Using Proportional Hazard Models to Predict Price Changes of Oncology Drugs in the United States
Authors: Wang B, Tsang K
Conference: International Society for Pharmacoeconomics and Outcomes Research
Location: Berlin, Germany
Objectives: Predicting the price change percentages and timings of drugs is important to policy makers, pharmaceutical companies, and even investment firms. As a case study, we utilize a set of oncology drugs in the US and apply hazard models to perform the predictions.
Methods: Using data from First DataBank (2003-2012), we have a panel of ex-factory drug prices for drug packs for 18 brand names. We convert the data into survival time data by calculating the time duration between each price change, which results in a total of 200 price increases and 38 censored outcomes. In our hazard models, we include the FDA approval date for each drug as an exogenous variable to answer the following questions: 1) how is the percentage change in price related to the time since the last price change and the time since FDA approval, and 2) does the probability of a price change depend on the time since FDA approval? We use Cox Proportional Hazard models for prediction.
Results: The average “event” is a price increase of 5%. For percentage changes in price, we find that for each additional month of constant price, the subsequent price increase drops by 0.08%. For a second order effect, we find that the negative effect of time since last price change is decreasing. Also, time since FDA approval has a large and significant effect: for each additional month since FDA approval, the subsequent price increase drops by 0.03%. The average duration between events is 8.8 months. The Cox model shows that for each additional month since FDA approval, the “risk” of a price increase increases by 0.7%. Similarly, there is a second order effect showing this risk diminishing over time.
Conclusions: Hazard models can predict the timing and percentage of price changes in oncology drugs in the United States.