More than one way: exploring the capabilities of different estimation approaches to joint models for longitudinal and time-to-event outcomes

Abstract

The development of physical functioning after a caesura in an aged population is still widely unexplored. Analysis of this topic would need to model the longitudinal trajectories of physical functioning and simultaneously take terminal events (deaths) into account. Separate analysis of both results in biased estimates, since it neglects the inherent connection between the two outcomes. Thus, this type of data generating process is best modelled jointly. To facilitate this several software applications were made available. They differ in model formulation, estimation technique (likelihood-based, Bayesian inference, statistical boosting) and a comparison of the different approaches is necessary to identify their capabilities and limitations. Therefore, we compared the performance of the packages JM, joineRML, JMbayes and JMboost of the R software environment with respect to estimation accuracy, variable selection properties and prediction precision. With these findings we then illustrate the topic of physical functioning after a caesura with data from the German ageing survey (DEAS). The results suggest that in smaller data sets and theory driven modelling likelihood-based methods (expectation maximation, JM, joineRML) or Bayesian inference (JMbayes) are preferable, whereas statistical boosting (JMboost) is a better choice with high-dimensional data and data exploration settings.

Keywords: Bayesian statistics; expectation maximization; joint models; longitudinal and time-to-event; model-based statistical boosting.