Statistical issues with trial data and economic modeling for cost-effectiveness evaluation

Boyd, K. and Briggs, A. (2013) Statistical issues with trial data and economic modeling for cost-effectiveness evaluation. In: Tang, W. and Tu, X. (eds.) Modern Clinical Trial Analysis. Series: Applied bioinformatics and biostatistics in cancer research. Springer-Verlag: New York, NY, USA, pp. 149-166. ISBN 9781461443216 (doi:10.1007/978-1-4614-4322-3_6)

Full text not currently available from Enlighten.


Economic evaluations are undertaken to help inform decision making, for example, to help determine which health care interventions to fund given limited health care budgets. A systematic approach is taken to compare alternative interventions in terms of their costs and consequences. Cost–effectiveness analysis (CEA) in particular compares the difference in costs and effects between two or more alternatives, reporting the incremental difference as a cost per unit of outcome, known as an incremental cost–effectiveness ratio (ICER). Alternatively a CEA will report the net monetary benefit of an intervention; however, ICERs are the most popular method for presenting CEA results. The larger the value of the ICER, the more it costs per unit of effectiveness and therefore the less cost-effective the intervention is in comparison to the alternative. The ICER value can be compared against a monetary threshold to help aid decisions regarding appropriate resource allocation.

Item Type:Book Sections
Glasgow Author(s) Enlighten ID:Briggs, Professor Andrew and Boyd, Dr Kathleen
Authors: Boyd, K., and Briggs, A.
College/School:College of Medical Veterinary and Life Sciences > Institute of Health and Wellbeing > Health Economics and Health Technology Assessment
Related URLs:

University Staff: Request a correction | Enlighten Editors: Update this record