Methodological hurdles in conducting pharmacoeconomic analyses.
As total healthcare spending increases throughout the world, greater emphasis is being placed on research which demonstrates value for medical interventions, including new and existing pharmaceuticals. Pharmacoeconomic evaluations can assist manufacturers, insurers, clinicians, governmental agencies, policy-makers and consumers to make informed, appropriate decisions about adoption and application of new medications. Because of the far-reaching implications of this research, it is important that researchers adequately address methodological challenges. In this article, we describe the uses of results of pharmacoeconomic trials, identify and discuss various study designs and methods for gathering nonclinical outcome data which may differ significantly from clinical outcome data, and consider the importance and difficulty of incorporating the patients' experience into such trials. Researchers in this area must give specific consideration to sample size estimation for economic outcomes, and carefully handle time issues including duration of observation for complications and discounting of future health and financial consequences. Costs from different perspectives associated with resource use should be assembled in a standard fashion. Use of charges which may not be standardised across geographical or organisational boundaries are discouraged. Inclusion of appropriate health-related quality-of-life (HR-QOL) and utility instruments is increasingly important, but controversy over the best methods still exists. While there is little question of the importance of pharmacoeconomic evaluations, they are expensive. Well designed and executed pharmacoeconomic trials can justify this expense by helping decision-makers understand which treatments have value.
Rizzo
J D
JD
International Bone Marrow Transplant Registry/Autologous Blood and Marrow Transplant Registry, Medical College of Wisconsin, Milwaukee, USA.
Powe
N R
NR
D017850:Economics, Pharmaceutical / Q000706:statistics & numerical data*; D006801:Humans; D012107:Research Design