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Comparison of Different Methods for the Meta-Analysis of Diagnostic Test Accuracy Studies-A Simulation Study
Ferdinand V Stoye1, Olaf Raths1, Alexander Hapfelmeier2,3
1Biostatistics and Medical Biometry, Medical School OWL, Bielefeld University, Bielefeld, Germany.
Biometrical Journal. Biometrische Zeitschrift
|July 3, 2026
Summary
Multiple threshold methods (MTM) outperform single threshold methods (STM) in diagnostic test accuracy meta-analysis, especially when considering all available data. MTM provides better threshold-dependent estimates for sensitivity and specificity.
Area of Science:
- Medical Statistics
- Diagnostic Test Accuracy
- Meta-Analysis
Background:
- Meta-analysis of diagnostic test accuracy studies typically uses single threshold methods (STM), which may ignore valuable data from studies reporting multiple thresholds.
- Multiple threshold methods (MTM) have been developed to incorporate all available information, enabling inference across all diagnostic thresholds.
- Comparing the performance of various STM and MTM is crucial for advancing diagnostic accuracy meta-analysis.
Purpose of the Study:
- To compare the performance of five single threshold methods (STM) and six multiple threshold methods (MTM) in diagnostic test accuracy meta-analysis.
- To evaluate how different data-generation mechanisms, including outcome type and number of thresholds, affect model performance.
- To identify the advantages of MTM, particularly in obtaining threshold-dependent estimates of sensitivity and specificity.
Main Methods:
- A simulation study was conducted, varying eight parameter dimensions in data generation, including continuous or ordinal index test outcomes and varying numbers of diagnostic thresholds.
- Five STM and six MTM were compared based on bias, empirical coverage, and convergence.
- Two real-data examples (type 2 diabetes diagnosis and anxiety disorder screening) were used to illustrate differences in model estimates.
Main Results:
- Model performance was comparable across methods regarding bias, empirical coverage, and convergence.
- A logit generalized linear mixed model (GLMM) of the MTM type demonstrated superior performance in many simulated scenarios.
- Model performance was most strongly influenced by the outcome type of the index test, with the number of thresholds having a minor impact.
Conclusions:
- Multiple threshold methods (MTM) offer a significant advantage by providing threshold-dependent estimates of sensitivity and specificity, utilizing all available study data.
- The choice of model is most critical when dealing with different types of index test outcomes.
- Real-data applications highlight variations in estimates between STM and MTM, which can be mitigated by bias adjustment.
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