The covariate-adjusted ROC curve: the concept and its importance, review of inferential methods, and a new Bayesian estimator

Accurate diagnosis of disease is of fundamental importance in clinical
practice and medical research. Before a medical diagnostic test is routinely
used in practice, its ability to distinguish between diseased and nondiseased
states must be rigorously assessed. The receiver operating characteristic
(ROC) curve is the most popular used tool for evaluating the diagnostic
accuracy of continuous-outcome tests. It has been acknowledged that several
factors (e.g., subject-specific characteristics such as age and/or gender) can
affect the test outcomes and accuracy beyond disease status. Recently, the
covariate-adjusted ROC curve has been proposed and successfully applied as
a global summary measure of diagnostic accuracy that takes covariate information
into account. The aim of this paper is three-fold. First, we motivate the
importance of including covariate-information, whenever available, in ROC
analysis and, in particular, how the covariate-adjusted ROC curve is an important
tool in this context. Second, we review and provide insight on the
existing approaches for estimating the covariate-adjusted ROC curve. Third,
we develop a highly flexible Bayesian method, based on the combination
of a Dirichlet process mixture of additive normal models and the Bayesian
bootstrap, for conducting inference about the covariate-adjusted ROC curve.
A simulation study is conducted to assess the performance of the different
methods and it also demonstrates the ability of our proposed Bayesian model
to successfully recover the true covariate-adjusted ROC curve and to produce
valid inferences in a variety of complex scenarios. The methods are applied
to an endocrine study where the goal is to assess the accuracy of the body
mass index, adjusted for age and gender, for detecting clusters of cardiovascular
disease risk factors.