Power analysis in a SMART design: sample size estimation for determining the best embedded dynamic treatment regime
Power analysis in a SMART design: sample size estimation for determining the best embedded dynamic treatment regime
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DOI：
10.1093/biostatistics/kxy064
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发表时间：
2020-07-01
期刊：
影响因子：
2.1
通讯作者：
Ertefaie, Ashkan
中科院分区：
文献类型：
--
作者：
Artman, William J.;Nahum-Shani, Inbal;Ertefaie, Ashkan
序贯、多分配、随机试验(SMART)设计通过提供用于比较针对单个患者量身定做的两个以上治疗序列的手段，即动态治疗方案(DTR)，在精确医学领域中变得越来越受欢迎。以证据为基础的DDR的构建有望取代普遍存在于患者护理中的临时、一刀切的决定。然而，由于设计中嵌入的DTR之间的关联结构(EDTR)，在调整智能设计的大小方面存在巨大的统计挑战。由于SMARTS的主要目标是构建最佳EDTR，因此研究人员感兴趣的是，根据筛选出低于最佳EDTR一定数量的EDTR的能力来调整SMART的大小，这是现有方法无法完成的。在本文中，我们通过开发一个严格的功率分析框架来填补这一空白，该框架利用了最佳方法的多重比较。我们的方法使用蒙特卡罗模拟来计算在任意SMART中注册的个人数量。我们通过广泛的模拟研究来评估我们的方法。我们通过回顾计算纳曲酮(EXTEND)试验延长治疗有效性的威力来说明我们的方法。实施我们方法的R包可从全面R档案网络下载。
Sequential, multiple assignment, randomized trial (SMART) designs have become increasingly popular in the field of precision medicine by providing a means for comparing more than two sequences of treatments tailored to the individual patient, i.e., dynamic treatment regime (DTR). The construction of evidence-based DTRs promises a replacement to ad hoc one-size-fits-all decisions pervasive in patient care. However, there are substantial statistical challenges in sizing SMART designs due to the correlation structure between the DTRs embedded in the design (EDTR). Since a primary goal of SMARTs is the construction of an optimal EDTR, investigators are interested in sizing SMARTs based on the ability to screen out EDTRs inferior to the optimal EDTR by a given amount which cannot be done using existing methods. In this article, we fill this gap by developing a rigorous power analysis framework that leverages the multiple comparisons with the best methodology. Our method employs Monte Carlo simulation to compute the number of individuals to enroll in an arbitrary SMART. We evaluate our method through extensive simulation studies. We illustrate our method by retrospectively computing the power in the Extending Treatment Effectiveness of Naltrexone (EXTEND) trial. An R package implementing our methodology is available to download from the Comprehensive R Archive Network.