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<p> We developed and tested procedures to rank the performance of security analysts according to the timeliness of their earning forecasts. We compared leaders and followers among analysts on various performance attributes, such as accuracy, boldness, experience, brokerage size and so on. We also use discriminant analysis and logistic regression model to examine what attributes have an effect on the classification. Further, we examined whether the timeliness of forecasts is related to their impact on stock prices. We found that the lead analysts identified by the measure of forecast timeliness have a greater impact on stock price than follower analysts. Our initial sample includes all firms on the Institutional Brokers Estimate System (I/B/E/S) database and security return data on the daily CRSP file for the years 1994 through 2003.</p> / Thesis / Master of Science (MSc)

Poliomyelitis is a foremost cause of paralysis among preventable diseases among children and adolescents globally. It is caused by persistent infection with poliovirus (PV). The PV infection does not always cause paralysis. A lack of immunization always increases the risk of paralytic polio. Genetic factors also been shown to affect the risk of developing the disease. The aim of this thesis is to investigate whether there are any genetic associations to paralytic poliomyelitis. This is based on a model for understanding its nature as a complex disease, where many genes are involved in contributing to the disease state. This is a population-based case-control study to identify genetic loci that influence disease risk. The study examined the association of genetic variation in single nucleotide polymorphisms (SNPs) across the genome with paralytic poliomyelitis susceptibility in the United States and Canadian survivors of poliomyelitis population, using a genome-wide association study (GWAS) approach. No association was observed. Loci that have been previously implicated were not found to affect the susceptibility to poliomyelitis in this study. The thesis consists of four chapters. Chapter 1 describes the epidemiology, pathogenesis and management of poliomyelitis. Chapter 2 gives an overview of the genomics of infectious diseases in general. Chapter 3 introduces the study population and presents the genome-wide analysis and associations with logistic regression to identify loci explore genes that might be associated with paralytic poliomyelitis and presents results. Chapter 4 discusses the implications of the results and explains future directions. / Thesis / Master of Science (MSc)

Hemorrhagic fever viruses are a major public health threat in Sub-Saharan Africa. These kinds of viruses cause symptoms ranging from non-specific fevers and body aches to severe, life-threatening bleeding, shock, and multi-organ failure. Previously discovered hemorrhagic fever viruses can cause recurrent or seasonal outbreaks, but new ones continue to emerge. In order to combat these viruses, we need to better understand the aspects of pathogenesis that lead to mortality or survival. I will present analysis of the host immune response to two hemorrhagic fever viruses, Lassa virus and Bundibugyo virus, and how the host response can be used to predict mortality in these diseases. Lassa virus (LASV) was identified over 50 years ago, but it remains understudied and has hence been denoted a “Neglected Tropical Disease”. Clinical studies and experiments were run by our collaborators in Nigeria and Germany. In all, longitudinal blood samples were collected for over two hundred Nigerian Lassa Fever patients and concentrations of over 60 proteins analyzed. I processed the datasets, performed statistical testing, and created logistic regression models for each protein. This modeling allowed me to determine which proteins could be used as a predictive biomarker of mortality and the level of that protein that could best stratify patients who died and survived. I then compared protein levels for the best biomarkers and other markers in the same biological pathways with those of healthy and other febrile illness (non-Lassa Fever) controls. I examined the best biomarkers over time for their utility as biomarkers at later timepoints in hospitalization. Finally, I produced an application using RShiny that incorporated these and other exploratory analyses of the data, which allows users to visualize all the data we had in addition to the plots that were published. The filovirus Bundibugyo ebolavirus (BDBV), a relative of the more well-known Ebola virus (EBOV), first caused an outbreak in people fifteen years ago. Animal models are still being developed and characterized for this virus. Our collaborators in Texas experimentally infected cynomolgus macaques with BDBV and gave them post-exposure treatment with a VSV-based vaccine. These collaborators performed RNA-Seq on longitudinal samples from the infected macaques and sent me these data for analysis. I wrote pipelines to perform RNA-Seq and differential expression analyses on over 600 samples, of which I will focus on a subset here. I found differentially expressed genes for different subsets of the data, and I examined these gene lists using gene set enrichment analysis. I then generated logistic regression models to find differentially expressed genes that could predict mortality or survival. Many of these genes could accurately predict outcome at either late or early timepoints. I then used the top genes found by logistic regression to generate random forest models that could predict mortality over the entire course of disease. / 2025-03-20T00:00:00Z

The U.S. health care system is one of the most expensive health care systems in the world, yet it is not as efficient as it is expected. Studies have shown that the use of expensive imaging procedures, such as CT scans, was significantly increasing for the past few years. However, the increased number of CT scans may not help to improve quality of care. No studies are conducted on investigate geographic variation on CT scan usage rate. This research is the first one to examine CT scan usage rate among counties and to examine variation caused by patient and hospital characteristics. We used the 2007 HCUP-SID database provided data for the research. GIS graph was used to illustrate geographic variation on CT scan usage in New York State. Contingency tables were developed to evaluate to what extent patient and hospital characteristics contribute to the variation. A logistic regression model was built to control the variation caused by patient and hospital characteristics in order to find variation contributed by other potential factors such as availability of CT scanners and radiologists. Significant geographic variation of CT scan usage rate in the county level of New York State was found. Patient demographics, insurance status and medical conditions as well as hospital bed size and teaching status were contributing factors to the variation. After controlling these factors, significant geographic variation was still found. It indicates that other potential reasons would influence the technology use. / Master of Science