Course 6
Date
21 – 23 January 2027
Faculty
Prof. Georgia Salanti
Institute of Social and Preventive Medicine, University of Bern, Switzerland
Dr. Guido Schwarzer
Institute of Medical Biometry and Statistics, University of Freiburg, Germany
Prof. Orestis Efthimiou
Institute of Social and Preventive Medicine, University of Bern, Switzerland
Venue
Wengen, Switzerland
Course description
Network meta-analysis (NMA) has become an essential tool in modern medical research, enabling the simultaneous comparison of multiple interventions within a single coherent framework. NMA is increasingly used to inform clinical guidelines, health technology assessments, and decision-making in evidence-based medicine.
This three-day intensive course provides a comprehensive introduction to the principles, methods, and practical application of NMA. It is designed for researchers and analysts with an understanding of conventional meta-analysis who wish to extend their skills to more complex evidence synthesis methods. Through a combination of lectures and hands-on computer sessions, participants will learn how to plan, conduct, and critically appraise NMAs.
Participants will explore statistical models for NMA in both frequentist and Bayesian frameworks, with guided implementation using R packages. Special attention is given to assessing inconsistency within networks and understanding how violations of assumptions can affect results. Further sessions focus on presenting and interpreting findings, including treatment ranking and visualization techniques. Advanced topics include component NMA for analysing data on composite treatments and methods for handling rare outcomes. The course concludes with presenting CINeMA, a framework for evaluating the credibility of NMA findings. By the end of the course, participants will be equipped with the skills required to conduct robust network meta-analyses and to interpret their findings in applied medical research settings.
Participants must be statistically literate with good understandings of linear regression. Computer practical sessions use R packages and require basic R software experience. Some knowledge on the methodology of systematic reviews is also expected. No previous understanding of Bayesian methods is required.
Course objectives
By the end of this course participants will have:
- the basic principles, steps, and statistical methods involved in network meta-analysis
- how to evaluate the assumptions underpinning network meta-analysis
- how to perform network meta-analysis in a frequentist or a Bayesian setting using R packages
- how to present and interpret the results from a network meta-analysis, and how to evaluate their credibility
Course audience
Researchers in health sciences with experience in or understanding of meta-analysis, who wish to expand their knowledge and skills within the context of comparative effectiveness research
Course outline
The course runs over three days and consists of lectures, group work, and computer practical sessions.
We start early in the morning by reviewing the material presented in the previous day. During extended afternoon breaks, participants review course materials, catch up on email, or ski. We reconvene at 4:30.
Thursday, 21 January 8:15 am – 12:15 pm | 4:30 pm – 6:30 pm
- Revisiting the random-effect meta-analysis and meta-regression models
- The concept of indirect treatment comparisons
- Assumptions underpinning network meta-analysis
Friday, 22 January 8:15 am – 12:15 pm | 4:30 pm – 6:30 pm
- Network meta-analysis models in a frequentist and Bayesian setting
- Evaluation of inconsistency
- Presenting results from network meta-analyses
Saturday, 23 January 8:15 am – 12:15 pm | 1:15 pm – 3:15 pm
- Component network meta-analysis
- Network meta-analysis for rare outcomes
- Evaluating the confidence in network meta-analysis results using CINeMA
Credits
1.0 ECTS
Course materials
Students should bring their own portable computers with the latest versions of R and RStudio installed.
We strongly recommend only bringing computers you have administration rights for to the course. Onsite University of Bern IT staff provides support upon e-mail () request.
Course fee
| PhD Bern: | CHF 600 |
| PhD other: | CHF 800 |
| Academic: | CHF 1’000 |
| Industry: | CHF 2’000 |
Registration
Go to registration information
Accommodation
Book your accommodation separately. Please see recommendations for special prices.