Department of Evaluation of Innovation and New Technologies, Fundació Clínic per a la Recerca Biomédica, Barcelona 08036, Spain.
Stat Med. 2012 Jun 30;31(14):1407-17. doi: 10.1002/sim.4488. Epub 2012 Feb 21.
Heterogeneity and small-study effects are major concerns for the validity of meta-analysis. Although random effects meta-analysis provides a partial solution to heterogeneity, neither takes into account the presence of small-study effects, although they can rarely be ruled out with certainty. In this paper, we facilitate a better understanding of the properties of a recently described regression-based approach to deriving a meta-analysis estimator robust to small-study effects and unexplainable heterogeneity. The weightings of studies in the meta-analysis are derived algebraically for the regression model and compared with the weightings allocated to studies by fixed and random effects models. These weightings are compared in case studies with and without small-study effects. The presence of small-study effects causes pooled estimates from fixed and random effects meta-analyses to differ, potentially markedly, as a result of the different weights allocated to individual studies. Because random effects meta-analysis gives more weight to smaller studies, it becomes more vulnerable to the small-study effects. The regression approach gives heavier weight to the larger studies than either the fixed or random effects models, leading to its dominance in the estimated pooled effect. The weighting properties of the proposed regression-derived meta-analysis estimator are presented and compared with those of the standard meta-analytic estimators. We propose that there is much to recommend the routine use of this model as a reliable way to derive a pooled meta-analysis estimate that is robust to potential small-study effects, while still accommodating heterogeneity, even though uncertainty will often be considerably larger than for standard estimators.
异质性和小样本效应是荟萃分析有效性的主要关注点。尽管随机效应荟萃分析为解决异质性提供了部分解决方案，但两者都没有考虑到小样本效应的存在，尽管它们几乎不可能被肯定排除。在本文中，我们促进了对最近描述的基于回归的方法的特性的更好理解，该方法可以得出一种稳健的荟萃分析估计值，不受小样本效应和无法解释的异质性的影响。荟萃分析中研究的权重是通过回归模型代数推导得出的，并与固定效应和随机效应模型分配给研究的权重进行了比较。这些权重在有小样本效应和无小样本效应的案例研究中进行了比较。由于固定效应和随机效应荟萃分析分配给个别研究的权重不同，小样本效应的存在会导致汇总估计值存在差异，差异可能非常显著。由于随机效应荟萃分析赋予较小研究的权重更大，因此它更容易受到小样本效应的影响。回归方法比固定或随机效应模型更重视较大的研究，从而导致其在估计的汇总效应中占据主导地位。提出了所提议的回归派生荟萃分析估计量的加权性质，并将其与标准荟萃分析估计量的加权性质进行了比较。我们建议，常规使用该模型作为一种可靠的方法来得出稳健的汇总荟萃分析估计值是有很多好处的，因为它可以克服潜在的小样本效应，同时仍然适应异质性，尽管不确定性通常会比标准估计量大得多。
