Xin Sun1, Thomas Faunce. 1. Department of Clinical Epidemiology and Biostatistics, McMaster University, 1200 Main Street West, HSC 2C7, L8N 3Z5 Hamilton, ON, Canada. sunx26@mcmaster.ca
Abstract
OBJECTIVE: Decision-analytical modelling is widely used in health-care economic evaluations, especially in situations where evaluators lack clinical trial data, and in circumstances where such evaluations factor into reimbursement pricing decisions. This paper aims to improve the understanding and use of modelling techniques in this context, with particular emphasis on Markov modelling. METHODS: We provide an overview, in this paper, of the principles and methodological details of decision-analytical modelling. We propose a common route for practicing modelling that accommodates any type of decision-analytical modelling techniques. We use the treatment of chronic hepatitis B as an example to indicate the process of development, presentation and analysis of the Markov model, and discuss the strengths, weaknesses and pitfalls of different approaches. CONCLUSIONS: Good practice of modelling requires careful planning, conduct and analysis of the model, and needs input from modellers and users.
OBJECTIVE: Decision-analytical modelling is widely used in health-care economic evaluations, especially in situations where evaluators lack clinical trial data, and in circumstances where such evaluations factor into reimbursement pricing decisions. This paper aims to improve the understanding and use of modelling techniques in this context, with particular emphasis on Markov modelling. METHODS: We provide an overview, in this paper, of the principles and methodological details of decision-analytical modelling. We propose a common route for practicing modelling that accommodates any type of decision-analytical modelling techniques. We use the treatment of chronic hepatitis B as an example to indicate the process of development, presentation and analysis of the Markov model, and discuss the strengths, weaknesses and pitfalls of different approaches. CONCLUSIONS: Good practice of modelling requires careful planning, conduct and analysis of the model, and needs input from modellers and users.
Authors: Paddy Gillespie; Eamon O'Shea; Andrew W Murphy; Susan M Smith; Mary C Byrne; Molly Byrne; Margaret E Cupples Journal: Eur J Health Econ Date: 2011-05-03