Idnay Betina, Fang Yilu, Butler Alex, Moran Joyce, Li Ziran, Lee Junghwan, Ta Casey, Liu Cong, Yuan Chi, Chen Huanyao, Stanley Edward, Hripcsak George, Larson Elaine, Marder Karen, Chung Wendy, Ruotolo Brenda, Weng Chunhua
Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA.
Department of Neurology, Columbia University Irving Medical Center, NY Research, New York, NY, USA.
J Clin Transl Sci. 2023 Sep 4;7(1):e199. doi: 10.1017/cts.2023.623. eCollection 2023.
Randomized clinical trials (RCT) are the foundation for medical advances, but participant recruitment remains a persistent barrier to their success. This retrospective data analysis aims to (1) identify clinical trial features associated with successful participant recruitment measured by accrual percentage and (2) compare the characteristics of the RCTs by assessing the most and least successful recruitment, which are indicated by varying thresholds of accrual percentage such as ≥ 90% vs ≤ 10%, ≥ 80% vs ≤ 20%, and ≥ 70% vs ≤ 30%.
Data from the internal research registry at Columbia University Irving Medical Center and Aggregated Analysis of ClinicalTrials.gov were collected for 393 randomized interventional treatment studies closed to further enrollment. We compared two regularized linear regression and six tree-based machine learning models for accrual percentage (i.e., reported accrual to date divided by the target accrual) prediction. The outperforming model and Tree SHapley Additive exPlanations were used for feature importance analysis for participant recruitment. The identified features were compared between the two subgroups.
CatBoost regressor outperformed the others. Key features positively associated with recruitment success, as measured by accrual percentage, include government funding and compensation. Meanwhile, cancer research and non-conventional recruitment methods (e.g., websites) are negatively associated with recruitment success. Statistically significant subgroup differences (corrected -value < .05) were found in 15 of the top 30 most important features.
This multi-source retrospective study highlighted key features influencing RCT participant recruitment, offering actionable steps for improvement, including flexible recruitment infrastructure and appropriate participant compensation.
随机临床试验（RCT）是医学进步的基础，但受试者招募仍然是其成功开展的一个长期障碍。这项回顾性数据分析旨在：（1）确定与以入组百分比衡量的成功受试者招募相关的临床试验特征；（2）通过评估入组百分比的不同阈值（如≥90%对≤10%、≥80%对≤20%、≥70%对≤30%）所表明的最成功和最不成功招募情况，比较随机对照试验的特征。
收集了哥伦比亚大学欧文医学中心内部研究登记处以及ClinicalTrials.gov汇总分析中393项已结束进一步入组的随机介入治疗研究的数据。我们比较了两种正则化线性回归模型和六种基于树的机器学习模型对入组百分比（即截至目前报告的入组人数除以目标入组人数）的预测情况。使用表现最佳的模型和树状Shapley加法解释进行受试者招募的特征重要性分析。在两个亚组之间比较所确定的特征。
CatBoost回归模型的表现优于其他模型。以入组百分比衡量，与招募成功呈正相关的关键特征包括政府资助和补偿。同时，癌症研究和非常规招募方法（如网站）与招募成功呈负相关。在30个最重要特征中的15个特征上发现了具有统计学意义的亚组差异（校正P值<0.05）。
这项多源回顾性研究突出了影响随机对照试验受试者招募的关键特征，提供了可采取的改进措施，包括灵活的招募基础设施和适当的受试者补偿。
