A Tutorial for Joint Modeling of Longitudinal and Time-to-Event Data in R

Authors

  • Sezen Cekic
    University of Geneva, Geneva, Switzerland
  • Stephen Aichele
    University of Geneva, Geneva, Switzerland; Colorado State University, Fort Collins, CO, USA
  • Andreas M. Brandmaier
    Max Planck Institute for Human Development, Berlin, Germany; Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany
  • Ylva Köhncke
    Max Planck Institute for Human Development, Berlin, Germany
  • Paolo Ghisletta
    University of Geneva, Geneva, Switzerland; Swiss Distance University Institute, Brig, Switzerland; Swiss National Centre of Competence in Research LIVES, University of Geneva, Geneva, Switzerland

Abstract

In biostatistics and medical research, longitudinal data are often composed of repeated assessments of a variable and dichotomous indicators to mark an event of interest. Consequently, joint modeling of longitudinal and time-to-event data has generated much interest in these disciplines over the previous decade. In behavioural sciences, too, often we are interested in relating individual trajectories and discrete events. Yet, joint modeling is rarely applied in behavioural sciences more generally. This tutorial presents an overview and general framework for joint modeling of longitudinal and time-to-event data, and fully illustrates its application in the context of a behavioral study with the JMbayes R package. In particular, the tutorial discusses practical topics, such as model selection and comparison, choice of joint modeling parameterization and interpretation of model parameters. In the end, this tutorial aims at introducing didactically the theory related to joint modeling and to introduce novice analysts to the use of the JMbayes package.