PDF(Pubmed)
Abstract：
The log response ratio, lnRR, is the most frequently used effect size statistic for meta-analysis in ecology. However, often missing standard deviations (SDs) prevent estimation of the sampling variance of lnRR. We propose new methods to deal with missing SDs via a weighted average coefficient of variation (CV) estimated from studies in the dataset that do report SDs. Across a suite of simulated conditions, we find that using the average CV to estimate sampling variances for all observations, regardless of missingness, performs with minimal bias. Surprisingly, even with missing SDs, this simple method outperforms the conventional approach (basing each effect size on its individual study-specific CV) with complete data. This is because the conventional method ultimately yields less precise estimates of the sampling variances than using the pooled CV from multiple studies. Our approach is broadly applicable and can be implemented in all meta-analyses of lnRR, regardless of \'missingness\'.
摘要：
对数响应比率，lnRR,是生态学荟萃分析中最常用的效应大小统计量。然而,经常缺少标准差(SD)会阻止对lnRR采样方差的估计。我们提出了新的方法来通过加权平均变异系数（CV）来处理缺失的SD，该系数是从报告SD的数据集中的研究中估计的。在一系列模拟条件下，我们发现使用平均CV来估计所有观测值的采样方差，不管不幸，以最小的偏差执行。令人惊讶的是,即使缺少SD，这种简单的方法优于具有完整数据的常规方法(每个效应大小基于其个体研究特定的CV).这是因为与使用来自多个研究的合并CV相比,常规方法最终得到的采样方差的精确估计更少。我们的方法广泛适用，可以在所有lnRR的荟萃分析中实施，不管“不幸”。
