A likelihood ratio approach to meta-analysis of diagnostic studies

Abstract

Objective: To develop a clinically and methodologically sound approach to diagnostic meta-analysis.

Methods: Two-step model was used involving four fictitious sets of 10 studies each with varying sensitivity and specificity; this was followed by the application of the method to data from a published systematic review of emergency ultrasound. Multidimensional test characteristics (relating to the detection or exclusion of the condition of interest) were described by likelihood ratio scatterplots and pooled likelihood ratios. Likelihood ratios summarise the ability of a test to revise the prior probability of disease. They can be summarised by established fixed-effects and random-effects methods.

Results: Likelihood ratios precisely describe both directions of test performance. By plotting positive against negative likelihood ratios, together with their 95% confidence intervals, a multidimensional forest plot is obtained that can be interpreted in analogy to therapeutic meta-analyses. There are accepted threshold values of positive and negative likelihood ratios (i.e. 10.0 and 0.1) to recommend a test for clinical use. In the matrix space, distinct test characteristics can even be assessed by eyeballing. With regard to data from the real meta-analysis, the suggested high discriminatory power of ultrasound was only partially qualified by likelihood ratios. The positive value confirms the reliability of a positive scan, whereas the negative value questions a normal sonogram.

Conclusions: A full characterisation of test performance requires multidimensional effect measures. Likelihood ratios are recommended descriptors of the two dimensions of diagnostic research evidence and provide a convenient means to visualise and to communicate results as weighted summary estimates of a diagnostic meta-analysis.