2026-10-13T00:45:28Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34048892 2026-10-01T17:38:02Z category_12 category_21 category_106 portal_5 item_type_3 month_year_10_2026

<p>Specificity-recalibrated planning values.</p> Han Liu (288039) Cell Biology Biotechnology Science Policy effective sample size distribution invitation targets based rule selected 15 %, compared validation resources jointly original validation sample negative validation samples negative validation participants 247 conditional invitations predictive width attainment conditional planning selected comparable assumed cost specified relative costs overplanning loss ratio 794 screening invitations positive planning proportion joint parameter uncertainty validation planning 708 invitations width floor separate cost parameter uncertainty positive proportion xlink "> theoretic recruitment survey design study examined point half planning current known within illustrative underplanning depression surveys data performance budget retained <div> <p>Background</p><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.</p> <p>Methods</p><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.</p> <p>Results</p><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.</p> <p>Conclusions</p><p>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.</p></div> 2026-10-01T17:37:52Z Dataset Dataset 10.1371/journal.pone.0359867.t003 https://figshare.com/articles/dataset/_p_Specificity-recalibrated_planning_values_p_/34048892 CC BY 4.0