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Lookup NU author(s): Professor Hongsheng DaiORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
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 the situation when the number of studies, denoted as $K$, is large, the cumulative Gaussian approximation errors from each study could make the final estimation unreliable. In the situation when $K$ is small, it is not realistic to assume the random-effect follows Gaussian distribution. In this paper, we present a novel empirical likelihood method 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. We establish the large-sample properties of the non-parametric estimator, and introduce a criterion governing the relationship between the number of studies, $K$, and the sample size of each study, $n_i$. Our methodology supersedes conventional meta-analysis techniques in both theoretical robustness and computational efficiency. We assess the performance of our proposed methods using simulation studies, and apply our proposed methods to two examples.
Author(s): Liang W, Huang H, Dai H, Wei Y
Publication type: Article
Publication status: Published
Journal: Journal of Nonparametric Statistics
Year: 2025
Pages: epub ahead of print
Online publication date: 15/04/2025
Acceptance date: 04/04/2025
Date deposited: 09/04/2025
ISSN (print): 1048-5252
ISSN (electronic): 1029-0311
Publisher: Taylor & Francis
URL: https://doi.org/10.1080/10485252.2025.2492254
DOI: 10.1080/10485252.2025.2492254
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