Network Meta-Analysis in Stata

The Network Meta-Analysis offers participants the opportunity to acquire both the theoretical and applied toolkit necessary to carry out a systematic review of scientific literature through a network meta-analysis. The network meta-analysis approach allows researchers to combine both direct and indirect evidence, relating to studies that compare different sets of treatments. The course focuses on the network meta-analysis commands currently available in Stata, through some user-contributed commands to perform a network meta-analysis in Stata 19.

In line with TStat’s general philosophy, the course has been developed to be interactive and largely applied in nature, restructured into a series of separate modules in order to facilitate the transition to an effective online teaching format. Despite the online nature of this course, participants will be able to both replicate the commands illustrated by the lecturer in real time using the databases provided at the beginning of the course and be expected to take part in applied data sections.

COURSE OUTCOME:
At the end of the course, participants are expected to:

• understand the fundamental concepts and principles of network meta-analysis;
• have attained an understanding of “real world” data issues related to network meta-analysis through the applied sections of the course; and
• be able to carry out a network meta-analysis (with the help of the Stata routine templates specifically developed for the course) independently, implementing the Stata network meta-analysis commands illustrated during the course in their own research context.

This course is of particular interest to graduate students, academic researchers and professionals working in the public health and medical sectors.

A good working knowledge of the basic principles of biostatistics, epidemiology and the topics covered in the Meta-Analysis in Stata course, as well as a basic knowledge of Stata.

1. Stages of a systematic review
2. Measures of treatment effect (odds ratio, risk ratio, difference in means, hazard ratio)
3. Statistical methods for meta-analyses: fixed-effect and random-effects models
4. Introduction to network meta-analyses
5. Network meta-analysis in Stata through the network suite of commands to assist with data preparation, and model estimation
6. Graphical tools for visualising the results of a network metaanalysis estimation using the network graphs package

USEFUL REFERENCES

• Schmid, C. H., Stijnen, T. & White, I. R. (2021). Handbook of Meta-Analysis. CRC Press.
• Palmer, T. M. & Sterne, J. A. C. (2016) Meta-Analysis in Stata: An Updated Collection from the Stata Journal. Second Edition. Stata Press.

The 2026 edition of this training course will be offered online on a part-time basis on the 7th-8th of May from 10:00 am to 1:30 pm CEST.

Full-time Students*: € 390.00
Ph.D. Students: € 500.00
Academic: € 555.00
Commercial: € 740.00

*To be eligible for student prices, participants must provide proof of their full-time student status for the current academic year. Our standard policy is to provide all full-time students, be they Undergraduates or Masters students, access to student participation rates. Part-time master and doctoral students who are also currently employed will however, be allocated academic status.

Fees are subject to VAT (applied at the current Italian rate of 22%). Under current EU fiscal regulations, VAT will not however applied to companies, Institutions or Universities providing a valid tax registration number.

The number of participants is limited to 8. Places will be allocated on a first come, first serve basis. The course will be officially confirmed, when at least 5 individuals are enrolled.

Course fees cover: I) teaching materials – copies of lecture slides, databases and Stata programs specifically developed for the course; ii) a temporary licence of StataNow™ valid for 30 days from the day before the course commences.

Individuals interested in attending this course must return their completed registration forms by e-mail to TStat by the 27th of April 2026.