Abstract
ObjectivesThis 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. MethodsThe 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. ResultsThe 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. ConclusionThe 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.
Highlights
When extensive heterogeneity was introduced, there was a substantial drop in performance for the bivariate model with a significant increase in type I error of up to 35%
We introduce the SCS method and demonstrate that its performance under systematic error was superior to that of the bivariate method currently being used
The discriminative capacity of a diagnostic test can be summarized by two main measuresdthe DOR and the area under the curve (AUC) [19,20]
Summary
Funding: This work was made possible by Program Grant #NPRP10-0129-170274 from the Qatar National Research Fund (a member of Qatar Foundation) to S.A.R.D. The findings herein reflect the work and are solely the responsibility of the authors. All authors had full access to all the data in the study, and the corresponding author (S.A.R.D.) had final responsibility for the decision to submit for publication and is the guarantor of this study.L.F-K. was supported by an Australian National Health and Medical Research Council Fellowship (APP1158469).
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