Physical Sciences and Mathematics Commons^™

Robustness Of The Within- And Between-Series Estimators To Non-Normal Multiple-Baseline Studies: A Monte Carlo Study, Seang-Hwane Joo

USF Tampa Graduate Theses and Dissertations

In single-case research, multiple-baseline (MB) design is the most widely used design in practical settings. It provides the opportunity to estimate the treatment effect based on not only within-series comparisons of treatment phase to baseline phase observations, but also time-specific between-series comparisons of observations from those that have started treatment to those that are still in the baseline. In MB studies, the average treatment effect and the variation of these effects across multiple participants can be estimated using various statistical modeling methods. Recently, two types of statistical modeling methods were proposed for analyzing MB studies: a) within-series model and b) …

A Latent Mixture Approach To Modeling Zero-Inflated Bivariate Ordinal Data, Rajendra Kadel

USF Tampa Graduate Theses and Dissertations

Multivariate ordinal response data, such as severity of pain, degree of disability, and satisfaction with a healthcare provider, are prevalent in many areas of research including public health, biomedical, and social science research. Ignoring the multivariate features of the response variables, that is, by not taking the correlation between the errors across models into account, may lead to substantially biased estimates and inference. In addition, such multivariate ordinal outcomes frequently exhibit a high percentage of zeros (zero inflation) at the lower end of the ordinal scales, as compared to what is expected under a multivariate ordinal distribution. Thus, zero inflation …

Statistical Estimation Of Physiologically-Based Pharmacokinetic Models: Identifiability, Variation, And Uncertainty With An Illustration Of Chronic Exposure To Dioxin And Dioxin-Like-Compounds., Zachary John Thompson

USF Tampa Graduate Theses and Dissertations

Assessment of human exposure to environmental chemicals is inherently subject to uncertainty and variability. There are data gaps concerning the inventory, source, duration, and intensity of exposure

as well as knowledge gaps regarding pharmacokinetics in general. These gaps result in uncertainties in exposure assessment.

The uncertainties compound further with variabilities due to population variations regarding stage of life, life style, and susceptibility,

etc. Use of physiologically-based pharmacokinetic (PBPK) models promises to reduce the uncertainties and enhance extrapolation between species, between routes, from high to low dose, and from acute to chronic exposure. However, fitting PBPK models is challenging because of …

Modeling Endogenous Treatment Eects With Heterogeneity: A Bayesian Nonparametric Approach, Xuequn Hu

USF Tampa Graduate Theses and Dissertations

This dissertation explores the estimation of endogenous treatment effects in the presence of heterogeneous responses. A Bayesian Nonparametric approach is taken to model the heterogeneity in treatment effects. Specifically, I adopt the Dirichlet Process Mixture (DPM) model to capture the heterogeneity and show that DPM often outperforms Finite Mixture Model (FMM) in providing more flexible function forms and thus better model fit. Rather than fixing the number of components in a mixture model, DPM allows the data and prior knowledge to determine the number of components in the data, thus providing an automatic mechanism for model selection.

Two DPM models …