Revisiting Youden's index as a useful measure of the misclassification error in meta-analysis of diagnostic studies.
The paper considers meta-analysis of diagnostic studies that use a continuous score for classification of study participants into healthy or diseased groups. Classification is often done on the basis of a threshold or cut-off value, which might vary between studies. Consequently, conventional meta-analysis methodology focusing solely on separate analysis of sensitivity and specificity might be confounded by a potentially unknown variation of the cut-off value. To cope with this phenomena it is suggested to use, instead, an overall estimate of the misclassification error previously suggested and used as Youden's index and; furthermore, it is argued that this index is less prone to between-study variation of cut-off values. A simple Mantel-Haenszel estimator as a summary measure of the overall misclassification error is suggested, which adjusts for a potential study effect. The measure of the misclassification error based on Youden's index is advantageous in that it easily allows an extension to a likelihood approach, which is then able to cope with unobserved heterogeneity via a nonparametric mixture model. All methods are illustrated at hand of an example on a diagnostic meta-analysis on duplex doppler ultrasound, with angiography as the standard for stroke prevention.
Böhning
Dankmar
D
Applied Statistics, School of Biological Sciences, University of Reading, Reading, UK. d.a.w.bohning@reading.ac.uk
Böhning
Walailuck
W
Holling
Heinz
H
D000792:Angiography / Q000706:statistics & numerical data; D001699:Biometry; D003955:Diagnostic Tests, Routine / Q000706:statistics & numerical data*; D006801:Humans; D015201:Meta-Analysis as Topic; D020521:Stroke / Q000000981:diagnostic imaging / Q000517:prevention & control; D018616:Ultrasonography, Doppler, Duplex / Q000706:statistics & numerical data