Trying to do better than average: a commentary on 'statistical inference for cost-effectiveness ratios'

Area of Science:

• Health Economics
• Biostatistics

Background:

• Hypothesis testing in cost-effectiveness analysis (CEA) is crucial for healthcare decision-making.
• Previous work by Laska, Meisner, and Siegel proposed methods based on average cost-effectiveness ratios.

Discussion:

• This paper critiques the use of average cost-effectiveness ratios, highlighting their potential to mislead.
• It argues that average ratios do not accurately represent the uncertainty inherent in cost-effectiveness data.

Key Insights:

• The estimation of confidence intervals around incremental cost-effectiveness ratios (ICERs) is presented as the statistically appropriate approach.
• Focusing on ICER confidence intervals provides a more robust measure of uncertainty and a better basis for decision-making.

Outlook:

• Recommends a shift in methodology towards interval estimation for more reliable CEA.
• Encourages further research into robust statistical methods for evaluating healthcare interventions.