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Linked Sensitivity Analysis, Calibration, and Uncertainty Analysis Using a System Dynamics Model for Stroke

Yuan Tian1, Kristen Hassmiller Lich2, Nathaniel D Osgood3

  • 1Program in Health Services & Systems Research, Duke-NUS Graduate Medical School Singapore, Singapore (YT, KE, DBM)

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|April 20, 2016
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Summary
This summary is machine-generated.

This study presents a systematic approach to improve confidence in complex health simulation models. Hypertension control for all veterans is the most effective intervention for improving quality-adjusted life years.

Keywords:
System Dynamicscalibrationsensitivity analysissimulation modelstrokeuncertainty analysis

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Area of Science:

  • Health services research
  • Simulation modeling
  • Decision analysis

Background:

  • Complex simulation models in health services research face challenges with increased parameters and uncertainty.
  • This uncertainty can reduce confidence in model-guided decision-making.

Purpose of the Study:

  • To demonstrate a systematic approach integrating sensitivity analysis, calibration, and uncertainty analysis.
  • The goal is to enhance confidence in complex health simulation models.

Main Methods:

  • Integrated Morris method (sensitivity analysis), multistart Powell hill-climbing algorithm and generalized likelihood uncertainty estimation (calibration), and Monte Carlo simulation (uncertainty analysis).
  • Applied to a System Dynamics stroke model for US veterans.

Main Results:

  • Sensitivity analysis identified 29 of 60 parameters needing calibration and 7 significantly influencing outcomes.
  • Hypertension control identified as the most impactful intervention for improving quality-adjusted life years.
  • Initial stroke incidence rate identified as the most influential uncertain parameter.

Conclusions:

  • A mixed approach rigorously examined uncertainty in stroke outcomes and model robustness.
  • Advocates for such integrated analysis to understand model limitations and guide future data collection.