双变量随机效应模型被视为同时建模研究特定的敏感度和特异度在诊断试验准确性元分析中的推荐方法。由于疾病状态的严重程度在研究之间可能有所不同，因此一个正确的分析应该考虑准确性度量与疾病患病率之间的相关性。为此，文献中提出了三元广义线性混合效应模型，尽管计算困难极大地限制了它们的适用性。此外，关注主要集中在队列研究，因为可以从中估计研究特定的疾病患病率，而对于病例对照研究的信息通常被忽视。为了克服这些限制，本文引入了一种三元近似正态模型，该模型同时考虑研究特定的疾病患病率和准确性度量，而对于病例对照研究则考虑敏感度和特异度。该模型是双变量正态混合效应模型的扩展，最初用于不考虑疾病患病率的元分析，并在估计的敏感度和特异度的对数的近似正态的研究内部分布下展开。近似内部样本协方差矩阵的分量被推导出来，并获得了闭式的似然函数。近似似然方法与基于精确内部样本分布的方法以及基于伪似然策略的修改方法进行比较，旨在减少计算工作量。比较是基于模拟研究在各种情况下进行的，并在针对真菌感染诊断试验准确性的元分析和非侵入性检测结肠癌试验的元分析中进行了说明。©2023该作者。由John Wiley & Sons Ltd出版的《医学统计学》出版。

Bivariate random-effects models represent a recommended approach for meta-analysis of diagnostic test accuracy, jointly modeling study-specific sensitivity and specificity. As the severity of the disease status can vary across studies, a proper analysis should account for the dependence of the accuracy measures on the disease prevalence. To this aim, trivariate generalized linear mixed-effects models have been proposed in the literature, although computational difficulties strongly limit their applicability. In addition, the attention has been mainly paid to cohort studies, where the study-specific disease prevalence can be estimated from, while information from case-control studies is often neglected. To overcome such limits, this article introduces a trivariate approximate normal model, which accounts for disease prevalence along with accuracy measures in cohort studies and sensitivity and specificity in case-control studies. The model represents an extension of the bivariate normal mixed-effects model originally developed for meta-analysis not accounting for disease prevalence, under an approximate normal within-study distribution for the logit of estimated sensitivity and specificity. The components of the approximate within-study covariance matrix are derived and the likelihood function is obtained in closed-form. The approximate likelihood approach is compared to that based on the exact within-study distribution and to its modifications following a pseudo-likelihood strategy aimed at reducing the computational effort. The comparison is based on simulation studies in a variety of scenarios, and illustrated in a meta-analysis about the accuracy of a test to diagnose fungal infection and a meta-analysis of a noninvasive test to detect colorectal cancer.© 2023 The Author. Statistics in Medicine published by John Wiley & Sons Ltd.