Bayesian estimation, simulation and uncertainty analysis: The cost-effectiveness of ganciclovir prophylaxis in liver transplantation

David J. Vanness, W. Ray Kim

Research output: Contribution to journalArticle

12 Scopus citations


This paper demonstrates the usefulness of combining simulation with Bayesian estimation methods in analysis of cost-effectiveness data collected alongside a clinical trial. Specifically, we use Markov Chain Monte Carlo (MCMC) to estimate a system of generalized linear models relating costs and outcomes to a disease process affected by treatment under alternative therapies. The MCMC draws are used as parameters in simulations which yield inference about the relative cost-effectiveness of the novel therapy under a variety of scenarios. Total parametric uncertainty is assessed directly by examining the joint distribution of simulated average incremental cost and effectiveness. The approach allows flexibility in assessing treatment in various counterfactual premises and quantifies the global effect of parametric uncertainty on a decision-maker's confidence in adopting one therapy over the other.

Original languageEnglish (US)
Pages (from-to)551-566
Number of pages16
JournalHealth Economics
Issue number6
StatePublished - Sep 1 2002



  • Bayesian analysis
  • Cost-effectiveness
  • Markov Chain Monte Carlo
  • Uncertainty

ASJC Scopus subject areas

  • Health Policy

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