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Bivariate hierarchical model for the Meta- analysis of diagnostic tests in studies with binary responses: its application from SAS and R

Abstract:

Studies on the precision of diagnostic tests usually report the number of true positives, false positives, true negatives, and false negatives. There is generally a negative association between the number of true positives and true negatives, as studies that adopt less strict criteria to declare a test as positive need higher sensitivities and lower specifici¬ties. Given this particularity, the bivariate nature of the data must be preserved, by modeling sensitivity and specificity together. In this paper, we will use the bivariate hierarchical model applied to a meta-analysis data set which was an update to a previous systematic review of diagnostic tests for chronic Chagas disease. Our modeling frame¬work was implemented with SAS NLMIXED procedure, making it pos¬sible to obtain summary measures for sensitivity and specificity, with values ofO. 725and0.995, respectively, out of a total of35 studies with 6057patients.