Using decision analysis for intervention value efficiency to select optimized interventions in the multiphase optimization strategy.
Using decision analysis for intervention value efficiency to select optimized interventions in the multiphase optimization strategy.
复制标题
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
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发表时间：
2024
期刊：
影响因子：
--
通讯作者：
Collins,LindaM
中科院分区：
文献类型：
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作者：
Strayhorn,JillianC;Cleland,CharlesM;Vanness,DavidJ;Wilton,Leo;Gwadz,Marya;Collins,LindaM
优化多组分行为和生物行为干预提出了一个复杂的决策问题。为了使干预措施既有效又易于执行，在决定选择哪些组成部分纳入时，可能有必要权衡有效性和可执行性。不同的组成部分可能对一系列结果变量具有不同的有效性。此外，不同的决策者将以不同的目标和偏好来处理这个问题。多阶段优化策略（MOST）决策方法的最新进展为干预科学家提供了新的可能性，可以根据各种决策者的偏好优化干预措施，包括涉及多个结果变量的干预措施。在这项研究中，我们介绍了决策分析干预价值效率（DAIVE），决策框架中使用的MOST，结合这些新的决策方法。我们应用DAIVE选择优化的干预措施的基础上的经验数据从factorial optimization trial.MethodWe定义各种假设的决策者的偏好，和我们应用DAIVE来确定优化的干预措施适合于每一个cases.ResultsWe演示如何DAIVE可以用来作出决策的组成优化的干预措施和如何选择优化的干预措施可以根据不同的决策-制造商偏好和目标。结论我们为想要应用DAIVE根据自己的析因优化试验的数据选择优化干预措施的干预科学家提供建议。
ObjectiveOptimizing multicomponent behavioral and biobehavioral interventions presents a complex decision problem. To arrive at an intervention that is both effective and readily implementable, it may be necessary to weigh effectiveness against implementability when deciding which components to select for inclusion. Different components may have differential effectiveness on an array of outcome variables. Moreover, different decision-makers will approach this problem with different objectives and preferences. Recent advances in decision-making methodology in the multiphase optimization strategy (MOST) have opened new possibilities for intervention scientists to optimize interventions based on a wide variety of decision-maker preferences, including those that involve multiple outcome variables. In this study, we introduce decision analysis for intervention value efficiency (DAIVE), a decision-making framework for use in MOST that incorporates these new decision-making methods. We apply DAIVE to select optimized interventions based on empirical data from a factorial optimization trial.MethodWe define various sets of hypothetical decision-maker preferences, and we apply DAIVE to identify optimized interventions appropriate to each case.ResultsWe demonstrate how DAIVE can be used to make decisions about the composition of optimized interventions and how the choice of optimized intervention can differ according to decision-maker preferences and objectives.ConclusionsWe offer recommendations for intervention scientists who want to apply DAIVE to select optimized interventions based on data from their own factorial optimization trials.