Diagnostic Test Accuracy Meta-Analysis: A Practical Guide to Hierarchical Models

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Abstract
Background: Accurate evaluation of diagnostic tests is essential to guide clinical decision-making, particularly in surgical practice. Systematic reviews and meta-analyses of diagnostic test accuracy (DTA) are key for evidence synthesis; however, traditional approaches, including univariate pooling or simplified summary ROC (SROC) models such as the Moses–Littenberg method, often yield biased and clinically misleading estimates.Methods: This article presents a methodological guide to hierarchical random-effects models for DTA meta-analysis, structured around current evidence and best practices. Based on this framework, a simulated dataset was generated, and a comprehensive meta-analysis was performed. The analysis illustrates key methodological concepts, interpretation of model outputs, and the use of complementary tools, including likelihood ratios, scattergrams, meta-regression, publication bias assessment, and outlier detection. It also provides a critical comparison of Stata commands for DTA meta-analysis (metandi, midas, metadta), outlining their methodological strengths and limitations to guide researchers in tool selectionResults: The traditional meta-analysis, performed with Meta-DiSc 1.4, applied the DerSimonian–Laird and Moses–Littenberg methods, produced separate sensitivity and specificity pooled estimates with artificially narrow confidence intervals and a symmetric, theoretical SROC curve extrapolated beyond the observed data range, thereby ignoring threshold variability and underestimating between-study heterogeneity. In contrast, the hierarchical random-effects model provided more realistic and clinically interpretable estimates. Joint modeling of sensitivity and specificity revealed substantial between-study variability, a strong negative correlation consistent with a threshold effect, an elliptical confidence region around the summary point (reflecting uncertainty in mean sensitivity/specificity), together with a broader prediction region indicating where 95% of future studies might fall. Influence diagnostics identified outliers and highly influential studies. Conclusions: Promoting the correct application and interpretation of hierarchical models in DTA meta-analyses is essential to ensure high-quality, reliable, and scientifically robust evidence.

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