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James K. Hammitt, Statistical Analysis of Cost-Effectiveness Data by A. R. Willan, and A. H. Briggs, Biometrics, Volume 63, Issue 4, December 2007, Pages 1302–1303, https://doi.org/10.1111/j.1541-0420.2007.00905_8.x
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Confronting ever-increasing health-care costs, how can policy makers determine which medicines, surgeries, screening programs, and other interventions to support? One response, favored by national health authorities in the United Kingdom, Canada, Australia, and elsewhere, is to use cost-effectiveness analysis (CEA) to compare the costs and health benefits of innovations with existing alternatives and to adopt innovations that promise adequate “value for money.”
This book describes the statistical analysis of cost and effectiveness data collected in randomized clinical trials. Patients are randomly assigned to a standard (S) or alternative treatment (T) and the analyst seeks to estimate the between-treatment differences in cost (including medical supplies, physicians' and patients' time) and effectiveness (often measured by gain in survival probability, life expectancy, or quality-adjusted life expectancy). The conventional summary measure is the incremental cost-effectiveness ratio (ICER), defined as the difference in cost divided by the difference in effectiveness. If the additional cost is small compared with the health benefit the treatment is “cost-effective” and presumably should be adopted.
