{
  "abstract": "Meta-analysis of diagnostic test accuracy (DTA) studies is commonly based on bivariate generalized linear mixed models (GLMMs) that assume normally distributed study-specific random effects. In practice, this distributional assumption is rarely examined, particularly in individual participant data meta-analysis (IPDMA), where empirical Bayes (EB) estimates exhibit heterogeneous precision across studies. Applying standard multivariate normality tests to such estimates can therefore lead to misleading inference. We propose a weighted extension of the Henze-Zirkler (HZ) test to assess bivariate normality of random effects, accounting for study-specific precision via inverse-variance weighting. To obtain valid finite-sample inference, we develop a parametric bootstrap calibration procedure. Extensive simulation studies showed that the classical HZ test applied to EB estimates can exhibit substantial Type I error inflation, whereas the proposed weighted procedures maintain nominal error rates while retaining power to detect departures from normality. The proposed method provides a practical, statistically principled diagnostic tool for assessing random-effects distributional assumptions in hierarchical models, with particular relevance to IPDMA of DTA studies. Although motivated by IPDMA, the proposed approach also extends to aggregate-data diagnostic test accuracy meta-analysis based on study-level 2-by-2 tables.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.03333",
  "author_total": 2,
  "authors": [
    {
      "name": "Diribsa T. Bedada"
    },
    {
      "name": "Zelalem F. Negeri"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8w2w7pxawnst1.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.03333",
  "fulltext_url": null,
  "id": "8w2w7pxawnst1",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.03333",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "stat.AP"
    ],
    [
      "arxiv",
      "stat.CO"
    ],
    [
      "arxiv",
      "stat.ME"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2611.03333",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-02",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8w2w7pxawnst1.ris",
  "sources": [
    {
      "first_seen": "2026-11-03T12:21:21Z",
      "native_id": "2611.03333",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.03333"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.AP",
      "subfield": "statistics"
    }
  ],
  "title": "Methods for Assessing Distributional Assumptions in Individual Participant Data Meta-Analysis of Diagnostic Tests",
  "title_text": "Methods for Assessing Distributional Assumptions in Individual Participant Data Meta-Analysis of Diagnostic Tests",
  "type": "preprint",
  "updated_at": "2026-11-05T15:20:35Z",
  "url": "https://research.draconitic.com/paper/8w2w7pxawnst1",
  "venue": "arXiv",
  "versions": []
}