Two New Covariate Adjustment Methods for Non-Inferiority Assessment of Binary Clinical Trials Data

Abstract

In clinical trials, examining the adjusted treatment difference has become the preferred way to establish non-inferiority (NI) in cases involving a binary endpoint. However, current methods are inadequate in the area of covariate adjustment. In this paper, we introduce two new methods, nonparametric and parametric, of using the probability and probability (P-P) curve to address the issue of unadjusted categorical covariates in the traditional assessment of NI in clinical trials. We also show that the area under the P-P curve is a valid alternative for assessing NI using the adjusted treatment difference, and we compute this area using Mann–Whitney nonparametric statistics. Our simulation studies demonstrate that our proposed methods can not only control type I error at a predefined significance level but also achieve higher statistical power than those of traditional parametric and nonparametric methods that overlook covariate adjustment, especially when covariates are unbalanced in the two treatment groups. We illustrate the effectiveness of our methodology with data from clinical trials of a therapy for coronary heart disease.

Key Words:

• Clinical trials
• Covariate adjustment
• Effect modification
• Mann–Whitney test
• Nonparametric

ACKNOWLEDGMENTS

The authors thank Harbin's Pharmaceutical Plant No. 2 for providing the data for their study. This research is partially supported by a grant from the National Natural Science Foundation of China (NSFC 30728019). Xiao-Hua Zhou, PhD, is presently a core investigator and Biostatistics Unit Director at the Northwest HSR&D Center of Excellence, Department of Veterans Affairs Medical Center, Seattle, WA. The views expressed in this article are those of the authors and do not necessarily represent the views of the Department of Veterans Affairs.

Notes

*Indicates that results are consistent with that of the conventional method (p value =.0671).