论文标题
双变量混合效应元回归的聚类量估计器
Cluster-Robust Estimators for Bivariate Mixed-Effects Meta-Regression
论文作者
论文摘要
荟萃分析经常包括根据一组共同的研究参与者报告多个效应大小的试验。这些效应大小通常会相关。群集稳定方差估计器是综合依赖效应的富有成果的方法。但是，当研究数量很少时，最新的稳健估计器可能会产生膨胀的1型错误。我们提出了两个新的簇量估计器，以提高样本性能较小。对于两个新估计量，这个想法是仅使用帽子矩阵的对角线条目来改变残差的估计差异。我们的建议在渐近上等同于先前建议的群集量估计量，例如偏差减少线性化方法。我们将方法应用于现实世界数据，并在广泛的仿真研究中比较和对比它们的性能。我们专注于双变量元回归，尽管这些方法可以更广泛地应用。
Meta-analyses frequently include trials that report multiple effect sizes based on a common set of study participants. These effect sizes will generally be correlated. Cluster-robust variance-covariance estimators are a fruitful approach for synthesizing dependent effects. However, when the number of studies is small, state-of-the-art robust estimators can yield inflated Type 1 errors. We present two new cluster-robust estimators, in order to improve small sample performance. For both new estimators the idea is to transform the estimated variances of the residuals using only the diagonal entries of the hat matrix. Our proposals are asymptotically equivalent to previously suggested cluster-robust estimators such as the bias reduced linearization approach. We apply the methods to real world data and compare and contrast their performance in an extensive simulation study. We focus on bivariate meta-regression, although the approaches can be applied more generally.
