Controlling for Confounding when Association is Quantified by Area Under the ROC Curve

Abstract

CONTROLLING FOR CONFOUNDING WHEN ASSOCIATION IS QUANTIFIED BY AREA UNDER THE ROC CURVE By Hadiza I. Galadima, Ph.D. A dissertation submitted in partial fulfillment of the requirement for the degree of Doctor of Philosophy at Virginia Commonwealth University. Virginia Commonwealth University, 2015 Advisor: Donna K. McClish, Ph.D. Professor, Department of Biostatistics In the medical literature, there has been an increased interest in evaluating association between exposure and outcomes using nonrandomized observational studies. However, because assignments to exposure are not done randomly in observational studies, comparisons of outcomes between exposed and non-exposed subjects must account for the effect of confounders. Propensity score methods have been widely used to control for confounding, when estimating exposure effect. Previous studies have shown that conditioning on the propensity score results in biased estimation of odds ratio and hazard ratio. However, there is a lack of research into the performance of propensity score methods for estimating the area under the ROC curve (AUC). In this dissertation, we propose AUC as measure of effect when outcomes are continuous. The AUC is interpreted as the probability that a randomly selected non-exposed subject has a better response than a randomly selected exposed subject. The aim of this research is to examine methods to control for confounding when association between exposure and outcomes is