Date of Award
Open Access Dissertation
Since first introduced by Dorfman in 1943, pooled testing has been widely used as a cost and time effective testing protocol in the variety of applications. This dis- sertation consists of three projects that reveal the use of pooling techniques in the disease prevention from the perspective of regression. For disease monitoring and control, individual covariates information are often of practical interest and yield meaningful interpretations. It is natural to model the outcome of interest, which can be either a disease status (binary) or a biomarker concentration index (continuous), with individual-specific covariates through a regression analysis. Chapter 2 focuses on the pooled biomarker assessment, where a pooling procedure is implemented to measure a continuous outcome of interest. A semi-parametric single-index model is developed to model the mean trend of biomarker concentration. In spite of pooled biomarker assessment, this dissertation also focuses on the group testing problems in infectious disease studies. In Chapter 3, we propose a multivariate logistic regres- sion model for the multiple-infection group testing data. To facilitate the variable selection and model interpretation, we further develop a regularized approach which selects the active risk factors for each infection. Other than significant cost savings, pooling strategy provides more precise biomarker mean curve estimations (in Chapter 2), and more accurate variable selections (in Chapter 3). With these cheerful benefits from pooling strategy, for the purpose of promoting group testing to laboratories, in Chapter 4, we further discuss how to simplify the pooled testing routine realistically without significant impairments on regression estimation.
Lin, J.(2019). Regression for Pooled Testing Data with Biomedical Applications. (Doctoral dissertation). Retrieved from https://scholarcommons.sc.edu/etd/5331