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

项目总结： 许多物质使用障碍的周期性和异质性突出表明有必要 调整治疗类型或剂量，以适应特定和不断变化的需求 个人。这项建议的动机是延长纳曲酮的治疗效果。 和可卡因依赖试验的适应性治疗，序贯多重分配 随机试验(SMART)旨在寻找(个性化的)酒精救援疗法 或/和可卡因依赖患者。这些试验中的主要挑战之一是高比率的 不遵守所分配的治疗方案。这一功能使其几乎不可能 调查人员将充分探索使用 数据。我们的首要目标是通过开发和开发 随后，将新的统计方法应用于数据。 SMART试验是一个多阶段试验，可以为适应性治疗策略的设计提供信息 (ATS)正式制定个体化治疗计划，以及当前治疗策略 可能取决于患者过去的医疗和治疗历史。最优的ATS是指 最大化感兴趣的特定健康结果。分析智能数据的现有方法 仅限于意向治疗(ITT)分析。也就是说，每个阶段的治疗效果是 根据个体在该阶段被随机分配到的治疗组进行估计 不管个人是否遵守了分配给他们的治疗。一个主要问题 实验操作和结果之间的关系可能是 对治疗不依从感到困惑。 我们开发了可用于调整数据分析中的违规行为的方法 用聪明的方式收集。具体地说，我们扩展了主要地层框架和贝叶斯 多阶段随机试验设置，并提出新的方法来估计 不同ATS下的平均结局。我们还提出了一种新的贝叶斯机器学习方法 可用于构建深度定制(即个性化)治疗策略的方法 考虑到患者的人口统计因素、精神健康指标和酒精 使用强迫饮酒和酗酒渴求量表、身体综合评分。 最后，我们将利用R开发易于使用、公开可用的开源软件 和实现我们的方法的Python语言。这将提供一个可扩展的平台 这将有助于研究人员为患有慢性阻塞性肺疾病的患者开发新的最佳ATS 酒精中毒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.