A Markov decision process for response-adaptive randomization in clinical trials

被引:0
|
作者
Merrell, David [1 ,4 ]
Chandereng, Thevaa [2 ]
Park, Yeonhee [3 ]
机构
[1] Univ Wisconsin Madison, Dept Comp Sci, Madison, WI USA
[2] Columbia Univ, Dept Biostat, New York, NY USA
[3] Univ Wisconsin Madison, Dept Biostat & Med Informat, Madison, WI USA
[4] 1210 W Dayton St, Madison, WI 53703 USA
基金
美国国家卫生研究院;
关键词
Clinical trials; Adaptive trials; Response -adaptive randomization; Reinforcement learning; SEQUENTIAL ALLOCATION; DESIGN;
D O I
10.1016/j.csda.2022.107599
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
In clinical trials, response-adaptive randomization (RAR) has the appealing ability to assign more subjects to better-performing treatments based on interim results. Traditional RAR strategies alter the randomization ratio on a patient-by-patient basis. An alternate approach is blocked RAR, which groups patients together in blocks and recomputes the randomization ratio in a block-wise fashion; past works show that this provides robustness against time-trend bias. However, blocked RAR poses additional questions: how many blocks should there be, and how many patients should each block contain? TRIALMDP is an algorithm that designs two-armed blocked RAR clinical trials. It differs from other trial design approaches in that it optimizes the size and number of blocks in addition to their treatment allocations. More precisely, the algorithm yields an adaptive policy that chooses the size and allocation ratio of the next block, based on results seen up to that point in the trial. TRIALMDP is related to past works that compute optimal trial designs via dynamic programming. The algorithm maximizes a utility function balancing (i) statistical power, (ii) patient outcomes, and (iii) the number of blocks. It attains significant improvements in utility over a suite of baseline designs, and gives useful control over the tradeoff between statistical power and patient outcomes. It is well suited for small trials that assign high cost to patient failures.(c) 2022 Elsevier B.V. All rights reserved.
引用
收藏
页数:11
相关论文
共 50 条
  • [1] Rejoinder: Response-Adaptive Randomization in Clinical Trials
    Robertson, David S.
    Lee, Kim May
    Lopez-Kolkovska, Boryana C.
    Villar, Sofia S.
    [J]. STATISTICAL SCIENCE, 2023, 38 (02) : 233 - 239
  • [2] Response-adaptive randomization for clinical trials with continuous outcomes
    Zhang, Lanju
    Rosenberger, William F.
    [J]. BIOMETRICS, 2006, 62 (02) : 562 - 569
  • [3] Response-adaptive randomization for clinical trials with adjustment for covariate imbalance
    Ning, Jing
    Huang, Xuelin
    [J]. STATISTICS IN MEDICINE, 2010, 29 (17) : 1761 - 1768
  • [4] Block response-adaptive randomization in clinical trials with binary endpoints
    Magirr, Dominic
    [J]. PHARMACEUTICAL STATISTICS, 2011, 10 (04) : 341 - 346
  • [5] Optimized Response-Adaptive Clinical Trials: Sequential Treatment Allocation Based on Markov Decision Problems
    Smith, James
    [J]. INTERFACES, 2015, 45 (04) : 367 - 368
  • [6] Response-adaptive randomization for survival trials: the parametric approach
    Zhang, Lanju
    Rosenberger, William F.
    [J]. JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS, 2007, 56 : 153 - 165
  • [7] Comment: Response-Adaptive Randomization in Clinical Trials: From Myths to Practical Considerations
    Duan, Yunshan
    Mueller, Peter
    Ji, Yuan
    [J]. STATISTICAL SCIENCE, 2023, 38 (02) : 212 - 215
  • [8] Response-Adaptive Randomization in Clinical Trials: From Myths to Practical Considerations1
    Robertson, David S.
    Lee, Kim May
    Lopez-Kolkovska, Boryana C.
    Villar, Sofia S.
    [J]. STATISTICAL SCIENCE, 2023, 38 (02) : 185 - 208
  • [9] Robustness of response-adaptive randomization
    Ye, Xiaoqing
    Hu, Feifang
    Ma, Wei
    [J]. BIOMETRICS, 2024, 80 (02)
  • [10] A surrogate-primary replacement algorithm for response-adaptive randomization in stroke clinical trials
    Nowacki, Amy S.
    Zhao, Wenle
    Palesch, Yuko Y.
    [J]. STATISTICAL METHODS IN MEDICAL RESEARCH, 2017, 26 (03) : 1078 - 1092