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Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
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Assessing uncertainty in microsimulation modelling with application to cancer screening interventions

K A Cronin1, J M Legler, R D Etzioni

  • 1Biometry Branch, National Cancer Institute, Bethesda, MD 20892, USA. cronink@dcpcepn.nci.nih.gov

Statistics in Medicine
|November 20, 1998
PubMed
Summary

Microsimulation models, often used for chronic disease interventions like cancer screening, can now quantify parameter uncertainty. A Bayesian approach with response surface modeling improves the reliability of microsimulation results for prostate cancer screening.

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Area of Science:

  • Computational epidemiology
  • Health economics modeling
  • Biostatistics

Background:

  • Microsimulation is increasingly used for complex health interventions, particularly chronic diseases like cancer.
  • Parameter uncertainty is a significant challenge in microsimulation, often unquantified in results.
  • Existing methods lack robust approaches to address uncertainty in microsimulation model parameters.

Purpose of the Study:

  • To introduce a Bayesian framework for quantifying parameter uncertainty in microsimulation models.
  • To develop a method for integrating parameter uncertainty into the analysis of simulation outcomes.
  • To demonstrate the application of this method to prostate cancer screening interventions.

Main Methods:

  • Designed a simulation experiment for comprehensive parameter space coverage.
  • Developed a response surface model to approximate simulation outcomes based on parameters.
  • Combined response surface with Bayesian parameter distributions to quantify outcome variability.

Main Results:

  • The proposed Bayesian approach effectively quantifies uncertainty in microsimulation outcomes.
  • Demonstrated the method's utility in assessing prostate specific antigen (PSA) screening's impact on prostate cancer mortality.
  • The response surface approach provides a computationally efficient way to explore parameter uncertainty.

Conclusions:

  • The Bayesian framework offers a robust solution for uncertainty quantification in microsimulation.
  • This method enhances the reliability and interpretability of simulation studies for health interventions.
  • Accurate quantification of parameter uncertainty is crucial for evidence-based decision-making in public health policy.