Modelling multiple thresholds in meta-analysis of diagnostic test accuracy studies. Issue 1 (December 2016)

• Record Type: Journal Article
• Authors: Steinhauser, Susanne; Schumacher, Martin; Rücker, Gerta
• Is Part Of: BMC medical research methodology. Volume 16:Issue 1(2016)
• Page Start: 1
• Page End: 15
• Publication Date: 2016-12
• Subjects: Diagnostic accuracy study -- Meta-analysis -- Biomarker -- Threshold -- ROC curve; Medicine -- Research -- Methodology -- Periodicals; 610.72
• DOI: 10.1186/s12874-016-0196-1
• Languages: English
• ISSNs: 1471-2288

Abstract

Abstract Background In meta-analyses of diagnostic test accuracy, routinely only one pair of sensitivity and specificity per study is used. However, for tests based on a biomarker or a questionnaire often more than one threshold and the corresponding values of true positives, true negatives, false positives and false negatives are known. Methods We present a new meta-analysis approach using this additional information. It is based on the idea of estimating the distribution functions of the underlying biomarker or questionnaire within the non-diseased and diseased individuals. Assuming a normal or logistic distribution, we estimate the distribution parameters in both groups applying a linear mixed effects model to the transformed data. The model accounts for across-study heterogeneity and dependence of sensitivity and specificity. In addition, a simulation study is presented. Results We obtain a summary receiver operating characteristic (SROC) curve as well as the pooled sensitivity and specificity at every specific threshold. Furthermore, the determination of an optimal threshold across studies is possible through maximization of the Youden index. We demonstrate our approach using two meta-analyses of B type natriuretic peptide in heart failure and procalcitonin as a marker for sepsis. Conclusions Our approach uses all the available information and results in an estimation not only of the performance of the biomarker but also of the threshold at which the optimal performance can be expected.