Overview

This course is for health economists, statisticians, and decision modellers interested in the extension of pair-wise meta-analysis to network meta-analysis (NMA)¹. The course is appropriate for anyone involved in health technology assessment who has a strong quantitative background.

The course focuses on Bayesian methods for statistically combining evidence from trials that form a network of treatment comparisons. The assumptions underlying both pair-wise meta-analysis and NMA are critically examined. The course also covers methods for detecting and managing heterogeneity and inconsistency, and how to embed the NMA in a cost-effectiveness analysis.

The Bayesian Markov chain Monte Carlo package WinBUGS is used on the course, and delegates who are new to WinBUGS should, when they have completed the course, be able to understand and code WinBUGS analyses of both NMA and other simple hierarchical models. However, the concepts learned on the course are entirely applicable to other Bayesian software, including OpenBUGS, JAGS and STAN.

This is an informal, hands-on, online course, based on a mixture of pre-recorded lectures, practical exercises with pre-recorded solutions, discussion boards, and live Q&A with practical support sessions. In the live practical support sessions tutors will go through solutions to exercises and participants will be able to access help from course tutors to complete the exercises in WinBUGS.

The course is a collaboration between the Department of Population Health Sciences, University of Bristol, the Centre for Reviews and Dissemination, University of York, and the Department of Health Sciences, University of Leicester.

The methods taught on the course are designed to be fully compatible with the NICE Guide to the methods of technology appraisal 2022 , the NICE Decision Support Unit Technical Support Documents on Evidence Synthesis, and with the Report of the ISPOR Task Force on Indirect Comparisons.

Participants will receive a free electronic copy of the 2018 book Network meta-analysis for decision making. Material from Chapters 1-4, 7-8 and 12 will be covered on this course.²

¹ NMA has also been called: mixed treatment comparisons, and multiple treatment meta-analysis; indirect treatment comparisons are another type of NMA.

² A limited number of hard-copies are available. Contact mpes-admin@bristol.ac.uk if you would like to discuss this option.

What you will learn

By the end of the course participants will be able to:

• conduct pair-wise, indirect comparison and network meta-analysis using WinBUGS Bayesian software;
• adjust for covariates;
• integrate statistical evidence synthesis with probabilistic cost-effectiveness analysis;
• assess the degree of heterogeneity and inconsistency in RCT data;
• conduct network meta-analysis for binary, continuous, and hazard ratio outcomes;
• understand the assumptions and potential pitfalls in Pairwise and Network MA;
• participants will acquire an introductory understanding of Bayesian methods, hierarchical modelling, and be able to use WinBUGS software.

Who is this course for?

The course is suitable for (1) health economists undertaking health technology assessments, including in the context of cost-effectiveness analysis; (2) statisticians, familiar with the principles of meta-analysis, who wish to learn about Bayesian methods for evidence synthesis; and (3) researchers synthesising evidence from reviews of multiple treatments who have a strong quantitative background and experience of statistical or other coding.

The course is popular with those who wish to use the freely available NMA WinBUGS codes, and want to make sure they understand what the code is doing, the assumptions being made, and how to make changes to the code and interpret the outputs. Senior managers who are involved in guideline development or health technology assessment, who supervise the work of staff doing the “hands-on” work, but who will not be carrying out analyses themselves, may also wish to take the course, to become better informed about the many kinds of analyses that can be done, the assumptions being made and how to judge if they are met, and what kinds of checks and quality assurance should be carried out.

Course pre-requisites

Participants should:

• be familiar with the basic principles of meta-analysis and have a good working knowledge of logistic regression and statistical interaction
• have experience programming in standard languages or statistical software
• ideally have experience with probabilistic decision analysis in cost-effectiveness analysis (although this is not essential)

Before the course all participants must:

1. Download WinBUGS 1.4.3 from the BUGS Project website (https://www.mrc-bsu.cam.ac.uk/software/bugs/the-bugs-project-winbugs/ ) onto the computer or laptop that you will be using during the course. The best way to do this is to download a zipped version of the whole file structure and unzip it into Program Files or wherever you want it. We recommend you use the top option “Installation (any Windows computers)”, which includes the key for unrestricted use and has been updated to version 1.4.3. To check that you have the updated version: open WinBUGS, go to the “Help” menu and click “About WinBUGS”. This should show the current version as 1.4.3. The software is free.
   
