Statistical inference for decision curve analysis, with applications to cataract diagnosis

Statistical learning methods are widely used in medical literature for the purpose of diagnosis or prediction. Conventional accuracy assessment via sensitivity, specificity, and ROC curves does not fully account for clinical utility of a specific model. Decision curve analysis (DCA) becomes a novel...

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Bibliographic Details
Published in:Statistics in medicine 2020-09, Vol.39 (22), p.2980-3002
Main Authors: Sande, Sumaiya Z., Li, Jialiang, D'Agostino, Ralph, Yin Wong, Tien, Cheng, Ching‐Yu
Format: Article
Language:English
Subjects:
Cataracts
Decision analysis
decision curve
diagnostic medicine
Medical diagnosis
risk prediction
Statistical inference
threshold
utility
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Summary:Statistical learning methods are widely used in medical literature for the purpose of diagnosis or prediction. Conventional accuracy assessment via sensitivity, specificity, and ROC curves does not fully account for clinical utility of a specific model. Decision curve analysis (DCA) becomes a novel complement as it incorporates a clinical judgment of the relative value of benefits (treating a true positive case) and harms (treating a false positive case) associated with prediction models. The preference of a patient or a policy‐maker is formulated statistically as the underlying threshold probability, above which the patient would choose to be treated. Net benefit is then calculated for possible threshold probability, which places benefits and harms on the same scale. We consider the inference problems for DCA in this paper. Interval estimation procedure and inference methodology are provided after we derive the relevant asymptotic properties. Our formulation can accommodate the classification problems with multiple categories. We carry out numerical studies to assess the performance of the proposed methods. An eye disease dataset is analyzed to illustrate our proposals.
ISSN:0277-6715
1097-0258
DOI:10.1002/sim.8588