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...

Full description

Saved in:
Bibliographic Details
Published in:Statistics in medicine Vol. 39; no. 22; pp. 2980 - 3002
Main Authors: Sande, Sumaiya Z., Li, Jialiang, D'Agostino, Ralph, Yin Wong, Tien, Cheng, Ching‐Yu
Format: Journal Article
Language:English
Published: England Wiley Subscription Services, Inc 30.09.2020
Subjects:
Cataracts
Decision analysis
decision curve
diagnostic medicine
Medical diagnosis
risk prediction
Statistical inference
threshold
utility
utility
threshold
decision curve
diagnostic medicine
risk prediction
ISSN:0277-6715, 1097-0258, 1097-0258
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
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.
Bibliography:Funding information
Academic Research Funds, R‐155‐000‐195‐114; R‐155‐000‐205‐114; Tier 2 MOE funds in Singapore, MOE2017‐T2‐2‐082
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:0277-6715
1097-0258
1097-0258
DOI:10.1002/sim.8588