OBJECTIVE: To show how the bivariate random effects meta-analysis model can be used to study the relation between the explanatory variables and the performance of diagnostic tests as characterized by a summary receiver operating characteristic curve (SROCC). STUDY DESIGN AND SETTING: The subject is discussed by means of a data example in which sensitivity and specificity are available for 149 studies on one of three tests for the diagnosis of coronary artery disease. The focus is on comparing SROCCs between different tests adjusted for potential confounders, but the methods can be applied much more generally. RESULTS: Different types of SROCCs can be calculated. The influence of explanatory variables on an SROCC is an ensemble of sensitivity and specificity regression coefficients and covariance parameters. The regression coefficients of the SROCC are estimated and tested, and the percentage explained variability is determined. Under certain assumptions, the SROCCs of different covariate values do not cross. If these are fulfilled, it is much easier to describe the influence of explanatory variables. Conclusions can depend on the type of SROCC. CONCLUSION: The bivariate random effects meta-analysis model is an appropriate and convenient framework to investigate the effect of covariates on the performance of diagnostic tests as measured by SROCCs.
OBJECTIVE: To show how the bivariate random effects meta-analysis model can be used to study the relation between the explanatory variables and the performance of diagnostic tests as characterized by a summary receiver operating characteristic curve (SROCC). STUDY DESIGN AND SETTING: The subject is discussed by means of a data example in which sensitivity and specificity are available for 149 studies on one of three tests for the diagnosis of coronary artery disease. The focus is on comparing SROCCs between different tests adjusted for potential confounders, but the methods can be applied much more generally. RESULTS: Different types of SROCCs can be calculated. The influence of explanatory variables on an SROCC is an ensemble of sensitivity and specificity regression coefficients and covariance parameters. The regression coefficients of the SROCC are estimated and tested, and the percentage explained variability is determined. Under certain assumptions, the SROCCs of different covariate values do not cross. If these are fulfilled, it is much easier to describe the influence of explanatory variables. Conclusions can depend on the type of SROCC. CONCLUSION: The bivariate random effects meta-analysis model is an appropriate and convenient framework to investigate the effect of covariates on the performance of diagnostic tests as measured by SROCCs.