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. 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. 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. 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. •Meta-analyses of diagnostic studies are currently undertaken by synthesizing sensitivity (Se) and specificity (Sp) pairs and the bivariate linear mixed models are the most popular method for synthesis.•The Se/Sp pairs are components of the diagnostic odds ratio (DOR) but are threshold variant unlike the DOR and this study proposes that synthesis models should start from the DOR and then split this into its components.•Simulation demonstrates better performance of this method compared to the traditional bivariate approach.•Software is available to run this new method