Longitudinal data occur frequently in medical studies and covariates measured by
error are typical features of such data. Generalized linear mixed models (GLMMs)
are commonly used to analyse longitudinal data. It is typically assumed that
the random effects covariance matrix is constant across the subject (and among
subjects) in these models. In many situations, however, this correlation structure
may differ among subjects and ignoring this heterogeneity can cause the biased estimates
of model parameters. In this thesis, following Lee et al. (2012), we propose
an approach to properly model the random effects covariance matrix based on covariates
in the class of GLMMs where we also have covariates measured by error.
The resulting parameters from this decomposition have a sensible interpretation
and can easily be modelled without the concern of positive definiteness of the
resulting estimator. The performance of the proposed approach is evaluated through
simulation studies which show that the proposed method performs very well
in terms biases and mean square errors as well as coverage rates. The proposed
method is also analysed using a data from Manitoba Follow-up Study. / February 2017
| Identifer | oai:union.ndltd.org:MANITOBA/oai:mspace.lib.umanitoba.ca:1993/31988 |
| Date | 05 January 2017 |
| Creators | Hoque, Md. Erfanul |
| Contributors | Torabi, Mahmoud (Statistics), Wang, Liqun (Statistics) Tate, Robert B. (Community Health Sciences) |
| Source Sets | University of Manitoba Canada |
| Detected Language | English |
Page generated in 0.002 seconds