title: A Bayesian three-parameter logistic model for early- and late-onset DLTs in oncology Phase I studies.
abstract: We introduce a three-parameter logistic model to analyze the dose limiting toxicity (DLT) as a time-to-event endpoint in oncology Phase I trials. In the proposed model, patients are allowed to stay on trial without the constraint of a maximum follow-up time. Our model accommodates late-onset DLT as well as early-onset DLT, by both of which the dose recommendation is informed. A Bayesian approach is used to incorporate prior knowledge of the test treatment into dose recommendation. Simulation examples show that our proposed model has good operating characteristics in assessing the maximum tolerated dose (MTD).
authors: a Sanofi Biostatistics, Sanofi-Aventis US LLC , Cambridge , Massachusetts , USA.
authors: Wei
authors: W
authors: Zheng
authors: a Sanofi Biostatistics, Sanofi-Aventis US LLC , Cambridge , Massachusetts , USA.
authors: Yang
authors: Y
authors: Zhao
authors: a Sanofi Biostatistics, Sanofi-Aventis US LLC , Cambridge , Massachusetts , USA.
authors: Yuefeng
authors: Y
authors: Lu
authors: b Incyte Corporation , Wilmington , Delaware , USA.
authors: Harry
authors: H
authors: Miao
authors: c Department of Computer Science , University of Science and Technology of China , Suzhou , Jiangsu , China.
authors: Hengchang
authors: H
authors: Liu
mesh_terms: D000970:Antineoplastic Agents / Q000008:administration & dosage* / Q000009:adverse effects*; D001499:Bayes Theorem; D017321:Clinical Trials, Phase I as Topic / Q000706:statistics & numerical data*; D003198:Computer Simulation; D004305:Dose-Response Relationship, Drug; D006801:Humans; D016015:Logistic Models; D008495:Medical Oncology / Q000706:statistics & numerical data*
chemical_list: D000970:Antineoplastic Agents
keywords: Bayesian; CRM; Phase I; late toxicity