Enrollment Success, Factors, and Prediction Models in Cancer Trials (2008-2019)

JCO Oncol Pract. 2023 Nov;19(11):1058-1068. doi: 10.1200/OP.23.00147. Epub 2023 Oct 4.

Abstract

Purpose: To investigate the enrollment success rate of cancer clinical trials conducted in 2008-2019 and various factors lowering the enrollment success rate.

Methods: This is a cross-sectional study with clinical trial information from the largest registration database ClinicalTrials.gov. Enrollment success rate was defined as actual enrollment greater or equal to 85% of the estimated enrollment goal. The association between trial characteristics and enrollment success was evaluated using the multivariable logistic regression.

Results: A total of 4,004 trials in breast, lung, and colorectal cancers were included. The overall enrollment success rate was 49.1%. Compared with 2008-2010 (51.5%) and 2011-2013 (52.1%), the enrollment success rate is lower in 2014-2016 (46.5%) and 2017-2019 (36.4%). Regression analyses found trial activation year, phase I, phase I/phase II, and phase II (v phase III), sponsor agency of government (v industry), not requiring healthy volunteers, and estimated enrollment of 50-100, 100-200, 200, and >500 (v 0-50) were associated with a lower enrollment success rate (P < .05). However, trials with placebo comparator, ≥5 locations (v 1 location), and a higher number of secondary end points (eg, ≥5 v 0) were associated with a higher enrollment success rate (P < .05). The AUC for prediction of the final logistic regression models for all trials and specific trial groups ranged from 0.69 to 0.76.

Conclusion: This large-scale study supports a lower enrollment success rate over years in cancer clinical trials. Identified factors for enrollment success can be used to develop and improve recruitment strategies for future cancer trials.

MeSH terms

  • Cross-Sectional Studies
  • Humans
  • Logistic Models
  • Neoplasms* / epidemiology
  • Neoplasms* / therapy
  • Patient Selection