Yuan Ji1, Yisheng Li, B Nebiyou Bekele. 1. Department of Bioinformatics and Computational Biology, University of Texas, M. D. Anderson Cancer Center, Houston, TX, USA. yuanji@mdanderson.org
Abstract
BACKGROUND: Most phase I clinical trials conducted at the M. D. Anderson Cancer Center use the algorithmic 3 + 3 design, despite the availability of more advanced model-based designs such as the continual reassessment method. PURPOSE: Through simple statistical modeling and computing, we develop a dose-finding design that can be easily understood and implemented by non-statisticians. METHODS: We propose a beta/binomial Bayesian model and a probabilistic up-and-down rule that allow all possible dose-assignment actions to be tabulated in a spreadsheet. We have developed an Excel macro (available at http://odin.mdacc. tmc.edu/~yuanj) that generates trial monitoring tables, which contain the dose-assignment actions corresponding to various toxicity outcomes. RESULTS: The new design outperforms the 3 + 3 design and performs comparably to other model-based methods in the literature. LIMITATIONS: The proposed method assumes that the observed toxicity is a binary variable and that toxicity increases with dose level. CONCLUSION: The new dose-finding design enables physicians to readily determine dose assignments for new patients by referencing a trial monitoring table.
BACKGROUND: Most phase I clinical trials conducted at the M. D. Anderson Cancer Center use the algorithmic 3 + 3 design, despite the availability of more advanced model-based designs such as the continual reassessment method. PURPOSE: Through simple statistical modeling and computing, we develop a dose-finding design that can be easily understood and implemented by non-statisticians. METHODS: We propose a beta/binomial Bayesian model and a probabilistic up-and-down rule that allow all possible dose-assignment actions to be tabulated in a spreadsheet. We have developed an Excel macro (available at http://odin.mdacc. tmc.edu/~yuanj) that generates trial monitoring tables, which contain the dose-assignment actions corresponding to various toxicity outcomes. RESULTS: The new design outperforms the 3 + 3 design and performs comparably to other model-based methods in the literature. LIMITATIONS: The proposed method assumes that the observed toxicity is a binary variable and that toxicity increases with dose level. CONCLUSION: The new dose-finding design enables physicians to readily determine dose assignments for new patients by referencing a trial monitoring table.
Authors: Suzanne Leijen; Robin M J M van Geel; Anna C Pavlick; Raoul Tibes; Lee Rosen; Albiruni R Abdul Razak; Raymond Lam; Tim Demuth; Shelonitda Rose; Mark A Lee; Tomoko Freshwater; Stuart Shumway; Li Wen Liang; Amit M Oza; Jan H M Schellens; Geoffrey I Shapiro Journal: J Clin Oncol Date: 2016-10-31 Impact factor: 44.544
Authors: Andrew J Wagner; Udai Banerji; Amit Mahipal; Neeta Somaiah; Heather Hirsch; Craig Fancourt; Amy O Johnson-Levonas; Raymond Lam; Amy K Meister; Giuseppe Russo; Clayton D Knox; Shelonitda Rose; David S Hong Journal: J Clin Oncol Date: 2017-02-27 Impact factor: 44.544
Authors: Yuan Ji; Lei Feng; Ping Liu; Elizabeth J Shpall; Partow Kebriaei; Richard Champlin; Donald Berry; Laurence J N Cooper Journal: J Biopharm Stat Date: 2012 Impact factor: 1.051
Authors: Daniel R Gomez; Michael Gillin; Zhongxing Liao; Caimiao Wei; Steven H Lin; Cameron Swanick; Tina Alvarado; Ritsuko Komaki; James D Cox; Joe Y Chang Journal: Int J Radiat Oncol Biol Phys Date: 2013-05-18 Impact factor: 7.038