{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T18:07:42Z","timestamp":1790878062362,"version":"4.1.0"},"reference-count":21,"publisher":"Public Library of Science (PLoS)","issue":"10","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:sec id=\"sec001\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec002\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec003\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec004\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>At k\u2009=\u20093, the loss-based rule selected a screening-positive planning proportion of 0.1862, reducing conditional invitation requirements by 39.38% relative to p\u2009=\u20090.5. Joint survey\u2013validation allocation improved simulated precision at comparable assumed cost. These findings support allocating recruitment and diagnostic-validation resources jointly when planning current-prevalence surveys.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1371\/journal.pone.0359867","type":"journal-article","created":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T17:37:42Z","timestamp":1790876262000},"page":"e0359867","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Decision-theoretic recruitment and validation planning for depression surveys: An illustrative Chinese application"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-0413-7026","authenticated-orcid":true,"given":"Han","family":"Liu","sequence":"first","affiliation":[{"name":"Institute of Epidemiology and Public Health, University College London","place":["London, United Kingdom"],"department":["Department of Behavioural Science and 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