Probabilistic Analysis of Cost-Effectiveness Models: Choosing between Treatment Strategies for Gastroesophageal Reflux Disease

Andrew H. Briggs, Ron Goeree, Gord Blackhouse and Bernie J. O’Brien
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Andrew H. Briggs: Health Economics Research Centre, University of Oxford, Institute of Health Sciences, Headington, Oxford, United Kingdom, Centre for Evaluation of Medicines, St Joseph’s Hospital and Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ontario, Canada
Ron Goeree: Centre for Evaluation of Medicines, St Joseph’s Hospital and Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ontario, Canada
Gord Blackhouse: Centre for Evaluation of Medicines, St Joseph’s Hospital and Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ontario, Canada
Bernie J. O’Brien: Centre for Evaluation of Medicines, St Joseph’s Hospital and Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ontario, Canada

Medical Decision Making, 2002, vol. 22, issue 4, 290-308

Abstract: When choosing between mutually exclusive treatment options, it is common to construct a cost-effectiveness frontier on the cost-effectiveness plane that represents efficient points from among the treatment choices. Treatment options internal to the frontier are considered inefficient and are excluded either by strict dominance or by appealing to the principle of extended dominance. However, when uncertainty is considered, options excluded under the baseline analysis may form part of the cost-effectiveness frontier. By adopting a Bayesian approach, where distributions for model parameters are specified, uncertainty in the decision concerning which treatment option should be implemented is addressed directly. The approach is illustrated using an example from a recently published cost-effectiveness analysis of different possible treatment strategies for gastroesophageal reflux disease. It is argued that probabilistic analyses should be encouraged because they have potential to quantify the strength of evidence in favor of particular treatment choices.

Keywords: economic evaluation; probabilistic sensitivity analysis; Bayesian methods; uncertainty; simulation (search for similar items in EconPapers)
Date: 2002
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Citations: View citations in EconPapers (25)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:22:y:2002:i:4:p:290-308

DOI: 10.1177/0272989X0202200408

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