Robust inference methods of diagnostic test accuracy meta-analysis for influential outlying studies via density power divergence

2026-04-30

AI Summary

This study addresses the sensitivity of the conventional bivariate random-effects model to outlying studies in meta-analyses of diagnostic test accuracy, which can lead to biased estimates. To mitigate this issue, the authors propose a frequentist robust inference framework based on density power divergence, incorporating a tuning parameter that automatically down-weights anomalous studies. The method employs the Hyvärinen score for data-driven, adaptive selection of this tuning parameter, thereby enhancing robustness without requiring manual calibration. In addition to effectively identifying and attenuating the influence of outliers, the approach provides a quantitative measure of each study’s contribution to the pooled estimate, facilitating transparent sensitivity analyses. Simulation results demonstrate that, in the presence of outliers, the proposed method substantially reduces estimation bias and root mean squared error while improving confidence interval coverage, with its practical utility further illustrated in a meta-analysis of Mini-Mental State Examination (MMSE) data.

Abstract

In diagnostic test accuracy meta-analysis (DTA-MA), standard inference methods using bivariate random-effects models for jointly synthesizing sensitivity and specificity can be sensitive to outlying studies and may yield misleading conclusions. In this article, we propose frequentist outlier-robust statistical inference methods for DTA-MA based on density power divergence. The proposed methods automatically downweight influential outlying studies by modifying the estimating function using the robust divergence with a tuning parameter. To achieve robust yet statistically efficient inference in the presence of outlying studies, the proposed methods incorporate practical strategies for selecting the tuning parameter, including a data-adaptive criterion based on the Hyvärinen score. We also quantify the contributions of individual studies to the robust pooled estimates, facilitating interpretation of how outlying studies affect the results. We illustrate the effectiveness of the proposed methods through an application to a DTA-MA of the Mini-Mental State Examination. Simulation studies showed that the proposed methods reduced bias and root mean squared error relative to existing methods and improved coverage probability in the presence of outliers. The proposed methods enable a sensitivity analysis to assess whether the main results obtained using standard methods are driven by outlying studies.

Problem

• diagnostic test accuracy meta-analysis
• outlying studies
• robust inference
• bivariate random-effects models
• sensitivity and specificity

Innovation

• density power divergence
• robust inference
• diagnostic test accuracy meta-analysis
• outlier downweighting
• Hyvärinen score