Use this URL to cite or link to this record in EThOS:
Title: Modelling of mean-covariance structures in marginal structural models
Author: Qu, Chen
ISNI:       0000 0004 7658 2668
Awarding Body: University of Manchester
Current Institution: University of Manchester
Date of Award: 2018
Availability of Full Text:
Access from EThOS:
Access from Institution:
In epidemiologic studies, marginal structural models (MSMs) are used for properly estimating the causal effect of a time-dependent treatment, especially when confounders are present. Estimating the mean structure in the marginal structural model framework has been studied for a long period, but there has been little research conducted on modelling of variance or covariance structures. According to the generalised estimating equations (GEE) approach of Zeger and Liang (1999), Hernan, Brumback and Robins (2002) suggested a selected covariance structure such as compound symmetry and AR(1) etc. However, questions arise whether the assumed covariance structure is indeed correct and what the consequences might be otherwise. In this research, we propose to model the mean-covariance structures for marginal structural models within the framework of the weighted generalized estimating equations (WGEE). These models allow for appropriate adjustment for confounding. The proposed MSM approach yields unbiased estimators for both the mean and covariance parameters for longitudinal data with confounders. We demonstrate the use of the proposed approach in simulation studies and a real data analysis. Then the proposed MSMs are extended to handle the missing at random dropout, the performance of the modified MSMs are discussed in the simulation studies and a real data analysis.
Supervisor: Pan, Jianxin Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: missing data ; confounding ; weighted generalized estimating equation ; generalised estimating equations ; marginal structural models ; mean-covariance modelling