Thesis (Ph. D.)--Ohio State University, 2005. / Title from first page of PDF file. Document formatted into pages; contains xi, 108 p.; also includes graphics (some col.) Includes bibliographical references (p. 101-108). Available online via OhioLINK's ETD Center

Thèse--Universit́e de Lausanne. / "Bibliographie": p. 63-64.

Thesis (M.S.)--University of Wisconsin--Madison, 1965. / eContent provider-neutral record in process. Description based on print version record. Bibliography: l. 134-141.

Thesis (Ph. D.)--Virginia Commonwealth University, 2006. / Prepared for: Dept. of Biostatistics. Bibliography: leaves 173-177. Also available online.

Thesis (Ph. D.) -- Virginia Commonwealth University, 2006. / Prepared for: Dept. of Biostatistics. Bibliography: leaves 96-101. Also available online.

Thesis (Ph. D.)--Virginia Commonwealth University, 2006. / Prepared for: Dept. of Biostatistics. Bibliography: leaves 121-124 . Also available online.

Thesis (Ph.D.)--University of Nebraska-Lincoln, 2007. / Title from title screen (site viewed Oct. 10, 2007). PDF text: viii, 207 p. : ill. (some col.) UMI publication number: AAT 3258734. Includes bibliographical references. Also available in microfilm and microfiche formats.

Thesis (Ph. D.)--University of Washington, 2000. / Vita. Includes bibliographical references (leaves 147-153).

In clinical trials, patients are enrolled into two treatment arms. A researcher may be interested in studying the effectiveness of a new drug or the comparison of two drugs for the treatment of a disease. This survival data is later analysed using the logrank test or the Cox regression model to detect differences in survivor functions. However, the power function of the logrank test depends solely on the number of patients enrolled into the study. Because statisticians will always minimise type I and type II errors, a researcher carrying out a clinical trial must define beforehand, the number of patients to be enrolled into the clinical study. Without proper sample size and power estimation a clinical trial may fail to detect a false hypothesis of the equality of survivor functions. This study presents through simulation, a way of power and sample size estimation for clinical trials that use the logrank test for their data analysis and suggests an easy method to estimate power and sample size in such clinical studies. Findings on power analysis and sample size estimation on logrank test are applied to two real examples: one is the Veterans' Administration Lung Cancer study; and the other is the data from a placebo controlled trial of gamma interferon in chronic granulotomous disease.

Human life and diseases are inseparable. For millions of years, humans and their ancestors suffered from diseases, caused by infectious pathogens (e.g., bacteria, viruses, parasites) and caused by our own bodies as they age and degenerate. Within the last century, with the advent of public health measures, improved nutrition and medicine, such as antibiotics, some of the infectious diseases have been controlled. However, infectious diseases still lead to most of the non-age related deaths in the world, especially in nations with insufficient health support. My research has taken the complex and dynamic contact networks as well as heterogeneity in disease transmission and recovery into account. Real social networks among individuals were used to generate an adjacency matrix in my formulas. Both, transition and recovery rates have been used as unique variables for each individual. I have used the forward Kolmogorov equation to solve the system. To control and prevent the infectious diseases such as influenza, sexually transmitted diseases, we have to model the dynamics of a particular disease, estimate the parameters, and forecast the behavior of the disease over time. The estimated parameters help us to design and implement interventions, such as vaccination, closure of public places, to limit the spread of diseases. R0, the reproduction number is an important parameter in epidemiology. R0 is the average number of secondary infections produced by a primary infection. If R0 is larger than one an epidemic will most likely happen, an R0 smaller than one suggests that the disease outbreak is local and will die out. In this study I have shown that R0 estimators that only use the the number of contacts and some network features such as covariance of coefficient are not enough to estimate the epidemic threshold. I have formulated R0 to consider both node degree distribution as well as the spectral gap in the eigenvalue of a weighted adjacency matrix of contact network. Only recently, researchers have developed theoretical approaches that can take into account dynamic networks and, independently, that can use genomic data of the pathogen, sampled from infected persons, to reconstruct the path of an epidemic. By considering the location and time of the sampled pathogen sequence data we can combine the sampled infection network and the mutational history of the pathogen to reconstruct a more accurate contact network. We can reconstruct this dynamic contact network using genetic data and epidemic parameters via a Hidden Markov Model. Sampled genome sequenced data of the pathogen are the observation and a set of dynamic networks are the hidden states in our HMM framework. The system switches between the set of dynamic contact networks to fit the best pattern to observation data. The outcome of such an analysis is the accurate dynamic network among samples of the pathogen. These set of dynamic networks capture the dynamics of the social contact network of the infected people. My model will most likely enable earlier detection of infectious disease spread in dynamic social networks than currently available methods. / A Dissertation submitted to the Department of Scientific Computing in partial fulfillment of the requirements for the degree of Doctor of Philosophy. / Summer Semester 2015. / July 17, 2015. / Includes bibliographical references. / Peter Beerli, Professor Directing Dissertation; Christopher Coutts, University Representative; Sachin Shanbhag, Committee Member; Dennis Slice, Committee Member; Alan Lemmon, Committee Member.