Validation and calibration of structural models that combine information from multiple sources

Area of Science:

• Medical modeling
• Quantitative inference
• Systems biology

Background:

• Mathematical models are increasingly utilized in medicine to represent structural relationships and synthesize multi-source data.
• Model validation and calibration are critical processes for ensuring the reliability and accuracy of these medical models.

Purpose of the Study:

• To provide an overview of methods for model validation and calibration in medicine.
• To survey studies comparing alternative approaches to model validation and calibration.

Main Methods:

• Review of existing literature on model validation and calibration techniques.
• Analysis of studies comparing different methodological approaches.

Main Results:

• Model validation involves comparing models against data, knowledge, and other models to assess credibility.
• Calibration aims to align model outputs with data, functioning as estimation for quantitative inference and forecasting.
• For qualitative insights, calibration is an informal process, not an estimation procedure.

Conclusions:

• Current research on validation and calibration is limited to methodological appraisals and case studies.
• Further research is needed to address complex decisions in developing and validating multi-source evidence models.