Measurement error bias in pharmaceutical cost‐effectiveness analysis

Ian C. Marschner

Applied Stochastic Models in Business and Industry, 2006, vol. 22, issue 5‐6, 621-630

Abstract: 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. Copyright © 2006 John Wiley & Sons, Ltd.

Date: 2006
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1002/asmb.644

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wly:apsmbi:v:22:y:2006:i:5-6:p:621-630

Access Statistics for this article

More articles in Applied Stochastic Models in Business and Industry from John Wiley & Sons
Bibliographic data for series maintained by Wiley Content Delivery ().