Introduction to Network Meta-Analysis

Coronavirus (COVID-19)

We may need to make responsive changes to our courses at short notice in order to follow the latest Public Health, Government and University guidance on coronavirus (COVID-19).


This course is temporarily suspended due to the Coronavirus pandemic. It is next expected to run in the 2021-22 short course programme.

Information on this page relates to the last run of the course and is for reference only. 

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Course dates

11 - 12 December 2019

Course duration

2 days (10.25 hours core teaching plus 1.5 hour optional session). Methods include formal lectures, group work and computer practicals.
Registration will start at 10.00am on the first day, with an optional Stata refresher session from 9am. The course will finish by 4.30pm on the final day.

Course tutors

Professor Nicky Welton and Dr Deborah Caldwell (course organisers) with others.

Course aims 

The aim of this course is to introduce network meta-analysis and show how the models can be estimated using Stata. Network meta-analysis (NMA) is a method that pools evidence from randomised controlled trials that compare two or more treatments, but where each trial may compare different treatments. NMA allows one to simultaneously estimate relative effectiveness for any pair of treatments in the evidence network.

Course objectives

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

  • understand what indirect comparisons and network meta-analysis (NMA) are and why they are used;
  • perform indirect comparisons using Stata;
  • perform network meta-analysis using Stata, with continuous and dichotomous data;
  • understand the assumptions made in NMA and use Stat to examine consistency;  
  • be aware of different techniques to present the results from NMA;
  • be able to critically appraise a paper that uses NMA.

Who the course is intended for

This course is designed for health services researchers, epidemiologists, statisticians, systematic reviewers and decision analysts.

Prerequisites for the course are:

Experience of meta-analysis in Stata (to the level covered by the course 'Introduction to Systematic Reviews and Meta-analysis'), and knowledge of statistical methods including logistic regression (to the level of the course 'Introduction to Linear and Logistic Regression Models').

An understanding of research designs (to the level of the course 'Introduction to Epidemiology') would also be helpful.

Course outline

  • Meta-analysis in Stata (refresher).
  • Introduction to indirect comparisons, and combining direct and indirect evidence.
  • Performing indirect comparisons in Stata, including practical.
  • Introduction to network meta-analysis.
  • Performing NMA in Stata, including practical.
  • Assumptions, ground rules and FAQs for network meta-analysis.
  • Systematic review to inform NMA.
  • Assessing Inconsistency in NMA.
  • NMA using other outcomes.
  • Explaining and Presenting Results.
  • Reporting and critical appraisal of NMA, including group work.

Please note: This course will be held in a computer lab, you will not need to bring a laptop. The course uses Stata, and it is expected that participants are familiar with meta-analysis in Stata . We also teach an alternative 3-day course on network meta-analysis using Bayesian methods in WinBUGS together with colleagues in Leicester. For details see here.

Recommended reading

Meta-analysis in Stata edited by Jonathan Sterne. Stata Press 2009.

Caldwell D. M. (2014) An overview of conducting systematic reviews with network meta-analysis. Systematic Reviews 3: 109.

Caldwell DM, Welton NJ, Ades AE. Mixed Treatment comparisons methods provide internally coherent treatment effect estimates based on overviews of reviews, and may reveal inconsistency. Journal of Clinical Epidemiology 2010; 63: 875-882.

Welton NJ, Sutton AJ, Cooper NJ, Abrams KR, Ades AE. Evidence synthesis for decision making in healthcare. Wiley. 2012.

Dias S. et al (2013) Medical Decision Making Special Issue on Evidence Synthesis in Decision Making.

An absolutely fantastic course - the best one I've been to, a higher quality than most of the courses I remember as a PhD student.

Course feedback, December 2019

Course fee


More information on course fees, fee waivers and reduced prices.

Course refreshments

We provide morning and afternoon refreshment breaks, including tea and coffee, biscuits and fresh fruit.

If you have specific dietary needs we ask that you let us know in advance.

Lunch is not included. There are a range of local cafes and supermarkets nearby for students to purchase lunch. 

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