In a sequential multiple-assignment randomized trial (SMART), a sequence of treatments is given to a patient over multiple stages. In each stage, randomization may be done to allocate patients to different treatment groups. Even though SMART designs are getting popular among clinical researchers, the methodologies for adaptive randomization at different stages of a SMART are few and not sophisticated enough to handle the complexity of optimal allocation of treatments at every stage of a trial. Lack of optimal allocation methodologies can raise serious concerns about SMART designs from an ethical point of view. In this work, we develop an optimal adaptive allocation procedure to minimize the expected number of treatment failures for a SMART with a binary primary outcome. Issues related to optimal adaptive allocations are explored theoretically with supporting simulations. The applicability of the proposed methodology is demonstrated using a recently conducted SMART study named M-Bridge for developing universal and resource-efficient dynamic treatment regimes (DTRs) for incoming first-year college students as a bridge to desirable treatments to address alcohol-related risks.
Sample size considerations for comparing dynamic treatment regimens in a sequential multiple-assignment randomized trial with a continuous longitudinal outcome
Sample size estimation for comparing dynamic treatment regimens in a SMART: a Monte Carlo-based approach and case study with longitudinal overdispersed count outcomes
An Online Meta-Level Adaptive Design Framework with Targeted Learning Inference: Applications to Evaluating and Utilizing Surrogate Outcomes in Adaptive Designs
Division of Biostatistics, University of California, Berkeley（加州大学伯克利分校生物统计系）；Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center（弗雷德·哈钦森癌症中心疫苗与传染病部）；Department of Biostatistics, University of Washington（华盛顿大学生物统计系）
Data-Adaptive and Model-Robust Covariate Adjustment for Time-to-Event Outcomes in Stratified Randomized Trials
Cornell Tech, Cornell University（康奈尔科技校区，康奈尔大学）；Department of Biostatistics, Department of Population Health, New York University School of Medicine（纽约大学医学院生物统计学系、人口健康系）
Robust Hybrid Learning for Estimating Personalized Dynamic Treatment Regimens
动态治疗方案（DTR）是在每个阶段根据可能随时间变化的患者特征和前一阶段观察到的中间结果定制的顺序决策规则。许多疾病和病症的复杂性、患者异质性和慢性性要求学习最佳DTR，其随着时间的推移最好地动态调整治疗以适应每个个体的反应。个性化数据的激增（例如，遗传和成像数据）提供了深度定制的机会以及对统计方法学的新挑战。在这项工作中，我们提出了一种强大的混合方法，称为增强多阶段结果加权学习（AMOL），将结果加权学习和Q学习相结合，以确定最佳DTR序列多分配随机试验（SMART）。我们推广了结果加权学习（O-learning; Zhao et al. 2012），以允许负面结果;我们提出了减少O学习中权重变化的方法，以实现数值稳定性和更高的效率;最后，对于多阶段SMART研究，我们将双重鲁棒增强引入到基于机器学习的O学习中，通过在每个阶段从基于回归模型的Q学习中提取信息来提高效率。即使Q学习模型被错误指定，所提出的AMOL仍然有效。我们建立了AMOL的理论性质，包括估计规则的一致性和收敛到最优值函数的速度。AMOL相对于现有方法的比较优势在广泛的模拟研究和两个SMART数据集的应用中得到了证明：注意力缺陷和多动障碍（ADHD）的两阶段试验和重度抑郁症（MDD）的STAR*D试验。
Marginal Structural Models for Electricity Demand under Treatment-Confounder Feedback: A Continuous-Treatment Outcome-Adaptive and Fused LASSO Approach
A comparative study of augmented inverse propensity weighted estimators using outcome-oriented covariate selection via penalization with outcome-adaptive lasso
Data-adaptive gene and pathway-based tests forrare-variant associations with survival outcomes
Medical College of Wisconsin（威斯康星医学院）；German Cancer Research Center (DKFZ)（德国癌症研究中心）；University of Leicester（莱斯特大学）；Instituto de Investigación Sanitaria de Santiago de Compostela（圣地亚哥-德孔波斯特拉卫生研究所）；Fundación Pública Galega de Medicina Xenómica（加利西亚公共基因组医学基金会）；Biomedical Network on Rare Diseases (CIBERER)（罕见病生物医学网络）；Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）；Cross Cancer Institute, University of Alberta（阿尔伯塔大学交叉癌症研究所）；University of Manchester（曼彻斯特大学）；Ghent University / Ghent University Hospital（根特大学/根特大学医院）
