目的：调查2014年1月至11月国内期刊发表的诊断试验准确性（diagnostic test accuracy，DTA）Meta分析中简单合并模型与双变量模型的使用现状，分析两模型间结果的差异性，并探讨这种差异性与研究间异质性大小的关系。方法：对《中国生物医学文献数据库》2014年1月至11月收录的文献进行检索，纳入DTA Meta分析，描述模型使用的相关信息，提取四格表数据，使用简单合并模型和双变量模型进行重分析，用非参数检验比较模型结果间差值，定性探究灵敏度、特异度异质性大小与结果间差值的关系。结果：共纳入55篇文章，包括58个DTA Meta分析，其中25个Meta分析用于重分析。简单合并模型与双变量模型的使用比例分别为90.9%（50/55）、1.8%（1/55），使用其他合并模型或未合并灵敏度和特异度的文献比例为7.3%（4/55）。在50篇使用简单合并模型合并灵敏度和特异度的文章中，41篇（82.0%）存在误用Meta-disc软件的可能。两种模型所得灵敏度、特异度差值中位数均为0.011（P<0.001，P=0.031），灵敏度和特异度差值随着I²增大变异程度逐渐增大，I²大于75%时变异程度更为明显。结论：国内期刊发表的DTA Meta分析对灵敏度和特异度进行合并时大多使用简单合并模型，且Meta-disc软件常被误认为可对灵敏度和特异度进行随机效应合并；简单合并模型可能低估真实值，尤其研究间异质性大时其合并值与双变量模型间差异更为明显，研究者应当提高正确认识和选用合并方法的能力。

Objective：To investigate the use of simple pooling and bivariate model in meta-analyses of diagnostic test accuracy (DTA) published in Chinese journals (January to November, 2014), compare the differences of results from these two models, and explore the impact of between-study variability of sensitivity and specificity on the differences. Methods:DTA meta-analyses were searched through Chinese Biomedical Literature Database (January to November, 2014). Details in models and data for fourfold table were extracted. Descriptive analysis was conducted to investigate the prevalence of the use of simple pooling method and bivariate model in the included literature. Data were re-analyzed with the two models respectively. Differences in the results were examined by Wilcoxon signed rank test. How the results differences were affected by between-study variability of sensitivity and specificity, expressed by I², was explored. Results:The 55 systematic reviews, containing 58 DTA meta-analyses, were included and 25 DTA meta-analyses were eligible for re-analysis. Simple pooling was used in 50 （90.9%）systematic reviews and bivariate model in 1 (1.8%). The remaining 4 (7.3%) articles used other models pooling sensitivity and specificity or pooled neither of them. Of the reviews simply pooling sensitivity and specificity, 41(82.0%) were at the risk of wrongly using Meta-disc software. The differences in medians of sensitivity and specificity between two models were both 0.011( P<0.001, P=0.031 respectively). Greater differences could be found as I2 of sensitivity or specificity became larger, especially when I²＞75%.Conclusion: Most DTA meta-analyses published in Chinese journals(January to November, 2014) combine the sensitivity and specificity by simple pooling. Meta-disc software can pool the sensitivity and specificity only through fixed-effect model, but a high proportion of authors think it can implement random-effect model. Simple pooling tends to underestimate the results compared with bivariate model. The greater the between-study variance is, the more likely the simple pooling has larger deviation. It is necessary to increase the knowledge level of statistical methods and software for meta-analyses of DTA data.