Bayesian estimation of cost-effectiveness: An importance-sampling approach

Daniel F. Heitjan, Huiling Li

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


We describe a method for estimating the cost-effectiveness of a new treatment compared to a standard, using data from a comparative clinical trial. We quantify the clinical effectiveness as a binary variable indicating success or failure. The underlying statistical model assumes that costs are uncensored and follow separate gamma distributions in each of the groups defined by the four possible combinations of treatment arm and effectiveness outcome. The method is subjectivist, in that it represents prior uncertainty about model parameters with a probability distribution, which we update via Bayes's theorem to produce a posterior distribution. We approximate the posterior by importance sampling, a straightforward simulation method. We illustrate the method with an analysis of cost (derived from resource usage data) and effectiveness (measured by one-year survival) in a clinical trial in heart disease. The example demonstrates that the method is practical and provides for a flexible data analysis.

Original languageEnglish (US)
Pages (from-to)191-198
Number of pages8
JournalHealth Economics
Issue number2
StatePublished - Feb 2004


  • Bayesian inference
  • Clinical trials
  • Cost-effectiveness ratios
  • Importance sampling
  • Net health benefit

ASJC Scopus subject areas

  • Health Policy


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