Survival analysis is a well developed area which explores time to single
event analysis. In some cases, however, such methods may not adequately
capture the disease process as the disease progression may involve intermediate
events of interest. Multistate models incorporate multiple events
or states. This thesis proposes to demystify the theory of multistate models
through an application based approach. We present the key components of
multistate models, relevant derivations, model diagnostics and techniques
for modeling the effect of covariates on transition intensities.
The methods that are developed in the thesis are applied to HIV and
TB data partly sourced from CAPRISA and the HPP programmes in the
University of KwaZulu-Natal. HIV progression is investigated through the
application of a five state Markov model with reversible transitions such
that state 1: CD4 count 500, state 2: 350 CD4 count < 500, state 3:
200 CD4 count < 350, state 4: CD4 count < 200 and state 5: ARV initiation.
The mean sojourn time in each state and transition probabilities
are presented as well as the effect of covariates namely age, gender and
baseline CD4 count on transition rates.
A key finding, consistent with previous research, is that the rate of decline
in CD4 count tends to decrease at lower levels of the marker. Further,
patients enrolling with a CD4 count less than 350 had a far lower chance
of immune recovery and a substantially higher chance of immune deterioration
compared to patients with a higher CD4 count. We noted that older
patients tend to progress more rapidly through the disease than younger
patients. / Thesis (M.Sc.)-University of KwaZulu-Natal, Westville, 2011.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:ukzn/oai:http://researchspace.ukzn.ac.za:10413/5810 |
| Date | January 2011 |
| Creators | Reddy, Tarylee. |
| Contributors | Mwambi, Henry G. |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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