发表机构
The Institute of Statistical Mathematics; The Graduate University for Advanced Studies; Institute of Medical Biometry and Statistics, Medical Faculty and Medical Center – University of Freiburg（日本统计科学研究所; 高级研究院; 弗赖堡大学医学院医学中心医学生物统计学与统计研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出一种置信分布传播方法，用于构建随机效应Meta分析的频率论预测区间，提升了覆盖率且宽度增加有限，相关功能已集成至R包cdmeta。
AI 中文摘要
预测区间在随机效应Meta分析中被越来越多地推荐，因为它们描述了未来研究或场景中预期真实效应的范围。传统频率论区间的有限样本覆盖率可能不足，原因是研究间方差的不确定性未被完全传播。我们提出一种置信分布传播方法，该方法将不确定性通过随机效应层级传递：从通过反转Cochran's Q精确分布得到的置信分布中抽样研究间方差，在每次抽样的条件下，从对应的正态置信分布中抽样平均效应，进而生成未来真实效应。预测限是所得蒙特卡洛分布的经验分位数。在所考察的所有场景中，与现有频率论区间（包括Nagashima-Noma-Furukawa置信分布自助法）相比，所提方法的覆盖率有所提升或保持，且预期宽度仅略有增加。同一蒙特卡洛样本还可生成平均效应和异质性度量的置信区间。该方法已在R包cdmeta中实现。
英文摘要
Prediction intervals are increasingly recommended in random-effects meta-analysis because they describe the range of true effects expected in a future study or setting. Conventional frequentist intervals can have inadequate finite-sample coverage because uncertainty in the between-study variance is not fully propagated. We propose a confidence-distribution propagation method that carries uncertainty through the random-effects hierarchy. The method samples the between-study variance from a confidence distribution obtained by inverting the exact distribution of Cochran's Q and, conditional on each draw, samples the average effect from its corresponding normal confidence distribution before generating a future true effect. Prediction limits are empirical quantiles of the resulting Monte Carlo distribution. Across the scenarios examined, the proposed method improved or maintained coverage relative to existing frequentist intervals, including the Nagashima-Noma-Furukawa confidence-distribution bootstrap, with generally modest increases in expected width. The same Monte Carlo sample also yields confidence intervals for the average effect and heterogeneity measures. The method is implemented in the R package cdmeta available at CRAN (https://doi.org/10.32614/CRAN.package.cdmeta).
CommentsThe R package cdmeta is available at CRAN (https://doi.org/10.32614/CRAN.package.cdmeta)
