Inference for Semiparametric AUC Regression Models with Discrete Covariates

Abstract

In this paper we consider clinical trials with two treatments and a non-normally distributed response variable. In addition, we focus on ap plications which include only discrete covariates and their interactions. For such applications, the semi-parametric Area Under the ROC Curve (AUC) regression model proposed by Dodd and Pepe (2003) can be used. However, because a logistic regression procedure is used to obtain parameter estimates and a bootstrapping method is needed for computing parameter standard errors, their method may be cumbersome to implement. In this paper we propose to use a set of AUC estimates to obtain parameter estimates and combine DeLong's method and the delta method for computing parameter standard errors. Our new method avoids heavy computation associated with the Dodd and Pepe's method and hence is easy to implement. We conduct simulation studies to show that the two methods yield similar results. Finally, we illustrate our new method using data from urinary incontinence clinical trials.