Cost-Effectiveness and Decision Modeling using R Workshop

August 23 – 25, 2023

The Decision Analysis in R for Technologies in Health (DARTH) workgroup is offering a workshop on cost-effectiveness and decision modeling using R from August 23 – 25, 2023 as a hybrid, hands-on seminar. HPM faculty Eva Enns, HPM alumni Fernando Alarid-Escudero and Hawre Jalal, 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 and their implementations in R. The workshop will also cover more advanced topics, such as probabilistic sensitivity analysis, model calibration and value of information analysis in R.

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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.

Petros Pechlivanoglou, PhD
Petros Pechlivanoglou, PhD, is a Scientist at the Hospital for Sick Children’s Research Institute and an Assistant Professor at the University of Toronto’s Institute of Health Policy, Management and Evaluation. After studying economics in his native Greece, he received both a Master’s in econometrics and a PhD in health econometrics from the University of Groningen. His research interests include: the use of health decision analysis in economic evaluation; bridging evidence synthesis, administrative data and decision analysis; and the application and extension of predictive models in health economics.

Hawre Jalal, MD, PhD
Hawre Jalal, MD, PhD, is an Associate Professor in the School of Epidemiology and Public Health at the University of Ottawa. His research focuses on using mathematical modeling, cost-effective analyses and econometrics approaches to address health policy and resource allocation questions. His current research involves modeling the opioid-epidemic and evaluating its mitigation strategies, and research in disability and aging. Hawre’s methodological interests involve value of information analysis, Bayesian calibration and communicating uncertainty to decision makers.

Eline Krijkamp, PhD
Eline Krijkamp, PhD, recently completed her PhD in epidemiology at the Netherlands Institute of Health Sciences. Her graduate work focused on promoting the use of open-source platforms, such as R, for decision analytic modeling and methodological innovations in microsimulation, cohort state transition models, and value of information analysis. Eline has a paradoxical relationship with choice: she doesn’t like having to make choices and yet, she is very enthusiastic about modelling choices in healthcare. Right now, she is building a decision model to predict the best choice for her future career path.

Alan Yang, MSc,
Alan Yang, MSc, is an analyst at the Hospital for Sick Children in Toronto, Canada, focusing on implementations of decision analysis in the R software and statistical applications for health economics. He holds a Master of Science in Biostatistics and an honours Bachelor of Science in Statistics from the University of Toronto. His areas of interest include decision analysis, R programming, survival analysis and simulation modeling.

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 2 nd 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).


Hybrid format: in-person in Toronto, Canada and online.

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