Response-adaptive designs for clinical trials: Simultaneous learning from multiple patients

被引:25
作者
Ahuja, Vishal [1 ]
Birge, John R. [1 ]
机构
[1] Univ Chicago, Booth Sch Business, Chicago, IL 60637 USA
基金
美国国家卫生研究院;
关键词
OR in health services; Adaptive clinical trials; Markov decision process; Bayesian learning; Stents; RANDOMIZATION; ASSIGNMENT; POLICIES; THERAPY;
D O I
10.1016/j.ejor.2015.06.077
中图分类号
C93 [管理学];
学科分类号
12 ; 1201 ; 1202 ; 120202 ;
摘要
Clinical trials have traditionally followed a fixed design, in which randomization probabilities of patients to various treatments remains fixed throughout the trial and specified in the protocol. The primary goal of this static design is to learn about the efficacy of treatments. Response-adaptive designs, on the other hand, allow clinicians to use the learning about treatment effectiveness to dynamically adjust randomization probabilities of patients to various treatments as the trial progresses. An ideal adaptive design is one where patients are treated as effectively as possible without sacrificing the potential learning or compromising the integrity of the trial. We propose such a design, termed Jointly Adaptive, that uses forward-looking algorithms to fully exploit learning from multiple patients simultaneously. Compared to the best existing implementable adaptive design that employs a multiarmed bandit framework in a setting where multiple patients arrive sequentially, we show that our proposed design improves health outcomes of patients in the trial by up to 8.6 percent, in expectation, under a set of considered scenarios. Further, we demonstrate our design's effectiveness using data from a recently conducted stent trial, This paper also adds to the general understanding of such models by showing the value and nature of improvements over heuristic solutions for problems with short delays in observing patient outcomes. We do this by showing the relative performance of these schemes for maximum expected patient health and maximum expected learning objectives, and by demonstrating the value of a restricted-optimal-policy approximation in a practical example. (C) 2015 Elsevier B.V. and Association of European Operational Research Societies (EURO) within the International Federation of Operational Research Societies (IFORS). All rights reserved.
引用
收藏
页码:619 / 633
页数:15
相关论文
共 63 条
[1]  
[Anonymous], 2010, AD DES CLIN TRIALS D
[2]  
[Anonymous], 2012, Forbes
[3]  
[Anonymous], 2013, Forbes
[4]  
[Anonymous], 2011, NYTimes
[5]  
[Anonymous], 2010, Guidance for Industry and FDA Staff - Non-Clinical Engineering Tests and Recommended Labeling for Intravascular Stents and Associated Delivery Systems
[6]  
[Anonymous], 2012, STIFLING NEW CURES T
[7]  
AptivSolutions, 2012, TOP BARR AD TRIAL IM
[8]   Optimal Hiring and Retention Policies for Heterogeneous Workers Who Learn [J].
Arlotto, Alessandro ;
Chick, Stephen E. ;
Gans, Noah .
MANAGEMENT SCIENCE, 2014, 60 (01) :110-129
[9]   Finite-time analysis of the multiarmed bandit problem [J].
Auer, P ;
Cesa-Bianchi, N ;
Fischer, P .
MACHINE LEARNING, 2002, 47 (2-3) :235-256
[10]   A partially observed Markov decision process for dynamic pricing [J].
Aviv, Y ;
Pazgal, A .
MANAGEMENT SCIENCE, 2005, 51 (09) :1400-1416