Evaluation of Efficiency in the Activation and Accrual of Interventional Clinical Trials at Cancer Centers
AuthorTate, Wendy Rose
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PublisherThe University of Arizona.
RightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
EmbargoRelease after 02-May-2018
AbstractBackground: Clinical trials represent a significant percentage of the time and cost to bring a drug through the development process and to Food and Drug Administration approval. Despite how critical these trials are to the drug development process, many studies are underpowered due to low accrual. This translates to valuable questions regarding the safety and effectiveness of new agents being left unanswered, requiring additional time and studies. A call for reform of the industry has been made by stakeholders in the clinical research enterprise; however, national change is slow. Thus sites that conduct clinical research must find methods to increase efficiency within the burdensome system currently in place. Throughout cancer centers adhering to the National Cancer Institute (NCI) Cancer Center Support Grant guidelines, efficiencies have been explored individually; however, there is a gap in knowledge on what factors affect sites system-wide. This dissertation seeks to examine factors that affect clinical trial efficiency in the areas of study activation looking at the outcome of local clinical trial accrual. Methods: Protocol and site-specific clinical trial administration data was collected regarding closed, interventional treatment and supportive care clinical trials from cancer centers adhering to NCI Cancer Center Support Grant guidelines during a five-year time period (2009-2014). Study characteristic analyses and hierarchical regression modeling was used to explore the effect of feasibility committee use and protocol workload on the outcomes of clinical trial accrual and time to activate a clinical trial. Sensitivity analyses were utilized when considering protocol workload to account for studies that had not yet closed to accrual, and thus were not included in this dataset. In addition, protocol- and site-specific variables were used to build regression models used to predict clinical trial accrual. Sensitivity, specificity, and accuracy were compared to the current standard, the institutional disease team. Results: Sixteen centers contributed a total of 5,787 protocols (range 93-697 studies). These studies accrued 49,319 subjects. Of all studies, 1,053 (18%) accrued zero subjects. Disease teams predicted 221% of actual accrual. Seven institutions submitted protocol workload information for 2,133 studies (36.9%) and 14,229 accruals (28.9%). Controlling for effect modifiers and interactions, and adjusting for institution, a statistically significant increase in clinical trial accrual and decrease in activation time was seen with the use of a feasibility committee. Regulatory protocol workload was significantly associated with clinical trial accrual and activation time; however, a single, definitive protocol workload was not identified that both minimized activation time and maximized clinical trial accrual. Protocol workload most often maximized accrual at workloads of between 3.5 and 5.0 protocols per staff member/FTE and minimized activation time at workloads between 1.0 and 1.9 protocols per staff member/FTE. Regression models predicted accrual more accurately than disease teams at all 16 centers, with site-specific models consistently having the best performance (versus an adjusted, hierarchical model). Conclusion: Despite institutional differences in variable association with accrual and activation times, the utilization of a feasibility committee was shown to improve clinical trial accrual as well as decrease activation time. Using systematic methods for examining study activation and accrual efficiencies resulted in the development of models that predicted clinical trial accrual better than the current standard (disease team prediction) at all participating centers. Further research is needed to better define and determine optimal workload. This information and these models may better inform study planning and resource allocation decisions by local stakeholders (administrators and investigators) in the clinical research enterprise.
Degree ProgramGraduate College