Event

This course explores how programming languages like R enhance model transparency, facilitate input parameter estimation and simulation, and improve the evaluation of structural and decision uncertainty in health economic modeling.

Technical Topics Include:

• Advantages of using R for cost-effectiveness analysis: Transparency, reproducibility, and flexibility
• Economic model types implemented in R:
  • Time-homogeneous and time-inhomogeneous Markov cohort models
  • Partitioned survival models
  • Semi-Markov individual patient simulations
• Model modification and customization:
  • Adding health states
  • Applying alternative time-to-event distributions
• Advanced economic evaluation methods:
  • Cost-effectiveness analysis (CEA)
  • Probabilistic sensitivity analysis (PSA)
  • Evaluating structural uncertainty
  • Value of information (VOI) analysis

This Course Includes Tools and Concepts That Can Be Immediately Applied, Including:

• Hands-on experience modifying R-based models for health economic evaluations
• Access to a GitHub repository with pre-built R code and R Markdown files for step-by-step analysis
• Practical exercises to develop, simulate, and analyze economic models in R
• Best practices for integrating R into cost-effectiveness modeling workflows

Participants will leave this course with the knowledge and skills to transition from Excel-based modeling to reproducible, transparent, and flexible health economic models in R.

PREREQUISITE: Participants who wish to gain hands-on experience are required to bring their laptops. To make this interactive aspect of the course as efficient as possible, all participants will have access to the GitHub repository prior to the course. It will contain R code to run the economic models and R Markdown files to explain and reproduce the analyses covered in the course.

Schedule:

LENGTH: 4 Hours | Course runs 1 day

Sunday, 13 May 2025 | Course runs 1 Day
1:00pm-5:00pm Eastern Daylight Time (EDT)