Forecasting Ontario Oncology Drug Expenditures: A Hybrid Approach to Improving Accuracy

Background The Provincial Drug Reimbursement Program (PDRP) at Cancer Care Ontario (CCO) is responsible for monitoring actual and projected outpatient intravenous cancer drug spending in the province. We developed a hybrid forecasting approach combining automated time-series forecasting with expert-customizable input. Objective Our objectives were to provide a flexible tool in which to incorporate multiple forecasts and to improve the accuracy of the resulting forecast. Methods The approach employed linear and non-linear time-series techniques and a combined hybrid model incorporating both approaches. We developed an interactive tool that incorporated the statistical models and identified the best performing forecast according to standard goodness-of-fit measures. Model selection procedures considered both the amount of historical expenditure data available per drug policy and the individual policy contributions to the overall budget. The user was allowed to customize forecasts based on knowledge of external factors related to policy or price changes and new drugs that come to market Results A comparison of 2016/17 fiscal year expenditures showed that all policies with a significant contribution to the overall budget were forecast with