Data-Driven Cutoff Selection for the Patient Health Questionnaire-9 Depression Screening Tool
Authors: Brooke Levis, Parash Mani Bhandari, Dipika Neupane, Suiqiong Fan, Ying Sun, Chen He, Yin Wu, Ankur Krishnan, Zelalem Negeri, Mahrukh Imran, Danielle B. Rice, Kira E. Riehm, Marleine Azar, Alexander W. Levis, Jill Boruff, Pim Cuijpers, Simon Gilbody, John P. A. Ioannidis, Lorie A. Kloda, Scott B. Patten, Roy C. Ziegelstein, Daphna Harel, Yemisi Takwoingi, Sarah Markham, Sultan H. Alamri, Dagmar Amtmann, Bruce Arroll, Liat Ayalon, Hamid R. Baradaran, Anna Beraldi, Charles N. Bernstein, Arvin Bhana, Charles H. Bombardier, Ryna Imma Buji, Peter Butterworth, Gregory Carter, Marcos H. Chagas, Juliana C. N. Chan, Lai Fong Chan, Dixon Chibanda, Kerrie Clover, Aaron Conway, Yeates Conwell, Federico M. Daray, Janneke M. de Man-van Ginkel, Jesse R. Fann, Felix H. Fischer, Sally Field, Jane R. W. Fisher, Daniel S. S. Fung, Bizu Gelaye, Leila Gholizadeh, Felicity Goodyear-Smith, Eric P. Green, Catherine G. Greeno, Brian J. Hall, Liisa Hantsoo, Martin Härter, Leanne Hides, Stevan E. Hobfoll, Simone Honikman, Thomas Hyphantis, Masatoshi Inagaki, Maria Iglesias-Gonzalez, Hong Jin Jeon, Nathalie Jetté, Mohammad E. Khamseh, Kim M. Kiely, Brandon A. Kohrt, Yunxin Kwan, Maria Asunción Lara, Holly F. Levin-Aspenson, Shen-Ing Liu, Manote Lotrakul, Sonia R. Loureiro, Bernd Löwe, Nagendra P. Luitel, Crick Lund, Ruth Ann Marrie, Laura Marsh, Brian P. Marx, Anthony McGuire, Sherina Mohd Sidik, Tiago N. Munhoz, Kumiko Muramatsu, Juliet E. M. Nakku, Laura Navarrete, Flávia L. Osório, Brian W. Pence, Philippe Persoons, Inge Petersen, Angelo Picardi, Stephanie L. Pugh, Terence J. Quinn, Elmars Rancans, Sujit D. Rathod, Katrin Reuter, Alasdair G. Rooney, Iná S. Santos, Miranda T. Schram, Juwita Shaaban, Eileen H. Shinn, Abbey Sidebottom, Adam Simning, Lena Spangenberg, Lesley Stafford, Sharon C. Sung, Keiko Suzuki, Pei Lin Lynnette Tan, Martin Taylor-Rowan, Thach D. Tran, Alyna Turner, Christina M. van der Feltz-Cornelis, Thandi van Heyningen, Paul A. Vöhringer, Lynne I. Wagner, Jian Li Wang, David Watson, Jennifer White, Mary A. Whooley, Kirsty Winkley, Karen Wynter, Mitsuhiko Yamada, Qing Zhi Zeng, Yuying Zhang, Brett D. Thombs, Andrea Benedetti
Journal: JAMA Network Open (2024)

Importance Test accuracy studies often use small datasets to simultaneously select an optimal cutoff score that maximizes test accuracy and generate accuracy estimates. Objective To evaluate the degree to which using data-driven methods to simultaneously select an optimal Patient Health Questionnaire-9 (PHQ-9) cutoff score and estimate accuracy yields (1) optimal cutoff scores that differ from the population-level optimal cutoff score and (2) biased accuracy estimates. Design, Setting, and Participants This study used cross-sectional data from an existing individual participant data meta-analysis (IPDMA) database on PHQ-9 screening accuracy to represent a hypothetical population. Studies in the IPDMA database compared participant PHQ-9 scores with a major depression classification. From the IPDMA population, 1000 studies of 100, 200, 500, and 1000 participants each were resampled. Main Outcomes and Measures For the full IPDMA population and each simulated study, an optimal cutoff score was selected by maximizing the Youden index. Accuracy estimates for optimal cutoff scores in simulated studies were compared with accuracy in the full population. Results The IPDMA database included 100 primary studies with 44 503 participants (4541 [10%] cases of major depression). The population-level optimal cutoff score was 8 or higher. Optimal cutoff scores in simulated studies ranged from 2 or higher to 21 or higher in samples of 100 participants and 5 or higher to 11 or higher in samples of 1000 participants. The percentage of simulated studies that identified the true optimal cutoff score of 8 or higher was 17% for samples of 100 participants and 33% for samples of 1000 participants. Compared with estimates for a cutoff score of 8 or higher in the population, sensitivity was overestimated by 6.4 (95% CI, 5.7-7.1) percentage points in samples of 100 participants, 4.9 (95% CI, 4.3-5.5) percentage points in samples of 200 participants, 2.2 (95% CI, 1.8-2.6) percentage points in samples of 500 participants, and 1.8 (95% CI, 1.5-2.1) percentage points in samples of 1000 participants. Specificity was within 1 percentage point across sample sizes. Conclusions and Relevance This study of cross-sectional data found that optimal cutoff scores and accuracy estimates differed substantially from population values when data-driven methods were used to simultaneously identify an optimal cutoff score and estimate accuracy. Users of diagnostic accuracy evidence should evaluate studies of accuracy with caution and ensure that cutoff score recommendations are based on adequately powered research or well-conducted meta-analyses.