%0 Journal Article
%T Dynamic Predictions with Time-Dependent Covariates in Survival Analysis using Joint Modeling and Landmarking
%A Dimitris Rizopoulos
%A Magdalena Murawska
%A Eleni-Rosalina Andrinopoulou
%A Geert Molenberghs
%A Johanna J. M. Takkenberg
%A Emmanuel Lesaffre
%J Statistics
%D 2013
%I arXiv
%X 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.
%U http://arxiv.org/abs/1306.6479v1