处理多变量和替代元分析中的部分缺失相关性
Dealing with partial missing correlations in multivariate and surrogate meta-analyses
AI总结：
针对双变量和替代元分析中部分缺失相关性的问题，提出基于Beta回归模型的多重插补方法，在随机缺失假设下通过随机过程进行插补，模拟研究表明该方法优于简单均值插补和完整案例分析。
摘要：
本文解决了双变量和替代元分析框架中部分缺失相关性的问题。虽然将分析限制在完整案例研究似乎是最直接的分析策略，但这种方法已被证明会导致估计结果出现显著的效率低下和潜在偏差。当前文献中的方法论贡献要么通过基于简化假设的似然框架下的聚合估计程序来规避这一限制，要么采用确定性插补策略，例如从观测单元导出的经验均值。在本文中，我们提出了一个多重插补框架，其中通过基于Beta回归模型的随机过程进行插补，从而明确考虑了观测缺失机制背后的随机缺失（MAR）假设。我们通过一项涵盖多种情景的广泛模拟研究证明了我们方法的有效性，并在MAR假设下将我们的提议与简单均值插补和完整案例分析进行了比较。
英文摘要：
This work addresses the issue of partially missing correlations within the framework of bivariate and surrogate meta-analyses. While restricting the analysis to complete-case studies may appear to constitute the most straightforward analytical strategy, such an approach has been demonstrated to yield substantial inefficiencies and potential bias in the resulting estimates. Current methodological contributions in the literature circumvent this limitation either through aggregate estimation procedures grounded in likelihood-based frameworks under simplifying assumptions, or by resorting to deterministic imputation strategies, such as the empirical mean derived from observed units. In the present paper, we propose a multiple imputation framework in which imputation is performed via stochastic procedures based on a Beta regression model, thereby explicitly accounting for the missing at random (MAR) assumption underlying the observed missingness mechanism. We demonstrate the effectiveness of our method through an extensive simulation study across various scenarios, comparing our proposal with simple mean imputation and complete-case analysis under the MAR assumption.
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