Master Bayesian and Freqeuntist Network Meta-Analysis

This course offers an in-depth exploration of Network Meta-Analysis (NMA), combining both Frequentist and Bayesian methodologies in line with the latest Cochrane guidelines. Participants will gain a comprehensive understanding of the theoretical foundations of NMA and the practical tools needed to conduct robust analyses in health research.

Key concepts include:

• Frequentist Approach: The course covers classical statistical techniques for conducting NMA, focusing on fixed-effects and random-effects models, consistency assumptions, and the interpretation of results.

• Bayesian Approach: A deeper dive into the Bayesian framework, including prior distributions, Markov Chain Monte Carlo (MCMC) simulations, and posterior analysis, emphasizing flexibility and handling of complex models.

• Cochrane Guidelines: All techniques are discussed within the framework of the Cochrane Collaboration’s methodological standards, ensuring that students understand the gold standard for evidence synthesis in systematic reviews.

• Theoretical Concepts: The course will delve into the theoretical underpinnings of NMA, such as transitivity, consistency, and heterogeneity, equipping students with the necessary tools to critically assess and apply these concepts in real-world scenarios.

By the end of the course, students will be proficient in applying both Frequentist and Bayesian approaches to NMA, capable of handling diverse datasets and drawing reliable conclusions from their analyses, all while adhering to internationally recognized standards. This course is ideal for researchers, clinicians, and statisticians who wish to expand their knowledge in evidence synthesis and systematic reviews.