  In biostatistics and medical research, longitudinal data are often composed
of repeated assessments of a variable (e.g., blood pressure or other
biomarkers) and dichotomous indicators to mark an event of interest (e.g.,
recovery from disease, or death). Consequently, joint modeling of longitudinal
and time-to-event data has generated much interest in these disciplines over
the previous decade. In psychology, too, often we are interested in relating
individual trajectories (e.g., cognitive performance or well-being across many
years) and discrete events (e.g., death, diagnosis of dementia, or of
depression). Yet, joint modeling are rarely applied in psychology and social
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 (cognitive aging)
study. We discuss practical topics, such as model selection and comparison for
both longitudinal and time-to-event data, choice of joint modeling
parameterization, and interpretation of model parameters. To do so, we examined
seven frequently used packages for joint modeling in the R language and
environment. We concluded that of these, JMbayes is especially attractive due
to its flexibility, its various parameterizations of the association structure,
and for its powerful and fully Bayesian implementation. We make available the R
syntax to apply the JMbayes package within our example.
