Empirical Bayes Estimates Generated in a Hierarchical Summary ROC Analysis Agreed Closely with Those of a Full Bayesian Analysis

Background And Objective: A range of fixed-effect and random-effects meta-analytic methods are available to obtain summary estimates of measures of diagnostic test accuracy. The hierarchical summary receiver operating characteristic (HSROC) model proposed by Rutter and Gatsonis in 2001 represents a general framework for the meta-analysis of diagnostic test studies that allows different parameters to be defined as a fixed effect or random effects within the same model. The Bayesian method used for fitting the model is complex, however, and the model is not widely used. The objective of this report is to show how the model may be fitted using the SAS procedure NLMIXED and to compare the results to the fully Bayesian analysis using an example.

Methods: The HSROC model, its assumptions, and its interpretation are described. The advantages of this model over the usual summary ROC (SROC) regression model are outlined. A complex example is used to compare the estimated SROC curves, expected operating points, and confidence intervals using the alternative approaches to fitting the model.

Results: The empirical Bayes estimates obtained using NLMIXED agree closely with those obtained using the fully Bayesian analysis.

Conclusion: This alternative and more straightforward method for fitting the HSROC model makes the model more accessible to meta-analysts.