Faculty of Statistics, Technical University of Dortmund, Dortmund, Germany.
Res Synth Methods. 2020 May;11(3):331-342. doi: 10.1002/jrsm.1388. Epub 2020 Jan 12.
The explanation of heterogeneity when synthesizing different studies is an important issue in meta-analysis. Besides including a heterogeneity parameter in the statistical model, it is also important to understand possible causes of between-study heterogeneity. One possibility is to incorporate study-specific covariates in the model that account for between-study variability. This leads to linear mixed-effects meta-regression models. A number of alternative methods have been proposed to estimate the (co)variance of the estimated regression coefficients in these models, which subsequently drives differences in the results of statistical methods. To quantify this, we compare the performance of hypothesis tests for moderator effects based upon different heteroscedasticity consistent covariance matrix estimators and the (untruncated) Knapp-Hartung method in an extensive simulation study. In particular, we investigate type 1 error and power under varying conditions regarding the underlying distributions, heterogeneity, effect sizes, number of independent studies, and their sample sizes. Based upon these results, we give recommendations for suitable inference choices in different scenarios and highlight the danger of using tests regarding the study-specific moderators based on inappropriate covariance estimators.
在进行荟萃分析时，解释异质性是一个重要问题。除了在统计模型中包含异质性参数外，了解研究之间异质性的可能原因也很重要。一种可能性是在模型中纳入研究特定的协变量，以解释研究之间的变异性。这导致了线性混合效应荟萃回归模型。已经提出了许多替代方法来估计这些模型中估计回归系数的（协）方差，这随后导致了统计方法结果的差异。为了量化这一点，我们在广泛的模拟研究中比较了基于不同异方差一致协方差矩阵估计量和（未截断的）Knapp-Hartung 方法的调节效应假设检验的性能。特别是，我们根据基础分布、异质性、效应大小、独立研究数量及其样本量的不同条件，研究了类型 1错误和功效。基于这些结果，我们针对不同情况下的适当推断选择提出了建议，并强调了基于不适当协方差估计量的针对研究特定调节因素的检验的危险。
