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Use of an angular transformation for ratio estimation in cost-effectiveness analysis.

J R Cook1, J F Heyse

  • 1Merck Research Laboratories, P.O. Box 4, West Point, PA 19486, USA.

Statistics in Medicine
|October 24, 2000
PubMed
Summary

This study reviews statistical methods for estimating confidence intervals for cost-effectiveness ratios (CE ratios). An angular transformation improves variance stabilization and interpretation of CE ratio results.

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Area of Science:

  • Health Economics
  • Biostatistics
  • Medical Decision Making

Background:

  • Economic evaluations are crucial for medical technologies, assessing costs versus clinical benefits.
  • Cost-effectiveness (CE) analysis quantifies the trade-off using the CE ratio: net incremental cost per unit of benefit.
  • Accurate statistical methods are needed for reliable confidence intervals of CE ratios.

Purpose of the Study:

  • To review and compare statistical methods for estimating 95% confidence intervals for cost-effectiveness ratios.
  • To introduce an angular transformation method for stabilizing CE ratio variance and improving interpretation.
  • To evaluate these methods using real-world clinical data.

Main Methods:

  • Review of existing statistical techniques for CE ratio confidence interval estimation.
  • Application of an angular transformation to standardize the CE ratio, stabilizing its variance.
  • Utilizing bootstrap and jack-knife resampling methods for confidence interval calculation in the transformed scale.
  • Comparison of methods using data from a long-term congestive heart failure mortality study.

Main Results:

  • An angular transformation of the standardized CE ratio stabilizes variance, enhancing interpretability.
  • Bootstrap and jack-knife methods provide reliable estimates for 95% confidence intervals on the transformed scale.
  • The proposed methods offer a clearer interpretation of study results compared to traditional approaches.

Conclusions:

  • The angular transformation method, combined with bootstrap or jack-knife techniques, provides a robust approach for estimating confidence intervals of cost-effectiveness ratios.
  • This statistical advancement aids in more accurate economic evaluations of medical technologies.
  • Improved confidence interval estimation supports better decision-making in healthcare resource allocation.