Bayesian nonparametric inference for the covariate-adjusted ROC curve

Vanda Inacio De Carvalho (University of Edinburgh)

Friday 9th November, 2018 15:00-16:00 Maths 311B

Abstract

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 through statistical analysis. The receiver operating characteristic (ROC) curve is the most popular used tool for evaluating the discriminatory ability of continuous-outcome diagnostic tests. It has been acknowledged that several factors (e.g., subject-specific characteristics, such as age and/or gender) can affect the test's 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. We develop a highly flexible nonparametric model for the covariate-adjusted ROC curve, based on a combination of a B-splines dependent Dirichlet process mixture model and the Bayesian bootstrap, that can respond to unanticipated features of the data (e.g., nonlinearities, skewness, multimodality, and/or excess of variability). Multiple simulation studies demonstrate the ability of our model to successfully recover the true covariate-adjusted ROC curve and to produce valid inferences in a variety of complex scenarios. Our methods are motivated by and applied to an endocrine dataset where the main goal is to assess the accuracy of the body mass index, adjusted for age and gender, for predicting clusters of cardiovascular disease risk factors.

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