A0755
Title: Meta-analysis through combining p-values
Authors: Zhongxue Chen - Arizona State University (United States) [presenting]
Abstract: Many meta-analysis approaches, such as fixed- and random-effect models, make some distributional assumptions. If those assumptions are violated, a meta-analysis can still be conducted by combining p-values. Combining information (p-values) obtained from individual studies to test whether there is an overall effect is an important task in statistical data analysis. Many classical statistical tests, such as chi-squared tests, can be viewed as being a p-value combination approach. It remains challenging to find powerful methods to combine p-values obtained from various sources. In this project, a class of p-value combination methods is studied based on the gamma distribution. It is shown that this class of tests is optimal under certain conditions, and several existing popular methods are equivalent to its special cases. An asymptotically uniformly most powerful p-value combination test based on the constrained likelihood ratio test is then studied. Numeric results from the simulation study and real data examples demonstrate that the proposed tests are robust and powerful under many conditions. They have potential broad applications in statistical inference.
