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We should not pool diagnostic likelihood ratios in systematic reviews
Aeilko H Zwinderman1, Patrick M Bossuyt
1Department of Clinical Epidemiology and Biostatistics, P.O. Box 22700, 1100 DD Amsterdam, The Netherlands. a.h.zwinderman@amc.uva.nl
Statistics in Medicine
|July 6, 2007
Summary
Meta-analysis of diagnostic test accuracy can be complex when using likelihood ratios. Simpler methods analyzing sensitivity and specificity are recommended over complicated models for likelihood ratios.
Area of Science:
- Medical Statistics
- Diagnostic Test Evaluation
Background:
- Diagnostic likelihood ratios are increasingly advocated for describing test accuracy.
- Likelihood ratios are utilized in medical journals and meta-analyses.
- Standard meta-analysis methods for likelihood ratios can present statistical challenges.
Purpose of the Study:
- To explore alternative statistical models for meta-analysis of diagnostic likelihood ratios.
- To address limitations of standard bivariate meta-analysis when using likelihood ratios.
Main Methods:
- Investigated three statistical models designed to avoid issues with likelihood ratio meta-analysis.
- Compared these models against the standard bivariate normal distribution approach.
Main Results:
- The proposed models avoid impossible probability distributions inherent in some likelihood ratio meta-analyses.
- However, these alternative models were found to be overly complicated.
Conclusions:
- Meta-analysis of diagnostic test accuracy using likelihood ratios presents significant statistical complexities.
- Consideration of meta-analysis of sensitivity and specificity is advised as a more practical alternative to complex likelihood ratio models.
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