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Dynamic Predictions and Prospective Accuracy in Joint Models for Longitudinal and Time‐to‐Event Data
Author(s) -
Rizopoulos Dimitris
Publication year - 2011
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2010.01546.x
Subject(s) - event (particle physics) , biostatistics , event data , longitudinal data , computer science , longitudinal study , longitudinal field , joint (building) , field (mathematics) , econometrics , statistics , data mining , machine learning , medicine , mathematics , epidemiology , engineering , covariate , architectural engineering , physics , quantum mechanics , pure mathematics , magnetic field
Summary 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.