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采用富集设计的序贯多组分配随机试验。

Sequential multiple assignment randomization trials with enrichment design.

作者信息

Liu Ying, Wang Yuanjia, Zeng Donglin

机构信息

Division of Biostatistics, Medical College of Wisconsin, Wisconsin, U.S.A.

Department of Biostatistics, Columbia University, New York, U.S.A.

出版信息

Biometrics. 2017 Jun;73(2):378-390. doi: 10.1111/biom.12576. Epub 2016 Sep 6.

Abstract

Sequential multiple assignment randomization trial (SMART) is a powerful design to study Dynamic Treatment Regimes (DTRs) and allows causal comparisons of DTRs. To handle practical challenges of SMART, we propose a SMART with Enrichment (SMARTER) design, which performs stage-wise enrichment for SMART. SMARTER can improve design efficiency, shorten the recruitment period, and partially reduce trial duration to make SMART more practical with limited time and resource. Specifically, at each subsequent stage of a SMART, we enrich the study sample with new patients who have received previous stages' treatments in a naturalistic fashion without randomization, and only randomize them among the current stage treatment options. One extreme case of the SMARTER is to synthesize separate independent single-stage randomized trials with patients who have received previous stage treatments. We show data from SMARTER allows for unbiased estimation of DTRs as SMART does under certain assumptions. Furthermore, we show analytically that the efficiency gain of the new design over SMART can be significant especially when the dropout rate is high. Lastly, extensive simulation studies are performed to demonstrate performance of SMARTER design, and sample size estimation in a scenario informed by real data from a SMART study is presented.

摘要

序贯多重分配随机化试验(SMART)是一种用于研究动态治疗方案(DTR)的强大设计,能够对DTR进行因果比较。为应对SMART的实际挑战,我们提出了一种带富集的SMART(SMARTER)设计,它对SMART进行分阶段富集。SMARTER可以提高设计效率、缩短招募期并部分缩短试验持续时间,从而在时间和资源有限的情况下使SMART更具实用性。具体而言,在SMART的每个后续阶段,我们以自然的方式(无需随机化)用接受过前期治疗的新患者丰富研究样本,并且仅在当前阶段的治疗方案中对他们进行随机化。SMARTER的一个极端情况是将单独的独立单阶段随机试验与接受过前期治疗的患者进行综合。我们表明,在某些假设下,来自SMARTER的数据能够像SMART那样对DTR进行无偏估计。此外,我们通过分析表明,新设计相对于SMART的效率提升可能会很显著,尤其是在失访率较高时。最后,进行了广泛的模拟研究以证明SMARTER设计的性能,并给出了在一个由SMART研究的真实数据提供信息的场景中的样本量估计。

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