ArticleBiostatistics (Oxford, England)2017
Inference for the difference in the area under the ROC curve derived from nested binary regression models.
Article in Biostatistics (Oxford, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
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- Attainment of Community-Based Goals Is Associated with Lower Risk of Hospital Readmission for Older Australians Accessing the Australian Transition Care Program.International journal of environmental research and public health · 2025Article
- R2ROC: an efficient method of comparing two or more correlated AUC from out-of-sample prediction using polygenic scores.Human genetics · 2024Article
- Identification of Novel Key Genes and Pathways in Multiple Sclerosis Based on Weighted Gene Coexpression Network Analysis and Long Noncoding RNA-Associated Competing Endogenous RNA Network.Oxidative medicine and cellular longevity · 2022Article
- Improvement of APACHE II score system for disease severity based on XGBoost algorithm.BMC medical informatics and decision making · 2021Article
- The added value of new covariates to the brier score in cox survival models.Lifetime data analysis · 2021Article
- Assessment of Weighted Gene Co-Expression Network Analysis to Explore Key Pathways and Novel Biomarkers in Muscular Dystrophy.Pharmacogenomics and personalized medicine · 2021Article
- Comprehensive analysis of lncRNA biomarkers in kidney renal clear cell carcinoma by lncRNA-mediated ceRNA network.PloS one · 2021Article
- Dynamic contrast-enhanced MRI model selection for predicting tumor aggressiveness in papillary thyroid cancers.NMR in biomedicine · 2020Article
- Semi-quantitative visual assessment of chest radiography is associated with clinical outcomes in critically ill patients.Respiratory research · 2019Article
- Prioritized concordance index for hierarchical survival outcomes.Statistics in medicine · 2019Article
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Authors and funding
4 authors.
Funding
Abstract
The area under the curve (AUC) statistic is a common measure of model performance in a binary regression model. Nested models are used to ascertain whether the AUC statistic increases when new factors enter the model. The regression coefficient estimates used in the AUC statistics are computed using the maximum rank correlation methodology. Typically, inference for the difference in AUC statistics from nested models is derived under asymptotic normality. In this work, it is demonstrated that the asymptotic normality is true only when at least one of the new factors is associated with the binary outcome. When none of the new factors are associated with the binary outcome, the asymptotic distribution for the difference in AUC statistics is a linear combination of chi-square random variables. Further, when at least one new factor is associated with the outcome and the population difference is small, a variance stabilizing reparameterization improves the asymptotic normality of the AUC difference statistic. A confidence interval using this reparameterization is developed and simulations are generated to determine their coverage properties. The derived confidence interval provides information on the magnitude of the added value of new factors and enables investigators to weigh the size of the improvement against potential costs associated with the new factors. A pancreatic cancer data example is used to illustrate this approach.
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