A0667
Title: A unifying dependent combination framework with applications to association tests
Authors: Xiufan Yu - University of Notre Dame (United States) [presenting]
Abstract: A novel meta-analysis framework is introduced to combine dependent tests under a general setting and utilize it to synthesize various association tests that are calculated from the same dataset. The development builds upon the classical meta-analysis methods of aggregating p-values and also a more recent general method of combining confidence distributions but makes generalizations to handle dependent tests. The proposed framework ensures rigorous statistical guarantees, and a comprehensive study is provided and compared with various existing dependent combination methods. Notably, it is demonstrated that the widely used Cauchy combination method for dependent tests, referred to as the vanilla Cauchy combination in this article, can be viewed as a special case within the framework. Moreover, the proposed framework provides a way to address the problem when the distributional assumptions underlying the vanilla Cauchy combination are violated. The numerical results demonstrate that ignoring the dependence among the to-be-combined components may lead to a severe size distortion phenomenon. Compared to the existing p-value combination methods, including the vanilla Cauchy combination method, the proposed combination framework can handle the dependence accurately and utilizes the information efficiently to construct tests with accurate size and enhanced power.
