Novel Longitudinal Methods for SMART Studies of Drug Abuse and HIV

 描述(由申请人提供)：药物使用和艾滋病毒的治疗通常需要关于治疗类型或提供的顺序的、个性化的决定。适应性干预是一种治疗设计，它使用来自患者的持续信息来指导是否以及如何随着时间的推移修改治疗。通过在需要的时候向需要的人提供适当的治疗，适应性干预措施有望改善更多人的长期结果，从而增加药物使用和艾滋病毒治疗在现实世界环境中的覆盖范围和影响。序贯多分配随机试验(SMART)是适应性干预科学的重要进展，是明确识别和构建有效适应性干预措施的实验设计。然而，为了使SMART最大限度地对药物使用和艾滋病毒干预科学家有用，需要开展方法学工作，以扩大分析SMART产生的数据的选择，这些选择在重要方面受到限制。特别是，在当今的药物使用和艾滋病毒研究中，绝大多数研究使用的现代纵向数据分析方法不适应也不能用于利用SMART的独特功能，因此它们的应用可能导致统计能力大大降低，甚至得出错误的结论。这使得使用SMART的调查人员不可能从纵向数据提供的强大、优雅和细微差别中科学地受益。我们的目标是通过开发和评估新的多水平方法来弥合这一关键差距，这些方法用于分析由SMART产生的纵向连续、二元和零膨胀的药物使用和艾滋病毒结果测量；开发新的样本量计算器，用于规划纵向连续、二元或零膨胀的结果测量；使用药物滥用和艾滋病毒的三项SMART研究的数据应用和说明这些方法；以及开发免费的、用户友好的资源来实施这些方法。该项目开发的方法将通过使药物使用和艾滋病毒科学家能够开发更有效的适应性干预措施来指导药物使用和艾滋病毒治疗的个体化，从而改善临床和公共卫生结果。

 DESCRIPTION (provided by applicant): The treatment of drug use and HIV often requires sequential, individualized decision-making concerning the type or delivery of treatment. An adaptive intervention is a treatment design that uses ongoing information from the patient to guide whether, and how to modify the treatment over time. By providing the appropriate treatment to those who need it, when they need it, adaptive interventions hold the promise of improving long-term outcomes for greater numbers of people, thereby increasing the reach and impact of drug use and HIV treatments in real-world settings. The sequential multiple assignment randomized trial (SMART), a major step forward in the science of adaptive interventions, is an experimental design explicitly for identifying and constructing efficacious adaptive interventions. However, in order to make the SMART maximally useful to drug use and HIV intervention scientists, methodological work is needed to expand the options for analyzing data that arise from a SMART, which are limited in important ways. In particular, modern longitudinal data analysis methods that are used by the great majority of studies in today's drug use and HIV research do not accommodate and cannot be used to take advantage of the unique features of a SMART, hence their application can lead to greatly reduced statistical power or even incorrect conclusions. This makes it impossible for investigators using SMARTs to scientifically benefit from the power, elegance, and nuance afforded by longitudinal data. Our objective is to bridge this critical gap by developing and evaluating new multilevel methods for analyzing longitudinal continuous, binary, and zero- inflated drug-use and HIV outcome measures arising from a SMART; developing new sample size calculators for planning SMART studies with longitudinal continuous, binary or zero- inflated outcome measures; applying and illustrating these methods using data from three SMART studies in drug abuse and HIV; and developing free, user-friendly resources to implement these methods. The methods developed in this project will improve clinical and public health outcomes by enabling drug use and HIV scientists to develop more potent adaptive interventions to guide the individualization of drug use and HIV treatments.