Optimizing substance misuse prevention and treatment interventions for enhanced public health impact: Incorporating Bayesian decision analytics into the multiphase optimization strategy

项目摘要 行为和生物行为干预在预防和治疗 药物滥用（SM）和艾滋病毒。制定具有最大公共卫生影响的干预措施是一个优先事项 对于NIDA。为了产生最大的公共卫生影响，干预措施不仅必须有效，而且必须负担得起， 易于实现，并且可缩放-即，能够有广泛的影响力。多阶段优化策略 （MOST）是一个创新的，受工程启发的框架，用于开发，优化和评估行为 以及对公众健康有重大影响的生物行为干预措施。在MOST中， 研究先于随机对照试验的评价。在优化阶段，一个随机的，有把握的 优化试验估计干预成分的单独和组合效应。然后基于 根据优化试验的结果，研究人员决定在优化的 决策的目标是确定一套干预措施， 最好的预期结果，同时保持负担得起。当前的优化决策方法 MOST阶段是基于经典的假设检验，一种频率论方法。然而，贝叶斯 方法能够更好地回答激励决策的问题，比如“什么是 一组特定的干预成分产生最佳结果（例如， 减少SM）？我们假设，贝叶斯决策分析方法的决策将更多 成功地确定最佳干预措施，更成功决策将产生预防和 对公共卫生产生更大影响的治疗干预措施。在专家团队的支持下， 导师（琳达博士M.柯林斯和大卫范尼斯博士），申请人将把贝叶斯方法纳入 MOST框架，通过评估一种新的战略，优化决策分析（SODA）。的 申请人将为SODA开发软件，评估SODA在蒙特卡罗模拟中的性能（目标1）， 然后使用SODA在一个由NIDA资助的SM和HIV领域的优化试验中做出决定， 心脏2（HTH 2; R 01 DA 040480; PI：Gwadz和柯林斯），针对两种行为结局（例如SM） 和生物学结果（例如HIV病毒载量）。最终，干预科学家将能够使用SODA， （c）各国应加强其自身对社会变革管理计划的应用，例如，优化其社会变革管理干预措施，以产生更大的公共卫生影响。这 F31奖学金将为申请人提供贝叶斯决策创新方法的前沿培训 分析，卫生经济学和决策科学;方法传播，特别是 数据可视化工具的开发; SM预防和治疗;以及科学写作，赠款写作， 以及负责任的研究行为F31还将为申请人提供关键的受保护时间， 朝着她的目标，一个富有成效的职业生涯作为一个独立的研究科学家在发展工作， 优化预防和治疗性传播疾病和艾滋病毒干预措施的方法。

PROJECT SUMMARY Behavioral and biobehavioral interventions play a critically important role in the prevention and treatment of substance misuse (SM) and HIV. Developing interventions that have maximal public health impact is a priority for NIDA. To have maximal public health impact, interventions must be not only effective, but also affordable, readily implementable, and scalable—i.e., capable of having wide reach. The multiphase optimization strategy (MOST) is an innovative, engineering-inspired framework for developing, optimizing, and evaluating behavioral and biobehavioral interventions that have high public health impact. In MOST, an optimization phase of research precedes evaluation by randomized control trial. In the optimization phase, a randomized, powered optimization trial estimates the individual and combined effects of intervention components. Then, based on the results of the optimization trial, investigators decide which components to include in the optimized intervention; the objective of decision-making is to identify the set of intervention components that yields the best expected outcome while remaining affordable. The current methods of decision-making in the optimization phase of MOST are based on classical hypothesis testing, a frequentist approach. However, Bayesian methods are better equipped to answer the questions that motivate decision-making, questions like “What is the probability that a particular set of intervention components yields the best outcome (e.g. the biggest reduction in SM)?” We hypothesize that a Bayesian decision analytic approach to decision-making will more successfully identify optimal interventions—and that more successful decision-making will yield prevention and treatment interventions that have greater public health impact. With the support of a team of expert, renowned mentors (Dr. Linda M. Collins and Dr. David Vanness), the applicant will incorporate Bayesian methods into the MOST framework by evaluating a novel strategy for optimization using decision analytics (SODA). The applicant will develop software for SODA, evaluate SODA's performance in Monte Carlo simulation (Aim 1), and then use SODA to make decisions in a NIDA-funded optimization trial in the SM and HIV area, Heart to Heart 2 (HTH2; R01 DA040480; PIs: Gwadz and Collins), which targets both behavioral outcomes (e.g. SM) and biological outcomes (e.g. HIV viral load). Eventually, intervention scientists will be able to use SODA in their own applications of MOST, e.g. to optimize their SM interventions for greater public health impact. This F31 fellowship will give the applicant cutting-edge training in innovative methodologies from Bayesian decision analysis, health economics, and decision sciences; in methods dissemination and, specifically, the development of data visualization tools; in SM prevention and treatment; and in scientific writing, grant-writing, and the responsible conduct of research. The F31 will also give the applicant crucial protected time to advance toward her goal of a productive career as an independent research scientist working in the development of methods for optimization of interventions for the prevention and treatment of SM and HIV.