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Bootstrap confidence intervals for cost-effectiveness ratios: some simulation results

M Tambour1, N Zethraeus

  • 1Centre for Health Economics, Stockholm School of Economics, Sweden. hemt@hhs.se

Health Economics
|July 4, 1998
PubMed
Summary
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This study introduces a bootstrap method for calculating confidence intervals in cost-effectiveness (CE) studies. The new approach provides more accurate confidence intervals for cost-effectiveness ratios compared to existing parametric methods.

Area of Science:

  • Health Economics
  • Biostatistics
  • Decision Analysis

Background:

  • Cost-effectiveness (CE) studies are crucial for healthcare decision-making.
  • Determining the statistical uncertainty of CE ratios, specifically confidence intervals, remains a challenge.
  • Existing parametric methods for CE ratio confidence intervals have limitations.

Purpose of the Study:

  • To present a bootstrap procedure for estimating bias-corrected confidence intervals for cost-effectiveness (CE) ratios.
  • To evaluate the performance of bootstrap confidence intervals for CE ratios using simulation.
  • To compare bootstrap methods with existing parametric approaches.

Main Methods:

  • A bootstrap procedure was developed to estimate bias-corrected confidence intervals for CE ratios.

Related Experiment Videos

  • A simulation study was conducted based on assumptions from previous research (Wakker and Klaassen).
  • Two variants of bootstrap confidence intervals for CE ratios were tested and compared.

    • Main Results:

    • The bootstrap procedure provides bias-corrected confidence intervals for CE ratios.
    • A bootstrap analogue of a parametric method yielded results similar to the parametric method.
    • Direct computation of bootstrap confidence intervals for the CE ratio resulted in intervals closer to the significance level.

    Conclusions:

    • Bootstrap methods offer a viable approach for constructing confidence intervals in CE analyses.
    • Directly computed bootstrap confidence intervals for CE ratios demonstrate improved accuracy in simulation.
    • This method enhances the reliability of statistical inference in cost-effectiveness studies.