta-analysis initiative: powering genetic discovery across human disease","volume":"2","author":"Zhou","year":"2022","journal-title":"Cell Genom"},{"key":"2026091614321287200_ocag083-B10","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1111\/rssa.12275","article-title":"A re-evaluation of fixed effect (s) meta-analysis","volume":"181","author":"Rice","year":"2018","journal-title":"J R Stat Soc A"},{"key":"2026091614321287200_ocag083-B11","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1136\/eb-2018-102911","article-title":"Practical guide to the meta-analysis of rare events","volume":"21","author":"Efthimiou","year":"2018","journal-title":"Evid Based Ment Health"},{"key":"2026091614321287200_ocag083-B12","doi-asserted-by":"crossref","first-page":"2263","DOI":"10.1002\/sim.10068","article-title":"Collaborative inference for treatment effect with distributed data-sharing management in multicenter studies","volume":"43","author":"Hu","year":"2024","journal-title":"Stat Med"},{"key":"2026091614321287200_ocag083-B13","doi-asserted-by":"crossref","first-page":"668","DOI":"10.1080\/01621459.2018.1429274","article-title":"Communication-efficient distributed statistical inference","volume":"114","author":"Jordan","journal-title":"J Am Stat Assoc"},{"key":"2026091614321287200_ocag083-B14","first-page":"30","article-title":"ODAL: a one-shot distributed algorithm to perform logistic regressions on electronic health records data from multiple clinical sites","volume":"24","author":"Duan","year":"2019","journal-title":"Pac Symp Biocomput"},{"key":"2026091614321287200_ocag083-B15","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1093\/jamia\/ocz199","article-title":"Learning from electronic health records across multiple sites: A communication-efficient and privacy-preserving distributed algorithm","volume":"27","author":"Duan","year":"2020","journal-title":"J Am Med Inform Assoc"},{"key":"2026091614321287200_ocag083-B16","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1093\/biomet\/asab007","article-title":"Heterogeneity-aware and communication-efficient distributed statistical inference","volume":"109","author":"Duan","year":"2022","journal-title":"Biometrika"},{"key":"2026091614321287200_ocag083-B17","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1111\/rssb.12352","article-title":"Renewable estimation and incremental inference in generalized linear models with streaming data sets","volume":"82","author":"Luo","year":"2020","journal-title":"J R Stat Soc B"},{"key":"2026091614321287200_ocag083-B18","doi-asserted-by":"crossref","first-page":"2734","DOI":"10.1002\/sim.10095","article-title":"Online causal inference with application to near real-time post-market vaccine safety surveillance","volume":"43","author":"Luo","year":"2024","journal-title":"Stat Med"},{"key":"2026091614321287200_ocag083-B19","doi-asserted-by":"crossref","first-page":"kxag001","DOI":"10.1093\/biostatistics\/kxag001","article-title":"A doubly robust framework for addressing outcome-dependent selection bias in multi-cohort EHR studies","volume":"27","author":"Kundu","year":"2026","journal-title":"Biostatistics"},{"key":"2026091614321287200_ocag083-B20","doi-asserted-by":"crossref","first-page":"2011","DOI":"10.1080\/01621459.2019.1677241","article-title":"Doubly robust inference with nonprobability survey samples","volume":"115","author":"Chen","year":"2020","journal-title":"J Am Stat Assoc"},{"key":"2026091614321287200_ocag083-B21","first-page":"40","article-title":"meta: An R package for meta-analysis. R.","volume":"7","author":"Schwarzer","year":"2007","journal-title":"News"},{"key":"2026091614321287200_ocag083-B22","doi-asserted-by":"crossref","first-page":"1965","DOI":"10.1002\/sim.8524","article-title":"An analytic framework for exploring sampling and observation process biases in genome and phenome-wide association studies using electronic health records","volume":"39","author":"Beesley","year":"2020","journal-title":"Stat Med"},{"key":"2026091614321287200_ocag083-B23","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1111\/rssb.12504","article-title":"Assumption-lean inference for generalised linear model parameters","volume":"84","author":"Vansteelandt","year":"2022","journal-title":"J R Stat Soc B"},{"key":"2026091614321287200_ocag083-B24","doi-asserted-by":"crossref","first-page":"809","DOI":"10.1038\/s41588-025-02109-1","article-title":"Secure and federated genome-wide association studies for biobank-scale datasets","volume":"57","author":"Cho","year":"2025","journal-title":"Nat Genet"}],"container-title":["Journal of the American Medical Informatics Association"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/jamia\/advance-article-pdf\/doi\/10.1093\/jamia\/ocag083\/68531687\/ocag083.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/jamia\/article-pdf\/33\/9\/1679\/68531687\/ocag083.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/jamia\/article-pdf\/33\/9\/1679\/68531687\/ocag083.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T18:32:19Z","timestamp":1789583539000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/jamia\/article\/33\/9\/1679\/8708528"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,16]]},"references-count":24,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2026,6,16]]},"published-print":{"date-parts":[[2026,9,1]]}},"URL":"https:\/\/doi.org\/10.1093\/jamia\/ocag083","relation":{},"ISSN":["1067-5027","1527-974X"],"issn-type":[{"value":"1067-5027","type":"print"},{"value":"1527-974X","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2026,9]]},"published":{"date-parts":[[2026,6,16]]}}}