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Activity Number: 244
Type: Contributed
Date/Time: Monday, August 4, 2014 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #312787 View Presentation
Title: Adjusting for Covariates Using Propensity Scores When Area Under the Curve Is Used as Measure of Risk Effect
Author(s): Hadiza Galadima*+ and Donna K. McClish
Companies: Virginia Commonwealth University and Virginia Commonwealth University
Keywords: AUC ; Propensity score ; covariate adjustment ; continuous data ; ROC
Abstract:

Propensity score (PS) methods have been widely used in epidemiologic studies to control for confounding when comparing risk groups. For continuous outcomes the mean difference between groups is a well-known measure of effect. However, when the probability that a randomly selected participant in one group (X) has a better response than a randomly selected participant in a second group (Y), i.e. P(X>Y) is used as a measure of effect, there is a lack of information about the performance of the PS methods. P(X>Y) is equivalent to the area under the receiver operating characteristic curve (AUC), a common measure for assessing accuracy of medical tests. We examine the performance of propensity score methods to control for confounding when AUC is used as the measure of group effect. This includes determining the optimal choice of variables for the propensity score. Furthermore, we explore two methods for estimating the AUC: one assuming a normal distribution and the other making no assumption about the underlying response distributions. We conduct a series of simulations to assess the performance of the methodology.


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