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Microsimulation Model Calibration with Approximate Bayesian Computation in R: A Tutorial.

Peter Shewmaker1, Stavroula A Chrysanthopoulou2, Rowan Iskandar3,4

  • 1Center for Gerontology and Healthcare Research, Brown University, Providence, RI, USA.

Medical Decision Making : an International Journal of the Society for Medical Decision Making
|March 21, 2022
PubMed
Summary
This summary is machine-generated.

This tutorial guides health policy modelers in using Approximate Bayesian Computation (ABC) for calibrating microsimulation models (MSMs). It provides practical steps and R code to enhance model accuracy and uncertainty evaluation.

Keywords:
approximate Bayesian computationcalibrationdementiamicrosimulation

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Area of Science:

  • Health economics and outcomes research
  • Mathematical modeling in public health
  • Computational statistics

Background:

  • Mathematical health policy models, such as microsimulation models (MSMs), are crucial for simulating complex health processes and predicting outcomes.
  • Model calibration, particularly Bayesian calibration, is essential for estimating parameter values to align model predictions with observed data.
  • Approximate Bayesian Computation (ABC) offers a method for calibrating complex models where the likelihood function is intractable.

Purpose of the Study:

  • To provide practical, step-by-step guidance on implementing Approximate Bayesian Computation (ABC) for calibrating microsimulation models (MSMs).
  • To address the limited practical guidance available in medical decision-making literature for applying ABC to MSMs.
  • To demonstrate the application of ABC for MSMs using case examples and provide accompanying R code.

Main Methods:

  • Description of the Bayesian calibration framework.
  • Introduction to the Approximate Bayesian Computation (ABC) approach for model calibration.
  • Step-by-step implementation guide for an ABC algorithm tailored for MSMs, including R code.

Main Results:

  • The tutorial details the application of ABC for calibrating MSMs, illustrated with two case examples.
  • The provided R code facilitates the implementation of ABC methods for researchers working with MSMs.
  • The methods enable better characterization and evaluation of parameter uncertainty in model outcomes.

Conclusions:

  • This work offers practical guidance for applying ABC to calibrate MSMs, enhancing their utility in health policy research.
  • The tutorial and code aim to bridge the gap in practical application of ABC for MSMs in medical decision-making.
  • Implementing ABC improves the reliability of MSMs by ensuring predictions align with observed data and by quantifying parameter uncertainty.