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On knowing what we do not know - An empirical comparison of methods to detect publication bias in meta-analysis

机译：了解我们不知道的内容-荟萃分析中检测出版物偏倚的方法的经验比较

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摘要

The performance of methods for detecting publication bias in meta-analysis was evaluated using Monte Carlo methods. Four methods of bias detection were investigated: Begg's rank correlation, Egger's regression, funnel plot regression, and trim and fill. Five factors were included in the simulation design: number of primary studies in each meta-analysis, sample sizes of primary studies, population variances in primary studies, magnitude of population effect size, and magnitude of selection bias. Results were evaluated in terms of Type I error control and statistical power. Results suggest poor Type I error control in many conditions for all of the methods examined. One exception was the Begg's rank correlation method using sample size rather than the estimated variance. Statistical power was typically very low for conditions in which Type I error rates were adequately controlled, although power increased with larger sample sizes in the primary studies and larger numbers of studies in the meta-analysis.
机译：使用蒙特卡洛方法评估了荟萃分析中检测出版物偏倚的方法的性能。研究了四种偏倚检测方法：Begg秩相关，Egger回归，漏斗图回归以及修剪和填充。模拟设计中包括五个因素：每个荟萃分析中的基础研究数量，基础研究的样本大小，基础研究中的总体差异，总体影响大小的大小和选择偏倚的大小。根据I类错误控制和统计功效评估了结果。结果表明，在许多情况下，对于所检查的所有方法，I型错误控制均较差。一个例外是使用样本量而不是估计方差的贝格秩相关法。对于I型错误率得到适当控制的条件，统计功效通常非常低，尽管随着主要研究中样本量的增加和荟萃分析中研究数量的增加，功效会增加。

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