title: Dynamic predictions and prospective accuracy in joint models for longitudinal and time-to-event data.
abstract: In longitudinal studies it is often of interest to investigate how a marker that is repeatedly measured in time is associated with a time to an event of interest. This type of research question has given rise to a rapidly developing field of biostatistics research that deals with the joint modeling of longitudinal and time-to-event data. In this article, we consider this modeling framework and focus particularly on the assessment of the predictive ability of the longitudinal marker for the time-to-event outcome. In particular, we start by presenting how survival probabilities can be estimated for future subjects based on their available longitudinal measurements and a fitted joint model. Following we derive accuracy measures under the joint modeling framework and assess how well the marker is capable of discriminating between subjects who experience the event within a medically meaningful time frame from subjects who do not. We illustrate our proposals on a real data set on human immunodeficiency virus infected patients for which we are interested in predicting the time-to-death using their longitudinal CD4 cell count measurements.
authors.affiliation: Department of Biostatistics, Erasmus Medical Center, PO Box 2040, 3000 CA Rotterdam, the Netherlands. d.rizopoulos@erasmusmc.nl
authors.forename: Dimitris
authors.initials: D
authors.lastname: Rizopoulos
mesh_terms: D001699:Biometry / Q000379:methods*; D018791:CD4 Lymphocyte Count; D015496:CD4-Positive T-Lymphocytes / Q000473:pathology; D015658:HIV Infections / Q000097:blood / Q000401:mortality; D006801:Humans; D008137:Longitudinal Studies; D011336:Probability; D016019:Survival Analysis; D013995:Time