{
  "abstract": "Synthetic data increasingly support model development and privacy-conscious sharing in biomedicine. Two-group studies require synthetic data to reproduce within-group structure and between-group differences, yet marginal agreement or predictive performance may obscure multivariate and conditional-dependence changes. We present a multidimensional validation framework for class-conditional synthetic data and apply it to three datasets spanning sample-size and dimensionality regimes. Two controls and four generators spanning mixture, interpolation, hybrid, and latent-variable architectures (GMM, SMOTE, GMM-SMOTE, and CVAE, respectively) were assessed using predictive utility, real-synthetic distinguishability, marginal agreement, PCA and t-SNE geometry, pairwise dependence, and Graphical LASSO networks. Noise perturbation, within-class permutation, and reverse ablation probed the sources of real-synthetic distinguishability. Across 18 dataset-method comparisons, discriminator AUC ranged from 0.55 to 1.00, while mean feature-level KS statistics ranged from 0.027 to 0.282. Thus, strong performance under individual criteria coexisted with detectable differences and lost or synthetic-only dependencies. Rather than assigning a single fidelity score, the framework supports multidimensional fidelity reporting as a minimum standard for shared synthetic biomedical data.",
  "abstract_shown": true,
  "affiliations": [
    "University of Guelph"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "name": "T. Tran"
    },
    {
      "name": "M. S. Ghaemi"
    },
    {
      "affs": [
        0
      ],
      "name": "C. S. Korosec"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8kv1fw2ng0zbb.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.08.31.26361878",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.08.31.26361878v1.full",
  "id": "8kv1fw2ng0zbb",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.08.31.26361878v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "medrxiv",
      "health informatics"
    ],
    [
      "openalex",
      "1702|Privacy-Preserving Technologies in Data"
    ]
  ],
  "openalex": "W7211934820",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.08.31.26361878v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-31",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8kv1fw2ng0zbb.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:54Z",
      "native_id": "10.64898/2026.08.31.26361878",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.08.31.26361878v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:health informatics",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Evaluating synthetic-data fidelity in two-group biomedical studies: a multidimensional validation framework",
  "title_text": "Evaluating synthetic-data fidelity in two-group biomedical studies: a multidimensional validation framework",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:54Z",
  "url": "https://research.draconitic.com/paper/8kv1fw2ng0zbb",
  "venue": "medRxiv",
  "versions": []
}