Dynamic Predictions and Prospective Accuracy in Joint Models for Longitudinal and Time-to-Event Data
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.
|Keywords||Area under the curve, Discrimination, ROC methodology, Shared parameter model, Survival analysis, Time-dependent covariates|
|Persistent URL||dx.doi.org/10.1111/j.1541-0420.2010.01546.x, hdl.handle.net/1765/33304|
Rizopoulos, D.. (2011). Dynamic Predictions and Prospective Accuracy in Joint Models for Longitudinal and Time-to-Event Data. Biometrics, 67(3), 819–829. doi:10.1111/j.1541-0420.2010.01546.x