Back to Annual Meeting Page		American Public Health Association 133rd Annual Meeting & Exposition December 10-14, 2005 Philadelphia, PA

Abstract #106538

Jeffrey S. Hoch, PhD, Department of Health Policy, Management and Evaluation, University of Toronto, Centre for Research on Inner City Health, 30 Bond Street, Toronto, ON M5B 1W8, Canada, (416) 864-6060, x2194, jeffrey.hoch@utoronto.ca

A decade ago, the term “stochastic cost-effectiveness” was introduced by researchers interested in “the feasibility and desirability of constructing statistical tests of economic hypotheses and estimation of cost-effectiveness ratios with associated 95% confidence intervals.” This was an important turning point. While economic evaluations of health care were abundant--with cost-effectiveness analyses (CEA) being the most popular type--statistical procedures for assessing the statistical uncertainty surrounding cost-effectiveness estimates were virtually non-existent. Economic evaluation (EE) was often seen as a branch of health economics divorced from mainstream techniques. The statistic of interest in CEA, the incremental cost-effectiveness ratio (ICER) is not amenable to regression-based methods. Through the years, key methodological improvements have had a major impact on the statistical analysis of cost-effectiveness data. Researchers undertaking CEA can now exploit the plethora of established statistical techniques through the use of the net-benefit regression framework (NBRF)--a recently suggested reformulation of the cost-effectiveness problem that avoids the reliance on the ICER and its associated statistical problems. This presentation will, in plain language, review highlights (such as the NBRF) of the first ten years of stochastic CEA. Emphasis will be given to how statistics, as a unified discipline, has been used to meet the challenges of characterizing uncertainty in EE. The presentation will also draw out themes shared by state of the art statistical work in stochastic CEA and the original 1994 paper that had the foresight to consider the “implications of moving economic appraisal away from deterministic models and toward the experimental paradigm.”

Learning Objectives: At the conclusion of the session, the participant (learner) in this session will be able to

• Articulate highlights of the first ten years of statistical cost-effectiveness analysis.
• Describe the growing importance of statistics in economic evaluation of health care.

Keywords: Methodology, Cost-Effectiveness

Presenting author's disclosure statement:

I wish to disclose that I have NO financial interests or other relationship with the manufactures of commercial products, suppliers of commercial services or commercial supporters.