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Discrete-Event Simulation Model for Cancer Interventions and Population Health in R (DESCIPHR): An Open-Source

Selina Pi1, Carolyn M Rutter2, Carlos Pineda-Antunez3

  • 1Department of Biomedical Data Science, School of Medicine, Stanford University, Palo Alto, CA.

Medrxiv : the Preprint Server for Health Sciences
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Summary
This summary is machine-generated.

This study introduces DESCIPHR, an open-source framework for cancer modeling using discrete-event simulation (DES) and Bayesian calibration. It aids health policy decisions by evaluating interventions and improving cancer care strategies.

Keywords:
Bayesian calibrationcancerdecision-analytic modelingdiscrete-event simulationmicrosimulationscreening

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Area of Science:

  • Decision Science
  • Health Policy
  • Computational Biology

Background:

  • Simulation models are crucial for health policy decisions, especially for cancer, a leading cause of death globally.
  • Discrete-event simulation (DES) and Bayesian calibration are powerful tools for modeling complex health conditions and handling data uncertainty.

Purpose of the Study:

  • To provide end-to-end guidance on structuring DES models for cancer progression, parameter estimation via Bayesian calibration, and policy evaluation.
  • To introduce DESCIPHR, an open-source framework integrating DES, Bayesian calibration, and screening strategy evaluation for cancer interventions.

Main Methods:

  • Developed DESCIPHR, an open-source framework with a flexible DES model for cancer natural history.
  • Implemented Bayesian calibration for parameter estimation, including an automated method for data-informed prior distributions.
  • Utilized a neural network emulator for enhanced Bayesian calibration accuracy and flexibility.

Main Results:

  • The DESCIPHR framework provides a structured approach to cancer modeling and intervention evaluation.
  • Automated prior distribution generation and neural network emulators improve the efficiency and accuracy of Bayesian calibration.
  • The codebase facilitates the integration of diverse data sources for robust health policy recommendations.

Conclusions:

  • DESCIPHR offers an adaptable template for constructing decision models in cancer research.
  • The framework supports the evaluation of cancer intervention risks and benefits, aiding policy decisions.
  • This work addresses the need for comprehensive guidance in applying DES and Bayesian calibration to cancer health policy.