A multiple regression model to explain the cost of brand-drugs

The goal of this study is to examine how four factors - level of competition, therapeutic purpose, age of the drug, and manufacturer play a role in the pricing of brand-name prescription drugs. Understanding how these factors contribute to high drug prices will allow players in this supply chain to negotiate more favorable contract terms. This can be a large benefit to society as this insight can lead to improved efficiency in pricing and increased savings, which can be passed to the consumer. We develop measures for these factors based on publicly available information. Using data on the wholesale prices of prescription drugs, we estimate a model for drug prices based on our measures of competition, therapeutic purpose, age, and manufacturer. Our analysis reveals that these factors are significant in estimating drug prices. We observe that proliferation of dosing levels tends to reduce the prices, therapeutic conditions which are both less common and more life-threatening lead to higher prices, older drugs are less expensive than newer drugs, and some manufacturers set prices systematically different from others even after controlling for other factors. These findings indicate that publicly observable factors can be used to explain drug prices.