Modeling Categorical Time-to-Event Data: The Example of Social Interaction Dynamics Captured With Event-Contingent Experience Sampling Methods

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 par-ticipants’ 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 meth-ods 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, N_events = 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. The study of behavior in daily life settings is facilitated by ambulatory assessment methods, which allow collecting data as people go about their daily life. Such intensive longitudinal data contain information about the time of a particular event, for example, when a social interaction with a friend took place. The tim-ing and type (interaction with, e.g., friend, family, and partner) of these events can be analyzed with survival models, which were originally developed for biomedical applications. Within the psychological literature, survival models are underrepresented. For this reason, we provide an in-depth tutorial and example appli-cation on survival models for repeated, multicategory event data.