Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.14/106280
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- Title
- Measurement error bias in pharmaceutical cost-effectiveness analysis
- Related
- Applied stochastic models in business and industry, Vol. 22, No. 5-6 (2006), p.621-630
- DOI
- 10.1002/asmb.644
- Publisher
- John Wiley & Sons
- Date
- 2006
- Author/Creator
- Marschner, Ian C
- Description
- Drug development in the pharmaceutical industry is increasingly influenced by measures of cost-effectiveness, such as cost per life-year gained, and some governments make decisions about whether to pay for drugs based on cost-effectiveness considerations. While cost per life-year gained is a key measure of cost-effectiveness, costs associated with the intermediate outcome of improving a biomarker, such as cholesterol level or blood pressure, provide important supplementary information, particularly where mortality data may be limited. In this case, cost-effectiveness can be interpreted as the additional cost per unit time of achieving an additional beneficial biomarker response to treatment. A problem in this context is that biomarker assessment is typically subject to measurement error which leads to bias in assessing the benefit of a drug, and hence in the assessment of its cost-effectiveness. We discuss the adjustment of cost-effectiveness analyses for measurement error and consider the potential magnitude of bias that can arise. Using example calculations in the context of cholesterol-lowering therapy, it is demonstrated that such biases can be significant, leading to costs being overestimated by in excess of 25%. Ignoring measurement error in cost-effectiveness analyses can, therefore, have a substantial effect on the interpretation of such analyses.
- Description
- 10 page(s)
- Subject Keyword
- bias
- Subject Keyword
- cost-effectiveness
- Subject Keyword
- drug development
- Subject Keyword
- measurement error
- Subject Keyword
- pharmaceutical industry
- Resource Type
- journal article
- Organisation
- Macquarie University. Dept. of Statistics
- Identifier
- http://hdl.handle.net/1959.14/106280
- Identifier
- ISSN:1524-1904
- Identifier
- mq-rm-2010000385
- Language
- eng
- Reviewed
