Meta-analysis for diagnostic test accuracy (DTA) has been one of standard research methods for evidence synthesis of diagnostic studies. In DTA meta-analysis, although publication bias is an important source of bias, there were no certain methods to detect such bias such as the Egger test in univariate meta-analysis. However, several recent studies have discussed these methods in the framework of multivariate meta-analysis, and some generalized Egger tests have been developed. A R package MVPBT (https://github.com/nomahi/MVPBT) is developed to implement the generalized Egger tests developed by Noma (2020; Biometrics 76, 1255-1259) for DTA meta-analysis. Noma's publication bias tests effectively incorporate the correlation information between multiple outcomes and are expected to improve the statistical powers. This paper provides a non-technical introduction for the publication bias tests of DTA meta-analysis using MVPBT.

翻译：诊断性测试精确度的元分析(DTA)是诊断性研究证据合成的标准研究方法之一,在DTA元分析中,虽然公布偏差是偏见的一个重要来源,但并没有某些方法来检测这种偏差,如在单象形元分析中进行的Egger测试,然而,最近的一些研究在多变元分析的框架内讨论了这些方法,并制定了一些通用的Egger测试,开发了一套R MVPBT(https://github.com/nomahi/MVPBT)软件,以实施由Noma(2020年);DTA元分析中开发的通用的Egger测试(生物测定76、1255-1259年);Noma的公布偏差测试有效地纳入了多种结果之间的关联信息,预计将改进统计能力;本文件对利用MVPBT进行DA元分析的公布偏差测试提供了非技术性的介绍。