When are summary ROC curves appropriate for diagnostic meta-analyses?
When are summary ROC curves appropriate for diagnostic meta-analyses?
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DOI：
10.1002/sim.3631
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发表时间：
2009-09-20
影响因子：
2
通讯作者：
Wardlaw, J. M.
中科院分区：
文献类型：
--
作者：
Chappell, F. M.;Raab, G. M.;Wardlaw, J. M.
诊断测试越来越多地通过系统性评价进行评估，这导致了分析此类数据的统计方法的最新发展。最常用的方法是汇总受试者工作特征（SROC）曲线，它可以用非线性双变量随机效应模型拟合。本文着重于解释和提出这些分析数据的实际问题。首先，许多荟萃分析可能无法获得SROC参数的可靠估计。其次，SROC模型可能不合适。在这些情况下，对真阳性率和假阳性率（TPR和FPRS）进行两项单变量荟萃分析的总结可能更合适。我们描述了在拟合这些模型时可能出现的问题类型，并提出了一种算法来指导此类研究的分析，并从已发表的数据分析中获得了插图。可以从（www.diagmeta.info）下载一组免费的R函数来执行这些分析。版权所有（C）2009约翰威利父子有限公司
Diagnostic tests are increasingly evaluated with systematic reviews and this has lead to the recent developments of statistical methods to analyse such data. The most commonly used method is the summary receiver operating characteristic (SROC) Curve, which can be fitted with a non-linear bivariate random-effects model. This paper focuses on the practical problems of interpreting and presenting data from such analyses. First, many meta-analyses may be underpowered to obtain reliable estimates of the SROC parameters. Second, the SROC model may be inappropriate. In these situations, a summary with two univariate meta-analyses of the true and false positive rates (TPRs and FPRS) may be more appropriate. We characterize the type of problems that can occur in fitting these models and present an algorithm to guide the analyst of such studies, with illustrations from analyses of published data. A set of R functions, freely available to perform these analyses, can be downloaded from (www.diagmeta.info). Copyright (C) 2009 John Wiley & Sons, Ltd.