Meta-Regression Analysis of Heterogeneity in the Effectiveness of BCG Vaccine Against Tuberculosis

Ali Hdi Hussein Al-Zubaidy, Yasamin; H. AL-Sharoot, Mohammed

doi:10.33916/qjae.2026.01299308

	Meta-Regression Analysis of Heterogeneity in the Effectiveness of BCG Vaccine Against Tuberculosis
AL-Qadisiyah Journal For Administrative and Economic sciences
Volume 28, Issue 1, April 2026, Pages 299-308 PDF (1.39 M)
Document Type: Research Paper
DOI: 10.33916/qjae.2026.01299308
Authors
Yasamin Ali Hdi Hussein Al-Zubaidy¹; Mohammed H. AL-Sharoot²
¹University of Qadisiyah
²University of AL-Qadisiya
Abstract
This study investigates heterogeneity in the reported effectiveness of the Bacillus Calmette Guérin (BCG) vaccine against tuberculosis (TB) using a meta-regression framework. Despite its long history, the BCG vaccine has shown wide variability in protection levels across studies, which necessitates a systematic quantitative assessment. Meta-regression was employed to identify study-level factors influencing vaccine effectiveness and to compare two classical estimators of between-study variance: DerSimonian–Laird (DL) and Restricted Maximum Likelihood (REML). A simulation study with 100 replications and a real-data analysis using 30 published clinical trials were conducted. Performance was evaluated using bias, average mean squared error (AMSE), and coverage probability. The results demonstrate that the REML estimator consistently provides lower bias, smaller AMSE, and more accurate coverage probabilities than DL, both in simulated and real settings. The real data analysis further revealed that geographic latitude, study design, and diagnostic methods significantly contribute to heterogeneity in BCG effectiveness. Overall, REML is recommended as a more reliable and efficient estimator for modeling heterogeneity in classical meta-regression applications.
Keywords
Meta-regression; BCG vaccine; tuberculosis; heterogeneity; DerSimonian– Laird; Restricted Maximum Likelihood

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