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

这个项目将研究新的方法来理解吸烟中随时间变化的因果中介效应 停止干预。当独立变量（例如，戒烟干预） 对结果有影响（例如，戒烟）通过第三变量（例如，减少食欲）。在 换句话说，我们将开发新的方法来了解戒烟的机制， 干预措施导致人们吸烟行为的变化，以及这些影响如何随时间变化。 了解干预措施如何产生效果是设计更强大，更有效的干预措施的关键。到 改善戒烟干预措施，这一建议的重点是改善方法的理解， 人们如何戒烟，并在戒烟干预后继续戒烟。 更广泛地说，我们建议开发的方法将帮助我们了解是什么导致行为 影响人们参与和维持健康行为的干预措施。 我们将开发和扩展分析密集的纵向数据和进行调解的方法 分析，以更好地研究随时间变化的影响，这是指一个变量对另一个变化的影响（即， 或长或短）。我们将根据潜在结果框架扩展方法， 因果推理，对调解中随时间变化的影响得出更有效的因果推理，然后应用 这些方法对两个戒烟干预研究。首先，我们将利用生态瞬间 研究人员使用了一项EMA评估数据，询问人们在吸烟期间的渴望，情绪和戒烟情况。 参加了一项随机对照试验，该试验是关于尼古丁替代疗法的联合治疗，单独使用尼古丁贴片， 和伐尼克兰开发调解模型，其中各种研究药物对吸烟的影响 禁欲是由欲望或情绪来调节的。这些影响可能随时间而变化。例如，研究药物 在戒烟后不久，可能对减少烟瘾非常有效，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，我们将评估身体活动/戒烟的效果 对每日步数的干预以及每日步数的增加是否反过来对吸烟产生影响 停止我们将在用户友好的软件中传播我们开发的方法（例如，R包），日志 文章，会议演示和研讨会，可以访问行为干预 研究人员方法和研究结果最终将使研究人员了解他们的干预措施是如何工作的。 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 通过在最相关的时间将最相关的调解人作为目标来停止。

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.