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

dc.creatorFischer, Felixes_ES
dc.creatorLevis, Brookees_ES
dc.creatorFalk, Carles_ES
dc.creatorSun, Yinges_ES
dc.creatorIoannidis, John P. A.es_ES
dc.creatorCuijpers, Pimes_ES
dc.creatorShrier, Ianes_ES
dc.creatorBenedetti, Andreaes_ES
dc.creatorThombs, Brett D.es_ES
dc.creatorDepression Screening Data (DEPRESSD) PHQ Collaborationes_ES
dc.creatorHe, Chenes_ES
dc.creatorKrishnan, Ankures_ES
dc.creatorWu, Yines_ES
dc.creatorNegeri, Zelalemes_ES
dc.creatorBhandari, Parash Manies_ES
dc.creatorNeupane, Dipikaes_ES
dc.creatorRice, Danielle B.es_ES
dc.creatorRiehm, Kira E.es_ES
dc.creatorSaadat, Nazanines_ES
dc.creatorAzar, Marleinees_ES
dc.creatorImran, Mahrukhes_ES
dc.creatorBoruff, Jilles_ES
dc.creatorKloda, Lorie A.es_ES
dc.creatorPatten, Scott B.es_ES
dc.creatorZiegelstein, Roy C.es_ES
dc.creatorMarkham, Sarahes_ES
dc.creatorAmtmann, Dagmares_ES
dc.creatorAyalon, Liates_ES
dc.creatorBaradaran, Hamid R.es_ES
dc.creatorBeraldi, Annaes_ES
dc.creatorBernstein, Charles N.es_ES
dc.creatorBombardier, Charles H.es_ES
dc.creatorCarter, Gregoryes_ES
dc.creatorChagas, Marcos H.es_ES
dc.creatorChibanda, Dixones_ES
dc.creatorClover, Kerriees_ES
dc.creatorConwell, Yeateses_ES
dc.creatorDiez-Quevedo, Crisantoes_ES
dc.creatorFann, Jesse R.es_ES
dc.creatorGibson, Lorna J.es_ES
dc.creatorGreen, Eric P.es_ES
dc.creatorGreeno, Catherine G.es_ES
dc.creatorJetté, Nathaliees_ES
dc.creatorKhamseh, Mohammad E.es_ES
dc.creatorKwan, Yunxines_ES
dc.creatorLara, Maria Asunciónes_ES
dc.creatorLoureiro, Sonia R.es_ES
dc.creatorLöwe, Berndes_ES
dc.creatorMarrie, Ruth Annes_ES
dc.creatorMarsh, Lauraes_ES
dc.creatorMarx, Brian P.es_ES
dc.creatorNavarrete, Lauraes_ES
dc.creatorOsório, Flávia L.es_ES
dc.creatorPicardi, Angeloes_ES
dc.creatorPugh, Stephanie L.es_ES
dc.creatorQuinn, Terence J.es_ES
dc.creatorRooney, Alasdair G.es_ES
dc.creatorShinn, Eileen H.es_ES
dc.creatorSidebottom, Abbeyes_ES
dc.creatorSimning, Adames_ES
dc.creatorSpangenberg, Lenaes_ES
dc.creatorTan, Pei Lin Lynnettees_ES
dc.creatorTaylor-Rowan, Martines_ES
dc.creatorTurner, Alynaes_ES
dc.creatorWeert, Henk C. vanes_ES
dc.creatorWagner, Lynne I.es_ES
dc.creatorWhite, Jenniferes_ES
dc.date2021
dc.date.accessioned2024-04-25T20:23:00Z
dc.date.available2024-04-25T20:23:00Z
dc.date.issued2021
dc.identifierJC78DIEP21es_ES
dc.identifier.issn0033-2917
dc.identifier.urihttp://repositorio.inprf.gob.mx/handle/123456789/7956
dc.identifier.urihttps://doi.org/10.1017/S0033291721000131
dc.descriptionBackground: 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.es_ES
dc.formatPDFes_ES
dc.language.isoenges_ES
dc.publisherCambridge University Presses_ES
dc.relation52(15):1-12
dc.rightsAcceso Cerradoes_ES
dc.titleComparison of different scoring methods based on latent variable models of the PHQ-9: an individual participant data meta-analysises_ES
dc.typeArtículoes_ES
dc.contributor.affiliationDepartment of Psychosomatic Medicine, Center for Internal Medicine and Dermatology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
dc.contributor.emailFelix.Fischer@charite.de (Felix Fischer)
dc.relation.jnabreviadoPSYCHOL MED
dc.relation.journalPsychological Medicine
dc.identifier.placeInglaterra
dc.date.published2021
dc.identifier.organizacionInstituto Nacional de Psiquiatría Ramón de la Fuente Muñiz
dc.identifier.eissn1469-8978
dc.identifier.doi10.1017/S0033291721000131
dc.subject.kwConfirmatory factor analysis
dc.subject.kwDepression
dc.subject.kwLatent variable modeling
dc.subject.kwScreening


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