Ecological momentary assessment (EMA) studies investigate intensive repeated observations of the current behavior and experiences of subjects in real time. In particular, such studies aim to minimize recall bias and maximize ecological validity, thereby strengthening the investigation and inference of microprocesses that influence behavior in real-world contexts by gathering intensive information on the temporal patterning of behavior of study subjects. Throughout this paper, we focus on the data analysis of an EMA study that examined behavior of intermittent smokers (ITS). Specifically, we sought to explore the pattern of clustered smoking behavior of ITS, or smoking ‘bouts’, as well as the covariates that predict such smoking behavior. To do this, in this paper we introduce a framework for characterizing the temporal behavior of ITS via the functions of event gap time to distinguish the smoking bouts. We used the time-varying coefficient models for the cumulative log gap time and to characterize the temporal patterns of smoking behavior, while simultaneously adjusting for behavioral covariates, and incorporated the inverse probability weighting into the models to accommodate missing data. Simulation studies showed that irrespective of whether missing by design or missing at random, the model was able to reliably determine prespecified time-varying functional forms of a given covariate coefficient, provided the the within-subject level was small.

生态瞬时评估 (EMA) 研究实时调查对受试者当前行为和经历的密集重复观察。特别是，此类研究旨在最大限度地减少回忆偏差并最大限度地提高生态有效性，从而通过收集有关研究对象行为时间模式的密集信息，加强对影响现实世界环境中行为的微过程的调查和推理。在整篇论文中，我们专注于 EMA 研究的数据分析，该研究检查了间歇性吸烟者 (ITS) 的行为。具体来说，我们试图探索 ITS 或吸烟“发作”的聚集吸烟行为模式，以及预测这种吸烟行为的协变量。去做这个，在本文中，我们介绍了一个框架，用于通过事件间隔时间的功能来表征 ITS 的时间行为，以区分吸烟发作。我们将时变系数模型用于累积对数间隙时间并表征吸烟行为的时间模式，同时调整行为协变量，并将逆概率加权纳入模型以适应缺失数据。模拟研究表明，无论是设计缺失还是随机缺失，只要受试者内水平很小，该模型都能够可靠地确定给定协变量系数的预先指定的时变函数形式。我们将时变系数模型用于累积对数间隙时间并表征吸烟行为的时间模式，同时调整行为协变量，并将逆概率加权纳入模型以适应缺失数据。模拟研究表明，无论是设计缺失还是随机缺失，只要受试者内水平很小，该模型都能够可靠地确定给定协变量系数的预先指定的时变函数形式。我们将时变系数模型用于累积对数间隙时间并表征吸烟行为的时间模式，同时调整行为协变量，并将逆概率加权纳入模型以适应缺失数据。模拟研究表明，无论是设计缺失还是随机缺失，只要受试者内水平很小，该模型都能够可靠地确定给定协变量系数的预先指定的时变函数形式。