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The Australian Pharmaceutical Biostatistics Group (APBG) and the Statistical Society of Australia warmly invite you to a workshop on "Network meta-analysis and population adjustment for decision-making", presented by David Phillippo.
About the workshop:
Network meta-analysis (NMA) is a method for combining evidence from several studies on multiple treatments of interest to provide a consistent set of relative effect estimates and is widely used for healthcare decision-making and guideline development. More recently, population adjustment methods have been proposed that use individual patient data from one or more studies to relax the assumptions of NMA and adjust for differences in effect modifying variables between populations, or even to incorporate disconnected networks and single-arm studies. The methods are becoming increasingly common in technology appraisal submissions to reimbursement agencies and raise new questions and challenges for analysts and decision-makers.
About the presenter:
David Phillippo is a Senior Research Associate in Statistics at the University of Bristol, UK. His research focuses on methodology for evidence synthesis, Bayesian Network Meta-Analysis (NMA), population adjustment methods for indirect comparisons, and accounting for bias in clinical guidelines. He is the lead author of a recent Technical Support Document published by the NICE Decision Support Unit on population-adjusted indirect comparisons, and has developed new methods extending the NMA framework to incorporate population adjustment combining individual patient data and published summary data.
Target audience:
Day 1 of this course is aimed at statisticians, health economists, decision-makers, and systematic reviewers who are already familiar with pairwise meta-analysis, and who want to extend their knowledge to NMA and population adjustment methods. Participants will develop an understanding of these methods and the required assumptions and learn to assess and critique these types of analyses.
Day 2 is aimed at those with technical experience of meta-analysis who want to apply the knowledge learned on Day 1 to hands-on practical examples. Participants will gain experience implementing NMA and population-adjusted analyses in an R package based on the Bayesian modelling language Stan.
Prerequisites: Day 1:
- Participants should be familiar with meta-analysis and linear and logistic regression
Day 2:
- Participants should have attended Day 1 of the course
- Participants should have a working knowledge of R (installing packages, running a simple regression model)
Learning Objectives Day 1 - Develop an understanding of network meta-analysis and population adjustment methods and the required assumptions and learn to assess and critique these types of analyses.
Day 2 - Gain experience implementing NMA and population-adjusted analyses in an R package based on the Bayesian modelling language Stan.
Fees
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No of Days
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1 Day
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2 Day
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SSA/APBG members
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$350
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$700
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Non-members
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$400
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$800
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SSA/APBG full-time student members*
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$200
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$400
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Non-member full-time students*
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$230
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$460
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Early Bird SSA/APBG members
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$300
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$600
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Early Bird non-members
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$300
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$600
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Early Bird SSA/APBG full-time student members*
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$150
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$300
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Early Bird non-member full-time students*
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$180
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$360
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*SSA student membership is available for $20 (12 months) - click here to sign up
Deadlines
Early Bird registration closes on 30 June 2019. Regular registrations close on midnight on 27 October 2019.
Travel Expenses
Occasionally workshops have to be cancelled due to a lack of subscription. Early registration ensures that this will not happen. Please contact the SSA Office before making any travel arrangements to confirm that the workshop will go ahead, because the Society will not be held responsible for any travel or accommodation expenses incurred due to a workshop cancellation.
Cancellation Policy
Cancellations received prior to Friday, 25 October 2019 will be refunded, minus a $20 administration fee. From then onwards no part of the registration fee will be refunded. However, registrations are transferable within the same organisation. Please advise any changes to eo@statsoc.org.au.
@StatSocAus
