Department of Mathematics and Statistics, McMaster University, Hamilton, Canada.
Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Canada.
Stat Methods Med Res. 2020 Apr;29(4):1227-1242. doi: 10.1177/0962280219852747. Epub 2019 Jun 16.
Bivariate random-effects models are currently widely used to synthesize pairs of test sensitivity and specificity across studies. Inferences drawn based on these models may be distorted in the presence of outlying or influential studies. Currently, subjective methods such as inspection of forest plots are used to identify outlying studies in meta-analysis of diagnostic test accuracy studies. We proposed objective methods based on solid statistical reasoning for identifying outlying and/or influential studies. The proposed methods have been validated using simulation study and illustrated on two published meta-analysis data. Our methods outperform and neglect the subjectivity of the currently used ad hoc methods. The proposed methods can be used as a sensitivity analysis tool concurrently with the current bivariate random-effects models or as a preliminary analysis tool for robust models that accommodate outlying and/or influential studies in meta-analysis of diagnostic test accuracy studies.
双变量随机效应模型目前被广泛用于综合研究间的成对检测敏感性和特异性。在存在离群或有影响力的研究时，基于这些模型得出的推论可能会产生偏差。目前，诊断测试准确性研究的荟萃分析中，通常使用森林图检查等主观方法来识别离群研究。我们提出了基于坚实统计推理的客观方法，用于识别离群和/或有影响力的研究。所提出的方法已通过模拟研究进行了验证，并应用于两个已发表的荟萃分析数据进行了说明。我们的方法优于并忽略了当前使用的特定方法的主观性。所提出的方法可以作为敏感性分析工具，与当前的双变量随机效应模型同时使用，也可以作为在诊断测试准确性研究的荟萃分析中容纳离群和/或有影响力的研究的稳健模型的初步分析工具。
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