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A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
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PRE-CISE: A PRE-calibration Coverage, Identifiability, and SEnsitivity analysis workflow to streamline model

Valeria Gracia1, Jeremy D Goldhaber-Fiebert1,2, Fernando Alarid-Escudero1,2

  • 1Department of Health Policy, Stanford University School of Medicine, Stanford, CA, USA.

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Summary

We developed PRE-CISE, a workflow that improves model calibration by analyzing coverage, sensitivity, and collinearity. This method enhances the reliability of health policy analyses by refining parameters before intensive calibration.

Keywords:
Bayesian methodscalibrationhealth policy modelsinverse modelingnonidentifiabilitysensitivity analysis

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Area of Science:

  • Mathematical modeling in health sciences
  • Computational epidemiology
  • Biostatistics

Background:

  • Model calibration is crucial for health policy analysis.
  • Nonidentifiability issues can compromise model reliability.
  • Pre-calibration analysis is needed to streamline complex modeling workflows.

Purpose of the Study:

  • Introduce PRE-CISE, a pre-calibration workflow.
  • Integrate coverage analysis, local sensitivity, and collinearity diagnostics.
  • Streamline model calibration and address nonidentifiability.

Main Methods:

  • PRE-CISE involves coverage analysis, local sensitivity analysis, and collinearity diagnostics.
  • Prior distribution bounds are resized based on sensitivity analyses to improve coverage.
  • Identifiability is assessed using collinearity analysis.

Main Results:

  • Coverage analyses identified initial misfits, which were improved by resizing prior distribution bounds.
  • Local sensitivity analysis prioritized key parameters in a COVID-19 model, enhancing calibration efficiency.
  • Daily calibration targets in the COVID-19 model resulted in collinearity indices below practical thresholds.

Conclusions:

  • PRE-CISE offers a practical and transparent method for refining model parameters and calibration targets.
  • This workflow improves uncertainty reporting and strengthens health policy analysis reliability.
  • The approach is demonstrated on a Sick-Sicker Markov model and a COVID-19 case study.