Abstract
Background and Objectives
Studies of diagnostic accuracy most often report pairs of sensitivity and specificity.
We demonstrate the advantage of using bivariate meta-regression models to analyze
such data.
Methods
We discuss the methodology of both the summary Receiver Operating Characteristic (sROC)
and the bivariate approach by reanalyzing the data of a published meta-analysis.
Results
The sROC approach is the standard method for meta-analyzing diagnostic studies reporting
pairs of sensitivity and specificity. This method uses the diagnostic odds ratio as
the main outcome measure, which removes the effect of a possible threshold but at
the same time loses relevant clinical information about test performance. The bivariate
approach preserves the two-dimensional nature of the original data. Pairs of sensitivity
and specificity are jointly analyzed, incorporating any correlation that might exist
between these two measures using a random effects approach. Explanatory variables
can be added to the bivariate model and lead to separate effects on sensitivity and
specificity, rather than a net effect on the odds ratio scale as in the sROC approach.
The statistical properties of the bivariate model are sound and flexible.
Conclusion
The bivariate model can be seen as an improvement and extension of the traditional
sROC approach.
Keywords
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Article info
Publication history
Published online: August 08, 2005
Accepted:
February 21,
2005
Identification
Copyright
© 2005 Elsevier Inc. Published by Elsevier Inc. All rights reserved.