Journal of Data Science

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.

In this paper, the problem of determining which treatments are statistically significant when compared with a zero-dose or placebo control in a dose-response study is considered. Nonparametric meth- ods developed for the commonly used multiple comparison problem whenever the Jonckheere trend test (JT) is appropriate is extended to the multiple comparisons to control problem. We present four closed testing methods, of which two use an AUC regression model approach for determining the treatment arms that are statistically different from the zero-dose control. A simulation study is performed to compare the proposed methods with two existing rank-based nonparametric mul- tiple comparison procedures. The method is further illustrated using a problem from a clinical setting.