Modern Longitudinal Mediation Methods for Prevention Studies

项目摘要 对美国公众的健康至关重要的是鼓励健康促进行为和 阻止健康下降的行为。设计干预措施并评估其随时间推移的影响 需要对干预带来行为改变的过程有所了解。统计 调解分析用于揭示这些过程，并确定 导致健康促进行为的干预措施，并检测可能适得其反的成分 导致健康下降的行为。对一段时间内出现的结果进行统计调解分析 经过干预(例如，持续锻炼和健康饮食，减少饮酒)构成特殊情况 来自混杂变量的挑战，这些变量可能会导致行为发生变化，而不是由于 干预。随着时间的推移，分离这些混淆变量对行为的影响非常重要 评估真正的干预效果。这项建议解决了一类令人困惑的变量--时变 混杂因素(受干预影响的一段时间的变量，同时影响调解人和 结果)。拟议的研究涉及统计调解的两条发展路线；传统的 预防科学使用的方法和流行病学使用的潜在结果方法 生物统计学，并评估他们准确区分混杂影响和纵向影响的能力 治疗干预措施。这项工作将改进评估长期治疗效果的统计方法 健康保护性干预措施。拟议的研究培训计划将弥合方法之间的差距 预防科学中的传统方法和流行病学和生物统计学中估计纵向的新方法 以下目标中的中介效应：1)描述和比较统计推理和因果推理 跨越传统方法和新的潜在结果方法的假设，用于估计纵向 具有时变混杂的中介效应。2)进行蒙特卡罗模拟研究，以调查 所有方法的纵向中介效应估计量的统计性质。3)将这些方法应用于 从NIH资助的三个数据集中提取更多信息，以比较纵向中介效应估计 物质使用预防数据集。

Project Summary Critical to the health of the American public are interventions to encourage health promoting behaviors and to discourage health diminishing behaviors. Designing interventions and evaluating their impact over time requires an understanding of the processes by which interventions bring about behavior change. Statistical mediation analysis is used to reveal these processes and to identify the impact of individual components of the intervention that lead to health promoting behaviors and to detect possible counterproductive components that lead to health diminishing behaviors. Statistical mediation analysis of outcomes that emerge over time following an intervention (e.g., sustained exercise and healthy diet, reduced alcohol consumption) pose special challenges from confounding variables that may produce changes in behavior that are not due to the intervention. Separating the effects of these confounding variables on behavior over time is important for assessing true intervention effects. This proposal addresses a class of confounding variables—time-varying confounders (variables over time that are affected by the intervention and affect both the mediator and the outcome). The proposed research addresses two lines of development in statistical mediation; traditional approaches used in prevention science and potential outcomes approaches used in epidemiology and biostatistics, and evaluates their ability to accurately separate confounding influences from longitudinal effects of treatment interventions. The work will improve statistical methods for evaluating long-term treatment effects of health protective interventions. The proposed research training plan will bridge the gap between methods traditional in prevention science and new methods in epidemiology and biostatistics for estimating longitudinal mediated effects in the following aims: 1) Delineate and compare the statistical and causal inference assumptions across traditional methods and new potential outcomes methods for estimating longitudinal mediated effects with time-varying confounding. 2) Conduct a Monte Carlo simulation study to investigate the statistical properties of longitudinal mediated effect estimators of all methods. 3) Apply these methods to extract more information from three NIH funded datasets to compare longitudinal mediated effect estimates on substance use prevention datasets.