August 23 – 27, 2021
The Decision Analysis in R for Technologies in Health (DARTH)* workgroup in collaboration with the University of Minnesota Division of Health Policy and Management is offering a workshop on cost-effectiveness and decision modeling using R, August 23 – 27, 2021 as a virtual, hands-on seminar. Instructors Eva Enns, Karen Kuntz, Fernando Alarid-Escudero, and other DARTH co-instructors will cover the principles of cost-effectiveness analysis and decision analytic modeling and will guide participants in implementing decision trees, Markov models, and microsimulation models in R as well as how to use models for cost-effectiveness analysis and deterministic and probabilistic sensitivity analysis.
Participants will be expected to have some experience with decision modeling and/or basic concepts of economic evaluation. A self-paced module introducing R concepts specifically for decision modeling will be provided as part of your workshop registration.
August 23: Introduction to R for decision modelers
August 24: Introduction to cost-effectiveness and decision trees
August 25: Markov models
August 26: Microsimulation modeling
August 27: Sensitivity analysis
Healthcare resources are limited and policymakers are under increased pressure to make use of these resources as efficiently as possible. Cost-effectiveness analysis and decision analysis are quantitative decision-making frameworks used to formalize objectives, quantify tradeoffs, and ultimately support more informed decision making. These techniques have been applied to a wide range of health policy questions, including optimal cancer screening and treatment guidelines; technology reimbursement and coverage decisions; and hospital operations management.
R is an open-source software that provides a flexible environment where advanced statistical analyses can be combined with decision models of varying complexity within the same framework and the results can be presented in publication ready tabular and graphical forms. The fact that R is freely available also improves model transparency and reproducibility.
*The DARTH workgroup is a multi-institutional, multi-university collaborative effort comprised of researchers who have a passion for transparent and open-source solutions to decision analysis in health. The aim of this collaboration is to expand knowledge and develop educational materials that empower people to construct R-based decision models.
Visit the DARTH website for more information.
Eva A. Enns, PhD
Eva Enns, PhD, is an Assistant Professor at the University of Minnesota’s School of Public Health, in the Division of Health Policy and Management. She develops mathematical models of the spread of infectious diseases that help evaluate strategies aimed at mitigating disease impact through prevention and/or treatment. Her aim is to inform better infectious disease prevention, management, and control policies as well as to develop new methodologies or tools to better address issues surrounding infectious disease outbreaks.
Fernando Alarid-Escudero, PhD
Fernando Alarid-Escudero, PhD, is an Assistant Professor at the Center for Research and Teaching in Economics (CIDE) in Aguascalientes, Mexico, in the Drug Policy Program. He is also a member of the Cancer Intervention Surveillance Modeling Network (CISNET) consortium. He focuses on developing and applying new methods in medical decision making, simulation modeling and cost-effectiveness analysis. His applied work focuses on the use of simulation modeling to evaluate clinical and public health strategies, such as treatment and screening for cancer, including gastric, colorectal and cervical cancer.
Karen M. Kuntz, ScD
Karen Kuntz, ScD, is a Professor and Director of Graduate Studies in Health Services Research, Policy and Administration MS and Doctoral programs at the University of Minnesota School of Public Health, Division of Health Policy and Management. She is an internationally-recognized expert in the methods and applications of simulation modeling to address clinical and public health interventions. She is past president of the Society for Medical Decision Making (SMDM) – an organization dedicated to the methods and applications of decision modeling. More recently, she served as co-chair of the International Society for Pharmacoeconomics and Outcomes Research (ISPOR)-SMDM Modeling Good Research Practices Task Force (2010-2012) where she also served as co-leader on the state-transition modeling paper. She was also a member of the 2nd Panel on Cost-Effectiveness in Health and Medicine and served as the lead on the chapter on decision modeling. She is a firm believer in the added value that decision models can bring to real-world policy.
- Loved it and the group. There’s a real energy here, and the classroom and monitors were great. When my attention wandered around the room, there was always another monitor pulling me back. So the classroom layout improved my focus.
- The pattern of the course (description, example, exercise, answer) was very helpful to learning the material and ensuring people didn’t fall behind,
- I found the introduction of the topics via powerpoint and then the subsequent exercises in R to be very helpful.
- Course design was excellent. My only regret is that I should have flown out to Minnesota. Webinar was fine, but the in-person environment seemed more fun.
- Fernando and Eva, you’re brilliant… in very different ways, but brilliant nevertheless. Your knowledge of R is terrific; your understanding of the methods, sound; your experience applying what you know to actual projects, hence making you able to refer to real examples of applications, super useful; your presentation skills, outstanding. I particularly appreciated your consideration for those of us who were participating remotely. Sharing the code you have written is hugely valuable.
- Good coverages of both cost analysis and R skills. Excellent references.
- It was clear that all of the instructors genuinely cared if we were learning, were passionate about the topic, and eagerly encouraged questions.
- The instructors were wonderful and invested in helping us learn the topic. The topic, while a bit challenging for someone with no experience such as myself, was generally easy to understand due to the clarity of instruction.
- After taking the workshop, I will finish a paper because now I can easily adapt the code to my project’s needs. I will code more efficiently in R. I will consider using R for more of my statistical analysis because it is more efficient to do everything (visualization, data wrangling, etc. in one place).