A new method for synthesizing test accuracy data outperformed the bivariate method

Objectives: This study outlines the development of a new method (split component synthesis; SCS) for meta-analysis of diagnostic accuracy studies and assesses its performance against the commonly used bivariate random effects model. Methods: The SCS method summarizes the study-specific diagnostic odds ratio (on the ln(DOR) scale), which mainly reflects test discrimination rather than threshold effects, and then splits the summary ln(DOR) into its component parts, logit sensitivity (Se) and logit specificity (Sp). Performance of SCS estimator was assessed through simulation and compared against the bivariate random effects model estimator in terms of bias, mean squared error (MSE), and coverage probability across varying degrees of between-studies heterogeneity. Results: The SCS estimator for the DOR, Se, and Sp was less biased and had smaller MSE than the bivariate model estimator. Despite the wider width of the 95% confidence intervals under the bivariate model, the latter had a poorer coverage probability than that under the SCS method. Conclusion: The SCS estimator outperforms the bivariate model estimator and thus represents an improvement in the approach to diagnostic meta-analyses. The SCS method is available to researchers through the diagma module in Stata and the SCSmeta function in R. (c) 2020 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/).

Automated summary

The study found that the SCS method has less biased estimators for DOR, Se, and Sp, with smaller mean squared errors, compared to the bivariate model estimator. Despite wider confidence intervals under the bivariate model, it had a poorer coverage probability than the SCS method.