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Related Experiment Video

Updated: Jun 6, 2026

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Calibrating models in economic evaluation: a seven-step approach.

Tazio Vanni¹, Jonathan Karnon, Jason Madan

¹Department of Public Health and Policy, London School of Hygiene and Tropical Medicine, London, UK. tazio.vanni@lshtm.ac.uk

Pharmacoeconomics

|December 15, 2010

Summary

Mathematical models are crucial for economic evaluations of health interventions. This study guides the calibration of these models using observational data to improve parameter estimation and reduce uncertainty, enhancing credibility.

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Last Updated: Jun 6, 2026

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Area of Science:

Health economics
Mathematical modeling
Biostatistics

Background:

Mathematical models are essential in economic evaluation for estimating long-term costs and consequences of health interventions.
Model predictions rely on scientific knowledge and parameters with inherent uncertainty, necessitating validation against real-world data.

Purpose of the Study:

To provide guidance on the theoretical underpinnings and practical application of various calibration methods for disease models in economic evaluation.
To address the lack of standardized approaches in model calibration, which can undermine the credibility of economic evaluations.

Main Methods:

The article outlines a seven-step process for model calibration, detailing considerations for parameter selection, target data, goodness-of-fit measures, search strategies, convergence criteria, stopping rules, and integration of results.
Discusses different potential methods for each step, emphasizing features specific to disease models in economic evaluation.

Main Results:

Calibration compares model outputs with empirical data to identify parameter values that ensure a good fit.
The process helps in estimating uncertain parameters and defining model uncertainty, including correlations between parameters.

Conclusions:

Standardizing calibration methods and reporting is crucial to enhance the credibility and reduce skepticism surrounding economic evaluation models.
Further investigation into different calibration methodologies is recommended to refine the process.