论文标题
测试诊断荟萃分析中出版偏差的偏差：一项模拟研究
Testing for Publication Bias in Diagnostic Meta-Analysis: A Simulation Study
论文作者
论文摘要
本研究调查了几项统计检验的性能，以通过模拟来检测诊断荟萃分析中的出版偏见。虽然应使用双变量模型来汇总诊断性荟萃分析中的基本研究数据，但诊断准确性的单变量测量值得检测出版偏差。与较早的研究相反，该研究仅集中在诊断优势比或其对数（$ \lnΩ$）上，测试与四种不同的单变量诊断准确度相结合。对于测试和单变量度量的每种组合，在不同条件下都检查了I型错误率和统计功率。结果表明，在诊断荟萃分析中不能建议基于线性回归或等级相关的测试，因为I型错误率要么膨胀或功率太低，无论应用单变量措施如何。相比之下，修剪和填充和$ \lnΩ$的组合具有未充气或略微膨胀的I型错误率和中等到高功率，即使在极端情况下（至少是当每个荟萃分析的研究数量足够大时）。因此，我们建议将修剪和填充与$ \lnΩ$结合使用，以检测诊断性荟萃分析中的漏斗图不对称性。请引用本文发表在《医学统计数据》上（https://doi.org/10.1002/sim.6177）。
The present study investigates the performance of several statistical tests to detect publication bias in diagnostic meta-analysis by means of simulation. While bivariate models should be used to pool data from primary studies in diagnostic meta-analysis, univariate measures of diagnostic accuracy are preferable for the purpose of detecting publication bias. In contrast to earlier research, which focused solely on the diagnostic odds ratio or its logarithm ($\lnω$), the tests are combined with four different univariate measures of diagnostic accuracy. For each combination of test and univariate measure, both type I error rate and statistical power are examined under diverse conditions. The results indicate that tests based on linear regression or rank correlation cannot be recommended in diagnostic meta-analysis, because type I error rates are either inflated or power is too low, irrespective of the applied univariate measure. In contrast, the combination of trim and fill and $\lnω$ has non-inflated or only slightly inflated type I error rates and medium to high power, even under extreme circumstances (at least when the number of studies per meta-analysis is large enough). Therefore, we recommend the application of trim and fill combined with $\lnω$ to detect funnel plot asymmetry in diagnostic meta-analysis. Please cite this paper as published in Statistics in Medicine (https://doi.org/10.1002/sim.6177).
