标题： 埃丁顿的随机效应元分析方法 第二部分：预测 显示英文标题

标题： Edgington's Method for Random-Effects Meta-Analysis Part II: Prediction

摘要： 随机效应元分析中关于平均效应的统计推断在存在显著研究间异质性的情况下被认为不足。 预测分布非常适合量化异质性，因为它们在效应尺度上具有可解释性，并提供了关于未来事件的临床相关信息。 我们构建了预测分布，通过Edgington的$p$值组合方法和广义异质性统计量的置信分布来考虑不确定性。 模拟结果表明，当有三个以上研究时，95%的预测区间通常能够实现名义覆盖率，并且在20个或更少研究的情景中有效反映效应估计的偏度。 忽略异质性估计中的不确定性的模型通常无法实现正确的覆盖率，这突显了在随机效应元分析中进行这种调整的必要性。 显示英文摘要

摘要： Statistical inference about the average effect in random-effects meta-analysis has been considered insufficient in the presence of substantial between-study heterogeneity. Predictive distributions are well-suited for quantifying heterogeneity since they are interpretable on the effect scale and provide clinically relevant information about future events. We construct predictive distributions accounting for uncertainty through confidence distributions from Edgington's $p$-value combination method and the generalized heterogeneity statistic. Simulation results suggest that 95% prediction intervals typically achieve nominal coverage when more than three studies are available and effectively reflect skewness in effect estimates in scenarios with 20 or less studies. Formulations that ignore uncertainty in heterogeneity estimation typically fail to achieve correct coverage, underscoring the need for this adjustment in random-effects meta-analysis.