Cigarette smoking is a major global public health issue and the leading cause of preventable death in the United States. Toward a goal of designing better smoking cessation treatments, system identification techniques are applied to intervention data to describe smoking cessation as a process of behaviour change. System identification problems that draw from two modelling paradigms in quantitative psychology (statistical mediation and self-regulation) are considered, consisting of a series of continuous-time estimation problems. A continuous-time dynamic modelling approach is employed to describe the response of craving and smoking rates during a quit attempt, as captured in data from a smoking cessation clinical trial. The use of continuous-time models provide benefits of parsimony, ease of interpretation, and the opportunity to work with uneven or missing data.

中文翻译：

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戒烟干预的连续时间系统识别


吸烟是一个重大的全球公共卫生问题，也是美国可预防死亡的主要原因。为了设计更好的戒烟治疗的目标，系统识别技术应用于干预数据，将戒烟描述为行为改变的过程。考虑源自定量心理学中的两种建模范式（统计中介和自我调节）的系统识别问题，由一系列连续时间估计问题组成。采用连续时间动态建模方法来描述戒烟尝试期间烟瘾和吸烟率的反应，如戒烟临床试验的数据所捕获。使用连续时间模型具有简约性、易于解释以及处理不均匀或缺失数据的机会等优点。