September 22, 2022
Open to all ISPOR Members and Non-members
Title: Network Meta-Analysis - Special Topics
10:00AM EDT | 2:00PM UTC | 4:00PM CEST
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Description
The Statistical Methods in Health Economics and Outcome Research Special Interest Group is pleased to present two webinars on Network Meta-Analysis (NMA). The first one is an introduction to NMA by Emma Hawe. The second will cover special topics in NMA by Sofia Dias. Both webinars will be moderated by Gian Luca Di Tanna. The second webinar will feature more advanced content than the first webinar.
Learning Objectives:
• Understand when NMA is useful for decision-making using examples of its use at NICE (UK) and description in NICE guidance
• Understand the basic NMA model and its assumptions (including the use of prior distributions in a Bayesian framework)
• Have an overview of model extensions and their application in decision-making (including class effect and additive effects models, bias adjustment)
• Understand benefits of Bayesian approach for decision-making, including:
- sampling from the posterior distribution
- use of informative prior distributions and exact likelihood when data are sparse
- model customization
• Have an overview of recently developed extensions and packages to implement them, including model-based NMA (incorporating dose and time-course modelling) and population adjustment.
Gian Luca Di Tanna, PhD, MPhil, MSc, AStat, CStat, Head (Australia), Biostatistics and Data Science Division, The George Institute for Global Health, Newtown, Australia
Speaker:
Sofia Dias, PhD, Professor of Health Technology Assessment, Centre for Reviews and Dissemination, University of York, York, UK
Please note: On the day of the scheduled webinar, the first 1000 registered participants will be accepted into the webinar. For those who are unable to attend, or would like to review the webinar at a later date, the
full-length webinar recording will be made available at the ISPOR Educational Webinar Series webpage approximately 2 days after the scheduled Webinar.