Short course on Statistical Methods for the Value of Information Analysis
[This article was first published on Gianluca Baio's blog, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
We’re now ready to start the advertisement for our short course on Statistical Methods for the Value of Information Analysis (I’ve posted about this here). The course will be at UCL from the 8th to the 9th of June, later this year. I think we have been lucky to secure some funding and so it will be relatively cheap $-$ it’s also conveniently a week before the SMDM conference, so we’re hoping that some people may be travelling to London a little earlier to join us!Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
The first day will be much less technical and applied and in fact we are allowing a larger number of participants (up to 70) $-$ I think this may be interesting to people working in health economics and regulatory agencies, as well as those leading in trials designs and implementation. The second and third day will be much more technical and applied (that’s why we are considering a maximum of 30 participants) and we’ll have lots of computer practicals. I think these two days will be of more interest to statisticians and modellers.
Anna has made a very nice flyer which contains all the relevant information. But I’m also copying this right here:
Course Title: Statistical Methods for Value of Information Analysis
Course Dates: 8th – 10th June 2016
Course Location: University College, London
Course Lecturers:
Gianluca Baio (University College London)
Anna Heath (University College London)
Mark Strong (University of Sheffield)
Nicky Welton (University of Bristol, ConDuCT-II Hub for Trials Methodology Research)
Cost:
For 3 days: £300 (£150 for students)
For the 1st day only: £100 (£50 for students)
For 2nd and 3rd days: £200 (£100 for students)
This course is subsidised by MRC Network of Hubs for Trials Methodology Research and UCL. Travel bursaries are also available for students and hub network members, courtesy of the MRC Network of Hubs for Trials Methodology Research. To apply, contact [email protected]. Two free places available per hub.
Registration: Registration is open, please visit the UCL online store
Places available: 70 for Day 1; 30 for Days 2 and 3.
Course Description:
Day 1, 9:30 – 16:30:
- The interpretation of results from a probabilistic approach to cost-effectiveness analysis
- The interpretation of the expected value of perfect information (EVPI), expected value of partial perfect information (EVPPI) and expected value of sample information measures
- Possible uses of EVPPI for research prioritisation and adoption/reimbursement decisions
- Potential use of EVSI for designing new research studies
This day will be an introduction to value of information and has no pre-requisites. This day can be taken as a stand-alone course.
Day 2, 9:00 – 16:30:
- Simple probabilistic cost-effectiveness analysis in R, using the BCEA package
- Simulation approaches to the computation of EVPI and EVPPI, and computation using R
- Algebraic tricks that can be used to reduce computational burden of EVPPI, calculation using R
- The computational challenges for EVPPI and EVSI
This day will be a mixture of lectures and computer practical. Therefore, some knowledge of computer programming is preferable, ideally in R. Knowledge of value of information methods (or attendance to Day 1) are necessary.
Day 3, 9:30 – 16:15:
- Meta-modelling approaches for the computation of EVPPI
- The SAVI Web App for computation of EVPPI
- The R package BCEA for computation of EVPPI
This day follows from Day 2 and again will be a mixture of lectures and computer practicals.
Who should attend: Anyone with interest in VoI methods and their application in Health-Economic Evaluations.
To leave a comment for the author, please follow the link and comment on their blog: Gianluca Baio's blog.
R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.