Using Data Augmentation to Facilitate Conduct of Phase I-II Clinical Trials With Delayed Outcomes

作     者：Jin, Ick Hoon Liu, Suyu Thall, Peter F. Yuan, Ying 

作者机构：Univ Texas MD Anderson Canc Ctr Dept Biostat Houston TX 77030 USA 

出 版 物：《JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION》 (美国统计学会志)

年 卷 期：2014年第109卷第506期

页      面：525-536页

核心收录：

学科分类：0202[经济学-应用经济学] 02[经济学] 020208[经济学-统计学] 07[理学] 0714[理学-统计学（可授理学、经济学学位）] 

基　　金：NIH/NCI Cancer Center Support Grant [CA016672 36] NIH [R01 CA154591] NIH/NCI [R01 CA 83932] 

主　　题：Bayesian adaptive clinical design Data augmentation algorithm Dose-finding Missing data Phase I-II clinical trial design Piecewise exponential model 

摘      要：A practical impediment in adaptive clinical trials is that outcomes must be observed soon enough to apply decision rules to choose treatments for new patients. For example, if outcomes take up to six weeks to evaluate and the accrual rate is one patient per week, on average three new patients will be accrued while waiting to evaluate the outcomes of the previous three patients. The question is how to treat the new patients. This logistical problem persists throughout the trial. Various ad hoc practical solutions are used, none entirely satisfactory. We focus on this problem in phase I-II clinical trials that use binary toxicity and efficacy, defined in terms of event times, to choose doses adaptively for successive cohorts. We propose a general approach to this problem that treats late-onset outcomes as missing data, uses data augmentation to impute missing outcomes from posterior predictive distributions computed from partial follow-up times and complete outcome data, and applies the design s decision rules using the completed data. We illustrate the method with two cancer trials conducted using a phase I-II design based on efficacy-toxicity trade-offs, including a computer stimulation study. Supplementary materials for this article are available online.