基于精确似然的诊断研究荟萃分析中解决发表偏倚的似然敏感性分析
A likelihood-based sensitivity analysis for addressing publication bias in meta-analysis of diagnostic studies using exact likelihood
AI总结：
本文首次将 Copas t 统计量选择模型与双变量二项模型结合，用于诊断研究荟萃分析中的发表偏倚敏感性分析，以解决正态近似在稀疏数据下不适用的问题，并通过真实数据和模拟研究验证了方法的有效性。
摘要：
发表偏倚（PB）对荟萃分析构成重大威胁，因为产生显著结果的研究更有可能发表在科学期刊上。敏感性分析为解决发表偏倚并考察未发表研究的影响提供了一种灵活的方法。Copas 提出了一种基于 t 统计量的选择模型用于敏感性分析。该 t 统计量选择模型具有可解释性，能够对跨研究的偏倚发表抽样进行建模，正如漏斗图的不对称性所表明的那样。在诊断研究的荟萃分析中，汇总受试者工作特征曲线是综合各研究报告的敏感性和特异性双变量结果的重要工具。以往的研究基于双变量正态模型来处理发表偏倚，但这些方法依赖于对经验 logit 变换后的敏感性和特异性的正态近似，这不适用于稀疏数据场景。与双变量正态模型相比，双变量二项模型用精确的组内模型替代了组内模型中的正态近似，具有更好的有限样本性质。在本研究中，我们将 Copas t 统计量选择模型应用于使用双变量二项模型的诊断研究荟萃分析。据我们所知，这是首次将 Copas t 统计量选择模型应用于双变量二项模型的研究。我们通过多个真实世界的诊断研究荟萃分析和模拟研究评估了我们提出的方法。
英文摘要：
Publication bias (PB) poses a significant threat to meta-analysis, as studies yielding notable results are more likely to be published in scientific journals. Sensitivity analysis provides a flexible method to address PB and to examine the impact of unpublished studies. A selection model based on t-statistics to sensitivity analysis is proposed by Copas. This t-statistics selection model is interpretable and enables the modeling of biased publication sampling across studies, as indicated by the asymmetry in the funnel-plot. In meta-analysis of diagnostic studies, the summary receiver operating characteristic curve is an essential tool for synthesizing the bivariate outcomes of sensitivity and specificity reported by individual studies. Previous studies address PB upon the bivariate normal model but these methods rely on the normal approximation for the empirical logit-transformed sensitivity and specificity, which is not suitable for sparse data scenarios. Compared to the bivariate normal model, the bivariate binomial model which replaces the normal approximation in the within-study model with the exact within-study model has better finite sample properties. In this study, we applied the Copas t-statistics selection model to the meta-analysis of diagnostic studies using the bivariate binomial model. To our knowledge, this is the first study to apply the Copas t-statistics selection model to the bivariate binomial model. We have evaluated our proposed method through several real-world meta-analyses of diagnostic studies and simulation studies.
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