Data-driven methods distort optimal cutoffs and accuracy estimates of depression screening tools: a simulation study using individual participant data (CROSBI ID 305589)
Prilog u časopisu | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Bhandari, Parash Mani ; Levis, Brooke ; Neupane, Dipika ; Patten, Scott B. ; Shrier, Ian ; Thombs, Brett D. ; Benedetti, Andrea ; Sun, Ying ; He, Chen ; Rice, Danielle B. ; Krishnan, Ankur ; Wu, Yin ; Azar, Marleine ; Sanchez, Tatiana A. ; Chiovitti, Matthew J. ; Saadat, Nazanin ; Riehm, Kira E. ; Imran, Mahrukh ; Negeri, Zelalem ; Boruff, Jill T. ; Cuijpers, Pim ; Gilbody, Simon ; Ioannidis, John P.A. ; Kloda, Lorie A. ; Ziegelstein, Roy C. ; Comeau, Liane ; Mitchell, Nicholas D. ; Tonelli, Marcello ; Vigod, Simone N. ; Aceti, Franca ; Alvarado, Rubén ; Alvarado-Esquivel, Cosme ; Bakare, Muideen O. ; Barnes, Jacqueline ; Bavle, Amar D. ; Beck, Cheryl Tatano ; Bindt, Carola ; Boyce, Philip M. ; Bunevicius, Adomas ; Castro e Couto, Tiago ; Chaudron, Linda H. ; Correa, Humberto ; de Figueiredo, Felipe Pinheiro ; Eapen, Valsamma ; Favez, Nicolas ; Felice, Ethel ; Fernandes, Michelle ; Figueiredo, Barbara ; Fisher, Jane R.W. ; Garcia- Esteve, Lluïsa ; Giardinelli, Lisa ; Helle, Nadine ; Howard, Louise M. ; Khalifa, Dina Sami ; Kohlhoff, Jane ; Kozinszky, Zoltán ; Kusminskas, Laima ; Lelli, Lorenzo ; Leonardou, Angeliki A. ; Maes, Michael ; Meuti, Valentina ; Radoš, Sandra Nakić ; García, Purificación Navarro ; Nishi, Daisuke ; Luwa E-Andjafono, Daniel Okitundu ; Pawlby, Susan J. ; Quispel, Chantal ; Robertson-Blackmore, Emma ; Rochat, Tamsen J. ; Rowe, Heather J. ; Sharp, Deborah J. ; Siu, Bonnie W.M. ; Skalkidou, Alkistis ; Stein, Alan ; Stewart, Robert C. ; Su, Kuan-Pin ; Sundström-Poromaa, Inger ; Tadinac, Meri ; Tandon, S. Darius ; Tendais, Iva ; Thiagayson, Pavaani ; Töreki, Annamária ; Torres-Giménez, Anna ; Tran, Thach D. ; Trevillion, Kylee ; Turner, Katherine ; Vega-Dienstmaier, Johann M. ; Wynter, Karen ; Yonkers, Kimberly A.
DEPRESsion Screening Data (DEPRESSD) Collaboration
engleski
Data-driven methods distort optimal cutoffs and accuracy estimates of depression screening tools: a simulation study using individual participant data
Objective To evaluate, across multiple sample sizes, the degree that data-driven methods result in (1) optimal cutoffs different from population optimal cutoff and (2) bias in accuracy estimates. Study design and setting A total of 1, 000 samples of sample size 100, 200, 500 and 1, 000 each were randomly drawn to simulate studies of different sample sizes from a database (n = 13, 255) synthesized to assess Edinburgh Postnatal Depression Scale (EPDS) screening accuracy. Optimal cutoffs were selected by maximizing Youden's J (sensitivity+specificity–1). Optimal cutoffs and accuracy estimates in simulated samples were compared to population values. Results Optimal cutoffs in simulated samples ranged from ≥ 5 to ≥ 17 for n = 100, ≥ 6 to ≥ 16 for n = 200, ≥ 6 to ≥ 14 for n = 500, and ≥ 8 to ≥ 13 for n = 1, 000. Percentage of simulated samples identifying the population optimal cutoff (≥ 11) was 30% for n = 100, 35% for n = 200, 53% for n = 500, and 71% for n = 1, 000. Mean overestimation of sensitivity and underestimation of specificity were 6.5 percentage point (pp) and -1.3 pp for n = 100, 4.2 pp and -1.1 pp for n = 200, 1.8 pp and -1.0 pp for n = 500, and 1.4 pp and -1.0 pp for n = 1, 000. Conclusions Small accuracy studies may identify inaccurate optimal cutoff and overstate accuracy estimates with data-driven methods.
Optimal cutoff ; Accuracy estimates ; Bias ; Cherry-picking ; Data-driven methods Depression
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Podaci o izdanju
137
2021.
137-147
objavljeno
0895-4356
10.1016/j.jclinepi.2021.03.031
