Indiana University-Purdue University Indianapolis (IUPUI) / Joint models for longitudinal and time-to-event data has been introduced to study the
association between repeatedly measured exposures and the risk of an event. The use
of joint models allows a survival outcome to depend on some characteristic functions
from the longitudinal measures. Current estimation methods include a two-stage
approach, Bayesian and maximum likelihood estimation (MLEs) methods. The twostage
method is computationally straightforward but often yields biased estimates.
Bayesian and MLE methods rely on the joint likelihood of longitudinal and survival
outcomes and can be computationally intensive.
In this work, we propose a joint generalized estimating equation framework
using an inverse intensity weighting approach for parameter estimation from joint
models. The proposed method can be used to longitudinal outcomes from the exponential
family of distributions and is computationally e cient. The performance of
the proposed method is evaluated in simulation studies. The proposed method is used
in an aging cohort to determine the relationship between longitudinal biomarkers and
the risk of coronary artery disease.
| Identifer | oai:union.ndltd.org:IUPUI/oai:scholarworks.iupui.edu:1805/17117 |
| Date | 07 May 2018 |
| Creators | Zheng, Mengjie |
| Contributors | Gao, Sujuan, Xu, Huiping, Zhang, Jianjun, Zhang, Ying |
| Source Sets | Indiana University-Purdue University Indianapolis |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
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