  A key question in clinical practice is accurate prediction of patient
prognosis. To this end, nowadays, physicians have at their disposal a variety
of tests and biomarkers to aid them in optimizing medical care. These tests are
often performed on a regular basis in order to closely follow the progression
of the disease. In this setting it is of medical interest to optimally utilize
the recorded information and provide medically-relevant summary measures, such
as survival probabilities, that will aid in decision making. In this work we
present and compare two statistical techniques that provide dynamically-updated
estimates of survival probabilities, namely landmark analysis and joint models
for longitudinal and time-to-event data. Special attention is given to the
functional form linking the longitudinal and event time processes, and to
measures of discrimination and calibration in the context of dynamic
prediction.
