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Biostatistics Advance Access originally published online on August 29, 2007
Biostatistics 2008 9(2):308-320; doi:10.1093/biostatistics/kxm029
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© The Author 2007. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org.

Joint inference for nonlinear mixed-effects models and time to event at the presence of missing data

Lang Wu*

Department of Statistics, 333-6356 Agricultural road, University of British Columbia, Vancouver, British Columbia V6T 1Z2, Canada lang{at}stat.ubc.ca

X. Joan Hu

Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, British Columbia V5A 1S6, Canada

Hulin Wu

Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY 14642, USA

* To whom correspondence should be addressed.

In many longitudinal studies, the individual characteristics associated with the repeated measures may be possible covariates of the time to an event of interest, and thus, it is desirable to model the time-to-event process and the longitudinal process jointly. Statistical analyses may be further complicated in such studies with missing data such as informative dropouts. This article considers a nonlinear mixed-effects model for the longitudinal process and the Cox proportional hazards model for the time-to-event process. We provide a method for simultaneous likelihood inference on the 2 models and allow for nonignorable data missing. The approach is illustrated with a recent AIDS study by jointly modeling HIV viral dynamics and time to viral rebound.

Keywords: EM algorithm; longitudinal data; proportional hazards model; shared parameter model

Received April 28, 2006; revised June 20, 2007; revised July 19, 2007; accepted for publication July 19, 2007.


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