Integrating Stratum-specific Likelihood Ratios with the Analysis of ROC Curves

Abstract

Data used to construct receiver operating characteristic (ROC) curves and to calculate the area under the curve (ROC AUC) can be used to derive stratum-specific likelihood ratios (SSLRs) with their 95% confidence intervals (95% Cls). The purpose of this study was to determine whether useful information can be obtained by adding SSLRs to the analysis of ROC curves. The authors analyzed four previously reported sets of data: 1) serum creatine kinase (SCK) for diagnosing acute myocardial infarction (AMI) in the coronary care unit (CCU); 2) SCK in the evaluation of chest pain in the emergency center (EC); 3) four predictor variables in the diagnosis of strep throat; and 4) the ordinal assessment of computed tom ographic (CT) images. Use of SCK in the CCU produced four strata that had posttest probabilities that were highly discriminating, whereas SCK in the EC resulted in only two strata with limited discriminating ability. In either study the cutpoint at which the SSLR changed from less than to greater than 1.0 was higher than the reported upper normal for the test, thereby quantitating spectrum bias. The maximum number of strata of predictor signs and symptoms for strep throat was three rather than the five used in previous studies. With a larger sample size or pooling, four strata could probably be developed. With CT images, "definitely normal," "probably normal," and "questionable" were collapsed to one negative stratum. "Probably abnormal" became the true "questionable" stratum and "defi nitely abnormal" was the only positive stratum. The authors conclude that additional useful information is obtained by deriving stratum-specific likelihood ratios as part of the analysis of an ROC curve. Key words: diagnosis; decision making; ROC curves; likelihood ratios. (Med Decis Making 1993;13:141-151)