A discrete event model of clinical trial enrollment at eli lilly and company

Clinical trials constitute large, complex, and resource intensive activities for pharmaceutical companies. Accurate prediction of patient enrollment would represent a major step forward in optimizing clinical trials. Currently models for patient enrollment that are both accurate and fast are not available. We present a discrete event model of the patient enrollment process that is accurate and uses relatively small CPU times. This model is now being used on a regular basis to predict the enrollment of patients for large trials with around 13,000 patients and has led to significant reduction in the time it takes to make these predictions.