A posterior expected value approach to decision-making in the multiphase optimization strategy for intervention science.
A posterior expected value approach to decision-making in the multiphase optimization strategy for intervention science.
复制标题
干预科学多阶段优化策略中决策的后验期望值方法。
DOI：
10.1037/met0000569
复制
发表时间：
2023
影响因子：
7
通讯作者：
Vanness,DavidJ
中科院分区：
文献类型：
--
作者：
Strayhorn,JillianC;Collins,LindaM;Vanness,DavidJ
在目前的实践中，干预科学家应用多阶段优化策略（MOST）与2 k因子优化试验使用组件筛选方法（CSA）来选择干预组件，以纳入优化的干预。在这种方法中，科学家审查所有估计的主要效应和相互作用，以确定基于固定阈值的重要效应，然后根据这些重要效应做出组件选择的决策。我们提出了一种替代后验期望值方法的基础上贝叶斯决策理论。这种新方法的目的是更容易应用和更容易扩展到各种干预优化问题。我们使用Monte Carlo模拟来评估后验期望值方法和CSA（自动用于模拟目的）相对于两个基准的性能：随机成分选择和经典治疗包方法。我们发现，无论是后验期望值的方法和CSA产生了显着的性能增益相对于基准。我们还发现，后验期望值方法在模拟析因优化试验中的各种现实变化中，在总体准确性、灵敏度和特异性方面略优于CSA，但始终优于CSA。我们讨论了干预优化和有前途的未来方向在使用后验期望值在MOST决策的影响。（PsycInfo数据库记录（c）2023阿帕，保留所有权利）
In current practice, intervention scientists applying the multiphase optimization strategy (MOST) with a 2 k factorial optimization trial use a component screening approach (CSA) to select intervention components for inclusion in an optimized intervention. In this approach, scientists review all estimated main effects and interactions to identify the important ones based on a fixed threshold, and then base decisions about component selection on these important effects. We propose an alternative posterior expected value approach based on Bayesian decision theory. This new approach aims to be easier to apply and more readily extensible to a variety of intervention optimization problems. We used Monte Carlo simulation to evaluate the performance of a posterior expected value approach and CSA (automated for simulation purposes) relative to two benchmarks: random component selection, and the classical treatment package approach. We found that both the posterior expected value approach and CSA yielded substantial performance gains relative to the benchmarks. We also found that the posterior expected value approach outperformed CSA modestly but consistently in terms of overall accuracy, sensitivity, and specificity, across a wide range of realistic variations in simulated factorial optimization trials. We discuss implications for intervention optimization and promising future directions in the use of posterior expected value to make decisions in MOST.(PsycInfo Database Record (c) 2023 APA, all rights reserved)