Bayesian Adaptation of the Summary ROC Curve Method for Meta-analysis of Diagnostic Test Performance

Abstract

Meta-analytic methods for diagnostic test performance, Bayesian methods in particular, have not been well developed. The most commonly used method for meta-analysis of diagnostic test performance is the Summary Receiver Operator Characteristic (SROC) curve approach of Moses, Shapiro and Littenberg. In this paper, we provide a brief summary of the SROC method, then present a case study of a Bayesian adaptation of their SROC curve method that retains the simplicity of the original model while additionally incorporating uncertainty in the parameters, and can also easily be extended to incorporate the effect of covariates. We further derive a simple transformation which facilitates prior elicitation from clinicians. The method is applied to two datasets: an assessment of computed tomography for detecting metastases in non-small-cell lung cancer, and a novel dataset to assess the diagnostic performance of endoscopic ultrasound (EUS) in the detection of biliary obstructions relative to the current gold standard of endoscopic retrograde cholangiopancreatography (ERCP).

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