{"ID":2824312,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2601.06072","arxiv_id":"2601.06072","title":"PDA in Action: Ten Principles for High-Quality Multi-Site Clinical Evidence Generation","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 ten 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 ten 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 ten 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.","short_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 impor...","url_abs":"https://arxiv.org/abs/2601.06072","url_pdf":"https://arxiv.org/pdf/2601.06072v1","authors":"[\"Yong Chen\",\"Jiayi Tong\",\"Yiwen Lu\",\"Rui Duan\",\"Chongliang Luo\",\"Marc A. Suchard\",\"Patrick B. Ryan\",\"Andrew E. Williams\",\"John H. Holmes\",\"Jason H. Moore\",\"Hua Xu\",\"Yun Lu\",\"Raymond J. Carroll\",\"Scott L. Zeger\",\"George Hripcsak\",\"Martijn J. Schuemie\"]","published":"2025-12-29T12:08:18Z","proceeding":"cs.CY","tasks":"[\"cs.CY\"]","methods":"[]","has_code":false}