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Related Experiment Videos

Economic modeling and sensitivity analysis.

J W Hay1

  • 1Department of Pharmaceutical Economics and Policy, University of Southern California, Los Angeles, CA 90033, USA.

Value in Health : the Journal of the International Society for Pharmacoeconomics and Outcomes Research
|May 6, 2006
PubMed
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Pharmacoeconomics (PE) research credibility is questioned due to bias and lack of transparency. Standardizing models with software sharing or sensitivity analysis can improve validity and acceptance by decision-makers.

Area of Science:

  • Pharmacoeconomics
  • Health Economics
  • Health Services Research

Background:

  • Concerns regarding research credibility and bias in pharmacoeconomics (PE).
  • Issues include irreproducibility, inappropriate data use, lack of transparency, and difficulty comparing studies.
  • Skepticism about the validity, reliability, and relevance of PE studies for healthcare decision-makers.

Purpose of the Study:

  • To propose reporting standards to enhance the credibility of pharmacoeconomics research.
  • To address concerns about transparency and comparability in economic modeling.
  • To improve the acceptance and application of PE findings by policymakers.

Main Methods:

  • Utilized a case study in lipid pharmacoeconomics.
  • Proposed two general reporting standards for PE research.

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  • Recommended either software distribution for PE models or multivariate sensitivity analysis.

    • Main Results:

    • Software distribution allows validation and adaptation of PE models.
    • Multivariate sensitivity analysis enables consistent and comparable results across studies.
    • Implementation of these standards can increase trust in PE findings.

    Conclusions:

    • Adoption of standardized reporting, including software sharing or sensitivity analysis, is crucial for PE credibility.
    • Mandating these guidelines in scientific journals and by healthcare decision-makers will foster wider acceptance.
    • Standardization will establish pharmacoeconomics as a practical, real-world science.