A Machine Learning Approach for Recruitment Prediction in Clinical Trial Design
This work addresses the need for better recruitment prediction to optimize site selection and enrollment timelines in clinical trials, but it appears incremental as it builds on existing data and methods.
The paper tackled the problem of predicting patient recruitment rates per month at clinical trial sites to improve trial design, showing that machine learning methods can reduce error compared to current industry standards.
Significant advancements have been made in recent years to optimize patient recruitment for clinical trials, however, improved methods for patient recruitment prediction are needed to support trial site selection and to estimate appropriate enrollment timelines in the trial design stage. In this paper, using data from thousands of historical clinical trials, we explore machine learning methods to predict the number of patients enrolled per month at a clinical trial site over the course of a trial's enrollment duration. We show that these methods can reduce the error that is observed with current industry standards and propose opportunities for further improvement.