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GMDS 2012: 57. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (GMDS)

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie

16. - 20.09.2012, Braunschweig

Meta-analysis for diagnostic accuracy studies: A new statistical model using beta-binomial distributions and bivariate copulas

Meeting Abstract

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  • Oliver Kuß - Institut für Medizinische Epidemiologie, Biometrie und Informatik, Medizinische Fakultät, Martin-Luther-Universität Halle-Wittenberg, Halle (Saale), Deutschland
  • Annika Hoyer - Institut für Medizinische Epidemiologie, Biometrie und Informatik, Medizinische Fakultät, Martin-Luther-Universität Halle-Wittenberg, Halle (Saale), Deutschland
  • Alexander Solms - FU Berlin, Computational Physiology, Halle, Deutschland

GMDS 2012. 57. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e.V. (GMDS). Braunschweig, 16.-20.09.2012. Düsseldorf: German Medical Science GMS Publishing House; 2012. Doc12gmds135

DOI: 10.3205/12gmds135, URN: urn:nbn:de:0183-12gmds1359

Published: September 13, 2012

© 2012 Kuß et al.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by-nc-nd/3.0/deed.en). You are free: to Share – to copy, distribute and transmit the work, provided the original author and source are credited.


Outline

Text

There are still challenges when meta-analysing data from studies on diagnostic accuracy. This is mainly due to the bivariate nature of the response where information on sensitivity and specificity must be summarized while accounting for their association within a single trial. In this paper we propose a new statistical model for the meta-analysis for diagnostic accuracy studies. This model uses beta-binomial distributions for the marginal numbers of true positives and true negatives and links these margins by a bivariate copula distribution. The new model comes with all the features of the current standard model, a bivariate logistic regression model with random effects, but has the additional advantages of a closed likelihood function, a larger flexibility for the association structure of sensitivity and specificity, and of operating on the original scale between 0 and 1.

In a simulation study, which compares three copula models and two implementations of the standard model, the Plackett copula model outperforms its competitors. An example from a meta-analysis to judge the diagnostic accuracy of telomerase [1] for the diagnosis of primary bladder cancer is used for illustration.


References

1.
Glas AS, Roos D, Deutekom M, Zwinderman AH, Bossuyt PM, Kurth KH. Tumor markers in the diagnosis of primary bladder cancer. A systematic review. J Urol. 2003;169(6):1975-82.