Data-Driven Cutoff Selection for the Patient Health Questionnaire-9 Depression Screening Tool
Rattachement africain : ca, nl, gb, us, sa, nz, il, ir, Afrique du Sud, au, br, hk, cn, my, Zimbabwe, ar, de, sg, gr, jp, es, kr, mx, th, np, Ouganda, it, lv, cl. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
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.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Data-Driven Cutoff Selection for the Patient Health Questionnaire-9 Depression Screening Tool
- Date Crossref
- 22/11/2024
- Éditeur
- American Medical Association (AMA)
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Jewish General Hospital pays non établi dans la noticeÉtablissement de santé
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McGill University Department of Epidemiology pays non établi dans la noticeUniversité ou école supérieure
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University of Waterloo Department of Statistics and Actuarial Science pays non établi dans la noticeUniversité ou école supérieure
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McMaster University Department of Psychiatry and Behavioural Neurosciences pays non établi dans la noticeUniversité ou école supérieure
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Vrije Universiteit Amsterdam Department of Clinical pays non établi dans la noticeUniversité ou école supérieure
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University of York Hull York Medical School and the Department of Health Sciences pays non établi dans la noticeUniversité ou école supérieure
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Stanford University Department of Biomedical Data Science pays non établi dans la noticeUniversité ou école supérieure
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Johns Hopkins Medicine pays non établi dans la noticeÉtablissement de santé
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New York University Department of Applied Statistics pays non établi dans la noticeUniversité ou école supérieure
Jewish General Hospital, Department of Epidemiology — McGill University et Department of Statistics and Actuarial Science — University of Waterloo, avec 9 autres affiliations. Pays d’affiliation : Afrique du Sud, Zimbabwe, Ouganda.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.