Meta-Analyses, Evidence-Based Medicine, and Quality Assurance in Clinical Research
This lecture explores the central role of meta-analyses in evidence-based medicine (EBM) and their importance for developing clinical guidelines and therapy recommendations. Students will learn the fundamental principles of how to interpret meta-analytic data, including reading forest plots, understanding fixed vs. random effects models, and assessing heterogeneity among studies.
The session also introduces the GRADE framework (Grading of Recommendations, Assessment, Development and Evaluation), focusing on critical aspects such as imprecision, inconsistency, and publication bias, and how these influence the strength of clinical recommendations.
In parallel, the lecture emphasizes the importance of quality assurance in clinical trials, discussing risk-based monitoring, centralized statistical oversight, and modern regulatory approaches (FDA, EMA) designed to enhance data integrity and trial reliability.
Learning objectives
By the end of this lecture, students will be able to:
- Understand the role of meta-analyses in evidence-based medicine.
- Interpret forest plots and heterogeneity measures.
- Apply the principles of the GRADE system for evaluating evidence quality.
- Recognize the importance of quality assurance and monitoring in RCTs.
- Meta-analyses in clinical research and EBM framework.
- Forest plots, fixed vs. random effects, and heterogeneity.
- The GRADE system and interpretation of recommendations.
- Risk-based quality assurance and centralized monitoring.
- Regulatory perspectives: FDA and EMA initiatives.
