Developing Methodology to examine causal mediation of Time-Varying Effects in Smoking Cessation Treatments

本项目将研究了解吸烟时变因果中介效应的新方法。 戒烟干预。当一个自变量(例如，戒烟干预) 通过第三个变量对结果产生影响(例如，戒烟)(例如，减少渴求)。在……里面 换句话说，我们将开发新的方法来理解戒烟的机制 干预会导致人们吸烟行为的变化，以及这些影响如何随着时间的推移而变化。 了解干预措施是如何发挥作用的，是设计更有力、更有效干预措施的关键。至 改进戒烟干预措施，这项建议的重点是改进了解吸烟的方法 人们如何在戒烟干预后停止吸烟并继续戒烟。 更广泛地说，我们提议开发的方法将帮助我们理解是什么导致了行为 影响人们参与和维持健康行为的干预措施。 我们将开发和推广分析密集纵向数据和进行调解的方法 分析以更好地研究时变效应，时变效应指的是一个变量对另一个变量的变化的影响(即， 加强或减弱)随着时间的推移。我们将根据潜在的成果框架，扩展方法 因果推理，以便对调解中的时变效应得出更有效的因果推理，然后应用 这些方法对两项戒烟干预研究。首先，我们将用生态瞬间 评估(EMA)数据，询问人们在工作期间的渴望、情绪和戒烟情况 参加联合尼古丁替代疗法的随机对照试验，单独使用尼古丁贴片， 和varenicline开发中介模型，在其中研究各种药物对吸烟的影响 禁欲是由欲望或情绪调节的。这些影响可能会随着时间的推移而变化。例如，一种研究药物 可能在戒烟后不久就能非常有效地减少食欲，但随着时间的推移，这种效果可能会减弱。 其次，使用计步器的数据，我们将评估体育活动/戒烟的效果 对每日步数的干预以及每日步数的增加是否会对吸烟产生影响 停止。我们将在用户友好的软件(例如，R包)中传播我们开发的方法， 可获得行为干预的文章、会议演示文稿和研讨会 研究人员。方法和发现最终将使研究人员了解他们的干预措施是如何起作用的。 有了这些知识，他们将能够设计出更有效和更具成本效益的吸烟干预措施 在最相关的时间以最相关的调解人(S)为目标停止。

This project will examine new methods for understanding time-varying causal mediation effects in smoking cessation interventions. Mediation occurs when an independent variable (e.g., smoking cessation intervention) has an effect on an outcome (e.g., smoking abstinence) through a third variable (e.g., reduction in craving). In other words, we will develop new ways to understand the mechanisms by which smoking cessation interventions cause changes in people's smoking behavior, and how these effects vary over time. Understanding how interventions have their effect is key to designing more powerful, efficient interventions. To improve smoking cessation interventions, this proposal focuses on improving methods for the understanding of how people stop smoking and continue to abstain from smoking following a smoking cessation intervention. More broadly, the methods that we propose to develop will help us understand what causes behavioral interventions to affect people's engagement in and maintenance of healthy behaviors. We will develop and extend methods for analyzing intensive longitudinal data and conducting mediation analysis to better study time-varying effects, which refer to effects of one variable on another that change (i.e., strengthen or diminish) over time. We will extend methods, based on the potential outcomes framework for causal inference, to draw more valid causal inferences about time-varying effects in mediation and then apply these methods to two smoking cessation intervention studies. First, we will use ecological momentary assessment (EMA) data that asked people about their craving, mood, and smoking abstinence while they were enrolled in a randomized controlled trial of combination nicotine replacement therapy, the nicotine patch alone, and varenicline to develop mediation models in which the effect of the various study drugs on smoking abstinence is mediated by cravings or mood. These effects may vary over time. For example, a study drug may be very effective at reducing cravings shortly after the quit day, but the effect may weaken over time. Second, using data from pedometers, we will assess the effect of a physical activity/ smoking cessation intervention on daily step count and whether increases in daily step counts in turn have an effect on smoking cessation. We will disseminate the methods we develop in user-friendly software (e.g., R packages), journal articles, and conference presentations and workshops that are accessible to behavioral intervention researchers. Methods and findings will ultimately allow researchers to understand how their interventions work. With this knowledge, they will be able to design more efficacious and cost-effective interventions for smoking cessation by targeting the most relevant mediator(s) at the most relevant time.