Random Effects and Recurrent Events in Survival Analysis

A key feature of time-to-event data is the presence of censored or truncated observations and standard methods of analyzing such data are often covered in introductory statistics courses. However, methods for analyzing recurrent events some of which may be censored, are often not covered in these introductory classes. Likewise, random effects are well-studied when modeling continuous or dichotomous outcomes with many books/resources available on this topic. But when analyzing time-to-event outcomes, the incorporation of random effects is challenging beyond the use of a few basic models. Theory is well developed, however there is a need to adapt the myriad methods available in published literature to real life applications. As the field of Biostatistics & Data Science advances, recurrent events and clustered survival data are increasingly seen in practice. As lot of time is required to understand theory, many practicing statisticians are tempted to fit only the very basic models to such data often ignoring the rich possibilities of advanced models that could be fit to these data. This course provides an opportunity to learn about advanced modeling for such data keeping in mind the underlying assumptions of the models. Real life examples will be covered using the R/SAS software and will focus on how to choose the most appropriate methods of analyzing such data.