Statistical versus quantitative significance in the socioeconomic evaluation of medicines.
This article examines the twin concepts of the statistical significance and quantitative importance of observed differences in studies comparing medicines in terms of economic parameters such as cost-effectiveness and measures of health-related quality of life (HRQOL).  Central to the design and interpretation of any comparative study, such as a randomised controlled trial, is some prior judgement about the order of magnitude of a difference that would make one switch from one therapy to another.  Starting with current definitions of clinically important differences we argue by analogy that the importance of differences in HRQOL require a shift of focus from the physician to the patient for preferences and judgements concerning what is important to them.  Whether an intervention offers sufficient value for money (cost effectiveness or cost utility) to warrant resources being reallocated to it is a collective decision requiring the input of public preferences about the relative importance of alternative therapies and health benefits.  Ultimately, the importance of the health benefits offered by a new drug is revealed by societal willingness-to-pay.  This may be stated implicitly through committees using cost-effectiveness 'league tables' for decision making, or explicitly by consumer surveys of willingness-to-pay in the context of cost-benefit analysis and stemming from the theoretical foundation of welfare economics.
Department of Clinical Epidemiology and Biostatistics, McMaster University, and Centre for Evaluation of Medicines, St. Joseph's Hospital, Hamilton, Ontario, Canada.
B J
BJ
O'Brien
M F
MF
Drummond
D003362:Cost-Benefit Analysis / Q000706:statistics & numerical data; D004341:Drug Evaluation / Q000379:methods* / Q000639:trends; D005544:Forecasting; D006801:Humans; D011788:Quality of Life; D016032:Randomized Controlled Trials as Topic / Q000379:methods / Q000706:statistics & numerical data; D012959:Socioeconomic Factors; D013269:Stochastic Processes