TY - JOUR
T1 - A unified Bayesian framework for exact inference of area under the receiver operating characteristic curve
AU - Lin, Ruitao
AU - Chan, KC Gary
AU - Shi, Haolun
N1 - Funding Information:
Lin's research was partially supported by National Cancer Institute Award Numbers 5P30CA016672 and 5P50CA221703, and Cancer Prevention & Research Institute in Texas (CPRIT) Award RR190079. Chan's research was partially supported by National Institute of Health (NIH) Award Number 1R01HL122212.
Publisher Copyright:
© The Author(s) 2021.
PY - 2021/10
Y1 - 2021/10
N2 - The area under the receiver operating characteristic curve is a widely used measure for evaluating the performance of a diagnostic test. Common approaches for inference on area under the receiver operating characteristic curve are usually based upon approximation. For example, the normal approximation based inference tends to suffer from the problem of low accuracy for small sample size. Frequentist empirical likelihood based approaches for area under the receiver operating characteristic curve estimation may perform better, but are usually conducted through approximation in order to reduce the computational burden, thus the inference is not exact. By contrast, we proposed an exact inferential procedure by adapting the empirical likelihood into a Bayesian framework and draw inference from the posterior samples of the area under the receiver operating characteristic curve obtained via a Gibbs sampler. The full conditional distributions within the Gibbs sampler only involve empirical likelihoods with linear constraints, which greatly simplify the computation. To further enhance the applicability and flexibility of the Bayesian empirical likelihood, we extend our method to the estimation of partial area under the receiver operating characteristic curve, comparison of multiple tests, and the doubly robust estimation of area under the receiver operating characteristic curve in the presence of missing test results. Simulation studies confirm the desirable performance of the proposed methods, and a real application is presented to illustrate its usefulness.
AB - The area under the receiver operating characteristic curve is a widely used measure for evaluating the performance of a diagnostic test. Common approaches for inference on area under the receiver operating characteristic curve are usually based upon approximation. For example, the normal approximation based inference tends to suffer from the problem of low accuracy for small sample size. Frequentist empirical likelihood based approaches for area under the receiver operating characteristic curve estimation may perform better, but are usually conducted through approximation in order to reduce the computational burden, thus the inference is not exact. By contrast, we proposed an exact inferential procedure by adapting the empirical likelihood into a Bayesian framework and draw inference from the posterior samples of the area under the receiver operating characteristic curve obtained via a Gibbs sampler. The full conditional distributions within the Gibbs sampler only involve empirical likelihoods with linear constraints, which greatly simplify the computation. To further enhance the applicability and flexibility of the Bayesian empirical likelihood, we extend our method to the estimation of partial area under the receiver operating characteristic curve, comparison of multiple tests, and the doubly robust estimation of area under the receiver operating characteristic curve in the presence of missing test results. Simulation studies confirm the desirable performance of the proposed methods, and a real application is presented to illustrate its usefulness.
KW - Area under the receiver operating characteristic curve
KW - Bayesian nonparametrics
KW - U-statistics
KW - double robustness
KW - empirical likelihood
KW - missing data
UR - http://www.scopus.com/inward/record.url?scp=85114108830&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85114108830&partnerID=8YFLogxK
U2 - 10.1177/09622802211037070
DO - 10.1177/09622802211037070
M3 - Article
C2 - 34468238
AN - SCOPUS:85114108830
SN - 0962-2802
VL - 30
SP - 2269
EP - 2287
JO - Statistical Methods in Medical Research
JF - Statistical Methods in Medical Research
IS - 10
ER -