   
2. For those using a Mac: To run WinBUGS on a Mac you will first need to install a Windows emulator (eg WINE). You can then install WinBUGS on that and it should work. For more precise instructions please check for advice online.
   
   
3. Make sure you run the example in the Tutorial. Go To "Help", User Manual, then click on "Introduction".
   
   
4. Try to run the BLOCKER example in the online Help, (Examples Vol 1_Blocker).  NB you need to use initial values Inits2 for this to run.
   
   
5. Note there are some optional sessions using/demonstrating R and MetaInsight. Those interested in the optional practical in R should install a recent version of R.

Note on OpenBUGS: We strongly recommend that you use WinBUGS 1.4.3 during the course. Participants wishing to use OpenBUGS should be aware that although the basic code should run, some files may not open properly and are advised to have a copy of WinBUGS 1.4.3 on their laptops to deal with such files.

Fees

• Student: £550*
• Academic, public sector: £900
• Commercial sector: £1,600

*Evidence of current student status will be required. If you have questions about this please contact the course administrator.

See our booking terms & conditions.

Contacts

For further information please email mpes-admin@bristol.ac.uk 

For enquiries about course content, Nicky Welton Nicky.Welton@bristol.ac.uk or Sofia Dias Sofia.Dias@york.ac.uk

Course format

This is an informal, hands-on online course, based on a mixture of lectures and practical work using published datasets. The course will be taught online over a two-week period using a mixture of pre-recorded lectures, practical exercises with pre-recorded solutions, discussion boards, live Q&A, and practical support sessions. Participants can work through the materials in their own time, post queries to the discussion board, and attend the live sessions for discussions on the materials covered in specific sessions. In the live practical support sessions participants will be able to access help from course tutors with the exercises in WinBUGS. All materials will be made available to participants 1 week in advance of the course and all content including lecture videos will remain available for 3 months after the course ends. To get the most from the course participants should attempt the practicals in advance of the practical support session for that topic. There will be an opportunity to book a short slot with a tutor for advice on participants’ own evidence synthesis project.

Live Q&A and Practical Support Sessions: 2pm – 4pm BST on:

Mon 18^th, Tue 19^th, Wed 20^th, Thurs 21^st,  Mon 25^th, Tue 26^th, Wed 27^th, Thurs 28^th September

Time commitment

We expect that the time taken to complete the core material is approximately 30 hours depending on participants’ previous experience. There are approximately 3 hours of further optional material.

Course programme

The course covers the following sessions.

SESSION 1: Introductions: NMA, Pairwise M-A, and Bayesian Inference

• Introduction to Indirect Comparisons and Network Meta-Analyses
• Pairwise Meta-Analysis: A Refresher
• Introduction to Bayesian Inference

SESSION 2: Bayesian Meta-Analysis in WinBUGS

• Bayesian Meta-Analysis in WinBUGS: binary outcomes

SESSION 3 NMA in WinBUGS

• Network meta-analysis in WinBUGS: binary outcomes
• **OPTIONAL** Multivariate Normal code for studies with 3 or more arms
• Assessing Model Fit in Bayesian Models
• **OPTIONAL** Outlier Detection Using Cross Validation

SESSION 4: Integrating NMA and Decision Modelling, and Streamlining Analyses

• Integrating NMA and Decision Modelling
• **OPTIONAL** Streamlining Analyses: R2WinBUGS
• **OPTIONAL** Streamlining Analyses: MetaInsight

SESSION 5: Inconsistency and Meta-Regression

• Inconsistency: methods and implications
• **OPTIONAL** Node-splitting using gemtc in R
• Incorporating Covariate Effects in pairwise meta-analysis

SESSION 6: Other Outcomes

• NMA with Continuous Outcomes
• NMA with Hazard Ratios
• Choosing the right code/model flow chart

SESSION 7: Further Topics

• Network Meta-Regression
• Modelling Treatment Class Effects
• *OPTIONAL*: Further Topics
• GUEST LECTURE: Population Adjusted Indirect Comparisons
• *OPTIONAL* Book a session to discuss participants own evidence synthesis project