The depth of information collected in participants' daily lives with active (e.g., experience sampling surveys) and passive (e.g., smartphone sensors) ambulatory measurement methods is immense. When measuring participants' behaviors in daily life, the timing of particular events-such as social interactions-is often recorded. These data facilitate the investigation of new types of research questions about the timing of those events, including whether individuals' affective state is associated with the rate of social interactions (binary event occurrence) and what types of social interactions are likely to occur (multicategory event occurrences, e.g., interactions with friends or family). Although survival analysis methods have been used to analyze time-to-event data in longitudinal settings for several decades, these methods have not yet been incorporated into ambulatory assessment research. This article illustrates how multilevel and multistate survival analysis methods can be used to model the social interaction dynamics captured in intensive longitudinal data, specifically when individuals exhibit particular categories of behavior. We provide an introduction to these models and a tutorial on how the timing and type of social interactions can be modeled using the R statistical programming language. Using event-contingent reports (N = 150, Nevents = 64,112) obtained in an ambulatory study of interpersonal interactions, we further exemplify an empirical application case. In sum, this article demonstrates how survival models can advance the understanding of (social interaction) dynamics that unfold in daily life. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

中文翻译：

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对分类事件时间数据进行建模：使用事件相关经验采样方法捕获的社交互动动态示例。

通过主动（例如，经验抽样调查）和被动（例如，智能手机传感器）动态测量方法在参与者的日常生活中收集的信息深度是巨大的。当测量参与者在日常生活中的行为时，通常会记录特定事件的时间（例如社交互动）。这些数据有助于调查有关这些事件发生时间的新型研究问题，包括个人的情感状态是否与社交互动的速率（二元事件发生）相关以及可能发生什么类型的社交互动（多类别事件）事件，例如与朋友或家人的互动）。尽管生存分析方法几十年来一直被用来分析纵向环境中的事件发生时间数据，但这些方法尚未纳入动态评估研究中。本文说明了如何使用多层次和多状态生存分析方法对密集纵向数据中捕获的社会互动动态进行建模，特别是当个体表现出特定类别的行为时。我们介绍了这些模型，并提供了有关如何使用 R 统计编程语言对社交互动的时间和类型进行建模的教程。使用在人际互动流动研究中获得的事件相关报告（N = 150，Nevents = 64,112），我们进一步举例说明了一个实证应用案例。总之，本文展示了生存模型如何促进对日常生活中展开的（社会互动）动态的理解。（PsycInfo 数据库记录 (c) 2023 APA，保留所有权利）。