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: 10226847
• 负责人: Jillian Claire Strayhorn
• 金额: $ 3.67万
• 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/10226847
• 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; 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; data visualization; health economics; implementation cost; improved; innovation; intervention effect; method development; mortality; motivational enhancement therapy; multiphase optimization strategy; novel strategies; peer coaching; preventive intervention; 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.

项目总结 行为和生物行为干预在预防和治疗肺炎方面发挥着至关重要的作用。 物质滥用(SM)和艾滋病毒。制定具有最大公共卫生影响的干预措施是当务之急 为了NIDA。为了最大限度地影响公共卫生，干预措施不仅必须有效，而且必须负担得起， 易于实现、可扩展--即能够广泛应用。多阶段优化策略 (MOST)是一个受工程启发的创新框架，用于开发、优化和评估行为 以及对公众健康有很大影响的生物行为干预。在大多数情况下，优化阶段 研究先于随机对照试验进行评价。在优化阶段，一个随机的、有动力的 最优化试验估计干预成分的单独效应和综合效应。然后，基于 根据优化试验的结果，调查人员决定将哪些组件包括在优化的 干预；决策的目标是确定一组能够产生 最好的预期结果，同时又能负担得起。优化中的现行决策方法 大多数阶段都是基于经典的假设检验，这是一种频率主义方法。然而，贝叶斯 方法可以更好地回答激励决策的问题，比如“什么是 一组特定干预组件产生最佳结果的概率(例如，最大 SM减少)？“我们假设，决策的贝叶斯决策分析方法将比 成功确定最佳干预措施--更成功决策将产生预防和 对公共卫生有更大影响的治疗干预措施。在专家团队的支持下，著名的 导师(Linda M.Collins博士和David Vanness博士)，申请者将把贝叶斯方法纳入 通过评估一种使用决策分析(SODA)进行优化的新策略，MOST框架。这个 申请者将开发苏打水软件，在蒙特卡罗模拟中评估苏打水的性能(目标1)， 然后用苏打水在NIDA资助的SM和HIV领域的优化试验中做出决定，心脏到 心脏2(HTH2；R01 DA040480；PI：Gwadz and Collins)，它针对两种行为结果(例如SM) 和生物学结果(例如艾滋病毒病毒载量)。最终，干预科学家将能够将苏打水用于 他们自己应用MOST，例如优化他们的SM干预措施，以产生更大的公共卫生影响。这 F31奖学金将为申请者提供来自贝叶斯决策的创新方法的前沿培训 分析、卫生经济学和决策科学；在方法传播方面，特别是 开发数据可视化工具；在SM预防和治疗方面；在科学写作、赠款写作方面， 以及负责任的研究行为。F31还将为申请者提供关键的受保护时间来推进 她的目标是成为一名独立研究科学家，致力于 优化SM和HIV防治干预措施的方法。

项目成果

The multiphase optimization strategy (MOST) in child maltreatment prevention research.

• DOI: 10.1007/s10826-021-02062-7
• 发表时间: 2021-10
• 期刊: Journal of child and family studies
• 影响因子: 2.1
• 作者: Guastaferro K;Strayhorn JC;Collins LM
• 通讯作者: Collins LM

Using decision analysis for intervention value efficiency to select optimized interventions in the multiphase optimization strategy.

使用干预价值效率决策分析来选择多阶段优化策略中的优化干预措施。

• DOI: 10.1037/hea0001318
• 发表时间: 2024
• 期刊: Health psychology : official journal of the Division of Health Psychology, American Psychological Association
• 作者: Strayhorn,JillianC;Cleland,CharlesM;Vanness,DavidJ;Wilton,Leo;Gwadz,Marya;Collins,LindaM
• 通讯作者: Collins,LindaM

Multiphase optimization strategy: How to build more effective, affordable, scalable and efficient social and behavioural oral health interventions.

多阶段优化策略：如何建立更有效、负担得起、可扩展和高效的社会和行为口腔健康干预措施。

• DOI: 10.1111/cdoe.12784
• 发表时间: 2023
• 期刊: Community dentistry and oral epidemiology
• 影响因子: 2.3
• 作者: Guastaferro,Kate;Strayhorn,JillianC
• 通讯作者: Strayhorn,JillianC