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Confidence Intervals for Cost - Effectiveness Ratios

Author

Listed:
  • Joseph Gardiner
  • Andrew Hogan
  • Margaret Holmes-Rovner
  • David Rovner
  • Lawrence Griffith
  • Joel Kupersmith

Abstract

The problem of variability in computed cost-effectiveness ratios (CERs) is usually addressed by performing sensitivity analyses to determine the effects on these ratios of plausible ranges of values of input parameters. However, the sampling variation that exists in these estimated parameters can be utilized to obtain confidence intervals for cost-effectiveness ratios. As cost-effectiveness analysis becomes more widely used, new techniques need to be de veloped for establishing when a difference in strategies evaluated is meaningful. A first step is to establish the precision of the CER itself. The authors estimate the precision of a CER in the context of a statistical model in which the primary outcome is survival, with cost and effectiveness defined in terms of the underlying survival distribution (S). Effectiveness (α) is measured by life expectancy, restricted to a finite time horizon and discounted at a fixed rate r, α = √ e- rt S(t)dt. Cumulative cost (β) per patient is regarded as resource utilization an d incurred randomly over time depending on the survival experience of the patient, p = √ e - rt S(t)dC(t), where C(t) is the total potential resources utilized up to time t. Average cost effectiveness (ACE) of a single strategy is β/α, and when comparing two strategies, the CER is Δβ/Δα, the ratio of the incremental cost to the difference in mean survival. Utilizing the sampling distribution of the Kaplan-Meier estimate of S yields standard errors and confidence intervals for ACE and CER. The technique is applied to survival data from 218 previously studied patients to assess 95% confidence intervals for the CER and ACE of the implantable cardioverter defibrillator as compared with electrophysiology-guided therapy. Key words: cost-effectiveness analysis; sensitivity analysis; Kaplan-Meier estimate; av erage cost-effectiveness; cardioverter defibrillator; survival data. (Med Decis Making 1995;15:254-263)

Suggested Citation

  • Joseph Gardiner & Andrew Hogan & Margaret Holmes-Rovner & David Rovner & Lawrence Griffith & Joel Kupersmith, 1995. "Confidence Intervals for Cost - Effectiveness Ratios," Medical Decision Making, , vol. 15(3), pages 254-263, August.
    • Handle: RePEc:sae:medema:v:15:y:1995:i:3:p:254-263
    DOI: 10.1177/0272989X9501500308
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    References listed on IDEAS

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    1. Phelan, Michael J., 1990. "Estimating the transition probabilities from censored Markov renewal processes," Statistics & Probability Letters, Elsevier, vol. 10(1), pages 43-47, June.
    2. Peter Doubilet & Colin B. Begg & Milton C. Weinstein & Peter Braun & Barbara J. McNeil, 1985. "Probabilistic Sensitivity Analysis Using Monte Carlo Simulation," Medical Decision Making, , vol. 5(2), pages 157-177, June.
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    Cited by:

    1. Joseph C. Gardiner & Marianne Huebner & James Jetton & Cathy J. Bradley, 2000. "Power and sample assessments for tests of hypotheses on cost‐effectiveness ratios," Health Economics, John Wiley & Sons, Ltd., vol. 9(3), pages 227-234, April.
    2. Ernst, Richard, 2017. "Theories of Health Care Cost-Effectiveness Analysis," SocArXiv gjbcp, Center for Open Science.
    3. Elena Polverejan & Joseph C. Gardiner & Cathy J. Bradley & Margaret Holmes‐Rovner & David Rovner, 2003. "Estimating mean hospital cost as a function of length of stay and patient characteristics," Health Economics, John Wiley & Sons, Ltd., vol. 12(11), pages 935-947, November.

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