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A Bayesian framework for health economic evaluation in studies with missing data.

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This summary is machine-generated.

This study introduces a Bayesian framework for health economic evaluations with missing data, addressing "missing not at random" (MNAR) scenarios. It offers a practical sensitivity analysis using expert opinion to ensure robust cost-effectiveness findings.

Keywords:
Bayesian analysiscost-effectiveness analysisexpert elicitationmissing not at randompattern-mixture model

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Area of Science:

  • Health Economics
  • Biostatistics
  • Medical Decision Making

Background:

  • Missing data in health economics studies often rely on "missing at random" assumptions, which may not hold true.
  • The probability of missing data can be linked to unobserved patient outcomes, violating this assumption.
  • Methodological guidelines advocate for sensitivity analyses to address potential
  • "missing not at random" (MNAR) data, yet practical approaches are scarce in health economics.

Purpose of the Study:

  • To propose a Bayesian framework for cost-effectiveness analyses (CEA) with missing outcome or cost data.
  • To develop a practical and accessible method for sensitivity analysis in CEA under MNAR assumptions.
  • To provide software tools for implementing the proposed Bayesian framework and sensitivity analysis.

Main Methods:

  • Developed a Bayesian framework tailored for cost-effectiveness analyses (CEA) involving missing data.
  • Incorporated a novel, accessible sensitivity analysis approach within the framework, utilizing expert opinion.
  • Applied the framework to a CEA comparing endovascular strategy versus open repair for ruptured abdominal aortic aneurysm.

Main Results:

  • The proposed Bayesian framework accommodates missing data in CEA, including scenarios where data are "missing not at random" (MNAR).
  • The sensitivity analysis allows for the incorporation of expert judgment to assess the impact of MNAR data on results.
  • Demonstrated the framework's utility and provided software for practical implementation in health economic evaluations.

Conclusions:

  • The Bayesian framework offers a robust approach to handle missing data in CEA, particularly when data are MNAR.
  • The integrated sensitivity analysis enhances the reliability of cost-effectiveness conclusions by accounting for potential biases due to missing data.
  • This work provides valuable tools for health economists to conduct more rigorous analyses when faced with missing data challenges.