Statistical methods in disease risk analysis, disease testing and nutrition epidemiology

The thesis is composed of three separated projects: disease risk scoring systems (chapter 2), statistical tests for proportion difference in one-to-two matched binary data (chapter 3) and bivariate measurement error model for nutrition epidemiology (chapter 4). In the first project, we propose to use group lasso algorithm for logistic regression to construct a risk scoring system for predicting disease in swine. We choose the penalty parameter for the group lasso through leave-one-out cross validation and use the area under the receiver operating characteristic curve as criterion. We show our proposed scoring system is superior to existing methods. The second project was originally motivated by the pooling of diagnostic tests. We proposed exact and asymptotic tests for one-to-two matched binary data. Unlike other existing methods, our procedure doesn\u27t rely on a mutual independence assumption. The emphasis on dependence among observations from the same matched set is natural and appealing, as much in human health as it is in veterinary medicine. It can be applied to many kinds of diagnostic studies with a one-to-two matched data structure. Our method can also be generalized to one-to-N matched case in a straightforward manner. In the third paper we consider the problem of estimating the joint distribution of two correlated random variables where one of the variables is observed with error. DKM is first used to adjust the univariate measurement error. A Gaussian copula is then used to model the correlation structure between the two variables after error adjustment

Molecular Weight Distributions and Size-dependent Composition of Dissolved Organic Matter in the Aquatic Continuum

Dissolved organic matter (DOM) in aquatic environments is one of the most important carbon reservoirs in global carbon cycling. The molecular weight of DOM is strongly related to a great number of biogeochemical reactions, influencing ecological functions and the fate of bioactive elements in aquatic ecosystems. A new technique coupling flow field-flow fractionation with fluorescence excitation-emission matrix (EEM) and parallel factor (PARAFAC) analysis was developed to elucidate the variations in DOM composition and optical properties with molecular weight in the individual samples and their changes along the aquatic continuum. Based on the novel coupling technique, variations in DOM characteristics were investigated across a trophic gradient from the lower Fox River to open Green Bay. Humic-like components were predominantly partitioned in the lower molecular weight fractions while protein-like components were mostly in the higher molecular weight fractions. Furthermore, unique fluorescent components in the river and open bay waters were observed, which would not have been identified using classic EEM-PARAFAC approach. Three fluorescent DOM components, including two humic-like and one protein-like components, were first reported for the lower Yukon River. Together with seasonal changes in chemical composition and optical properties between different DOM size-fractions, we provide a baseline dataset for future trend studies in the context of environmental change in Arctic river basins. A data fusion technique was applied to jointly analyze multiple datasets including fluorescence EEM and Fourier transform infrared spectra to decipher potential internal connections between fluorophores and chemical bonds. Humic-like components were mostly associated with C-H, C=C, and C-O bonds, while protein-like components were correlated more with C-N and N-H bonds. Based on year-long time-series sampling, seasonal variations of DOM size and composition were further investigated in two contrasting aquatic environments, i.e., the terrestrial-dominated Milwaukee River, with environmental changes and anthropogenic impacts from metropolitan Milwaukee, and the eutrophic Veterans Park Lagoon, where seasonal Microcystis blooms have been observed over the past decades. In addition to differences in DOM sources and composition, which were correlated to hydrological conditions and biomass, factors controlling DOM dynamics and seasonality were distinctive between the two aquatic ecosystems