Skip to main content

A Bayesian approach to Markov modelling in cost-effectiveness analyses: application to taxane use in advanced breast cancer

Buy Article:

$51.00 plus tax (Refund Policy)

Abstract:

Summary.

The paper demonstrates how cost-effectiveness decision analysis may be implemented from a Bayesian perspective, using Markov chain Monte Carlo simulation methods for both the synthesis of relevant evidence input into the model and the evaluation of the model itself. The desirable aspects of a Bayesian approach for this type of analysis include the incorporation of full parameter uncertainty, the ability to perform all the analysis, including each meta-analysis, in a single coherent model and the incorporation of expert opinion either directly or regarding the relative credibility of different data sources. The method is described, and its ease of implementation demonstrated, through a practical example to evaluate the cost-effectiveness of using taxanes for the second-line treatment of advanced breast cancer compared with conventional treatment. For completeness, the results from the Markov chain Monte Carlo simulation model are compared and contrasted with those from a classical Monte Carlo simulation model.

Keywords: Bayesian methods; Breast cancer; Cost-effectiveness decision analysis; Elicitation; Markov models; Meta-analysis; Taxanes

Document Type: Research Article

DOI: https://doi.org/10.1111/1467-985X.00283

Affiliations: University of Leicester, UK

Publication date: 2003-10-01

  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more