The performance of tests of publication bias and other sample size effects in systematic reviews of diagnostic test accuracy was assessed
The performance of tests of publication bias and other sample size effects in systematic reviews of diagnostic test accuracy was assessed
复制标题
DOI：
10.1016/j.jclinepi.2005.01.016
复制
发表时间：
2005-09-01
影响因子：
7.2
通讯作者：
Irwig, L
中科院分区：
文献类型：
--
作者：
Deeks, JJ;Macaskill, P;Irwig, L
背景和目的：发表偏差和其他样本量效应是检验准确性的荟萃分析的问题，就像随机试验一样。我们调查了标准漏斗图和检验在应用于检验准确度的荟萃分析时的局限性，并寻找改进的方法。方法：在模拟的检验准确度的荟萃分析中，估计和比较现有的和替代的样本量效应检验的I型和II型错误率。结果：当疾病患病率不同于50%，当阈值更倾向于敏感性而不是特异性或相反时，Begg、Egger和MacAskill测试的I型错误率对于典型的诊断优势比(DOR)被夸大。基于有效样本量函数的回归和相关性检验对样本量效应的检验是有效的，即使偶尔是保守的。经验证据表明，它们有足够的力量成为有用的测试。然而，当DORS是异质性时，所有的漏斗图不对称检验的威力都很低。结论：现有的使用优势比标准误差的检验如果应用于测试准确度的荟萃分析，可能会产生严重的误导。应使用有效样本量漏斗图和非对称性的相关回归检验来检测发表偏差和其他与样本量相关的效应。(C)2005 Elsevier Inc.保留所有权利。
Background and Objective: Publication bias and other sample size effects are issues for meta-analyses of test accuracy, as for randomized trials. We investigate limitations of standard funnel plots and tests when applied to meta-analyses of test accuracy and look for improved methods.Methods: Type I and type II error rates for existing and alternative tests of sample size effects were estimated and compared in simulated meta-analyses of test accuracy.Results: Type I error rates for the Begg, Egger, and Macaskill tests are inflated for typical diagnostic odds ratios (DOR), when disease prevalence differs from 50% and when thresholds favor sensitivity over specificity or vice versa. Regression and correlation tests based on functions of effective sample size are valid, if occasionally conservative, tests for sample size effects. Empirical evidence suggests that they have adequate power to be useful tests. When DORs are heterogeneous, however, all tests of funnel plot asymmetry have low power.Conclusion: Existing tests that use standard errors of odds ratios are likely to be seriously misleading if applied to meta-analyses of test accuracy. The effective sample size funnel plot and associated regression test of asymmetry should be used to detect publication bias and other sample size related effects. (c) 2005 Elsevier Inc. All rights reserved.