Application of Summary Receiver Operating Characteristics (sROC) Analysis to Diagnostic Clinical Testing

Summary receiver operating characteristics (sROC) analysis is a recently developed statistical technique that can be applied to meta-analysis of diagnostic tests. This technique can overcome some of the limitations associated with pooling the sensitivities and specificities of published studies. The sROC curve is initially constructed by plotting the sensitivity (true positivity) and false positivity (1 - specificity) of each study. After mathematical manipulation of the true and false positivities, linear regression is performed to calculate the slope and y-intercept. These coefficients are then entered into the sROC equation to generate the sROC curve. There are three commonly used methods to assess the accuracy of the test: the exact area under the curve (AUC) for the sROC function, the homogeneous AUC, and the index Q*. Statistical formulas can compare these values from different diagnostic tests. With the introduction of sROC software and better understanding of this method, the application of sROC analysis should continue to increase.