Comparison of different scoring methods based on latent variable models of the PHQ-9: an individual participant data meta-analysis

Details

• Alternative Title:
  Psychol Med
• Personal Author:
  Fischer, Felix ; Levis, Brooke ; Falk, Carl ; Sun, Ying ; Ioannidis, John P. A. ; Cuijpers, Pim ; Shrier, Ian ; Benedetti, Andrea ; Thombs, Brett D.
• Corporate Authors:
  Depression Screening Data (DEPRESSD) PHQ Collaboration
• Description:
  Background:
  
  Previous research on the depression scale of the Patient Health Questionnaire (PHQ-9) has found that different latent factor models have maximized empirical measures of goodness-of-fit. The clinical relevance of these differences is unclear. We aimed to investigate whether depression screening accuracy may be improved by employing latent factor model-based scoring rather than sum scores.
  
  Methods:
  
  We used an individual participant data meta-analysis (IPDMA) database compiled to assess the screening accuracy of the PHQ-9. We included studies that used the Structured Clinical Interview for DSM (SCID) as reference standard and split those into calibration and validation datasets. In the calibration dataset, we estimated unidimensional, two-dimensional (separating cognitive/affective and somatic symptoms of depression), and bi-factor models, and the respective cut-offs to maximize combined sensitivity and specificity. In the validation dataset, we assessed the differences in (combined) sensitivity and specificity between the latent variable approaches and the optimal sum score (≥10), using bootstrapping to estimate 95% confidence intervals for the differences.
  
  Results:
  
  The calibration dataset included 24 studies (4378 participants, 652 major depression cases); the validation dataset 17 studies (4252 participants, 568 cases). In the validation dataset, optimal cut-offs of the unidimensional, two-dimensional and bi-factor models had higher sensitivity (by 0.036, 0.050, 0.049 points, respectively) but lower specificity (0.017, 0.026, 0.019, respectively) compared to the sum score cut-off of ≥10.
  
  Conclusions:
  
  In a comprehensive dataset of diagnostic studies, scoring using complex latent variable models do not improve screening accuracy of the PHQ-9 meaningfully as compared to the simple sum score approach.
  
  
• Subjects:
  Article Confirmatory Factor Analysis Depression Latent Variable Modeling Screening
• Source:
  Psychol Med. :1-12
• Pubmed ID:
  33612144
• Pubmed Central ID:
  PMC9393567
• Document Type:
  Journal Article
• Funding:
  R34 MH072925/MH/NIMH NIH HHSUnited States/ ; R01 MH069666/MH/NIMH NIH HHSUnited States/ ; R49 CE002093/CE/NCIPC CDC HHSUnited States/ ; P30 DK050456/DK/NIDDK NIH HHSUnited States/ ; T32 GM007356/GM/NIGMS NIH HHSUnited States/ ; TL1 RR024135/RR/NCRR NIH HHSUnited States/ ; K07 CA093512/CA/NCI NIH HHSUnited States/ ; U10 CA037422/CA/NCI NIH HHSUnited States/ ; U10 CA021661/CA/NCI NIH HHSUnited States/ ; U10 CA180822/CA/NCI NIH HHSUnited States/ ; R36 HS018246/HS/AHRQ HHSUnited States/ ; U10 CA180868/CA/NCI NIH HHSUnited States/ ; K02 MH065919/MH/NIMH NIH HHSUnited States/ ; R24 MH071604/MH/NIMH NIH HHSUnited States/
• Collection(s):
  CDC Public Access
• Main Document Checksum:
  urn:sha256:60dfc035dab3c81c391debe54460d8dc8842ba1bf008b7ac784730143a3605ad
• Download URL:
  https://stacks.cdc.gov/view/cdc/120526/cdc_120526_DS1.pdf
• File Type:
  [PDF - 519.21 KB ]