Meta-analysis of the accuracy of two diagnostic tests used in combination: application to the ddimer test and the wells score for the diagnosis of deep vein thrombosis

OBJECTIVES: It is standard practice for diagnostic tests to be evaluated against gold standards in isolation. In routine clinical practice, however, it is commonplace for multiple tests to be used before making definitive diagnoses. This article describes a meta-analytic modeling framework developed to estimate the accuracy of the combination of two diagnostic tests, accounting for the likely nonindependence of the tests. METHODS: A novel multicomponent framework was developed to synthesize information available on different parameters in the model. This allows data to be included from studies evaluating single tests or both tests. Different likelihoods were specified for the different sources of data and linked by means of common parameters. The framework was applied to evaluate the diagnostic accuracy of the Ddimer test and the Wells score for deep vein thrombosis, and the results were compared with those of a model in which independence of tests was assumed. All models were evaluated by using Bayesian Markov chain Monte Carlo simulation methods. RESULTS: The results showed the importance of allowing for the (likely) nonindependence of tests in the meta-analysis model when evaluating a combination of diagnostic tests. The analysis also highlighted the relatively limited impact of those studies that evaluated only one of the two tests of interest. CONCLUSIONS: The models developed allowed the assumption of independence between diagnostic tests to be relaxed while combining a broad array of relevant information from disparate studies. The framework also raises questions regarding the utility of studies limited to the evaluation of single diagnostic tests

Novel presentational approaches were developed for reporting network meta-analysis

OBJECTIVES: To present graphical tools for reporting network meta-analysis (NMA) results aiming to increase the accessibility, transparency, interpretability, and acceptability of NMA analyses. STUDY DESIGN AND SETTINGS: The key components of NMA results were identified based on recommendations by agencies such as the National Institute for Health and Care Excellence (United Kingdom). Three novel graphs were designed to amalgamate the identified components using familiar graphical tools such as the bar, line, or pie charts and adhering to good graphical design principles. RESULTS: Three key components for presentation of NMA results were identified, namely relative effects and their uncertainty, probability of an intervention being best, and between-study heterogeneity. Two of the three graphs developed present results (for each pairwise comparison of interventions in the network) obtained from both NMA and standard pairwise meta-analysis for easy comparison. They also include options to display the probability best, ranking statistics, heterogeneity, and prediction intervals. The third graph presents rankings of interventions in terms of their effectiveness to enable clinicians to easily identify "top-ranking" interventions. CONCLUSIONS: The graphical tools presented can display results tailored to the research question of interest, and targeted at a whole spectrum of users from the technical analyst to the nontechnical clinician