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Related Experiment Videos

Confidence intervals for cost-effectiveness ratios

J Gardiner1, A Hogan, M Holmes-Rovner

  • 1Biostatistics Unit, College of Human Medicine, Michigan State University, East Lansing 48824, USA.

Medical Decision Making : an International Journal of the Society for Medical Decision Making
|July 1, 1995
PubMed
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This study introduces a statistical method to calculate confidence intervals for cost-effectiveness ratios (CERs), improving precision in healthcare decision-making. It enhances the reliability of comparing medical interventions by quantifying uncertainty in their economic value.

Area of Science:

  • Health Economics
  • Biostatistics
  • Medical Decision Making

Background:

  • Cost-effectiveness analysis (CEA) often relies on sensitivity analyses to address parameter variability.
  • Existing methods lack robust ways to establish the statistical significance of differences between strategies.
  • Quantifying the precision of cost-effectiveness ratios (CERs) is crucial for meaningful comparisons.

Purpose of the Study:

  • To develop and apply a statistical method for estimating confidence intervals of CERs.
  • To address the need for improved precision assessment in cost-effectiveness analysis.
  • To provide a framework for determining when differences in strategy effectiveness are statistically meaningful.

Main Methods:

  • Developed a statistical model defining effectiveness (life expectancy) and cost based on survival distributions (S).

Related Experiment Videos

  • Utilized the sampling distribution of the Kaplan-Meier estimate of S to derive standard errors.
  • Calculated 95% confidence intervals for average cost-effectiveness (ACE) and CER using survival data.

    • Main Results:

    • Successfully estimated standard errors and confidence intervals for ACE and CER.
    • Applied the technique to patient data, yielding 95% confidence intervals for the CER and ACE.
    • Demonstrated the utility of the method for assessing the precision of economic evaluations.

    Conclusions:

    • The proposed method effectively utilizes sampling variation to provide confidence intervals for CERs.
    • This approach enhances the statistical rigor of cost-effectiveness analyses.
    • The technique offers a valuable tool for decision-makers evaluating the economic value of healthcare interventions.