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Cost-effectiveness Analysis in R Using a Multi-state Modeling Survival Analysis Framework: A Tutorial.

Claire Williams1, James D Lewsey1, Andrew H Briggs1

  • 1Health Economics and Health Technology Assessment, Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK (CW, JDL, AHB).

Medical Decision Making : an International Journal of the Society for Medical Decision Making
|June 10, 2016
PubMed
Summary
This summary is machine-generated.

This tutorial demonstrates cost-effectiveness analysis using R and multi-state modeling. This transparent, reproducible syntax-based approach offers advantages over spreadsheet methods for health economic evaluations.

Keywords:
Markov modelscost-effectiveness analysisprobabilistic sensitivity analysissurvival analysis

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Area of Science:

  • Health Economics
  • Biostatistics
  • Computational Statistics

Background:

  • Cost-effectiveness analysis (CEA) is crucial for healthcare decision-making.
  • Traditional modeling in spreadsheets lacks transparency and reproducibility.
  • Multi-state modeling offers a robust framework for analyzing complex health trajectories.

Purpose of the Study:

  • To provide a step-by-step tutorial for performing CEA using multi-state modeling in R.
  • To highlight the advantages of a syntax-based approach for transparency and reproducibility.
  • To demonstrate advanced techniques including state-arrival extended models and sensitivity analyses.

Main Methods:

  • Utilized the R statistical package, specifically adapting functions from the 'mstate' package.
  • Employed parametric multi-state modeling for survival curve extrapolation.
  • Incorporated state-arrival extended models to test the Markov property with patient history covariates.

Main Results:

  • Successfully built and validated multi-state survival models.
  • Performed deterministic and probabilistic sensitivity analyses for robust CEA.
  • Generated cost-effectiveness planes and acceptability curves for clear result visualization.

Conclusions:

  • Multi-state modeling in R provides a transparent, reproducible, and advantageous approach to CEA.
  • The tutorial and provided R functions facilitate the application of these advanced methods.
  • This methodology enhances the reliability and interpretability of health economic evaluations.