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Screening and surveillance are routinely used in medicine for early detectionof disease and close monitoring of progression. Biomarkers are one of theprimarily tools used for these tasks, but their successful translation toclinical practice is closely linked to their ability to accurately predictclinical endpoints during follow-up. Motivated by a study of patients whoreceived a human tissue valve in the aortic position, in this work we areinterested in optimizing and personalizing screening intervals for longitudinalbiomarker measurements. Our aim in this paper is twofold: First, toappropriately select the model to use at time t, the time point the patient wasstill event-free, and second, based on this model to select the optimal timepoint u > t to plan the next measurement. To achieve these two goals we developmeasures based on information theory quantities that assess the information wegain for the conditional survival process given the history of the subject thatincludes both baseline information and his/her accumulated longitudinalmeasurements.

Personalized Screening Intervals for Biomarkers using Joint Models for Longitudinal and Survival Data