DLMM as a lossless one-shot algorithm for collaborative multi-site distributed linear mixed models

Chongliang Luo, Md. Nazmul Islam, Natalie E. Sheils, John Buresh, Jenna Reps, Martijn J. Schuemie, Patrick B. Ryan, Mackenzie Edmondson, Rui Duan, Jiayi Tong, Arielle Marks-Anglin, Jiang Bian, Zhaoyi Chen, Talita Duarte-Salles, Sergio Fernández-Bertolín, Thomas Falconer, Chungsoo Kim, Rae Woong Park, Stephen R. Pfohl, Nigam H. Shah, Andrew E. Williams, Hua Xu, Yujia Zhou, Ebbing Lautenbach, Jalpa A. Doshi, Rachel M. Werner, David A. Asch and Yong Chen ()
Additional contact information
Chongliang Luo: University of Pennsylvania
Md. Nazmul Islam: Optum Labs
Natalie E. Sheils: Optum Labs
John Buresh: Optum Labs
Jenna Reps: Janssen Research and Development LLC
Martijn J. Schuemie: Janssen Research and Development LLC
Patrick B. Ryan: Janssen Research and Development LLC
Mackenzie Edmondson: University of Pennsylvania
Rui Duan: University of Pennsylvania
Jiayi Tong: University of Pennsylvania
Arielle Marks-Anglin: University of Pennsylvania
Jiang Bian: University of Florida
Zhaoyi Chen: University of Florida
Talita Duarte-Salles: Fundacio Institut Universitari per a la recerca a l’Atencio Primaria de Salut Jordi Gol i Gurina (IDIAPJGol)
Sergio Fernández-Bertolín: Fundacio Institut Universitari per a la recerca a l’Atencio Primaria de Salut Jordi Gol i Gurina (IDIAPJGol)
Thomas Falconer: Columbia University
Chungsoo Kim: Ajou University Graduate School of Medicine
Rae Woong Park: Ajou University Graduate School of Medicine
Stephen R. Pfohl: Stanford Center for Biomedical Informatics Research
Nigam H. Shah: Stanford Center for Biomedical Informatics Research
Andrew E. Williams: Tufts University School of Medicine
Hua Xu: The University of Texas Health Science Center at Houston
Yujia Zhou: The University of Texas Health Science Center at Houston
Ebbing Lautenbach: University of Pennsylvania
Jalpa A. Doshi: University of Pennsylvania
Rachel M. Werner: University of Pennsylvania
David A. Asch: University of Pennsylvania
Yong Chen: University of Pennsylvania

Nature Communications, 2022, vol. 13, issue 1, 1-10

Abstract: Abstract Linear mixed models are commonly used in healthcare-based association analyses for analyzing multi-site data with heterogeneous site-specific random effects. Due to regulations for protecting patients’ privacy, sensitive individual patient data (IPD) typically cannot be shared across sites. We propose an algorithm for fitting distributed linear mixed models (DLMMs) without sharing IPD across sites. This algorithm achieves results identical to those achieved using pooled IPD from multiple sites (i.e., the same effect size and standard error estimates), hence demonstrating the lossless property. The algorithm requires each site to contribute minimal aggregated data in only one round of communication. We demonstrate the lossless property of the proposed DLMM algorithm by investigating the associations between demographic and clinical characteristics and length of hospital stay in COVID-19 patients using administrative claims from the UnitedHealth Group Clinical Discovery Database. We extend this association study by incorporating 120,609 COVID-19 patients from 11 collaborative data sources worldwide.

Date: 2022
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DOI: 10.1038/s41467-022-29160-4

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