Steps for Diagnostic Precision Meta-Analysis in Binary Response Studies: A Case of the Application by Hierarchical Modeling

The discriminatory capacity of a test is commonly expressed in terms of sensitivity and specificity, and there is generally a compromise relationship between these two measures, as an increasing threshold for defining the positivity of the test results in a decrease in sensitivity and an increase in specificity. Recommended methods for the meta-analysis of diagnostic tests, such as the bivariate model, focus on estimating a summary sensitivity and specificity at a common threshold, while the Hierarchical Summary Receiver Operating Characteristic (HSROC) model focuses on estimating a summary curve from studies that have used different thresholds. Therefore, we will explain the hierarchical modeling for meta-analysis of the study of precision in diagnostic tests, and we will design a decision scheme that helps to understand the models to choose the most appropriate in situations of heterogeneity in the studies, and illustrate its application in a systematic review, studying the properties and assumptions of meta-analytic procedures to synthesize the quantitative evidence of the parameters. For which, we used a systematic review that obtained summary estimates for the diagnosis of invasive aspergillosis, being our modeling framework the NLMIXED procedure of SAS, obtaining summary estimates for sensitivity and specificity for the bivariate model of 0.7708 and 0.8521, from a total of 27 studies involving 3,943 patients, and for the HSROC case the values were 0.7304 and 0.8867 respectively. Finally, we hope that this article will provide clinicians with a sufficient understanding of the terminology and statistical methods, obtaining plausible interpretations of the results in systematic reviews.