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

优化药物滥用预防和治疗干预措施以增强公共卫生影响：将贝叶斯决策分析纳入多阶段优化策略

• 批准号: 10066662
• 负责人: Jillian Claire Strayhorn
• 金额: $ 3.62万
• 依托单位: PENNSYLVANIA STATE UNIVERSITY, THE
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2022-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10066662
• 关键词: Address; Area; Bayesian Analysis; Bayesian Method; Behavioral; Biological; Budgets; Case Study; Characteristics; Complex; Computer software; Consultations; Data; Data Set; Decision Analysis; Decision Making; Development; Effectiveness of Interventions; Engineering; Evaluation; Experimental Designs; Fellowship; Funding; Goals; Grant; HIV; Health; Health Technology; Individual; Intervention; Investments; Joints; Mentors; Methods; Monte Carlo Method; Morbidity - disease rate; National Institute of Drug Abuse; Outcome; Performance; Phase; Plant Roots; Play; Prevention; Preventive Intervention; Probability; Public Health; Randomized; Randomized Controlled Trials; Research; Research Personnel; Resources; Role; Sample Size; Science; Scientist; Specific qualifier value; Technology Assessment; Testing; Time; Training; Uncertainty; United States; United States National Institutes of Health; Viral Load result; Visualization; Visualization software; Writing; analytical method; base; behavioral outcome; biobehavior; career; comorbidity; control trial; cost; data visualization; health economics; improved; innovation; intervention effect; method development; mortality; motivational enhancement therapy; multiphase optimization strategy; novel strategies; peer coaching; programs; responsible research conduct; simulation; software development; substance misuse; substance misuse prevention

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.

项目摘要 行为和生物行为干预措施在预防和治疗中起着至关重要的作用 物质小姐（SM）和艾滋病毒。制定具有最大公共卫生影响的干预措施是优先事项 对于NIDA。为了产生最大的公共卫生影响，干预措施不仅有效，而且还必须负担得起， 易于实施，可扩展的 - 即能够具有广泛的影响力。多相优化策略 （大多数）是一个创新的，工程启发的框架，用于开发，优化和评估行为 以及对公共卫生影响很大的生物行为干预措施。在大多数中，一个优化阶段 研究先于通过随机对照试验进行评估。在优化阶段，随机，有动力 优化试验估计干预组件的个体和综合效应。然后，基于 优化试验的结果，研究人员决定在优化中包括哪些组件 干涉;决策的目的是确定一组产生的干预组件 最好的预期结果，同时保持负担得起。优化中当前决策的方法 大多数阶段基于经典假设检验，即一种频率方法。但是，贝叶斯 方法可以更好地回答混合决策制定的问题，例如“什么是 特定的一组干预组件产生最佳结果的可能性（例如，最大的 减少SM）？”我们假设贝叶斯决策方法的决策方法将更多 成功识别最佳干预措施，并且更成功的决策将产生预防和 公共卫生影响更大的治疗干预措施。在一个著名的专家团队的支持下 导师（Linda M. Collins博士和David Vanness博士），适用的将贝叶斯方法纳入 大多数框架通过评估使用决策分析（SODA）优化的新型策略。这 申请人将开发苏打软件，评估苏打水在蒙特卡洛模拟中的性能（AIM 1）， 然后使用苏打 心脏2（HTH2; R01 DA040480; PIS：GWADZ和Collins），它针对这两个行为结果（例如SM） 和生物结局（例如HIV病毒载量）。最终，干预科学家将能够在 他们自己的大多数应用，例如为了优化其SM干预措施，以实现更大的公共卫生影响。这 F31奖学金将为贝叶斯决定提供创新方法的申请人尖端培训 分析，健康经济学和决策科学；在方法传播中，特别是 开发数据可视化工具；在SM预防和治疗中；以及科学写作，授予写作， 以及负责任的研究。 F31还将给出适用的关键保护时间 她的目标是作为一名独立研究科学家，从事富有成效的职业的目标 优化预防和治疗SM和HIV的干预措施的方法。