*********************************
There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
*********************************
Advisors:
Brani Vidakovi, Ph.D. (BME)
Paul Griffin, Ph.D. (Penn State University)
Committee:
Melissa Kemp, Ph.D. (BME)
David Goldsman, Ph.D. (ISYE)
Susan Griffin, Ph.D. (Centers for Disease Control and Prevention)
The field of medicine is regularly faced with the challenge of utilizing information that is complicated or difficult to characterize. Physicians often must use their best judgment in reaching decisions or recommendations for treatment in the clinical setting. The goal of this thesis is to use innovative statistical tools in tackling three specific challenges of this nature from current healthcare applications.
The first aim focuses on developing a novel approach to meta-analysis when combining binary data from multiple studies of paired design, particularly in cases of high heterogeneity between a low or moderate number of studies. This approach uses a Rasch model for translating data from multiple paired studies into a unified structure, allowing for proper handling of variability associated with both pair and study effects. Analysis is performed using a Bayesian hierarchical structure, accounting for heterogeneity in a direct way within the variances of the generating distributions of model parameters. This method is applied to the debated topic within the dental community of the comparative effectiveness of materials used for pit-and-fissure sealants.
The second and third aims have applications in early detection of breast cancer. The interpretation of mammograms is often difficult since signs of early disease are often minuscule, and the appearance of even normal tissue can be highly variable and complex. When dealing with high frequency and irregular data, as in most medical images, the behaviors of these complex structures are often impossible to quantify by standard modeling techniques. Scaling is a commonly occurring phenomenon in high-frequency data. The second aim in this research was to develop and evaluate a wavelet-based scaling estimator that reduces the information in a mammogram down to an informative, low-dimensional quantification of the innate scaling behavior, optimized for use in classifying the tissue as cancerous or non-cancerous.
The third aim focuses on enhancing the visualization and the assessment of microcalcifications that are too small to capture well on screening mammograms. Using scale-mixing discrete wavelet transform methods, the existing detail information contained in a very small and course image is used to impute scaled details at finer levels. These details are then used to produce an image of much higher resolution than the original, improving the visualization of the object, allowing for more accurate feature assessment for diagnosis.
