Modeling categorical time-to-event data: The example of social interaction dynamics captured with event-contingent experience sampling methods

Abstract

The depth of information collected in participant’s 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 – are 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 (multi-category 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 paper illustrates how multilevel and multistate survival analysis method 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.

Capturing the Social Life of a Person by Integrating Experience-Sampling Methodology and Personal-Social-Network Assessments

“…Researchers can, for example, investigate associations between mood and quality of social interactions with a particular friend or colleague if they know about characteristics of the participant’s relationship with this person. Currently, most ESM studies do not distinguish between different interaction partners but assess only the social role of an interaction partner (e.g., friend, family member, colleague, or superior) and thereby summarize multiple different people in one category (Elmer et al, 2023). Therefore, the effects of specific interaction partners remain masked even though such information may be relevant for (idiographic) research and personalized therapy (Stadel et al, 2023; von Klipstein et al, 2023).…”