{
  "abstract": "Background: Survey planning must balance recruitment losses against uncertainty in screening yield, diagnostic accuracy and survey design. This study examined how these considerations determine invitation requirements and diagnostic-validation allocation. Methods: A Beta planning distribution used 170 PHQ-9-positive results among 1,045 Chinese community participants and an effective sample size of 100. Asymmetric recruitment loss selected conditional and joint-distribution invitation targets. A separate cost-plus-variance Bayes criterion jointly allocated survey clusters and independent reference-positive and reference-negative validation samples. Parameter uncertainty and future-data performance were evaluated by integration and simulation. Results: With an illustrative underplanning-to-overplanning loss ratio of 3, conditional planning selected a screening-positive proportion of 0.1862 and 4,794 screening invitations. Integrating design uncertainty increased this target to 5,346. Current-prevalence planning required 9,247 conditional invitations or 11,866 under joint parameter uncertainty when diagnostic accuracy was known within each scenario. The original validation sample imposed a reference-point half-width floor of 0.0292, exceeding the 0.020 target. Under specified relative costs, the joint allocation used 21,708 invitations and 240/4,538 reference-positive/reference-negative validation participants. Its prior-predictive width attainment was 53.15%, compared with 0.03% when nearly the same budget retained the original validation sample. Increasing the precision valuation fourfold raised attainment to 10,000/10,000 simulations, with 95.31% coverage. Uncorrected specificity shifts produced substantial bias despite larger samples. Conclusions: At k = 3, the loss-based rule selected a screening-positive planning proportion of 0.1862, reducing conditional invitation requirements by 39.38% relative to p = 0.5. Joint survey–validation allocation improved simulated precision at comparable assumed cost. These findings support allocating recruitment and diagnostic-validation resources jointly when planning current-prevalence surveys.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Behavioural Science and Health, Institute of Epidemiology and Public Health, University College London, London, United Kingdom"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Han Liu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8zy8mkhv5n6xz.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0359867",
  "fulltext_url": null,
  "id": "8zy8mkhv5n6xz",
  "issns": [
    "1932-6203"
  ],
  "landing_url": "https://doi.org/10.1371/journal.pone.0359867",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "3312|Survey Methodology and Nonresponse"
    ],
    [
      "plos",
      "/Engineering and technology/Electronics engineering/Electronics/Comparators"
    ],
    [
      "plos",
      "/Medicine and health sciences/Health care/Health care policy/Screening guidelines"
    ],
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      "plos",
      "/Medicine and health sciences/Mental health and psychiatry/Mood disorders/Depression"
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      "/Research and analysis methods/Research assessment/Reproducibility"
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      "plos",
      "/Research and analysis methods/Research design/Survey research/Surveys"
    ]
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  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0359867&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-01",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/8zy8mkhv5n6xz.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:37Z",
      "native_id": "10.1371/journal.pone.0359867",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0359867"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "plos:algebra",
      "subfield": "algebra-number-theory"
    },
    {
      "field": "mathematics",
      "origin": "plos:statistical methods",
      "subfield": "statistics"
    },
    {
      "field": "medicine",
      "origin": "plos:mental health and psychiatry",
      "subfield": "psychiatry"
    }
  ],
  "title": "Decision-theoretic recruitment and validation planning for depression surveys: An illustrative Chinese application",
  "title_text": "Decision-theoretic recruitment and validation planning for depression surveys: An illustrative Chinese application",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:37Z",
  "url": "https://research.draconitic.com/paper/8zy8mkhv5n6xz",
  "venue": "PLOS One",
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}