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

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

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

TitleJoint inference for nonlinear mixed-effects models and time to event at the presence of missing data
Publication TypeJournal Article
Year of Publication2008
AuthorsWU, LANG, X. Hu, J, Wu, H
JournalBiostatistics
Volume9
Pagination308–320
ISSN1465-4644, 1468-4357
KeywordsEM algorithm, Longitudinal data, proportional hazards model, shared parameter model
AbstractIn 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.
URLhttp://biostatistics.oxfordjournals.org/content/9/2/308
DOI10.1093/biostatistics/kxm029