Sequential multiple assignment randomized trial (SMART) with adaptive randomization for quality improvement in depression treatment program.
Sequential multiple assignment randomized trial (SMART) with adaptive randomization for quality improvement in depression treatment program.
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顺序多重分配随机试验（SMART）具有自适应随机化，以改善抑郁症治疗计划的质量。
DOI：
10.1111/biom.12258
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发表时间：
2015-06
期刊：
影响因子：
1.9
通讯作者：
Davidson KW
中科院分区：
文献类型：
--
作者：
Cheung YK;Chakraborty B;Davidson KW
实施研究是将最先进的治疗方法从临床疗效研究部署到治疗计划中的重要工具，具有了解治疗有效性和提高入组计划患者护理质量的双重目标。本文设计了一种急性冠脉综合征后抑郁患者的动态治疗方案。我们介绍了一种新的自适应随机化方案的序贯多重分配随机试验的DTRs。我们的方法调整随机化概率，以支持在Q学习中使用的具有相对上级Q函数的治疗序列。所提出的方法解决了实施研究的三个主要问题：它允许纳入历史数据或意见，它包括随机学习的目的，它的目的是通过适应整个程序来改善护理。我们演示了如何应用我们的方法来设计一个抑郁症治疗方案，使用以前的研究数据。通过模拟，我们说明了从历史数据的输入是重要的计划的性能所衡量的预期结果的参与者，但也表明，自适应随机化方案是能够补偿不良指定的历史输入，改善病人的结果在一个合理的范围内。仿真结果还证实，所提出的设计允许有效的学习的治疗，减轻灾难的维度。
Implementation study is an important tool for deploying state-of-the-art treatments from clinical efficacy studies into a treatment program, with the dual goals of learning about effectiveness of the treatments and improving the quality of care for patients enrolled into the program. In this article, we deal with the design of a treatment program of dynamic treatment regimens (DTRs) for patients with depression post acute coronary syndrome. We introduce a novel adaptive randomization scheme for a sequential multiple assignment randomized trial of DTRs. Our approach adapts the randomization probabilities to favor treatment sequences having comparatively superior Q-functions used in Q-learning. The proposed approach addresses three main concerns of an implementation study: it allows incorporation of historical data or opinions, it includes randomization for learning purposes, and it aims to improve care via adaptation throughout the program. We demonstrate how to apply our method to design a depression treatment program using data from a previous study. By simulation, we illustrate that the inputs from historical data are important for the program performance measured by the expected outcomes of the enrollees, but also show that the adaptive randomization scheme is able to compensate poorly specified historical inputs by improving patient outcomes within a reasonable horizon. The simulation results also confirm that the proposed design allows efficient learning of the treatments by alleviating the curse of dimensionality.
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DOI：
10.1016/s0197-2456(03)00112-0
发表时间：
2004-02-01
期刊：
CONTROLLED CLINICAL TRIALS
影响因子：
--
作者：
Rush, AJ;Fava, M;Niederehe, G
通讯作者：
Niederehe, G
DOI：
10.1191/1740774s04cn002oa
发表时间：
2004-02-01
期刊：
Clinical trials (London, England)
影响因子：
--
作者：
Lavori, Philip W;Dawson, Ree
通讯作者：
Dawson, Ree
DOI：
10.1080/01621459.2014.926815
发表时间：
2015-06-01
影响因子：
3.7
作者：
Lee J;Thall PF;Ji Y;Müller P
通讯作者：
Müller P
影响因子：
2
作者：
Lee, Shing M.;Cheung, Ying Kuen
通讯作者：
Cheung, Ying Kuen
影响因子：
2
作者：
Thall, Peter F.;Wooten, Leiko H.;Tannir, Nizar M.
通讯作者：
Tannir, Nizar M.