Go to  Advanced Search

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

Show full item record

Files in this item

Files Size Format Description   View
ubc_2010_fall_wang_xu.pdf 314.0Kb Adobe Portable Document Format   View/Open
 
Title: Joint inference for longitudinal and survival data with incomplete time-dependent covariates
Author: Wang, Xu
Degree Master of Science - MSc
Program Statistics
Copyright Date: 2010
Publicly Available in cIRcle 2010-08-27
Abstract: 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.
URI: http://hdl.handle.net/2429/27842
Scholarly Level: Graduate

This item appears in the following Collection(s)

Show full item record

All items in cIRcle are protected by copyright, with all rights reserved.

UBC Library
1961 East Mall
Vancouver, B.C.
Canada V6T 1Z1
Tel: 604-822-6375
Fax: 604-822-3893