• Zhengjia Chen, Ye Cui, Taofeek K Owonikoko, Zhibo Wang, Zheng Li, Ruiyan Luo, Michael Kutner, Fadlo R Khuri, and Jeanne Kowalski.
• Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322, United States; Biostatistics and Bioinformatics Shared Resource, Winship Cancer Institute, Emory University, Atlanta, GA 30322, United States. Electronic address: zchen38@emory.edu.
• Contemp Clin Trials. 2014 Mar 1; 37 (2): 322-32.

AbstractThe primary purposes of Phase I cancer clinical trials are to determine the maximum tolerated dose (MTD) and the treatment schedule of a new drug. Phase I trials usually involve a small number of patients so that fully utilizing all toxicity information including time to event toxicity data is key to improving the trial efficiency and the accuracy of MTD estimation. Chen et al. proposed a novel normalized equivalent toxicity score (NETS) system to fully utilize multiple toxicities per patient instead of a binary indicator of dose limiting toxicity (DLT). Cheung and Chappell developed the time to toxicity event (TITE) approach to incorporate time to toxicity event data. Escalation with overdose control (EWOC) is an adaptive Bayesian Phase I design which can allow rapid dose escalation while controlling the probability of overdosing patients. In this manuscript, we use EWOC as a framework and integrate it with the NETS system and the TITE approach to develop an advanced Phase I design entitled EWOC-NETS-TITE. We have conducted simulation studies to compare its operating characteristics using selected derived versions of EWOC because EWOC itself has already been extensively compared with common Phase I designs [3]. Simulation results demonstrate that EWOC-NETS-TITE can substantially improve the trial efficiency and accuracy of MTD determination as well as allow patients to be entered in a staggered fashion to significantly shorten trial duration. Moreover, user-friendly software for EWOC-NETS-TITE is under development.Copyright © 2014 Elsevier Inc. All rights reserved.

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