Structural equation modelling--based meta-analytic model for diagnostic test accuracy studies
Abstract
Bivariate random-effects models are commonly used to jointly synthesize sensitivity and specificity in diagnostic test accuracy (DTA) meta-analysis. However, existing approaches may be sensitive to distributional assumptions and may produce biased estimates of accuracy and between-study heterogeneity with sparse meta-analytic data. We proposed a novel bivariate structural equation modelling (BSEM) approach for DTA meta-analysis and evaluated its performance relative to the bivariate linear mixed model (BLMM) and bivariate generalized linear mixed model (BGLMM). We conducted extensive simulations under varying study, sample-size, accuracy, and heterogeneity conditions. We assessed model performance using bias, root mean squared error (RMSE), confidence interval width, empirical 95% coverage probability (CP), and convergence. We generated study-specific random effects from bivariate normal, t, and Laplace distributions to assess robustness to distributional misspecification. We also applied the models to two empirical DTA datasets. The BSEM generally produced smaller bias and RMSE and nominal-level CP for pooled sensitivity and specificity than the BLMM, while the BGLMM generally yielded results between the two approaches. The BLMM showed stable convergence but tended to underestimate between-study heterogeneity. We observed similar performance patterns under the three random-effects distributions. In the empirical applications, differences between model estimates were generally consistent with patterns observed in the simulations. Overall, the proposed BSEM provided a flexible alternative approach for jointly modelling sensitivity and specificity in DTA meta-analysis, with performance that was generally favourable relative to the BLMM and comparable to the BGLMM across the simulated and empirical settings considered.
Related
- MASEMiner – AI-Assisted Data Extraction for Meta-Analytic Structural Equation Modeling
- Universality in the Transition from Inspiral to Plunge for Extreme-Mass-Ratio-Inspirals: High-Accuracy Analytic Solutions and Catastrophe Theory
- Null Controllability of a Stochastic Parabolic Equation with a Space-Dependent Analytic Noise Coefficient
- Exact Analytic Solution for the Time-Fractional Hunter-Saxton Equation with Caputo derivative
- Equation free data-driven modelling of chaotic processes
BibTeX
@misc{negeri2026structural,
title = {{Structural equation modelling--based meta-analytic model for diagnostic test accuracy studies}},
author = {Zelalem F. Negeri},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.27408},
eprint = {2610.27408},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.27408},
}
RIS
TY - UNPB
TI - Structural equation modelling--based meta-analytic model for diagnostic test accuracy studies
AU - Zelalem F. Negeri
PY - 2026
DA - 2026/10/20
PB - arXiv
DO - 10.48550/arxiv.2610.27408
UR - https://arxiv.org/abs/2610.27408
AB - Bivariate random-effects models are commonly used to jointly synthesize sensitivity and specificity in diagnostic test accuracy (DTA) meta-analysis. However, existing approaches may be sensitive to distributional assumptions and may produce biased estimates of accuracy and between-study heterogeneity with sparse meta-analytic data. We proposed a novel bivariate structural equation modelling (BSEM) approach for DTA meta-analysis and evaluated its performance relative to the bivariate linear mixed model (BLMM) and bivariate generalized linear mixed model (BGLMM). We conducted extensive simulations under varying study, sample-size, accuracy, and heterogeneity conditions. We assessed model performance using bias, root mean squared error (RMSE), confidence interval width, empirical 95% coverage probability (CP), and convergence. We generated study-specific random effects from bivariate normal, t, and Laplace distributions to assess robustness to distributional misspecification. We also applied the models to two empirical DTA datasets. The BSEM generally produced smaller bias and RMSE and nominal-level CP for pooled sensitivity and specificity than the BLMM, while the BGLMM generally yielded results between the two approaches. The BLMM showed stable convergence but tended to underestimate between-study heterogeneity. We observed similar performance patterns under the three random-effects distributions. In the empirical applications, differences between model estimates were generally consistent with patterns observed in the simulations. Overall, the proposed BSEM provided a flexible alternative approach for jointly modelling sensitivity and specificity in DTA meta-analysis, with performance that was generally favourable relative to the BLMM and comparable to the BGLMM across the simulated and empirical settings considered.
ER -