TY - JOUR
T1 - Bayesian adaptive linearization method for phase I drug combination trials with dimension reduction
AU - Pan, Haitao
AU - Cheng, Cheng
AU - Yuan, Ying
N1 - Funding Information:
The authors thank the Editor, Associate Editor, and referees for their constructive comments that substantially improved the article. The research work of H. P. and C. C. was supported by the American Lebanese and Syrian Associated Charities. The research work of Y. Y. was supported in part by grants R01 CA154591, P50 CA098258, and P30 CA016672 from the U.S. National Cancer Institute.
Publisher Copyright:
© 2020 John Wiley & Sons Ltd
PY - 2020/9/1
Y1 - 2020/9/1
N2 - Many phase I drug combination designs have been proposed to find the maximum tolerated combination (MTC). Due to the two-dimension nature of drug combination trials, these designs typically require complicated statistical modeling and estimation, which limit their use in practice. In this article, we propose an easy-to-implement Bayesian phase I combination design, called Bayesian adaptive linearization method (BALM), to simplify the dose finding for drug combination trials. BALM takes the dimension reduction approach. It selects a subset of combinations, through a procedure called linearization, to convert the two-dimensional dose matrix into a string of combinations that are fully ordered in toxicity. As a result, existing single-agent dose-finding methods can be directly used to find the MTC. In case that the selected linear path does not contain the MTC, a dose-insertion procedure is performed to add new doses whose expected toxicity rate is equal to the target toxicity rate. Our simulation studies show that the proposed BALM design performs better than competing, more complicated combination designs.
AB - Many phase I drug combination designs have been proposed to find the maximum tolerated combination (MTC). Due to the two-dimension nature of drug combination trials, these designs typically require complicated statistical modeling and estimation, which limit their use in practice. In this article, we propose an easy-to-implement Bayesian phase I combination design, called Bayesian adaptive linearization method (BALM), to simplify the dose finding for drug combination trials. BALM takes the dimension reduction approach. It selects a subset of combinations, through a procedure called linearization, to convert the two-dimensional dose matrix into a string of combinations that are fully ordered in toxicity. As a result, existing single-agent dose-finding methods can be directly used to find the MTC. In case that the selected linear path does not contain the MTC, a dose-insertion procedure is performed to add new doses whose expected toxicity rate is equal to the target toxicity rate. Our simulation studies show that the proposed BALM design performs better than competing, more complicated combination designs.
KW - Bayesian adaptive design
KW - dose insertion
KW - maximum tolerated combination
KW - phase I combination trials
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U2 - 10.1002/pst.2013
DO - 10.1002/pst.2013
M3 - Article
C2 - 32248647
AN - SCOPUS:85082928528
SN - 1539-1604
VL - 19
SP - 561
EP - 582
JO - Pharmaceutical statistics
JF - Pharmaceutical statistics
IS - 5
ER -