{
  "abstract": "Randomized clinical trials remain the gold standard for evaluating treatment effects but are often infeasible for rapidly assessing drug repurposing opportunities, particularly in complex conditions such as Alzheimer’s disease. Target trial emulation (TTE) provides a principled framework for causal inference with electronic health records and, when coupled with modern machine learning, enables scalable analyses across distributed health systems. We present FLAT-WHITE (Federated Lossless Algorithm for Target Trial Emulation with Overlap Weighting for Healthcare Data Integration via Trusted and Efficient Communication), a novel federated causal machine learning pipeline for high-throughput treatment effect estimation without sharing patient-level data. FLAT-WHITE is a one-shot, lossless algorithm, requiring a single communication round while achieving results equivalent to centralized pooled analyses. With overlap weighting on the propensity score, it robustly addresses heterogeneity in treatment assignment and ensures valid causal inference in decentralized settings. We demonstrate FLAT-WHITE in a Alzheimer’s drug repurposing study, scanning 181 candidate drugs using data from four health learning systems encompassing approximately 150,000 patients with mild cognitive impairment. Findings were validated externally in 339,180 patients from the TriNetX global research network. FLAT-WHITE enables rapid, large-scale, and secure evaluation of treatment effects and establishes a foundation for collaborative clinical research across diverse healthcare systems.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD USA",
    "The Center for Health AI and Synthesis of Evidence (CHASE), University of Pennsylvania, Philadelphia, PA USA",
    "The Graduate Group in Applied Mathematics and Computational Science, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA USA",
    "Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA USA",
    "Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA USA",
    "Leonard Davis Institute for Health Economics, University of Pennsylvania, Pennsylvania, PA USA",
    "Department of Biostatistics and Health Data Science, University of Pittsburgh, Pittsburgh, PA USA",
    "Merck & Co Inc, Rahway, NJ USA",
    "Weill Cornell Medicine, Cornell University, New York, NY USA",
    "Department of Biostatistics and Health Data Science, Indiana University, Bloomington, IN USA",
    "Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL USA",
    "Department of Biomedical Informatics and Data Science, School of Medicine, Yale University, New Haven, CT USA",
    "Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China",
    "Statistical Research and Data Science Center, Pfizer Inc, New York, NY USA",
    "College of Population Health, Thomas Jefferson University, Philadelphia, PA USA",
    "Real-World Evidence and Epidemiology, Chief Medical Office, Pfizer, Collegeville, PA USA",
    "Penn Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA USA",
    "Penn Medicine Center for Evidence-based Practice (CEP), Philadelphia, PA USA"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 25,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Jiayi Tong"
    },
    {
      "affs": [
        1,
        2
      ],
      "name": "Lu Li"
    },
    {
      "affs": [
        1,
        3
      ],
      "name": "Dazheng Zhang",
      "orcid": "0000-0002-1431-0785"
    },
    {
      "affs": [
        4,
        5
      ],
      "name": "Kyra S. O’Brien"
    },
    {
      "affs": [
        1,
        2
      ],
      "name": "Yiwen Lu"
    },
    {
      "affs": [
        6
      ],
      "name": "Qiong Wu"
    },
    {
      "affs": [
        1,
        3
      ],
      "name": "Ting Zhou"
    },
    {
      "affs": [
        1,
        2
      ],
      "name": "Bingyu Zhang",
      "orcid": "0000-0001-7176-249X"
    },
    {
      "affs": [
        1,
        3
      ],
      "name": "Yuqing Lei"
    },
    {
      "affs": [
        1,
        3
      ],
      "name": "Huilin Tang"
    },
    {
      "affs": [
        7
      ],
      "name": "Naimin Jing"
    },
    {
      "affs": [
        8
      ],
      "name": "Haoyang Li",
      "orcid": "0000-0003-3544-5563"
    },
    {
      "affs": [
        8
      ],
      "name": "Zhenxing Xu"
    },
    {
      "affs": [
        9
      ],
      "name": "Yu Huang"
    },
    {
      "affs": [
        10
      ],
      "name": "Yu Hu"
    },
    {
      "affs": [
        11
      ],
      "name": "Yujia Zhou",
      "orcid": "0000-0003-0889-2261"
    },
    {
      "affs": [
        11
      ],
      "name": "Fongci Lin"
    },
    {
      "affs": [
        12
      ],
      "name": "Ying Jiang"
    },
    {
      "affs": [
        13
      ],
      "name": "Haitao Chu",
      "orcid": "0000-0003-0932-598X"
    },
    {
      "affs": [
        13,
        14,
        15
      ],
      "name": "Yong Chen"
    },
    {
      "affs": [
        8
      ],
      "name": "Fei Wang"
    },
    {
      "affs": [
        9
      ],
      "name": "Jiang Bian",
      "orcid": "0000-0002-2238-5429"
    },
    {
      "affs": [
        11
      ],
      "name": "Hua Xu"
    },
    {
      "affs": [
        4
      ],
      "name": "David A. Wolk"
    },
    {
      "affs": [
        1,
        2,
        3,
        16,
        17
      ],
      "name": "Yong Chen",
      "orcid": "0000-0003-0835-0788"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5m9jt57rssn0y.bib",
  "cited_by": 0,
  "doi": "10.1038/s44401-026-00160-6",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13714285/",
  "id": "5m9jt57rssn0y",
  "issns": [
    "3005-1959"
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  "landing_url": "https://doi.org/10.1038/s44401-026-00160-6",
  "language": "en",
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Article"
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      "openalex",
      "2613|Advanced Causal Inference Techniques"
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  "pdf_url": "https://www.nature.com/articles/s44401-026-00160-6.pdf",
  "pmcid": "PMC13714285",
  "pmid": "42946784",
  "published": "2026-10-27",
  "publisher": "Nature Publishing Group",
  "ris": "https://research.draconitic.com/paper/5m9jt57rssn0y.ris",
  "sources": [
    {
      "first_seen": "2026-11-01T12:21:49Z",
      "native_id": "PMC13714285",
      "source": "pmc",
      "url": "https://doi.org/10.1038/s44401-026-00160-6"
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  "subjects": [
    {
      "field": "mathematics",
      "origin": "openalex:2613|Advanced Causal Inference Techniques",
      "subfield": "statistics"
    }
  ],
  "title": "FLAT-WHITE: from target trial emulation to federated high-throughput causal machine learning using decentralized real-world data",
  "title_text": "FLAT-WHITE: from target trial emulation to federated high-throughput causal machine learning using decentralized real-world data",
  "type": "peer-reviewed",
  "updated_at": "2026-11-01T12:21:49Z",
  "url": "https://research.draconitic.com/paper/5m9jt57rssn0y",
  "venue": "npj Health Systems",
  "versions": []
}