Comparison of different scoring methods based on latent variable models of the PHQ-9
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Comparison of different scoring methods based on latent variable models of the PHQ-9 : an individual participant data meta-analysis. / Fischer, Felix; Levis, Brooke; Falk, Carl; Sun, Ying; Ioannidis, John P A; Cuijpers, Pim; Shrier, Ian; Benedetti, Andrea; Thombs, Brett D; Depression Screening Data (DEPRESSD) PHQ Collaboration.
In: PSYCHOL MED, Vol. 52, No. 15, 2022, p. 3472-3483.Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review
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TY - JOUR
T1 - Comparison of different scoring methods based on latent variable models of the PHQ-9
T2 - an individual participant data meta-analysis
AU - Fischer, Felix
AU - Levis, Brooke
AU - Falk, Carl
AU - Sun, Ying
AU - Ioannidis, John P A
AU - Cuijpers, Pim
AU - Shrier, Ian
AU - Benedetti, Andrea
AU - Thombs, Brett D
AU - Depression Screening Data (DEPRESSD) PHQ Collaboration
AU - Löwe, Bernd
PY - 2022
Y1 - 2022
N2 - 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 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.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.
AB - 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 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.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.
U2 - 10.1017/S0033291721000131
DO - 10.1017/S0033291721000131
M3 - SCORING: Journal article
C2 - 33612144
VL - 52
SP - 3472
EP - 3483
JO - PSYCHOL MED
JF - PSYCHOL MED
SN - 0033-2917
IS - 15
ER -