Date of Award

5-2017

Document Type

Open Access Dissertation

Department

Statistics

First Advisor

Joshua M. Tebbs

Abstract

Group testing, dating back to the early 1940s, was first proposed to screen for syphilis among US inductees during World War II (Dorfman, 1943). Since then, the benefits of reducing testing costs by employing group testing have been demonstrated in many areas, such as drug discovery, genetics, and infectious disease testing. Traditionally, statistical research in group testing has largely been motivated by applications involving a single infection. With the recent development of multiplex assays that can diagnose multiple infections simultaneously, generalizing the existing group testing literature to incorporate multiple infections is a natural and necessary next step. This dissertation consists of three research projects that extend group testing to multiple infections. In Chapter 2 and Chapter 3, we propose two different testing algorithms to accommodate the use of multiplex assays. Compared to the two-stage hierarchical group testing algorithms currently employed by the Infertility Prevention Project (IPP) in Iowa, our algorithms are proven to confer significant cost savings. In Chapter 4, we propose a semi-parametric regression framework to estimate individual-level marginal probability of infection from multiple infection group testing data. The performance of our testing algorithms and estimation techniques is evaluated through numerical study, simulation, and application to chlamydia and gonorrhea data collected by five states as part of the IPP.

Rights

© 2017, Peijie Hou

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