PDA in Action: Ten Principles for High-Quality Multi-Site Clinical Evidence Generation
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arXiv
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| Autori principali: | , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
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2025
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| _version_ | 1866909986367471616 |
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| author | Chen, Yong Tong, Jiayi Lu, Yiwen Duan, Rui Luo, Chongliang Suchard, Marc A. Ryan, Patrick B. Williams, Andrew E. Holmes, John H. Moore, Jason H. Xu, Hua Lu, Yun Carroll, Raymond J. Zeger, Scott L. Hripcsak, George Schuemie, Martijn J. |
| author_facet | Chen, Yong Tong, Jiayi Lu, Yiwen Duan, Rui Luo, Chongliang Suchard, Marc A. Ryan, Patrick B. Williams, Andrew E. Holmes, John H. Moore, Jason H. Xu, Hua Lu, Yun Carroll, Raymond J. Zeger, Scott L. Hripcsak, George Schuemie, Martijn J. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_06072 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PDA in Action: Ten Principles for High-Quality Multi-Site Clinical Evidence Generation Chen, Yong Tong, Jiayi Lu, Yiwen Duan, Rui Luo, Chongliang Suchard, Marc A. Ryan, Patrick B. Williams, Andrew E. Holmes, John H. Moore, Jason H. Xu, Hua Lu, Yun Carroll, Raymond J. Zeger, Scott L. Hripcsak, George Schuemie, Martijn J. Computers and Society 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. |
| title | PDA in Action: Ten Principles for High-Quality Multi-Site Clinical Evidence Generation |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2601.06072 |