biostudies-literatureNegeri ZFSwiss National Science FoundationWellcome Trust28https://www.ebi.ac.uk/biostudies/studies/S-EPMC10832258biostudies-literatureUnknown24(1)<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 witBMC medical research methodologyAn 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.PMC10832258071571082384/Z/07/Z125493211374/Z/18/ZGilbody SSu KPRice DBKhalifa DSBunevicius AGarcia-Esteve LCuijpers PToreki AKozinszky ZMarkham SSharp DJNeupane DKloda LAWu YHe CIoannidis JPATandon SDLevis BTorres-Gimenez ASun YVigod SNKusminskas LTadinac MFavez NAzar MKrishnan AVega-Dienstmaier JMBenedetti Avan Heyningen TNegeri ZFPatten SBChorwe-Sungani GBhandari PMImran MCouto TCERochat TJSmith-Nielsen JChiovitti MJThombs BDHonikman SMitchell NDField SDEPRESsion Screening Data (DEPRESSD) EPDS GroupRiehm KEZiegelstein RCBakare MOKohlhoff JNakic Rados SThiagayson PPawlby SJBoruff JTComeau LBeck CTfalseAn 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.<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 wit2024-01-01T00:00:00Z2024 Feb2026-07-15T01:22:09.831Z2025-04-05T21:27:16.724ZS-EPMC108322583830292810.1186/s12874-023-02134-w