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A Need for Change! A Coding Framework for Improving Transparency in Decision Modeling.

Fernando Alarid-Escudero1, Eline M Krijkamp2, Petros Pechlivanoglou3

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Summary

This study introduces a framework for R-based health decision modeling to enhance transparency and reproducibility. It provides coding recommendations and a modular structure for better model development and sharing in cost-effectiveness analyses (CEA).

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Area of Science:

  • Health Decision Sciences
  • Computational Modeling
  • Health Economics

Background:

  • Open-source programming languages like R offer potential for transparency and reproducibility in health decision sciences.
  • Challenges exist due to complex models, secondary focus on sharing, and varying programmer expertise.
  • Lack of standardized practices hinders model comprehension and collaboration.

Purpose of the Study:

  • To propose a high-level framework for model-based decision and cost-effectiveness analyses (CEA) in R.
  • To provide a conceptual, modular structure and coding recommendations for R-based decision modeling.
  • To improve transparency, reproducibility, and shareability of health decision models.

Main Methods:

  • Developed a framework with five common decision model components: inputs, implementation, calibration, validation, and analysis.
  • Included recommendations for good coding practices, such as file organization and naming conventions.
  • Demonstrated the framework using a functional testbed model hosted on GitHub.

Main Results:

  • The proposed framework offers a structured approach to developing decision models in R.
  • Coding recommendations aim to improve code readability and adherence to best practices.
  • A publicly available testbed model facilitates adoption and adaptation.

Conclusions:

  • The framework enhances code readability, model sharing, and reproducibility in health decision modeling.
  • It supports the development of transparent and shareable open-source decision models.
  • Facilitates the application of decision models for policy questions and value of information analyses.