PDA (Privacy-Preserving Distributed Algorithms) in action: ten principles for high-quality multi-site clinical evidence generation

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ID: 320788
2026
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Ranked #42 of 89 articles by views in Journal of the American Medical Informatics Association : JAMIA

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Abstract
BACKGROUND: Distributed Research Networks (DRNs) offer significant opportunities for collaborative multi-site research and have significantly advanced healthcare research based on clinical observational data. However, generating high-quality real-world evidence using fit-for-use data from multi-site studies faces important challenges, including biases associated with various types of heterogeneity within and across sites and data sharing difficulties. Over the last 10 years, Privacy-Preserving Distributed Algorithms (PDA) have been developed and utilized in numerous national and international real-world studies spanning diverse domains, from comparative effectiveness research, target trial emulation, to healthcare delivery, policy evaluation, and system performance assessment. Despite these advances, there remains a lack of comprehensive and clear guiding principles for generating high-quality real-world evidence through collaborative studies leveraging the methods under PDA. OBJECTIVE: The paper aims to establish 10 principles of best practice for conducting high-quality multi-site studies using PDA. These principles cover all phases of research, including study preparation, protocol development, analysis, and final reporting. DISCUSSION: The 10 principles for conducting a PDA study outline a principled, efficient, and transparent framework for employing distributed learning algorithms within DRNs to generate reliable and reproducible real-world evidence.
Reference Key
openalex_W7168155198 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Yong Chen, Jiayi Tong, Yiwen Lu, Rui Duan, Chongliang Luo, Marc A. Suchard, Patrick Ryan, Andrew E. Williams, J H Holmes, Jason H. Moore, Hua Xu, Yun Lu, Raymond J. Carroll, Scott L. Zeger, George Hripcsak, Martijn J. Schuemie
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Journal Journal of the American Medical Informatics Association : JAMIA
Year 2026
DOI
10.1093/jamia/ocag119
URL
https://doi.org/10.1093/jamia/ocag119
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