Multilevel models account for different levels of aggregation that may be
present in the data. Researchers are sometimes faced with the task of
analysing data that are collected at different levels such that attributes about
individual cases are provided as well as the attributes of groupings of these
individual cases. Data with multilevel structure is common in the social
sciences and other fields such as epidemiology. Ignoring hierarchies in data
(where they exist) can have damaging consequences to subsequent statistical
inference.
This study applied multilevel models from frequentist and Bayesian perspectives
to the Swaziland Demographic and Health Survey (SDHS) data.
The first model fitted to the data was a Bayesian generalised linear mixed
model (GLMM) using two estimation techniques: the Integrated Laplace
Approximation (INLA) and Monte Carlo Markov Chain (MCMC) methods.
The study aimed at identifying determinants of HIV in Swaziland and as
well as comparing the different statistical models. The outcome variable of
interest in this study is HIV status and it is binary, in all the models fitted
the logit link was used.
The results of the analysis showed that the INLA estimation approach
is superior to the MCMC approach in Bayesian GLMMs in terms of computational speed. The INLA approach produced the results within seconds compared to the many minutes taken by the MCMC methods. There were
minimal differences observed between the Bayesian multilevel model and
the frequentist multilevel model. A notable difference observed between the
Bayesian GLMMs and the the multilevel models is that of differing estimates
for cluster effects. In the Bayesian GLMM, the estimates for the cluster
effects are larger than the ones from the multilevel models. The inclusion
of cluster level variables in the multilevel models reduced the unexplained
group level variation.
In an attempt to identify key drivers of HIV in Swaziland, this study
found that age, age at first sex, marital status and the number of sexual
partners one had in the last 12 months are associated with HIV serostatus.
Weak between cluster variations were found in both men and women. / Thesis (M.Sc.)-University of KwaZulu-Natal, Pietermaritzburg, 2012.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:ukzn/oai:http://researchspace.ukzn.ac.za:10413/9229 |
| Date | January 2012 |
| Creators | Vilakati, Sifiso E. |
| Contributors | Achia, Thomas Noel Ochieng., Mwambi, Henry G. |
| Source Sets | South African National ETD Portal |
| Language | en_ZA |
| Detected Language | English |
| Type | Thesis |
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