arXiv:2610.33270v1 Announce Type: cross Abstract: Meta-analysis of diagnostic test accuracy (DTA) commonly relies on the bivariate linear mixed model (BLMM) and the bivariate generalized linear mixed model (BGLMM) to jointly synthesize diagnostic test sensitivity (Se) and specificity (Sp). However, reliance on asymptotic theory and random-effects distributional assumptions may affect finite-sample inference, particularly in small meta-analyses or when the random effects are non-normal. We proposed three bootstrap-based approaches: a nonparametric bootstrap (NPB), a parametric bootstrap (PB), and a bias-corrected parametric bootstrap (BCPB), with the latter two based on a fitted BGLMM. NPB model parameters were estimated using inverse-variance-covariance weighted least squares. Extensive simulations under bivariate normal, Student-t, and Laplace random-effects distributions evaluated finite-sample performance and robustness to departures from normality. Results were qualitatively similar across the three distributions. The bootstrap approaches generally outperformed BLMM in terms of bias, root mean squared error (RMSE), and coverage probability, particularly when diagnostic accuracy is high and the number of studies is small. The BCPB demonstrated the strongest finite-sample performance among the bootstrap approaches, frequently achieving the lowest bias and RMSE for pooled sensitivity and specificity while maintaining coverage probabilities close to the nominal level. The BGLMM also performed well across most settings. The NPB approach generally improved upon the BLMM while providing a flexible alternative without parametric random-effects assumptions. Real-data applications further illustrated the practical utility of the proposed approaches. Overall, the bootstrap methods provide useful alternatives for DTA meta-analysis, particularly when the number of studies is small or random-effects distributional assumptions are uncertain.