Uncovering key clinical trial features influencing recruitment

Abstract

Background: 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%.

Methods: 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.

Results: 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 p-value < .05) were found in 15 of the top 30 most important features.

Conclusion: 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.

Keywords: Clinical trials; SHAP; informatics; machine learning; research recruitment.

Figure 1.

Overall study methodology.

Figure 2.

Randomized clinical trials (RCT) selection in research compliance and administration system (RASCAL) and clinicalTrials.gov registries. Each box illustrates the number of RCTs after applying the inclusion and exclusion criteria. AACT = aggregate analysis of clinicaltrials.gov.

Figure 3.

Tree SHapley additive exPlanations (SHAP) summary plot with the Top 30 Most important features associated with RCT recruitment success. The SHAP values have been log scaled. *Features are continuous variables, whereas the others are binary variables. C16: congenital, hereditary, and neonatal diseases and abnormalities. CO4 = neoplasms; C10 = nervous system diseases; C06 = digestive system diseases; C19 = endocrine system diseases; C15 = hemic and lymphatic diseases.