Dynamic Predictions and Prospective Accuracy in Joint Models for Longitudinal and Time-to-Event Data
Article first published online: 9 FEB 2011
© 2011, The International Biometric Society
Volume 67, Issue 3, pages 819–829, September 2011
How to Cite
Rizopoulos, D. (2011), Dynamic Predictions and Prospective Accuracy in Joint Models for Longitudinal and Time-to-Event Data. Biometrics, 67: 819–829. doi: 10.1111/j.1541-0420.2010.01546.x
- Issue published online: 14 SEP 2011
- Article first published online: 9 FEB 2011
- Received February 2010. Revised October 2010. Accepted November 2010.
- Area under the curve;
- ROC methodology;
- Shared parameter model;
- Survival analysis;
- Time-dependent covariates
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.