Modeling Recurrent Events: A Tutorial Based on Relapse and Remitting Episodes during Medication-Assisted Treatment for Opioid Use Disorder

In health or medical studies, participants can often experience the outcome(s) of interest multiple times during the observation period, creating recurrent event data. Depending on the primary research objective, advanced statistical methods are required to correctly analyze this special type of data. This tutorial discusses 4 general frameworks, appropriate for analyzing recurrent events data: 1) extended Cox, 2) parametric survival, 3) longitudinal, and 4) multistate models. We present in detail the implementation of these methods, including a description of the required dataset structure, R code, and interpretation of results, using data from the CTN-0051 study, a randomized clinical trial comparing the effectiveness of opioid use disorder treatments. The objectives of 3 use case scenarios exemplify the usage and relevance of the methods for the analysis of recurrent events: 1) estimate adjusted effects, 2) make individual-level predictions, and 3) model a complicated process involving multidirectional transitions between disease states. We compare the methods, comment on their strengths and limitations, and make recommendations on the preferred method depending on the primary research objective. Highlights Recurrent events are a common phenomenon in experimental research settings, and their analysis requires advanced survival modeling approaches. This tutorial aims to explain and make these approaches more accessible with code and detailed instructions. We compare a detailed list of statistical methods for analyzing recurrent events and make suggestions on which one should be used depending on the study objective. This tutorial will enable researchers to make better use of recurrent events data.