Meta-analyses of diagnostic test accuracy are uncommon and often based on separate pooling of sensitivity and specificity, which can lead to biased estimates. Recently, several appropriate methods have been developed for meta-analysing diagnostic test data from primary studies. Primary studies usually only provide binary test data, for which Moses et al. have developed a method to estimate Summary Receiver Operating Characteristic Curves, thereby taking account of possible test threshold differences between studies. Several methods are also available for analysing multicategory and continuous test data. The usefulness of applying these methods is constrained by publication bias and the generally poor quality of primary studies of diagnostic test accuracy. Meta-analysts need to highlight important defects in quality and how they affect summary estimates to ensure that better primary studies are available for meta-analysis in the future.