As health economic decision making becomes increasing complex, there is a need for powerful programming tools to support statistical analysis.
R is an open source, high level programming language for statistical computing and graphics that is widely used and supported by statisticians, data scientists and health economists. Shiny is an R package that provides a powerful web framework for building interactive web applications, while taking advantage of the various possibilities for data analysis and data visualization that R provides. These features can be useful to enhance the presentation of study results, and to provide a more accessible, user-friendly and flexible interface for health economic models with high analytic capabilities. Join us to learn the challenges and opportunities of this approach, including:
- Possible use of R and R Shiny
- Interactive and web-based data presentation
- Interactive and web-based common user access (CUA)
This program will be beneficial for senior level pharmaceutical and medical device professionals working in:
- Heath economics
- Outcomes research
- Real world evidence
- Regulatory affairs
- Medical affairs
Emma Hernlund, PhD
Lead Consultant, Global Health Economics, ICON
Emma has worked in health economics and outcomes research since 2011, and has managed and supported a broad range of projects. She has extensive experience in health economic modelling, retrospective data studies, literature reviews and database mappings.
Moa Ivergard, MS
Senior Consultant, Global Health Economics, ICON
Moa has over ten years’ experience in health economics and outcomes research. At ICON, she is responsible for developing economic models in a variety of indications for HTA submissions, conducting retrospective data studies and developing interactive web applications for data visualisation.
Professor of Statistics and Health Economics, Department of Statistical Science, University College London
Gianluca leads the Statistics for Health Economic Evaluation research group within the department of Statistical Science at University College London, which research evolves around the development and application of Bayesian statistical methodology for health economic evaluation. Moreover, Gianluca collaborates with the UK National Institute for Health and Care Excellence (NICE) as a Scientific Advisor on Health Technology Appraisal projects.