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Statistical inference for cost-effectiveness ratios

E M Laska1, M Meisner, C Siegel

  • 1Statistical Sciences and Epidemiology Division, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY 10962, USA. laska@iris.rfmh.org

Health Economics
|May 1, 1997
PubMed
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This study presents statistical methods for comparing cost-effectiveness ratios of treatments. It introduces procedures to identify and eliminate inferior treatments, aiding in clinical decision-making for cost-effectiveness analysis.

Area of Science:

  • Health Economics
  • Biostatistics
  • Clinical Trial Analysis

Background:

  • Cost-effectiveness (C/E) analysis is crucial for healthcare decision-making.
  • Comparing incremental cost-effectiveness (delta C/ delta E) ratios requires robust statistical methods.
  • Existing methods for C/E ratio inference may have limitations in diverse clinical trial settings.

Purpose of the Study:

  • To develop and present statistical inference methods for individual and incremental cost-effectiveness ratios.
  • To introduce a statistical procedure for testing treatment dominance and eliminating inferior options.
  • To compare the efficacy of Bonferroni and Fieller-based confidence intervals for C/E ratio analysis.

Main Methods:

  • Statistical inference for cost-effectiveness (C/E) and incremental cost-effectiveness (delta C/ delta E) ratios.

Related Experiment Videos

  • Development of a statistical procedure to test for treatment dominance (admissibility).
  • Generalization of one-sided Bonferroni confidence intervals to two-sided intervals and comparison with Fieller-based intervals.

    • Main Results:

    • A lemma relating the relative magnitude of C/E ratios to the delta C/ delta E ratio was established.
    • The proposed dominance test can effectively eliminate inferior treatments.
    • Fieller-based confidence intervals were found to be shorter than Bonferroni intervals when assumptions are met, though Bonferroni intervals offer broader applicability.

    Conclusions:

    • The study provides valuable statistical tools for cost-effectiveness analysis in clinical trials.
    • The methods facilitate the identification of dominant and inferior treatments, supporting evidence-based healthcare choices.
    • Both Bonferroni and Fieller methods offer viable approaches for C/E ratio inference, with selection dependent on data characteristics and assumptions.