Department of Mathematics and Statistics, McMaster University, Hamilton, Ontario, Canada.
Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada.
Biom J. 2020 Sep;62(5):1223-1244. doi: 10.1002/bimj.201900184. Epub 2020 Feb 5.
Hierarchical models are recommended for meta-analyzing diagnostic test accuracy (DTA) studies. The bivariate random-effects model is currently widely used to synthesize a pair of test sensitivity and specificity using logit transformation across studies. This model assumes a bivariate normal distribution for the random-effects. However, this assumption is restrictive and can be violated. When the assumption fails, inferences could be misleading. In this paper, we extended the current bivariate random-effects model by assuming a flexible bivariate skew-normal distribution for the random-effects in order to robustly model logit sensitivities and logit specificities. The marginal distribution of the proposed model is analytically derived so that parameter estimation can be performed using standard likelihood methods. The method of weighted-average is adopted to estimate the overall logit-transformed sensitivity and specificity. An extensive simulation study is carried out to investigate the performance of the proposed model compared to other standard models. Overall, the proposed model performs better in terms of confidence interval width of the average logit-transformed sensitivity and specificity compared to the standard bivariate linear mixed model and bivariate generalized linear mixed model. Simulations have also shown that the proposed model performed better than the well-established bivariate linear mixed model in terms of bias and comparable with regards to the root mean squared error (RMSE) of the between-study (co)variances. The proposed method is also illustrated using a published meta-analysis data.
层级模型被推荐用于荟萃分析诊断测试准确性（DTA）研究。目前，双变量随机效应模型被广泛用于使用对数转换对来自不同研究的一对测试灵敏度和特异性进行综合。该模型假设随机效应的双变量正态分布。然而，该假设是限制性的，并且可能会被违反。当假设失败时，推断可能会产生误导。在本文中，我们通过假设随机效应的灵活双变量偏斜正态分布来扩展当前的双变量随机效应模型，以便稳健地对对数敏感和对数特异性进行建模。所提出模型的边缘分布是通过解析推导得出的，因此可以使用标准似然方法进行参数估计。采用加权平均法来估计整体对数转换的敏感性和特异性。进行了广泛的模拟研究，以调查与其他标准模型相比，所提出模型的性能。总体而言，与标准的双变量线性混合模型和双变量广义线性混合模型相比，所提出的模型在平均对数转换敏感性和特异性的置信区间宽度方面表现更好。模拟还表明，在所研究的（协）方差的偏差方面，该模型比既定的双变量线性混合模型表现更好，并且在 RMSE 方面具有可比性。还使用已发表的荟萃分析数据说明了该方法。
