Empirical Likelihood Based Summary ROC Curve for Meta-Analysis of Diagnostic Studies

TL;DR

This paper introduces an empirical likelihood-based method for selecting models to construct summary ROC curves in meta-analyses of diagnostic studies, especially effective with small sample sizes.

Contribution

It proposes a novel empirical likelihood approach for model selection in sROC curve construction, outperforming traditional likelihood methods in small-sample scenarios.

Findings

Empirical likelihood method performs better with limited studies.

Parametric likelihood methods often fail with small sample sizes.

Proposed method is recommended for meta-analyses with as few as 5 or 10 studies.

Abstract

Objectives: This study provides an effective model selection method based on the empirical likelihood approach for constructing summary receiver operating characteristic (sROC) curves from meta-analyses of diagnostic studies. Methods: We considered models from combinations of family indices and specific pairs of transformations, which cover several widely used methods for bivariate summary of sensitivity and specificity. Then a final model was selected using the proposed empirical likelihood method. Simulation scenarios were conducted based on different number of studies and different population distributions for the disease and non-disease cases. The performance of our proposal and other model selection criteria was also compared. Results: Although parametric likelihood-based methods are often applied in practice due to its asymptotic property, they fail to consistently choose appropriate models for summary under the limited number of studies. For these situations, our proposed method almost always performs better. Conclusion: When the number of studies is as small as 10 or 5, we recommend choosing a summary model via the proposed empirical likelihood method.