A comparison of confidence/credible interval methods for the area under the ROC curve for continuous diagnostic tests with small sample size.
The receiver operating characteristic (ROC) curve is frequently used as a measure of accuracy of continuous markers in diagnostic tests. The area under the ROC curve (AUC) is arguably the most widely used summary index for the ROC curve. Although the small sample size scenario is common in medical tests, a comprehensive study of small sample size properties of various methods for the construction of the confidence/credible interval (CI) for the AUC has been by and large missing in the literature. In this paper, we describe and compare 29 non-parametric and parametric methods for the construction of the CI for the AUC when the number of available observations is small. The methods considered include not only those that have been widely adopted, but also those that have been less frequently mentioned or, to our knowledge, never applied to the AUC context. To compare different methods, we carried out a simulation study with data generated from binormal models with equal and unequal variances and from exponential models with various parameters and with equal and unequal small sample sizes. We found that the larger the true AUC value and the smaller the sample size, the larger the discrepancy among the results of different approaches. When the model is correctly specified, the parametric approaches tend to outperform the non-parametric ones. Moreover, in the non-parametric domain, we found that a method based on the Mann-Whitney statistic is in general superior to the others. We further elucidate potential issues and provide possible solutions to along with general guidance on the CI construction for the AUC when the sample size is small. Finally, we illustrate the utility of different methods through real life examples.
1 Biometrics Research, Merck Research Lab, NJ, USA.
Dai
D
Feng
2 Department of Statistical Sciences, University of Padova, Italy.
Giuliana
G
Cortese
1 Biometrics Research, Merck Research Lab, NJ, USA.
Richard
R
Baumgartner
D000818:Animals; D019540:Area Under Curve; D015415:Biomarkers / Q000378:metabolism; D056808:Biostatistics / Q000379:methods*; D001932:Brain Neoplasms / Q000000981:diagnostic imaging; D003198:Computer Simulation; D016001:Confidence Intervals; D003955:Diagnostic Tests, Routine / Q000706:statistics & numerical data*; D005910:Glioma / Q000000981:diagnostic imaging; D006801:Humans; D000074021:Interatrial Block; D007668:Kidney / Q000293:injuries / Q000378:metabolism; D016013:Likelihood Functions; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method; D012372:ROC Curve; D018401:Sample Size; D018709:Statistics, Nonparametric
D015415:Biomarkers
AUC; Bayesian MCMC; Behrens–Fisher problem; Mann–Whitney; Wald statistic; bootstrap; empirical likelihood; higher order asymptotic; jackknife; kernel smoothing; profile likelihood; signed log-likelihood ratio statistic; small sample size