Using stated preference and revealed preference modeling to evaluate prescribing decisions.

Abstract

The use of stated preference analyses to evaluate choice of health care products has been growing in recent years. This paper shows how revealed preference data can be enriched with stated preference data and highlights the relative advantages of revealed and stated preference data. The techniques were applied to a study of determinants of physicians' prescriptions of alcoholism medications. Analyses were conducted on the relationship between physicians' perceptions of existing alcoholism medication attributes and their prescribing rates of those medications. Analyses were also conducted on physicians' decisions to prescribe hypothetical alcoholism medications with varying attributes such as efficacy, side effects, compliance, mode of action, and price. Finally, analyses were conducted on the combined stated and revealed preference data. Joint estimation suggests that parameters from the revealed and stated preference data are equal, up to scale. Joint analyses highlight how stated preference data can be used to estimate parameters for attributes that are not observed in the marketplace, that do not vary in the marketplace, or that are highly collinear with other attributes in actual markets.