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.
翻译:近些年来,在优化临床试验的病人征聘方面取得了显著进展,然而,需要改进病人征聘预测方法,以支持试验地点的选择,并估计试验设计阶段的适当入学时限。 在本文中,我们利用数千个历史临床试验的数据,探索机器学习方法,预测试验期间每月在临床试验地点就诊的病人人数。我们表明,这些方法可以减少与现行行业标准相一致的错误,并提出进一步改进的机会。