Guidelines for meta-analyses in pharmacology and biomedical research: A consensus framework for design, conduct, and reporting

Abstract Meta-analysis has become a cornerstone of evidence synthesis in pharmacological and biomedical research, providing a quantitative framework to combine findings across preclinical, clinical, and epidemiological studies. Evidence shows significant variability across these areas, with varying degrees of methodological rigor, reporting transparency, and interpretive validity among the studies reviewed. The Guidelines for Meta-Analysis in Pharmacology and Biomedical Research provide a consensus framework for conducting, designing, and reporting meta-analyses within experimental as well as clinical research and expand the PRISMA 2020 and other similar methodologies by emphasizing the need for reproducibility, transparency, and interpretive integrity across multiple data sources. The primary recommendations contained within these guidelines outline the need for (i) the registration of protocols, hypotheses defined a priori, (ii) rigorous and reproducible literature search strategies, (iii) quality of studies and risk of bias assessments, (iv) standardized methodologies for effect size calculation on efficacy, safety and mechanism functions, (v) rigorous evaluations for heterogeneity, publication bias, sensitivity analysis and alerting the scientific community to (vi) a transparent portrayal of limitations, context and translational significance. The intent of these Guidelines is to be a reference for authors of meta-analyses and systematic reviews whose work contributes to Advances in Translational Research and related pharmacology journals, thereby establishing an optimal standard of methodological excellence while improving the reliability, interpretability, and translational potential of biomedical and pharmacological meta-analyses. In addition, the Guidelines are a resource identifying the increased use of web-based analytical platforms that provide scientific researchers with opportunities to develop statistically integrated, transparent and reproducible data synthesis that adhere to modern standards for evidence-based evaluation.