Extended two-stage adaptive designs with three target responses for phase II clinical trials

作     者：Kim, Seongho Wong, Weng Kee 

作者机构：Karmanos Canc Inst Biostat Core Detroit MI USA UCLA Sch Publ Hlth Dept Biostat Los Angeles CA USA 

出 版 物：《STATISTICAL METHODS IN MEDICAL RESEARCH》 (医学研究统计方法)

年 卷 期：2018年第27卷第12期

页      面：3628-3642页

核心收录：

学科分类：0710[理学-生物学] 1204[管理学-公共管理] 02[经济学] 0202[经济学-应用经济学] 020208[经济学-统计学] 1001[医学-基础医学(可授医学、理学学位)] 07[理学] 0714[理学-统计学（可授理学、经济学学位）] 070103[理学-概率论与数理统计] 0701[理学-数学] 

基　　金：NIH Cancer Center Support Grant [P30 CA022453] NSF DMS-1312603 NIH R01GM107639 

主　　题：Adaptive design greedy algorithm particle swarm optimization power sequential design Simon's two-stage design 

摘      要：We develop a nature-inspired stochastic population-based algorithm and call it discrete particle swarm optimization to find extended two-stage adaptive optimal designs that allow three target response rates for the drug in a phase II trial. Our proposed designs include the celebrated Simon s two-stage design and its extension that allows two target response rates to be specified for the drug. We show that discrete particle swarm optimization not only frequently outperforms greedy algorithms, which are currently used to find such designs when there are only a few parameters;it is also capable of solving design problems posed here with more parameters that greedy algorithms cannot solve. In stage 1 of our proposed designs, futility is quickly assessed and if there are sufficient responders to move to stage 2, one tests one of the three target response rates of the drug, subject to various user-specified testing error rates. Our designs are therefore more flexible and interestingly, do not necessarily require larger expected sample size requirements than two-stage adaptive designs. Using a real adaptive trial for melanoma patients, we show our proposed design requires one half fewer subjects than the implemented design in the study.