  Screening and surveillance are routinely used in medicine for early detection
of disease and close monitoring of progression. Biomarkers are one of the
primarily tools used for these tasks, but their successful translation to
clinical practice is closely linked to their ability to accurately predict
clinical endpoints during follow-up. Motivated by a study of patients who
received a human tissue valve in the aortic position, in this work we are
interested in optimizing and personalizing screening intervals for longitudinal
biomarker measurements. Our aim in this paper is twofold: First, to
appropriately select the model to use at time t, the time point the patient was
still event-free, and second, based on this model to select the optimal time
point u > t to plan the next measurement. To achieve these two goals we develop
measures based on information theory quantities that assess the information we
gain for the conditional survival process given the history of the subject that
includes both baseline information and his/her accumulated longitudinal
measurements.
