论文标题
固定效应模型：荟萃分析最令人信服的模型，很少有研究
Fixed-effects model: the most convincing model for meta-analysis with few studies
论文作者
论文摘要
根据Davey等人的说法。 （2011年）在2008年1月发行的系统评价数据库中，总共有22,453个荟萃分析，每次荟萃分析中包含的研究中位数仅为三个。换句话说，文献中进行的大约一半或更多的荟萃分析仅包括两到三项研究。尽管当研究之间的异质性很大时，虽然共同效应模型（也称为固定效应模型）可能会导致误导性结果，但如果研究数量很少，基于随机效应模型的结论也可能不可靠。或者，固定效应模型避免了公共效应模型中的限制性假设，并且需要估计随机效应模型中研究间差异。但是，我们注意到，固定效应模型受到赞赏，直到最近才在实践中使用。在本文中，我们比较了所有三个模型，并在研究数量少时证明了固定效应模型的有用性。此外，我们为固定效应模型中未加权平均效应提出了一个新的估计量。模拟和真实示例还用于说明固定效应模型和新估计器的好处。
According to Davey et al. (2011) with a total of 22,453 meta-analyses from the January 2008 Issue of the Cochrane Database of Systematic Reviews, the median number of studies included in each meta-analysis is only three. In other words, about a half or more of meta-analyses conducted in the literature include only two or three studies. While the common-effect model (also referred to as the fixed-effect model) may lead to misleading results when the heterogeneity among studies is large, the conclusions based on the random-effects model may also be unreliable when the number of studies is small. Alternatively, the fixed-effects model avoids the restrictive assumption in the common-effect model and the need to estimate the between-study variance in the random-effects model. We note, however, that the fixed-effects model is under appreciated and rarely used in practice until recently. In this paper, we compare all three models and demonstrate the usefulness of the fixed-effects model when the number of studies is small. In addition, we propose a new estimator for the unweighted average effect in the fixed-effects model. Simulations and real examples are also used to illustrate the benefits of the fixed-effects model and the new estimator.
