A0354
Title: Robust inference for the unification of confidence intervals in meta-analysis
Authors: Hongsheng Dai - Newcastle University (United Kingdom) [presenting]
Wei Liang - Xiamen University (China)
Yinghui Wei - Plymouth University (United Kingdom)
Haicheng Huang - Xiamen University (China)
Abstract: Traditional meta-analysis assumes that the effect sizes estimated in individual studies follow a Gaussian distribution. However, this distributional assumption is not always satisfied in practice, leading to potentially biased results. In a situation where the number of studies is large, the cumulative Gaussian approximation errors from each study could render the final estimation unreliable. An empirical likelihood method is developed for combining confidence intervals under the meta-analysis framework. This method is free of the Gaussian assumption in effect size estimates from individual studies and from the random effects. This new methodology supersedes conventional meta-analysis techniques in both theoretical robustness and computational efficiency.
