Evaluation of strategies to combine multiple biomarkers in diagnostic testing.

• Main Author: Mohammed, Muna Balla Elshareef.
• Other Authors: Mwambi, Henry G.
• Language: en_ZA
• Published: 2013
• Subjects: Biochemical markers.; Drug development.; Theses > Statistics and actuarial science.
• Online Access: http://hdl.handle.net/10413/9231

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.