Multilevel joint modelling of hierarchical longitudinal and time-to-event data

İngilizce (OpenAlex)

Joint modelling of longitudinal and time-to-event data has attracted increasing interest in the literature over the last two decades. In practice, clinical studies are increasingly likely to record more complex data structures (such as multilevel longitudinal data) than single longitudinal and event-time data. This paper develops a methodology for multilevel joint models, accounting for complex longitudinal data, by focussing on random effect selection models, where information from the longitudinal trajectories is used to indicate the event-time process. The methodology is tested via simulation studies, and implemented in a real dataset. The results indicate that the multilevel joint models can provide better estimators when the model is specified correctly such that it utilizes all available data.