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

在部分符合性的情况下分析序贯、多重分配、随机试验

• 批准号: 10461789
• 负责人: Ashkan Ertefaie
• 金额: $ 38.53万
• 依托单位: UNIVERSITY OF ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-30 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10461789
• 关键词: Address; Adoption; Affect; Alcohol consumption; Alcoholism; Bayesian Method; Bayesian learning; Characteristics; Clinical Trials Design; Cocaine; Cocaine Dependence; Code; Computer software; Data; Data Analyses; Demographic Factors; Dose; Educational workshop; Effectiveness; Health; Heterogeneity; Individual; Intention; Joints; Language; Lead; Maps; Measures; Medical; Mental Health; Methodology; Methods; Naltrexone; Nature; Obsessive compulsive behavior; Outcome; Patients; Performance; Periodicity; Procedures; Pythons; Randomized; Recording of previous events; Research Personnel; Scheme; Scientist; Sequential Multiple Assignment Randomized Trial; Software Tools; Specific qualifier value; Statistical Methods; Stratification; Structure; Substance Use Disorder; Time; Treatment Effectiveness; alcohol abuse therapy; alcohol craving; arm; base; compliance behavior; design; dosage; drinking; improved; individualized medicine; interest; machine learning method; non-compliance; novel; open source; patient response; randomized trial; repository; response; statistics; symposium; therapy design; tool; treatment effect; treatment group; treatment planning; treatment response; treatment strategy; trial design; virtual

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.

项目摘要： 许多物质使用障碍的周期性和异构性质突出了需要 适应治疗的类型或剂量，以适应​​特定和不断变化的需求 个人。该提案是由纳曲酮的延长治疗效果所激发的 以及可卡因依赖试验的自适应治疗，顺序多重分配 随机试验（SMART）旨在找到酒精的（个性化）救援治疗 或/和可卡因依赖患者。这些试验的主要挑战之一是高率 不遵守指定的治疗。此功能使几乎不可能 调查人员充分探索使用使用的可能性 数据。我们的总体目的是通过发展和 随后将新的统计方法应用于数据。 聪明的试验是一项多阶段试验，可以告知自适应治疗策略的设计 （ATS）正式化了个性化的治疗计划以及当前治疗策略 可以取决于患者过去的医疗和治疗史。最佳ATS是 最大化特定的健康结果。分析智能数据的现有方法 仅限于意向治疗（ITT）分析。那就是每个阶段的治疗效果 根据该阶段随机分组的治疗组估计 不管个人是否遵守他们的分配治疗。一个主要问题 是实验操作与结果之间的关系可能是 被治疗违规的混淆。 我们开发可用于调整不符合数据的方法论 收集在智能中。具体来说，我们扩展了主要地层框架和贝叶斯 对多阶段随机试验设置设置和提出新的程序来估计 在不同ATS下的平均结果。我们还建议一种新颖的贝叶斯机器学习 可用于构建深度量身定制（即个性化的）治疗策略的方法 考虑到患者的人口因素，心理健康和酒精的措施 使用，强迫性饮酒和渴望量表，物理综合评分。 最后，我们将开发易于使用的公开开源软件，以利用R 以及实现我们方法的Python语言。这将提供一个可扩展的平台 这将帮助研究人员为患有患者的患者开发新的最佳ATS 酒精中毒和其他物质使用障碍。