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Organizational designs for achieving high treatment trial enrollment:
A fuzzy-set analysis of the community clinical oncology program
Weiner, B. J., Jacobs, S., Minasian, L., & Good, M. J. (2012). Organizational designs for achieving high treatment trial enrollment: A fuzzy-set analysis of the community clinical oncology program. Journal of Oncology Practice, 8(5), 287-291. https://doi.org/10.1200/JOP.2011.000507
Purpose: To examine the organizational design features that were consistently associated in 2010 with high levels of patient enrollment onto National Cancer Institute (NCI) cancer treatment trials among the oncology practices and hospitals participating in the NCI Community Clinical Oncology Program (CCOP).
Methods: Fuzzy-set qualitative comparative analysis was used to identify the recipes (ie, combinations of organizational design features) that CCOPs used to achieve high levels of patient enrollment onto NCI treatment trials in 2010. Four organizational design features were examined: number of open treatment trials with at least one patient enrolled, number of newly diagnosed patients with cancer, number of CCOP-affiliated physicians, and number of CCOP-affiliated hospitals or practices where patient enrollment could occur. Data were obtained from NCI data systems and CCOP grant progress reports.
Results: Two recipes were consistently associated with high levels of patient enrollment onto NCI treatment trials in 2010: having many open treatment trials and many new patients with cancer, and having many open treatment trials and many affiliated hospitals or practices. Together, these recipes accounted for nearly two thirds of CCOP membership in the high-performance set in 2010.
Conclusion: No single organizational design feature, by itself, was consistently associated with high levels of patient enrollment onto NCI treatment trials in 2010. Having a large menu of active treatment trials may be necessary to achieve high-patient enrollment performance, but this is not sufficient unless combined with either large patient volume or many participating sites.