title: Personalized screening intervals for biomarkers using joint models for longitudinal and survival data.
abstract: Screening and surveillance are routinely used in medicine for early detection of disease and close monitoring of progression. Motivated by a study of patients who received a human tissue valve in the aortic position, in this work we are interested in personalizing screening intervals for longitudinal biomarker measurements. Our aim in this paper is 2-fold: First, to appropriately select the model to use at the time point the patient was still event-free, and second, based on this model to select the optimal time point to plan the next measurement. To achieve these two goals, we combine information theory measures with optimal design concepts for the posterior predictive distribution of the survival process given the longitudinal history of the subject.
authors: Department of Biostatistics, Erasmus University Medical Center, 3000 CE Rotterdam, The Netherlands d.rizopoulos@erasmusmc.nl.
authors: Dimitris
authors: D
authors: Rizopoulos
authors: Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.
authors: Jeremy M G
authors: JM
authors: Taylor
authors: Department of Biostatistics, Erasmus University Medical Center, 3000 CE Rotterdam, The Netherlands.
authors: Joost
authors: J
authors: Van Rosmalen
authors: Department of Public Health, Erasmus University Medical Center, 3000 CE Rotterdam, The Netherlands.
authors: Ewout W
authors: EW
authors: Steyerberg
authors: Department of Cardiothoracic Surgery, Erasmus University Medical Center, 3000 CE Rotterdam, The Netherlands.
authors: Johanna J M
authors: JJ
authors: Takkenberg
mesh_terms: D015415:Biomarkers; D000066491:Clinical Decision-Making / Q000379:methods*; D006801:Humans; D015233:Models, Statistical; D057285:Precision Medicine
chemical_list: D015415:Biomarkers
keywords: Decision making; Information theory; Personalized medicine; Random effects