Thesis (MSc (School of health systems and public health))--University of Pretoria, 2009. / Summary in English. Includes bibliographical references.

Background/Aims: Case-control designs are commonly employed in genetic association studies. In addition to the primary trait of interest, data on additional secondary traits, related to the primary trait, are often collected. Traditional association analyses between genetic variants and secondary traits can be biased in such cases, and several methods have been proposed to address this issue, including the inverse-probability-of-sampling-weighted (IPW) approach and semi-parametric maximum likelihood (SPML) approach. Methods: Here, we propose a set of new estimating equation based approach that combines observed and counter-factual outcomes to provide unbiased estimation of genetic associations with secondary traits. We extend the estimating equation framework to both generalized linear models (GLM) and non-parametric regressions, and compare it with the existing approaches. Results: We demonstrate analytically and numerically that our proposed approach provides robust and fairly efficient unbiased estimation in all simulations we consider. Unlike existing methods, it is less sensitive to the sampling scheme and underlying disease model specification. In addition, we illustrate our new approach using two real data examples. The first one is to analyze the binary secondary trait diabetes under GLM framework using a stroke case-control study. The second one is to analyze the continuous secondary trait serum IgE levels under linear and quantile regression models using an asthma case-control study. Conclusion: The proposed new estimating equation approach is able to accommodate a wide range of regressions, and it outperforms the existing approaches in some scenarios we consider.

Thesis (M.S.)--University of Texas at El Paso, 2009. / Title from title screen. Vita. CD-ROM. Includes bibliographical references. Also available online.

Global spread of infectious disease threatens the well-being of human, domestic, and wildlife health. A proper understanding of global distribution of these diseases is an important part of disease management and policy making. However, data are subject to complexities by heterogeneity across host classes and space-time epidemic processes [Waller et al., 1997, Hosseini et al., 2006]. The use of frequentist methods in Biostatistics and epidemiology are common and are therefore extensively utilized in answering varied research questions. In this thesis, we proposed the Hierarchical Bayesian approach to study the spatial and the spatio-temporal pattern of tuberculosis in Kenya [Knorr-Held et al., 1998, Knorr-Held, 1999, L opez-Qu lez and Munoz, 2009, Waller et al., 1997, Julian Besag, 1991]. Space and time interaction of risk (ψ[ij]) is an important factor considered in this thesis. The Markov Chain Monte Carlo (MCMC) method via WinBUGS and R packages were used for simulations [Ntzoufras, 2011, Congdon, 2010, David et al., 1995, Gimenez et al., 2009, Brian, 2003], and the Deviance Information Criterion (DIC), proposed by [Spiegelhalter et al., 2002], used for models comparison and selection. Variation in TB risk is observed among Kenya counties and clustering among counties with high TB relative risk (RR). HIV prevalence is identified as the dominant determinant of TB. We found clustering and heterogeneity of risk among high rate counties and the overall TB risk is slightly decreasing from 2002-2009. Interaction of TB relative risk in space and time is found to be increasing among rural counties that share boundaries with urban counties with high TB risk. This is as a result of the ability of models to borrow strength from neighbouring counties, such that near by counties have similar risk. Although the approaches are less than ideal, we hope that our formulations provide a useful stepping stone in the development of spatial and spatio-temporal methodology for the statistical analysis of risk from TB in Kenya. / Thesis (M.Sc.)-University of KwaZulu-Natal, Pietermaritzburg, 2013.

Thesis (M.P.H.)--University of Texas Health Science Center at Houston, School of Public Health, 2008. / Source: Masters Abstracts International, Volume: 47-01, page: . Advisers: Robert E. Roberts; Michele Forman. Includes bibliographical references.

Doctor of Philosophy / Department of Civil Engineering / Sunanda Dissanayake / Lane-departure crashes are the most predominant crash type in Kansas which causes very high number of motor vehicle fatalities. Therefore, the Kansas Department of Transportation (KDOT) has implemented several different types of countermeasures to reduce the number of motor vehicle fatalities associated with lane-departure crashes. This research was conducted to estimate the safety effectiveness of commonly used lane-departure countermeasures in Kansas on all crashes and lane-departure crashes using Crash Modification Factors (CMFs). Paved shoulders, rumble strips, safety edge treatments and median cable barriers were identified as the commonly used lane-departure countermeasures on both tangent and curved road segments while chevrons and post-mounted delineators were identified as the most commonly used lane-departure countermeasures on curved road segments. This research proposes a state-of-art method of estimating CMFs using cross-sectional data for chevrons and post-mounted delineators. Furthermore, another state-of-art method is proposed in this research to estimate CMFs for safety edge treatments using before-and-after data. Considering the difficulties of finding the exact date of implementation of each countermeasure, both cross-sectional and before-and-after studies were employed to estimate the CMFs. Cross-sectional and case-control methods, which are the two major methods in cross-sectional studies were employed to estimate CMFs for paved shoulders, rumble strips, and median cable barriers. The conventional cross-sectional and case-control methods were modified when estimating CMFs for chevrons and post-mounted delineators by incorporating environmental and human behaviors in addition to geometric and traffic-related explanatory variables. The proposed method is novel and has not been used in the previous cross-sectional models available in the literature. Generalized linear regression models assuming negative binomial error structure were used to develop models for cross-sectional method to estimate CMFs while logistic regression models were used to estimate CMFs using case-control method. Results showed that incorporating environmental and human-related variables into cross-sectional models provide better model fit than in conventional cross-sectional models. To validate the developed models for cross-sectional method, mean of the residuals and the Root Mean Square Error (RMSE) were used. For the case-control method, Receiver Operational Characteristic (ROC) was used to evaluate the predictive power of models for a binary outcome using classification tables. However, it was seen that the case-control method is not suitable for estimating CMFs for all crashes since the range of the crash frequency is wide in each road segment. A regression-based method of estimating CMFs using before-and-after data was proposed to estimate CMFs for safety edge treatments. This method allows researchers to identify the safety effectiveness of an individual CMFs on road segments where multiple treatments have been applied at the same time. Since this method uses road geometric and traffic-related characteristics in addition to countermeasure information as the explanatory variables, the model itself would be the Safety Performance Function (SPF). Therefore, developing new SPF is not necessary. Finally, the CMFs were estimated using before-and-after Empirical Bayes method to validate the results from the regression-based method. The results of this study can be used as a decision-making tool when implementing lane-departure countermeasures on similar roadways in Kansas. Even though there are readily available CMFs from the national level studies, having more localized CMFs will be beneficial due to differences in traffic-related and geometric characteristics on different roadways.

Thesis (Ph. D.)--University of Texas Health Science Center at Houston, School of Public Health, 2007. / "December 2007." Source: Dissertation Abstracts International, Volume: 68-11, Section: B, page: 7220. Adviser: Stephen C. Waring. Includes bibliographical references (leaves 39-44).