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

description: <p>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.</p>