1. NCATS. Clinical and Translational Science Awards Program. Opportunities for Advancing Clinical and Translational Research, Chapter: 3, Leadership CTSA Fact Sheet [Internet], 2013 [cited July 2017]. (https://ncats.nih.gov/files/CTSA-factsheet.pdf)
2. United States. President’s Commission for the Study of Ethical Problems in Medicine and Biomedical and Behavioral Research; Implementing human research regulations. United States Code Annotated United States 1982; Title 42 Sect.
4. United States Department of Health and Human Services. Office of Inspector General, Institutional Review Boards: A System in Jeopardy “DRAFT”; A Time for Reform (OEI-01-97-00193) [Internet], 1998 [cited July 2017]. (https://oig.hhs.gov/oei/reports/oei-01-97-00193.pdf)
6. United States Government Accountability Office. Human Subjects Research: HHS Takes Steps to Strengthen Protections, But Concerns Remain (GAO-01-775T) [Internet], 2001 [cited July 2017]. (http://www.gao.gov/products/GAO-01-775T)
7. Institute of Medicine (US). Committee on assessing the system for protecting human research participants. In: Federman DD, Hanna KE, Rodriguez LL, eds. Responsible Research: A Systems Approach to Protecting Human Research Participants. Washington, DC: National Academies Press, 2002, pp. v-vi.
8. Institute of Medicine (US). Forum on Drug Discovery, Development, and Translation. Transforming Clinical Research in the United States: Challenges and Opportunities: Workshop Summary. Washington, DC: National Academies Press, 2010.
9. Institute of Medicine (US). Envisioning a Transformed Clinical Trials Enterprise in the United States: Establishing an Agenda for 2020: Workshop Summary. Washington, DC: National Academies Press, 2012.
10. National Academies of Sciences, Engineering, and Medicine. Optimizing the Nation’s Investment in Academic Research: A New Regulatory Framework for the 21
Century. Washington, DC: National Academies Press; 2016.
11. Caligiuri, M, et al. A multi-site study of performance drivers among Institutional Review Boards. Journal of Clinical and Translational Science 2017; 1: 192–197.
12. Sobolski, GK, Flores, L, Emanuel, EJ. Institutional review board review of multicenter studies. Annals of Internal Medicine 2007; 146: 759.
13. Anderson, EE. A qualitative study of non-affiliated, non-scientist institutional review board members. Accountability in Research 2006; 13: 135–155.
14. Steinbrook, R. Improving protection for research subjects. New England Journal of Medicine 2002; 346: 1425–1430.
15. Rubio, DM, et al. Developing common metrics for the Clinical and Translational Science Awards (CTSAs): lessons learned. Clinical and Translational Science 2015; 8: 451–459.
16. Dilts, DM, Sandler, AB. Invisible barriers to clinical trials: the impact of structural, infrastructural, and procedural barriers to opening oncology clinical trials. Journal of Clinical Oncology 2006; 24: 4545–4552.
17. Dilts, DM, et al. Steps and time to process clinical trials at the Cancer Therapy Evaluation Program. Journal of Clinical Oncology 2009; 27: 1761–1766.
18. Strasser, JE, Cola, PA, Rosenblum, D. Evaluating various areas of process improvement in an effort to improve clinical research: discussions from the 2012 Clinical Translational Science Award (CTSA) Clinical Research Management workshop. Clinical and Translational Science 2013; 6: 317–320.
19. Rosenblum, D, Alving, B. The role of the Clinical and Translational Science Awards program in improving the quality and efficiency of clinical research. Chest 2011; 140: 764–767.
20. Walden, D, et al. INCOSE Systems Engineering Handbook: A Guide for System Life Cycle Processes and Activities, 4th edition. Hoboken, NJ: Wiley, 2015, pp. 7.1.3–7.3.6.
22. Burnham, KP, Anderson, DR. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach, 2nd edition. New York, NY: Springer, 2002.
25. Zarin, D. Newsmaker interview: Debora Zarin. Unseen world of clinical trials emerges from U.S. database. Science 2011; 333: 145.
26. Koenker, R, Hallock, KF. Quantile regression. Journal of Economic Perspectives 2001; 15: 143–156.
27. Freedman, DA. Statistical Models: Theory and Practice. New York, NY: Cambridge University Press, 2009.
28. Nahar, A, et al. Quality Improvement and Cost Reduction Using Statistical Outlier Methods. Lake Tahoe, CA: Computer Design, 2009, pp. 64–69.
29. Hodge, VJ, Austin, J. A survey of outlier detection methodologies. Artificial Intelligence Review 2004; 22: 85–126.
30. Xu, S, et al. An improved methodology for outlier detection in dynamic datasets. Process Systems Engineering, The American Institute of Chemical Engineers 2015; 61: 419–433.
31. Shoenbill, K, et al. IRB process improvements: a machine learning analysis. Journal of Clinical Translational Science 2017; 1: 176–183.