Evidence map›Paper›PMID 42440280›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2026

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

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 and 6 more

Abstract read
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Yong ChenThe Center for Health AI and Synthesis of Evidence (CHASE), Perelman School of Medicine, The University of Pennsylvania, Philadelphia, PA, 19104, United States.ORCID 0000-0003-0835-0788
Jiayi TongThe Center for Health AI and Synthesis of Evidence (CHASE), Perelman School of Medicine, The University of Pennsylvania, Philadelphia, PA, 19104, United States.
Yiwen LuThe Graduate Group in Applied Mathematics and Computational Science, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, 19104, United States.
Rui DuanDepartment of Biostatistics, School of Public Health, Harvard University, Boston, MA, 02115, United States.ORCID 0000-0002-9261-4864
Chongliang LuoDivision of Public Health Sciences, Department of Surgery, Washington University in St. Louis, St. Louis, MO, 63110, United States.ORCID 0000-0003-3682-9454
Marc A SuchardDepartment of Veterans Affairs Informatics and Computing Infrastructure, Tennessee Valley Healthcare System VA, Nashville, TN, 37212, United States.ORCID 0000-0001-9818-479X
Patrick B RyanEpidemiology, Janssen Research & Development, Titusville, NJ, 08560, United States.
Andrew E WilliamsCenter for Advanced Healthcare Research Informatics, Tufts University School of Medicine, Boston, MA, 02111, United States.
John H HolmesDepartment of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, The University of Pennsylvania, Philadelphia, PA, 19104, United States.
Jason H MooreDepartment of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, United States.
Hua XuDepartment of Biomedical Informatics and Data Science, Yale University, New Haven, CT, 06510, United States.ORCID 0000-0002-5274-4672
Yun LuCenter for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, MD, 20993, United States.ORCID 0000-0001-9332-6832
Raymond J CarrollDepartment of Statistics, Texas A&M University, College Station, TX, 77840, United States.
Scott L ZegerDepartment of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, United States.
George HripcsakDepartment of Biomedical Informatics, Columbia University, New York City, NY, 10032, United States.
Martijn J SchuemieEpidemiology, Janssen Research & Development, Titusville, NJ, 08560, United States.ORCID 0000-0002-0817-5361

Funding

Coordinating Individually Measured Phenotypes to Advance Mental Health ResearchU24MH136069 · NIMH · YALE UNIVERSITY · PI Yong Chen, Cui Tao · 2024 to 2026
$9.8M
ReCARDO: Using Real-World Data to Derive Common Data Elements for Alzheimer's Disease and AD-Related Dementias Research Through Ontological InnovationU24AG098157 · NIA · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI Zoe Arvanitakis, Yong Chen · 2025 to 2026
$9.8M
PANDA-MSD: Predictive Analytics via Networked Distributed Algorithms for Multi-System DiseasesU01TR003709 · NCATS · UNIVERSITY OF PENNSYLVANIA · PI BIAN, JIANG, CHEN, YONG · 2022 to 2025
$4.7M
Federated and transfer learning methods for cross-ancestry and cross-phenotype integration of genomic datasetsR01GM148494 · NIGMS · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI Rui Duan · 2023 to 2026
$1.7M
NCATS NIH HHS U01 TR003709NIA NIH HHS U24 AG098157NIGMS NIH HHS R01 GM148494NIH HHS U01TR003709NIH HHS U24AG098157NIH HHS U24MH136069NIMH NIH HHS U24 MH136069
6 · The paper itself

Abstract

backgroundDistributed 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.

objectiveThe 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.

Indexed as

AlgorithmsComputer Communication NetworksConfidentialityMulticenter Studies as TopicHumansPrivacyResearch Designclinical observational datadistributed research networkprincipled Privacy-Preserving Distributed Algorithms (PDA)reliable clinical evidence generation

Identifiers

PMID42440280
PMCPMC13580733

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.