Analyzing Sequential, Multiple Assignment, Randomized Trials in the Presence of Partial Compliance

项目摘要： 许多物质使用障碍的周期性和异质性突出了需要 调整治疗的类型或剂量，以适应特定的和不断变化的需求， 个体这项建议的动机是扩大纳洛酮的治疗效果 和适应性治疗的依从性试验，序贯多重分配 随机试验（SMART），旨在寻找（个性化）酒精补救治疗 或/和可卡因依赖患者。这些试验的主要挑战之一是高感染率。 不遵守指定的治疗。这一特点使得它几乎不可能 研究人员充分探索使用高质量治疗策略的可能性， 数据我们的总体目标是通过发展和 然后对数据应用新的统计方法。 SMART试验是一项多阶段试验，可以为适应性治疗策略的设计提供信息 (ATS)它正式制定了个性化的治疗计划， 这取决于患者的既往病史和治疗史。最佳ATS是 最大化感兴趣的特定健康结果。分析SMART数据的现有方法 仅限于意向治疗（ITT）分析。即每个阶段的治疗效果是 根据该阶段个体随机分配至的治疗组估计 无论患者是否遵守指定的治疗。一个主要问题 实验操作和结果之间的关系 因治疗不依从而混淆。 我们开发的方法可用于调整数据分析中的违规行为 收集在智能。具体来说，我们扩展了主层框架和贝叶斯 多阶段随机试验设置的连接，并提出新的程序，估计 不同ATS下的平均结果。我们还提出了一种新的贝叶斯机器学习 可用于构建深度定制的方法（即，个体化）治疗策略 考虑到患者的人口统计学因素，心理健康和酒精的测量 使用，强迫性饮酒和酒精渴望量表，身体综合评分。 最后，我们将开发易于使用的，公开可用的开源软件，利用R 和Python语言来实现我们的方法。这将提供一个可扩展的平台 这将有助于研究人员为患有自闭症的患者开发新的最佳ATS 酗酒和其他物质使用障碍。

Project Summary: The cyclical and heterogeneous nature of many substance use disorders highlights the need to adapt the type or the dose of treatment to accommodate the specific and changing needs of individuals. This proposal is motivated by the Extending Treatment Effectiveness of Naltrexone and the Adaptive Treatment for Cocaine Dependence trials, sequential multiple assignment randomized trials (SMART) designed to find a (personalized) rescue treatment for alcohol or/and cocaine dependent patients. One of the main challenges in these trials is the high rate of noncompliance to the assigned treatments. This feature has made it virtually impossible for investigators to fully explore the possibility of building high quality treatment strategies using the data. Our overarching aim is to address this particular challenge through developing and subsequently applying new statistical methods to the data. A SMART trial is a multi-stage trial that can inform the design of an adaptive treatment strategy (ATS) which formalizes an individualized treatment plan and where current treatment strategy can depend on a patient's past medical and treatment history. An optimal ATS is one that maximizes a specified health outcome of interest. Existing methods in analyzing SMART data are limited to intention-to-treat (ITT) analyses. That is the treatment effect at each stage is estimated based on the treatment group to which an individual was randomized at that stage regardless of whether the individual complied with their assigned treatment. One major concern is that the relationship between the experimental manipulation and the outcome may be confounded by treatment noncompliance. We develop methodologies that can be used to adjust for noncompliance in analyzing data collected in SMARTs. Specifically, we extend the principal strata framework and Bayesian Copulas to multi-stage randomized trials setting and propose novel procedures that estimate the mean outcome under different ATSs. We also propose a novel Bayesian machine learning approach that can be used to construct deeply tailored (i.e., individualized) treatment strategies that take into account patients' demographic factors, measures of mental health and alcohol use, obsessive-compulsive drinking and alcohol craving scales, physical composite scores. Finally, we will develop easy-to-use, publicly available open-source software leveraging the R and Python languages that implements our methods. This will provide an expandable platform that will assist researchers in developing new optimal ATSs for patients suffering from alcoholism and other substance use disorders.