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Related Experiment Videos

Estimation, power and sample size calculations for stochastic cost and effectiveness analysis.

S D Walter1, Amiram Gafni, Stephen Birch

  • 1Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ontario, Canada. walter@mcmaster.ca

Pharmacoeconomics
|May 26, 2007
PubMed
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This study refines the rectangle method for healthcare economic evaluations. A modified approach corrects for multiple comparisons, providing more accurate confidence intervals for the incremental cost-effectiveness ratio (ICER) and improving sample size estimations.

Area of Science:

  • Health Economics
  • Decision Analysis
  • Statistical Modeling

Background:

  • Economic evaluations of healthcare programs face uncertainty.
  • The rectangle method uses separate confidence intervals for costs and effects to define a confidence region in the cost-effectiveness plane.
  • This method implicitly defines a confidence interval for the incremental cost-effectiveness ratio (ICER) and is used for sample size and power calculations.

Purpose of the Study:

  • To outline the existing rectangle method for cost-effectiveness analysis.
  • To propose a modification to the rectangle method that accounts for separate inferences in cost and effectiveness domains.
  • To correct for multiple statistical comparisons, which can lead to an overly small confidence rectangle and underestimated sample sizes.

Main Methods:

Related Experiment Videos

  • Review and outline the standard rectangle method for calculating confidence intervals in cost-effectiveness analysis.
  • Develop a modified rectangle method that incorporates corrections for multiple statistical comparisons.
  • Analyze the impact of the modification on the confidence rectangle, ICER confidence interval, and sample size estimations.
  • Investigate the arbitrariness in the placement of the confidence rectangle around null values.

Main Results:

  • The standard rectangle method can result in an overly small confidence rectangle and a too-narrow confidence interval for the ICER.
  • Sample sizes and study power are underestimated when using the standard rectangle method.
  • The proposed modification corrects for multiple comparisons, leading to more accurate confidence intervals and sample size estimates.
  • The placement of the confidence rectangle around null values can be arbitrary, affecting sample size and power calculations.

Conclusions:

  • The modified rectangle method provides a more accurate approach to estimating confidence intervals for ICERs in healthcare economic evaluations.
  • Correcting for multiple statistical comparisons is crucial for reliable sample size and power calculations.
  • Clearly defining the analytic goal (differential costs, effects, or ICER) is important when applying the rectangle method.
  • The choice of null values can influence sample size and power, highlighting the need for careful consideration in study design.