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Area of Science:

  • Health Economics
  • Computational Statistics
  • Health Services Research

Background:

  • Economic evaluations (EE) are crucial for healthcare resource allocation decisions.
  • Traditional cost-effectiveness analyses often rely on Microsoft Excel (ME).
  • There's a growing need for advanced software to improve model complexity, reproducibility, and transparency in EE.

Purpose of the Study:

  • To provide a step-by-step guide for implementing Markov models in R.
  • To facilitate the transition for health economists and MS Excel users to R for decision modeling.
  • To promote wider adoption of R in health economic modeling.

Main Methods:

  • Development of two distinct R code implementations for Markov models.
  • Detailed explanations accompanying each code snippet for user comprehension.
  • Focus on a beginner-friendly approach for users with limited R programming knowledge.

Main Results:

  • Demonstration of how to build and implement Markov models in R.
  • Illustrations of R's capabilities in handling complex decision-making models.
  • Guidance enabling users to switch from MS Excel to R for EE.

Conclusions:

  • R offers a powerful and reproducible alternative to MS Excel for economic evaluations.
  • This tutorial equips health economists with the necessary R skills for advanced decision modeling.
  • Adoption of R can enhance transparency and complexity management in healthcare decision-making.