Methods to Compare Mechanisms of Action in Substance Use Programs

描述（由申请人提供）：该R03小额资助机制由新的NIH首席研究员提交，将解决物质使用干预数据中介分析中的重要空白。指导程序开发的行为理论已经变得越来越复杂，导致针对多种机制的干预，或者被认为与使用有因果关系的中介。调解分析在探索这些物质使用途径和干预措施在改变这些发展轨迹中的作用方面发挥了重要作用。关于中介的假设也越来越复杂，现在经常包括关于中介效应如何相互比较的问题。这些介导效应的对比是项目评估中越来越重要的一个方面，因为它们使研究人员能够了解几种治疗或治疗成分的比较有效性。对中介效应的比较可以帮助研究人员更有效地为行为改变的最显著机制量身定制干预方案。在资源日益减少的情况下，这些信息对于制定具有成本效益的干预措施至关重要。不幸的是，在实践中，这些比较是基于无效的指标，如所讨论的效应的绝对大小或它们的t分数。这种对不恰当方法的依赖可能会模糊那些真正导致大多数行为改变的过程。同样，对群体特异性中介效应的粗略比较可能表明，一个群体不会从一个成分中受益，这一结论可以通过更严格的统计检验来支持或反驳。本研究将为应用物质使用研究人员提供所需的工具，通过解决两个具体目标，适当地进行中介效应的比较。首先是一个广泛的模拟研究，该研究将检查所有五种已知对比测试（沃尔德、百分位和偏差校正bootstrap、基于似然的置信区间和虚拟潜在变量测试）的统计特性。这些测试的统计属性（例如，功率，I型错误率）要么是未知的，要么是不清楚的，因此这些信息将为应该使用这些不同测试的情况（例如，假设类型，数据结构）提供指导。然后，模拟结果将为本研究的第二个组成部分提供信息：将对比方法与来自两个多成分物质使用预防计划的现有预防数据进行彻底应用。这两个项目都是许多预防设计的代表，具有多种介质和结果以及纵向自我报告数据收集等特点。这些特征，加上之前在两个数据集中发现的显著中介，使它们非常适合于如何制定和测试中介效应对比的教学演示。

DESCRIPTION (provided by applicant): This R03 small grant mechanism, submitted by a new NIH Principal Investigator, will address important gaps in mediation analysis of substance use intervention data. Behavioral theories guiding program development have become increasingly complex, resulting in interventions targeting multiple mechanisms, or mediators that are believed to be causally related to use. Mediation analysis has been instrumental in exploring these pathways to substance use and the role of interventions in altering these developmental trajectories. Hypotheses about mediation have also increased in complexity and now often include questions about how mediated effects compare to one another. These contrasts of mediated effects are an increasingly important aspect of program evaluation, as they allow researchers to understand the comparative effectiveness of several treatments or treatment components. Comparisons of mediated effects can help researchers more efficiently tailor intervention programs to the most salient mechanisms of behavior change. Such information is critical for developing cost-effective interventions in the face of decreasing availability of resources. Unfortunately, in practice these comparisons have been based on invalid metrics such as the absolute size of the effects in question or their t scores. Such reliance on improper methods can obscure the processes that truly account for the majority of behavior change. Similarly, crude comparisons of group-specific mediated effects may suggest that one group would not benefit from a component, a conclusion that could be supported or refuted by more stringent statistical examination. This study will provide applied substance use researchers with the tools needed to properly conduct comparisons of mediated effects by addressing two specific aims. First is an extensive simulation study that will examine the statistical properties of all five known contrast tests (Wald, percentile and bias-corrected bootstrap, likelihood-based confidence intervals, and a dummy latent variable test). The statistical properties (e.g., power, Type I error rate) of these tests are either unknown or unclear, so this information will provide guidance about the situations (e.g., types of hypotheses, data structures) in which these various tests should be employed. The simulation results will then inform the second component of this study: a thorough application of contrast methods to existing prevention data from two multicomponent substance use prevention programs. Both programs are representative of many prevention designs, with features such as multiple mediators and outcomes and longitudinal self-reported data collection. These features, together with previous findings of significant mediation in both data sets, make them well suited to a pedagogical demonstration of how to formulate and test mediated effect contrasts. PUBLIC HEALTH RELEVANCE: This study will increase the tools available to public health researchers as they attempt to uncover and change the mechanisms by which substance use occurs. Comparisons of these mechanisms, also called mediated effects, are important for determining how treatment and prevention programs achieve their effects and how program efficacy differs across groups based on factors such as gender and age. The results of this study will identify the best methods for making these comparisons and provide concrete pedagogical examples that applied substance use researchers can use to benefit their own evaluations.