Journal
BIOSTATISTICS
Volume 9, Issue 2, Pages 308-320Publisher
OXFORD UNIV PRESS
DOI: 10.1093/biostatistics/kxm029
Keywords
EM algorithm; longitudinal data; proportional hazards model; shared parameter model
Funding
- NIAID NIH HHS [AI055290, AI052765, R01-AI-56695] Funding Source: Medline
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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.
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