Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, 130 De Soto Street, Pittsburgh, PA 15213, USA.
Biostatistics. 2022 Oct 14;23(4):1182-1199. doi: 10.1093/biostatistics/kxab020.
Sequential multiple assignment randomized trials (SMARTs) are systematic and efficient media for comparing dynamic treatment regimes (DTRs), where each patient is involved in multiple stages of treatment with the randomization at each stage depending on the patient's previous treatment history and interim outcomes. Generally, patients enrolled in SMARTs are randomized equally to ethically acceptable treatment options regardless of how effective those treatments were during the previous stages, which results in some undesirable consequences in practice, such as low recruitment, less retention, and lower treatment adherence. In this article, we propose a response-adaptive SMART (RA-SMART) design where the allocation probabilities are imbalanced in favor of more promising treatments based on the accumulated information on treatment efficacy from previous patients and stages. The operating characteristics of the RA-SMART design relative to SMART design, including the consistency and efficiency of estimated response rate under each DTR, the power of identifying the optimal DTR, and the number of patients treated with the optimal and the worst DTRs, are assessed through extensive simulation studies. Some practical suggestions are discussed in the conclusion.
序贯多项分配随机试验（SMARTs）是比较动态治疗方案（DTRs）的系统和有效的媒介，其中每个患者都参与多个治疗阶段，每个阶段的随机化取决于患者之前的治疗史和中期结果。通常，无论这些治疗在之前的阶段有多有效，SMARTs 中招募的患者都会被平均随机分配到伦理上可接受的治疗选择，这在实践中会导致一些不理想的后果，例如低招募率、低保留率和低治疗依从性。在本文中，我们提出了一种基于累积信息的自适应 SMART（RA-SMART）设计，该设计根据来自之前患者和阶段的治疗效果的累积信息，对更有希望的治疗方法进行不平衡的分配概率。通过广泛的模拟研究，评估了 RA-SMART 设计相对于 SMART 设计的工作特性，包括在每个 DTR 下估计的响应率的一致性和效率、识别最优 DTR 的能力以及用最优和最差 DTR 治疗的患者数量。在结论中讨论了一些实际建议。
