Master Bayesian and Freqeuntist Network Meta-Analysis
Master Bayesian and Freqeuntist Network Meta-Analysis
Published 8/2025
Duration: 4h 41m | .MP4 1280x720 30 fps(r) | AAC, 44100 Hz, 2ch | 2.23 GB
Genre: eLearning | Language: English
Published 8/2025
Duration: 4h 41m | .MP4 1280x720 30 fps(r) | AAC, 44100 Hz, 2ch | 2.23 GB
Genre: eLearning | Language: English
Learn and master NMA with both approaches using Rstudio
What you'll learn
- How to read network plots and network meta-analysis studies according to official Cochrane guidelines
- How to construct your own datasets on excel sheets, do coding and perform analysis on Rstudio
- How to perform Frequentist and Bayesian Network meta-analysis (NMA) on Rstudio program
- How to do it yourself!
Requirements
- Participants must have initial knowledge of pairwise meta-analysis and Rstudio in order to grasp this advance course
Description
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.
Who this course is for:
- Medical students and graduates
- Researchers
- USMLE, UKMLA and AMC aspirants
More Info