Speaker: Jiacheng Wu, Graduate Student, UW Biostatistics
Abstract: In meta-analysis, random effects models are widely used to combine results from multiple studies while account for heterogeneity. Existing testing procedures rely on asymptotic results, but they may not work well as the number of studies is typically small to moderate. Another difficulty arises due to the nonstandard situation where the variance component might be exact zero, on the boundary of its parameter space. To address these challenges, we consider exact likelihood ratio tests for two hypotheses with boundary problems, including the global null and homogeneity. Based on spectral decomposition, we characterize exact distributions of the likelihood ratio test under the null and alternative hypotheses. This facilitates fast computation of not only the null distribution for p-values but also the power function, which allows comprehensive power comparison between tests based on random and fixed effects models. The proposed test performs well regardless the number of studies, and can have substantially higher power than tests based on fixed effects models when there is heterogeneity.