AI 中文总结
研究针对双研究元分析中统计挑战，探究基于埃丁顿p值组合方法的元分析，将其应用于1226项分析，该方法在同质性下校准，能自适应调整置信区间宽度，与固定效应元分析在多数情况下统计显著性一致，为现有方法提供补充。
AI 中文摘要
双研究元分析在证据综合中很常见，但带来重大统计挑战。由于仅有两项研究，研究间方差无法可靠估计，使标准随机效应方法不稳定。本文研究基于埃丁顿p值组合方法的元分析作为替代方法，将其应用于德国医疗质量与效率研究所的1226项双研究元分析。埃丁顿方法在同质性下校准，能根据观察到的研究间差异调整置信区间宽度，无需明确估计异质性。在所有检验的元分析中，置信区间包含研究特定估计且有信息性。该方法与固定效应元分析在91%的元分析中统计显著性一致，结果不一致时区间更宽，高度一致时更窄。加权扩展方法能在保留适应性的同时使点估计向更精确研究偏移。我们得出结论，埃丁顿方法为双研究元分析现有方法提供了原则性且实用的补充，介于标准固定效应和随机效应方法之间。
英文摘要
Two-study meta-analyses are common in evidence synthesis but pose major statistical challenges. With only two studies, the between-study variance cannot be reliably estimated, rendering standard random-effects methods unstable. Here, we investigate meta-analyses based on Edgington's p-value combination method as an alternative approach, applying it to 1226 two-study meta-analyses from the German Institute for Quality and Efficiency in Health Care (IQWiG). Like fixed-effect meta-analysis, Edgington's method is calibrated under homogeneity. However, it adapts confidence interval width to observed between-study discrepancy without requiring explicit heterogeneity estimation. In all of the examined meta-analyses, this leads to confidence intervals that contain both study-specific estimates but remain informative. Edgington's method agrees with fixed-effect meta-analysis on statistical significance (at two-sided $α$ = 0.05) in 91% of all meta-analyses, but can give wider intervals when study results are discrepant and narrower intervals when results are highly consistent. Weighted extensions of Edgington's method shift point estimates toward the more precise study while preserving much of this adaptive behavior. We conclude that Edgington's method offers a principled and practically useful complement to existing approaches for two-study meta-analysis, occupying a middle ground between standard fixed-effect and random-effects approaches.
