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Joint inference for longitudinal and survival data with incomplete time-dependent covariates

University of British Columbia

In many longitudinal studies, individual characteristics associated with their repeated measures may be covariates for the time to an event of interest. Thus, it is desirable to model both the survival process and the longitudinal process together. Statistical analysis may be complicated with missing data or measurement errors in the time-dependent covariates. This thesis considers a nonlinear mixed-effects model for the longitudinal process and the Cox proportional hazards model for the survival process. We provide a method based on the joint likelihood for nonignorable missing data, and we extend the method to the case of time-dependent covariates. We adapt a Monte Carlo EM algorithm to estimate the model parameters. We compare the method with the existing two-step method with some interesting findings. A real example from a recent HIV study is used as an illustration.

Text

2010-08-27

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/27842

Statistics

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/