{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Negeri ZF"],"funding":["Swiss National Science Foundation","Wellcome Trust"],"pagination":["28"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10832258"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["24(1)"],"pubmed_abstract":["<h4>Background</h4>Selective reporting of results from only well-performing cut-offs leads to biased estimates of accuracy in primary studies of questionnaire-based screening tools and in meta-analyses that synthesize results. Individual participant data meta-analysis (IPDMA) of sensitivity and specificity at each cut-off via bivariate random-effects models (BREMs) can overcome this problem. However, IPDMA is laborious and depends on the ability to successfully obtain primary datasets, and BREMs ignore the correlation between cut-offs within primary studies.<h4>Methods</h4>We compared the performance of three recent multiple cut-off models developed by Steinhauser et al., Jones et al., and Hoyer and Kuss, that account for missing cut-offs when meta-analyzing diagnostic accuracy studies wit"],"journal":["BMC medical research methodology"],"pubmed_title":["An empirical comparison of statistical methods for multiple cut-off diagnostic test accuracy meta-analysis of the Edinburgh postnatal depression scale (EPDS) depression screening tool using published results vs individual participant data."],"pmcid":["PMC10832258"],"funding_grant_id":["071571","082384/Z/07/Z","125493","211374/Z/18/Z"],"pubmed_authors":["Gilbody S","Su KP","Rice DB","Khalifa DS","Bunevicius A","Garcia-Esteve L","Cuijpers P","Toreki A","Kozinszky Z","Markham S","Sharp DJ","Neupane D","Kloda LA","Wu Y","He C","Ioannidis JPA","Tandon SD","Levis B","Torres-Gimenez A","Sun Y","Vigod SN","Kusminskas L","Tadinac M","Favez N","Azar M","Krishnan A","Vega-Dienstmaier JM","Benedetti A","van Heyningen T","Negeri ZF","Patten SB","Chorwe-Sungani G","Bhandari PM","Imran M","Couto TCE","Rochat TJ","Smith-Nielsen J","Chiovitti MJ","Thombs BD","Honikman S","Mitchell ND","Field S","DEPRESsion Screening Data (DEPRESSD) EPDS Group","Riehm KE","Ziegelstein RC","Bakare MO","Kohlhoff J","Nakic Rados S","Thiagayson P","Pawlby SJ","Boruff JT","Comeau L","Beck CT"],"additional_accession":[]},"is_claimable":false,"name":"An empirical comparison of statistical methods for multiple cut-off diagnostic test accuracy meta-analysis of the Edinburgh postnatal depression scale (EPDS) depression screening tool using published results vs individual participant data.","description":"<h4>Background</h4>Selective reporting of results from only well-performing cut-offs leads to biased estimates of accuracy in primary studies of questionnaire-based screening tools and in meta-analyses that synthesize results. Individual participant data meta-analysis (IPDMA) of sensitivity and specificity at each cut-off via bivariate random-effects models (BREMs) can overcome this problem. However, IPDMA is laborious and depends on the ability to successfully obtain primary datasets, and BREMs ignore the correlation between cut-offs within primary studies.<h4>Methods</h4>We compared the performance of three recent multiple cut-off models developed by Steinhauser et al., Jones et al., and Hoyer and Kuss, that account for missing cut-offs when meta-analyzing diagnostic accuracy studies wit","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Feb","modification":"2026-07-15T01:22:09.831Z","creation":"2025-04-05T21:27:16.724Z"},"accession":"S-EPMC10832258","cross_references":{"pubmed":["38302928"],"doi":["10.1186/s12874-023-02134-w"]}}