标题： 埃丁顿的随机效应元分析方法 第一部分：估计 显示英文标题

标题： Edgington's Method for Random-Effects Meta-Analysis Part I: Estimation

摘要： 元分析可以表述为将各研究中的$p$值组合成一个联合的$p$值函数，从该函数中可以得到点估计和置信区间。 我们扩展了基于组合$p$值函数的元分析估计框架，通过采用置信分布方法来纳入异质性估计的不确定性。 具体而言，根据从广义异质性统计量构建的异质性参数的置信分布，对 Edgington 方法的置信分布进行调整。 模拟结果表明，在大多数涉及多于三项研究和异质性的场景下，95% 的置信区间接近名义覆盖率。 在没有异质性或仅有三项研究的情况下，置信区间通常会过度覆盖，但通常比 Hartung-Knapp-Sidik-Jonkman 区间更窄。 在模型误设和中等到大异质性情况下，点估计器表现出较小的偏差。 Edgington 方法为经典方法提供了一种实用的替代方案，对异质性估计不确定性的调整通常能改善置信区间的覆盖率。 显示英文摘要

摘要： Meta-analysis can be formulated as combining $p$-values across studies into a joint $p$-value function, from which point estimates and confidence intervals can be derived. We extend the meta-analytic estimation framework based on combined $p$-value functions to incorporate uncertainty in heterogeneity estimation by employing a confidence distribution approach. Specifically, the confidence distribution of Edgington's method is adjusted according to the confidence distribution of the heterogeneity parameter constructed from the generalized heterogeneity statistic. Simulation results suggest that 95% confidence intervals approach nominal coverage under most scenarios involving more than three studies and heterogeneity. Under no heterogeneity or for only three studies, the confidence interval typically overcovers, but is often narrower than the Hartung-Knapp-Sidik-Jonkman interval. The point estimator exhibits small bias under model misspecification and moderate to large heterogeneity. Edgington's method provides a practical alternative to classical approaches, with adjustment for heterogeneity estimation uncertainty often improving confidence interval coverage.