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A Tutorial on Time-Dependent Cohort State-Transition Models in R Using a Cost-Effectiveness Analysis Example.

Fernando Alarid-Escudero1,2, Eline Krijkamp3,4, Eva A Enns5

  • 1Department of Health Policy, School of Medicine, and Stanford Health Policy, Freeman-Spogli Institute for International Studies, Stanford University, Stanford, California, USA.

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Summary

This tutorial demonstrates adding time-dependent factors to cohort state-transition models (cSTMs) in R. It enhances cost-effectiveness analysis by incorporating simulation-time and state-residence time dependencies for more realistic modeling.

Keywords:
R softwarecohort state-transition modelscost-effectiveness analysismarkov modelstime-dependenttutorial

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Area of Science:

  • Health economics
  • Computational epidemiology
  • Biostatistics

Background:

  • Cohort state-transition models (cSTMs) are widely used in health economics and epidemiology.
  • Traditional cSTMs often assume time-invariant transition probabilities, which may not reflect real-world scenarios.
  • Time-dependent factors are crucial for accurate modeling of disease progression and intervention effects.

Purpose of the Study:

  • To illustrate the implementation of time-dependent cohort state-transition models (cSTMs) in R.
  • To demonstrate two specific types of time dependence: simulation-time dependence and state-residence time dependence.
  • To apply these enhanced models to a cost-effectiveness analysis and obtain epidemiological outcomes.

Main Methods:

  • The study extends previous work on cSTMs in R by incorporating time-dependent transition probabilities and rewards.
  • Simulation-time dependence allows probabilities to vary based on time elapsed since the simulation's start.
  • State-residence time dependence uses tunnel states to track time spent in specific health states, capturing patient history.

Main Results:

  • The tutorial provides R code and mathematical notation for building and analyzing time-dependent cSTMs.
  • Demonstrates the application of these models in cost-effectiveness and probabilistic sensitivity analyses.
  • Enables the extraction of epidemiological outcomes like survival probability and disease prevalence for model validation.

Conclusions:

  • Time-dependent cSTMs offer a more realistic approach for health economic and epidemiological modeling.
  • The presented R implementation facilitates the incorporation of complex temporal dynamics into cSTMs.
  • This methodology supports robust cost-effectiveness analyses and model calibration using epidemiological data.