Introduction to Network Meta-Analysis
Network meta-analysis (NMA) is a method that pools evidence from randomised controlled trials that compare two or more interventions, but where each trial may compare different interventions. NMA allows one to simultaneously estimate relative effectiveness for any pair of interventions in the evidence network.
| Dates | 1 - 5 June 2026 |
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| Fee | £625 |
| Format | Online |
| Audience | Open to all applicants (prerequisites apply) |
Course profile
This course aims to introduce network meta-analysis and show how the models can be estimated using R.
Please click on the sections below for more information.
This course is delivered online, via live sessions, over 5 consecutive mornings from 9.30am to 1pm.
By the end of the course participants should be able to:
1. describe what indirect comparisons and network meta-analysis (NMA) are and why they are used;
2. perform indirect comparisons using R;
3. perform network meta-analysis using R, with continuous and dichotomous data;
4. be aware of the assumptions made in NMA and use R to examine consistency;
5. interpret different techniques used to present the results from NMA;
6. appreciate how NMA can be used to combine evidence on complex interventions; and
7. critically appraise a paper that uses NMA.
This course will cover:
1. introduction to indirect comparisons, and combining direct and indirect evidence;
2. introduction to network meta-analysis;
3. performing NMA in R, including practicals;
4. interpreting and presenting Results;
5. assumptions made in network meta-analysis;
6. assessing Inconsistency in NMA;
7. systematic review to inform NMA;
8. NMA for complex interventions;
9. NMA for continuous outcomes; and
10. critical appraisal of NMA, including group work.
Please note: The course gives an introduction to network meta-analysis using R.
Staff in the BEAM centre also teach an intermediate to advanced level course on network meta-analysis using WinBUGS which is more focused on NMA for decision modelling.
To make sure the course is suitable for you and you will benefit from attending, please ensure you meet the following prerequisites before booking:
| Knowledge | Experience of pairwise meta-analysis (to the level covered by the course Introduction to Systematic Reviews and Meta-analysis), understanding of statistical methods including logistic regression (to the level of the course Introduction to Linear and Logistic Regression Models), and basic experience with R. |
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| Software | You must have R (version 4.2 or higher) and RStudio installed in advance of the course. This course will use RStudio Desktop (Open Source version). This is compatible with Windows, Mac and Linux and is freely available from: https://rstudio.com/products/rstudio/download/ Go to R Installation Instructions for help getting set up in advance of the course starting. |
| Recommendation | We recommend the use of two screens, to follow the worksheets whilst working in practical computing sessions. However, this is not essential. |
Before booking this course, please make sure you read the information provided above about the target audience and prerequisites. It is important that you have access to the relevant IT resources needed for the course and meet the knowledge prerequisites to ensure you can get the most from the course.
Bookings are taken via our online booking system, for which you must register an account. To check if you are eligible for free or discounted courses please see our fees and voucher packs page. All bookings are subject to our terms & conditions, which can be read in full here.
For help and support with booking a course refer to our booking information page, FAQs or feel free to contact us directly. For available payment options please see: How to pay your short course fees.
Participants are granted access to our virtual learning platform (Blackboard Ultra) 1 to 2 weeks in advance of the course. This allows time for any pre-course work to be completed and to familiarise with the platform.
To gain the most from the course, we recommend that you attend in full and participate in all interactive components. We endeavour to record all live lecture sessions and upload these to the online learning environment within 24 hours. This allows course participants to review these sessions at leisure and revisit them multiple times. Please note that we do not record breakout sessions.
All course participants retain access to the online learning materials and recordings for 5 months after the course.
University of Bristol staff and postgraduate students who do not wish to attend the full course may instead register for access to the 'Materials & Recordings' version of this course: Further information and bookings.
100% of attendees recommend this course*.
*Attendee feedback from 2026.
Here is a sample of feedback from the last run of the course:
“Content covered was excellent, very broad scope of topics that were covered in detail in a relatively short space of time.” - Course feedback, June 2026.
“I really liked that the course covered the whole process, not just the theory ,from understanding why we use NMA, to actually running indirect comparisons and network meta-analyses in R, to checking the assumptions and appraising the results. Doing it hands-on in R made it click, and ending with a real paper to critique pulled everything together nicely.” - Course feedback, June 2026.
“Overall, this training has significantly improved my understanding of network meta-analysis and increased my confidence in applying these methods in my research.” - Course feedback, June 2026.
“Really liked in this short course that the tutor was going through the actual practical instead of mandatory break-out rooms.” - Course feedback, June 2026.
“The course was smooth and balanced. There was no overload of material.” - Course feedback, June 2026.
“The training provided a clear introduction to the principles and methodology of network meta-analysis, including both theoretical concepts and practical applications.” - Course feedback, June 2026.