Schwarzer Guido, Antes Gerd, Schumacher Martin
Freiburg Center for Data Analysis and Modelling, University of Freiburg, Germany.
Stat Med. 2002 Sep 15;21(17):2465-77. doi: 10.1002/sim.1224.
The use of meta-analysis to combine results of several trials is still increasing in the medical field. The validity of a meta-analysis may be affected by various sources of bias (for example, publication bias, language bias). Therefore, an analysis of bias should be an integral part of any systematic review. Statistical tests and graphical methods have been developed for this purpose. In this paper, two statistical tests for the detection of bias in meta-analysis were investigated in a simulation study. Binary outcome data, which are very common in medical applications, were considered and relative effect measures (odds ratios, relative risk) were used for pooling. Sample sizes were generated according to findings in a survey of eight German medical journals. Simulation results indicate an inflation of type I error rates for both tests when the data are sparse. Results get worse with increasing treatment effect and number of trials combined. Valid statistical tests for the detection of bias in meta-analysis with sparse data need to be developed.
在医学领域，使用荟萃分析来合并多个试验的结果仍在增加。荟萃分析的有效性可能会受到各种偏倚来源的影响（例如，发表偏倚、语言偏倚）。因此，偏倚分析应该是任何系统评价不可或缺的一部分。为此已经开发了统计检验和图形方法。在本文中，在一项模拟研究中对两种用于检测荟萃分析中偏倚的统计检验进行了研究。考虑了在医学应用中非常常见的二元结局数据，并使用相对效应量（比值比、相对风险）进行合并。样本量根据对八本德国医学期刊的调查结果生成。模拟结果表明，当数据稀疏时，两种检验的I型错误率都会膨胀。随着治疗效果的增加和合并试验数量的增加，结果会变得更糟。需要开发用于检测稀疏数据荟萃分析中偏倚的有效统计检验。
