Nonparametric meta-analysis for diagnostic accuracy studies
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Nonparametric meta-analysis for diagnostic accuracy studies. / Zapf, Antonia; Hoyer, Annika; Kramer, Katharina; Kuss, Oliver.
In: STAT MED, Vol. 34, No. 29, 20.12.2015, p. 3831-3841.Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review
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TY - JOUR
T1 - Nonparametric meta-analysis for diagnostic accuracy studies
AU - Zapf, Antonia
AU - Hoyer, Annika
AU - Kramer, Katharina
AU - Kuss, Oliver
N1 - Copyright © 2015 John Wiley & Sons, Ltd.
PY - 2015/12/20
Y1 - 2015/12/20
N2 - Summarizing the information of many studies using a meta-analysis becomes more and more important, also in the field of diagnostic studies. The special challenge in meta-analysis of diagnostic accuracy studies is that in general sensitivity and specificity are co-primary endpoints. Across the studies both endpoints are correlated, and this correlation has to be considered in the analysis. The standard approach for such a meta-analysis is the bivariate logistic random effects model. An alternative approach is to use marginal beta-binomial distributions for the true positives and the true negatives, linked by copula distributions. In this article, we propose a new, nonparametric approach of analysis, which has greater flexibility with respect to the correlation structure, and always converges. In a simulation study, it becomes apparent that the empirical coverage of all three approaches is in general below the nominal level. Regarding bias, empirical coverage, and mean squared error the nonparametric model is often superior to the standard model, and comparable with the copula model. The three approaches are also applied to two example meta-analyses.
AB - Summarizing the information of many studies using a meta-analysis becomes more and more important, also in the field of diagnostic studies. The special challenge in meta-analysis of diagnostic accuracy studies is that in general sensitivity and specificity are co-primary endpoints. Across the studies both endpoints are correlated, and this correlation has to be considered in the analysis. The standard approach for such a meta-analysis is the bivariate logistic random effects model. An alternative approach is to use marginal beta-binomial distributions for the true positives and the true negatives, linked by copula distributions. In this article, we propose a new, nonparametric approach of analysis, which has greater flexibility with respect to the correlation structure, and always converges. In a simulation study, it becomes apparent that the empirical coverage of all three approaches is in general below the nominal level. Regarding bias, empirical coverage, and mean squared error the nonparametric model is often superior to the standard model, and comparable with the copula model. The three approaches are also applied to two example meta-analyses.
KW - Aorta
KW - Computer Simulation
KW - Coronary Artery Disease
KW - Diagnostic Techniques and Procedures
KW - Echocardiography, Transesophageal
KW - Humans
KW - Meta-Analysis as Topic
KW - Models, Statistical
KW - Sensitivity and Specificity
KW - Statistics, Nonparametric
KW - Journal Article
KW - Research Support, Non-U.S. Gov't
U2 - 10.1002/sim.6583
DO - 10.1002/sim.6583
M3 - SCORING: Journal article
C2 - 26174020
VL - 34
SP - 3831
EP - 3841
JO - STAT MED
JF - STAT MED
SN - 0277-6715
IS - 29
ER -