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Related Experiment Videos

Estimating confidence intervals for cost-effectiveness ratios: an example from a randomized trial

M A Chaudhary1, S C Stearns

  • 1Department of Biostatistics, University of North Carolina at Chapel Hill, NC 27599-7400, USA.

Statistics in Medicine
|July 15, 1996
PubMed
Summary
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Calculating cost-effectiveness ratios accurately is crucial for healthcare programs. This study compares methods for confidence intervals, finding that accounting for skewed data is essential for reliable results.

Area of Science:

  • Health Economics
  • Biostatistics
  • Public Health

Background:

  • Cost-effectiveness ratios (CERs) are vital for healthcare program evaluation.
  • Traditionally, CERs are presented as point estimates without confidence intervals (CIs).
  • Recent literature emphasizes the necessity of CIs for robust CER analysis.

Purpose of the Study:

  • To compare different statistical methods for calculating CIs for CERs.
  • To evaluate the performance of these methods in the context of a randomized intervention for the Early and Periodic Screening, Diagnosis and Treatment (EPSDT) program.

Main Methods:

  • Comparison of parametric and non-parametric bootstrap methods for CI estimation.
  • Evaluation of methods considering skewness in ratio estimators versus those assuming normal distribution.

Related Experiment Videos

  • Analysis of cost-effectiveness data from a randomized trial.

    • Main Results:

    • Methods accounting for skewness in the ratio estimator are preferable to those assuming normality.
    • Non-parametric bootstrap methods yield CIs comparable to parametric methods adjusting for skewness.
    • Modest sample sizes in trials can lead to wide CIs for CERs, exceeding deterministic sensitivity analysis bounds.

    Conclusions:

    • Accurate CI calculation for CERs is critical in healthcare economic evaluations.
    • Skewness-adjusted and non-parametric bootstrap methods offer reliable approaches for CI estimation.
    • The wide CIs observed highlight the need for careful interpretation of CERs, especially with limited sample sizes.