A challenge in clinical medicine is that of correct diagnosis of disease. Medical researchers invest
considerable time and effort to enhance accurate disease diagnosis. Diagnostic tests are important
components in modern medical practice. The receiver operating characteristic (ROC) is a commonly
used statistical tool for describing the discriminatory accuracy and performance of a diagnostic
test. A popular summary index of discriminatory accuracy is the area under ROC curve (AUC).
In the era of high-dimensional data, scientists are evaluating hundreds to multiple thousands of
biomarkers simultaneously. A critical challenge is the combination of these markers into models
that give insight into disease. In infectious disease, markers are often evaluated in the host as well
as in the microorganism or virus causing infection, adding more complexity to the analysis. In
addition to providing an improved understanding of factors associated with infection and disease
development, combinations of relevant markers is important to diagnose and treat disease. Taken
together, this presents many novel and major challenges to, and extends the role of, the statistical
analyst.
In this thesis, we will address the problem of how to select from multiple markers using existing
methods. Logistic regression models offer a simple method for combining markers. We applied
resampling methods (e.g., Cross-Validation and bootstrap) to adjust for overfitting associated with
model selection. We simulated several multivariate models to evaluate the performance of the resampling
approaches in this setting. We applied the methods to data collected from a study of
tuberculosis immune reconstitution inflammatory syndrome (TB-IRIS) in Cape Town. Baseline levels
of five biomarkers were evaluated and we used this dataset to evaluate whether a combination
of these biomarkers could accurately discriminate between Tuberculosis Immune Reconstitution
Inflammatory Syndrome (TB-IRIS) and non TB-IRIS patients, applying AUC analysis and resampling
methods. / 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:10413/9231 |
| Date | January 2012 |
| Creators | Mohammed, Muna Balla Elshareef. |
| Contributors | Mwambi, Henry G., Dodd, Lori E. |
| Source Sets | South African National ETD Portal |
| Language | en_ZA |
| Detected Language | English |
| Type | Thesis |
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