Author:
Mu Rongji,Hu Zongliang,Xu Guoying,Pan Haitao
Abstract
Abstract
Background
With the emergence of molecularly targeted agents and immunotherapies, the landscape of phase I trials in oncology has been changed. Though these new therapeutic agents are very likely induce multiple low- or moderate-grade toxicities instead of DLT, most of the existing phase I trial designs account for the binary toxicity outcomes. Motivated by a pediatric phase I trial of solid tumor with a continuous outcome, we propose an adaptive generalized Bayesian optimal interval design with shrinkage boundaries, gBOINS, which can account for continuous, toxicity grades endpoints and regard the conventional binary endpoint as a special case.
Result
The proposed gBOINS design enjoys convergence properties, e.g., the induced interval shrinks to the toxicity target and the recommended dose converges to the true maximum tolerated dose with increased sample size.
Conclusion
The proposed gBOINS design is transparent and simple to implement. We show that the gBOINS design has the desirable finite property of coherence and large-sample property of consistency. Numerical studies show that the proposed gBOINS design yields good performance and is comparable with or superior to the competing design.
Funder
National Natural Science Foundation of China
China Postdoctoral Science Foundation
American Lebanese Syrian Associated Charities
Guangdong Basic and Applied Basic Research Foundation
Natural Science Foundation of Guangdong Province
Publisher
Springer Science and Business Media LLC
Subject
Health Informatics,Epidemiology
Cited by
1 articles.
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