摘要：In longitudinal studies subjects are measured for one or more response variable, over
time. Although the underlying evolution of such response variables is continuous in
time, in practice the measurements are observed at discrete time points. In longitudinal
clinical trials it is also common to observe relevant events, generating time-to-event
data. If both types of data are available, we might be interested in the association
between the two processes, longitudinal and time-to-event. Commonly, when death
is considered the event, the observation sequence of longitudinal measurements is
terminated by the event process. When the two observed processes are related, the
analysis of the data set should be suited to the specific objectives. We distinguish
three situations: if the interest is to analyse the longitudinal outcome response variable
with drop-out at the time-to-event; to analyse time-to-event, whilst exploiting
correlation with a noisy version of a time-varying risk factor; or to analyse the relationship
between the two processes. Joint models assume a full distribution for the joint
distribution of longitudinal and time-to-event processes, which includes a description
of the relation between the two processes.
