Incremental Cost-Effectiveness Ratio

An Incremental Cost-Effectiveness Ratio is a ratio/difference in cost between two treatment interventions over the difference in their treatment effect.

• See: Cost-Effectiveness Analysis, Quality-Adjusted Life Years, Health Economics, Confidence Interval Estimation.

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References

2015

• (Wikipedia, 2015) ⇒ http://en.wikipedia.org/wiki/incremental_cost-effectiveness_ratio Retrieved:2015-2-28.
  • The incremental cost-effectiveness ratio (ICER) is an equation used commonly in health economics to provide a practical approach to decision making regarding health interventions. It is typically used in cost-effectiveness analysis. ICER is the ratio of the change in costs to incremental benefits of a therapeutic intervention or treatment. The equation for ICER is: ICER = (C1 – C2) / (E1 – E2)
    where C1 and E1 are the cost and effect in the intervention or treatment group and where C2 and E2 are the cost and effect in the control care group. Costs are usually described in monetary units while benefits/effect in health status is measured in terms of quality-adjusted life years (QALYs) gained or lost.

1997

• (Briggs et al., 1997) ⇒ Andrew H. Briggs, David E. Wonderling, and Christopher Z. Mooney. (1997). “Pulling cost‐effectiveness analysis up by its bootstraps: A non‐parametric approach to confidence interval estimation." Health economics, 6(4).
  • ABSTRACT: The statistic of interest in the economic evaluation of health care interventions is the incremental cost effectiveness ratio (ICER), which is defined as the difference in cost between two treatment interventions over the difference in their effect. Where patient-specific data on costs and health outcomes are available, it is natural to attempt to quantify uncertainty in the estimated ICER using confidence intervals. Recent articles have focused on parametric methods for constructing confidence intervals. In this paper, we describe the construction of non-parametric bootstrap confidence intervals. The advantage of such intervals is that they do not depend on parametric assumptions of the sampling distribution of the ICER. We present a detailed description of the non-parametric bootstrap applied to data from a clinical trial, in order to demonstrate the strengths and weaknesses of the approach. By examining the bootstrap confidence limits successively as the number of bootstrap replications increases, we conclude that percentile bootstrap confidence interval methods provide a promising approach to estimating the uncertainty of ICER point estimates. However, successive bootstrap estimates of bias and standard error suggests that these may be unstable; accordingly, we strongly recommend a cautious interpretation of such estimates.

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