Comparison of different scoring methods based on latent variable models of the PHQ-9: an individual participant data meta-analysis release_dwtvsmpcazhinlxhkms2cmmdrq

by felix fischer, Brooke Levis, Carl Falk, Ying Sun, John Ioannidis, Pim Cuijpers, Ian Shrier, Andrea Benedetti, Brett Thombs, the Depression Screening Data (DEPRESSD) PHQ Collaboration

Published in Psychological Medicine by Cambridge University Press (CUP).

p1-12 (2021)


<jats:title>Abstract</jats:title> <jats:sec id="S0033291721000131_sec_a1"> <jats:title>Background</jats:title> 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. </jats:sec> <jats:sec id="S0033291721000131_sec_a2" sec-type="methods"> <jats:title>Methods</jats:title> 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 a 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. </jats:sec> <jats:sec id="S0033291721000131_sec_a3" sec-type="results"> <jats:title>Results</jats:title> 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. </jats:sec> <jats:sec id="S0033291721000131_sec_a4" sec-type="conclusions"> <jats:title>Conclusions</jats:title> 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. </jats:sec>
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