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An R-Based Landscape Validation of a Competing Risk Model
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An Introductory Tutorial on Cohort State-Transition Models in R Using a Cost-Effectiveness Analysis Example.

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Summary

This tutorial introduces time-independent cohort state-transition models (cSTMs) for medical decision-making. We demonstrate R implementation for simulating health strategies and conducting cost-effectiveness analysis.

Keywords:
Markov modelsR softwarecohort state-transition modelscost-effectiveness analysistutorial

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Area of Science:

  • Health economics
  • Biostatistics
  • Decision science

Background:

  • Decision models simulate long-term strategy consequences under uncertainty.
  • Cohort state-transition models (cSTMs) are common in medical decision-making.
  • This tutorial focuses on time-independent cSTMs where probabilities are constant.

Purpose of the Study:

  • To provide a tutorial on implementing time-independent cSTMs in R.
  • To illustrate cost-effectiveness analysis using a previously published decision model.
  • To facilitate wider adoption of cSTMs through open-source code.

Main Methods:

  • Implementation of time-independent cSTM in R.
  • Simulation of a hypothetical cohort's health state transitions.
  • Calculation of costs and effectiveness outcomes.
  • Conducting a cost-effectiveness analysis with probabilistic sensitivity analysis.

Main Results:

  • Demonstration of a functional time-independent cSTM in R.
  • Successful cost-effectiveness analysis of multiple strategies.
  • Provision of open-source R code for reproducibility and application.

Conclusions:

  • Time-independent cSTMs are valuable tools for medical decision-making.
  • R is a suitable platform for implementing and analyzing cSTMs.
  • The provided code facilitates the application of cSTMs in health economics research.