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Meta-Analysis
. 2015 Nov-Dec;16(6):1188-96.
doi: 10.3348/kjr.2015.16.6.1188. Epub 2015 Oct 26.

Systematic Review and Meta-Analysis of Studies Evaluating Diagnostic Test Accuracy: A Practical Review for Clinical Researchers-Part II. Statistical Methods of Meta-Analysis

Juneyoung Lee  1 , Kyung Won Kim  2 , Sang Hyun Choi  2 , Jimi Huh  2 , Seong Ho Park  2
Affiliations
Meta-Analysis

Systematic Review and Meta-Analysis of Studies Evaluating Diagnostic Test Accuracy: A Practical Review for Clinical Researchers-Part II. Statistical Methods of Meta-Analysis

Juneyoung Lee et al. Korean J Radiol. 2015 Nov-Dec.

Abstract

Meta-analysis of diagnostic test accuracy studies differs from the usual meta-analysis of therapeutic/interventional studies in that, it is required to simultaneously analyze a pair of two outcome measures such as sensitivity and specificity, instead of a single outcome. Since sensitivity and specificity are generally inversely correlated and could be affected by a threshold effect, more sophisticated statistical methods are required for the meta-analysis of diagnostic test accuracy. Hierarchical models including the bivariate model and the hierarchical summary receiver operating characteristic model are increasingly being accepted as standard methods for meta-analysis of diagnostic test accuracy studies. We provide a conceptual review of statistical methods currently used and recommended for meta-analysis of diagnostic test accuracy studies. This article could serve as a methodological reference for those who perform systematic review and meta-analysis of diagnostic test accuracy studies.

Keywords: Diagnostic test accuracy; Meta-analysis; Systematic review.

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Figures

Fig. 1
Fig. 1. Examples of forest plot, separate pooling of sensitivity and specificity, and construction of Moses-Littenberg SROC curve (method currently not recommended) using Meta-disc software.
A. Use of Meta-disc. First, data are entered in data window (1). In analyze tab, choose Plots function (2). Then, select plot to draw from new pop-up window (3). Results can be reviewed in Results window (4). B. Moses-Littenberg SROC curve. SROC curves and summary estimates, including area under ROC curve (AUC) and Q* index are presented. SROC = summary receiver operating characteristic
Fig. 2
Fig. 2. Example of meta-analysis with hierarchical modeling (method currently recommended). Metandi module in STATA is used.
A. Data input. Simply click data editor button (1) and enter data in Data Editor window (2). B. Calculation of summary estimates. Summary estimates of sensitivity, specificity, DOR, LR+, and LR- can be obtained using command "metandi tp fp fn tn". C. HSROC curve is obtained using command "metandiplot tp fp fn tn". Circles represent estimates of individual primary studies, and square indicates summary points of sensitivity and specificity. HSROC curve is plotted as curvilinear line passing through summary point. 95% confidence region and 95% prediction region are also provided. DOR = diagnostic odds ratio, HSROC = hierarchical summary receiver operating characteristic, LR = likelihood ratio
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