Time-Varying Path-Specific Direct and Indirect Effects: A Novel Approach to Examine Dynamic Behavioral Processes with Application to Smoking Cessation

Behavioral processes are often complex, and vary over time, requiring intensive longitudinal data to effectively capture the dynamic elements involved. For example, examining daily socio-behavioral and treatment adherence data collected during a smoking quit attempt, can reveal how, when, and why withdrawal symptoms change, offering insight into critical windows of relapse-risk in the cessation process. However, analytical methods (e.g., time-varying causal mediation methods), that can translate such intensive longitudinal data into time-varying causal effects remain limited, hindering a deeper understanding of these dynamic behavioral processes. We propose a new approach, augmented mediational g-formula with a two-step estimation strategy, to estimate time-varying causal (in)direct effects. Its performance was evaluated via simulation, comparing bias, precision, and alignment with the product-of-coefficients approach. The optimal approach identified by the simulation study was applied to data from the Wisconsin Smokers’ Health Study II, for assessing the effect of randomized pharmacological treatment assignment (exposure) on daily smoking cessation outcome(s), mediated via daily treatment adherence, in the presence of a time-varying confounder (daily stress). Daily stress was due to social contextual factors but not affected by the exposure. Within its scope, this study serves as a preliminary framework for studying the causal structure of time-varying bio-behavioral processes.

行为过程往往兼具复杂性与时变性，需借助密集型纵向数据（intensive longitudinal data）方能有效捕捉其中蕴含的动态特征。例如，针对戒烟尝试期间采集的每日社会行为与治疗依从性数据展开分析，可揭示戒断症状的变化规律、发生时机与诱因，进而明确戒烟过程中复发风险的关键窗口期。然而，能够将此类密集型纵向数据转化为可解释的时变因果效应的分析方法，例如时变因果中介分析方法（time-varying causal mediation methods），仍较为匮乏，这极大限制了学界对这类动态行为过程的深入认知。为此，我们提出一种结合两步估计策略的增强型中介g公式（augmented mediational g-formula）新方法，用于估计时变因果直接与间接效应。我们通过仿真实验评估了该方法的性能，对比了其偏倚、精度与系数乘积法的契合度。本研究通过仿真实验确定了最优方法，并将其应用于威斯康星吸烟者健康研究二期（Wisconsin Smokers’ Health Study II）的数据：在存在时变混杂因素（time-varying confounder）的场景下，评估以每日治疗依从性为中介的随机药物治疗分配（暴露因素）对每日戒烟结局的影响。其中，每日应激水平由社会背景因素驱动，且不受该暴露因素的影响。在本研究的研究范围内，其可为时变生物行为过程的因果结构研究提供初步的分析框架。