Microsimulation Modeling for Health Decision Sciences Using R: A Tutorial
This tutorial teaches how to build microsimulation models in health decision science using the R programming language. It explains step by step how to create models that simulate individual patient paths over time, making results more transparent and reproducible.
At a glance
Use when
Modeling heterogeneous populations, incorporating statistical models directly, needing reproducible and transparent workflows, simulating complex event sequences
Avoid when
Simple homogeneous cohorts suffice, when users lack R programming skills, or when computational resources are limited
Inputs
Patient-level data or distributions for health states, transition probabilities, costs, utilities, and model parameters
Outputs
Simulated individual trajectories, aggregated outcomes (e.g., life years, QALYs, costs), cost-effectiveness results
How it works
The tutorial provides a structured guide for implementing microsimulation models in R, including generic, adaptable code. It covers model setup, simulation of individual trajectories, vectorization for computational efficiency, integration of statistical analyses within models, and reproducible reporting. The approach contrasts with traditional cohort models by enabling heterogeneity and complex event dependencies.
- HTA domains
- Clinical Effectiveness, Costs & Economic Evaluation, Patient and Social Aspects
- Assumptions
- Individuals can be modeled stochastically over time; R is used as the primary environment; vectorization improves performance; statistical and decision modeling are integrated
- Strengths
- Enables individual-level modeling with heterogeneity, supports integration of statistical models, promotes transparency and reproducibility, leverages R's extensive packages, uses efficient vectorized operations
- Limitations
- Requires proficiency in R programming, higher computational demands than cohort models, steeper learning curve for beginners
- Also known as
- R microsimulation tutorial, Microsimulation in R tutorial
Questions this answers
- › How to implement microsimulation models in R?
- › How to improve computational efficiency in microsimulation using vectorization?
- › How to integrate statistical analyses within decision models in R?
- › How to ensure transparency and reproducibility in microsimulation models?
- › How to structure R code for flexible and transferable health decision models?
- › How to extend basic models to more complex structures in R?
References & sources
Similar by meaning
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