Bayesian estimation of cost-effectiveness ratios from clinical trials

Daniel F. Heitjan, Alan J. Moskowitz, William Whang

Research output: Contribution to journalReview article

78 Scopus citations

Abstract

Estimation of the incremental cost-effectiveness ratio (ICER) is difficult for several reasons: treatments that decrease both cost and effectiveness and treatments that increase both cost and effectiveness can yield identical values of the ICER; the ICER is a discontinuous function of the mean difference in effectiveness; and the standard estimate of the ICER is a ratio. To address these difficulties, we have developed a Bayesian methodology that involves computing posterior probabilities for the four quadrants and separate interval estimates of ICER for the quadrants of interest. We compute these quantities by simulating draws from the posterior distribution of the cost and effectiveness parameters and tabulating the appropriate posterior probabilities and quantiles. We demonstrate the method by re-analysing three previously published clinical trials.

Original languageEnglish (US)
Pages (from-to)191-201
Number of pages11
JournalHealth Economics
Volume8
Issue number3
DOIs
StatePublished - May 1 1999

Keywords

  • Bayesian inference
  • Clinical trials
  • Confidence intervals
  • Cost-effectiveness ratios
  • Net health benefit

ASJC Scopus subject areas

  • Health Policy

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