R for trial and model-based cost-effectiveness analysis
We are excited to announce the R for Health Technology Assessment (HTA) workshop that will be held on Thursday 8th, Friday 9th, and Monday 12th June 2023.
Friday 9th will be an in-person day-long, hybrid event hosted by at ARRC/Alan Maynard, University of York, while the other days will be online only. Our programme will be announced in April. The overall goal is to present interesting and enlightening presentations on the use of R that will engage an audience of those working in the field of health technology assessment and related analysis. Sessions may cover some or all of the following:
- New methods and applications for economic modelling using R
- Efficient modelling for economic evaluation using dedicated R packages
- Improving modelling for HTA using R – Lessons from industry and academia
- Teaching economic evaluation and HTA using R
The call for abstracts is not restricted by topic.
We look forward to seeing you there!
Call for abstracts
Deadline 24 March 2023
We are seeking abstracts for the R for Health Technology Assessment (HTA) workshop that will be held on Thursday 8th, Friday 9th, and Monday 12th June 2023. Friday 9th will be in-person day-long hybrid event hosted by the University of York, while other days will be online only. Our call for abstracts is broad and relates to no specific topic area. The overall goal is to present interesting and enlightening presentations on the use of R that will engage an audience of those working in the field of health technology assessment and related analysis. While the call is broad, the following considerations will help guide abstract preparation.
We are inviting submissions aimed at an audience with a broad range of expertise with R. Abstracts aimed at beginners are welcome and should be accessible and offer teaching insights. Abstracts for more advanced R users are also invited and can be technically ambitious and presume a knowledge of R and HTA modelling methods. Naturally, more advanced presentations should still attempt to convey broad principles alongside technical detail in order to remain relevant for less expert attendees. Please clearly indicate in your abstract if your submission is aimed at more or less experienced R users.
We welcome any topic you consider interesting and worth sharing. We suggest keeping the scope of the presentation sufficiently narrow to permit a meaningful exposition of a method, use of code or output presentation. Abstracts exemplifying the application of particular packages (not necessarily your own) are welcome. We also welcome the presentations on problems that analysts have encountered and are seeking to prompt discussion on possible solutions. Such problem-related abstracts should address a clearly-defined issue and outline some candidate approaches in order to frame the discussion. We also welcome abstracts related to the practicalities of R modelling, such as cluster or cloud implementation, handling of large datasets and runtime or variance reduction. Submissions may relate to applied analyses or methods research. Similarly, they may relate to simulation modelling or data analysis. Please clearly indicate which best corresponds to your submission in your abstract.
Abstracts should be 300 words or less excluding title and author information. Structured abstracts are encouraged using the format: background; analysis; discussion. Include all co-author name and institutional affiliations.