published_or_final_version / abstract / toc / Statistics and Actuarial Science / Master / Master of Philosophy

In cross-cultural studies, respondents from specific cultures may have different product preferences and scale usage. Combining data from different cultures will result in departures from the basic assumptions of analysis of variance (ANOVA) and loss of power in testing capability of finding product and culture differences. However, the result of violations on power of ANOVA is unknown by sensory researchers. The objectives of this research were by simulating consumer product evaluation data, to evaluate the robustness and testing power of ANOVA under different cross-cultural situations. The study was conducted in two parts. First, an Empirical Logit simulation model was employed for generating sensory data. This model included respondent, product, consumer segment and product by segment interaction effects. Four underlying distributions: Binomial, Beta-Binomial, Hypergeometric, and Beta-Hypergeometric were used to increase or decrease the dispersion of the responses. Alternatively, instead of using these four distributions, the same applications were achieved by a binning step. The entire simulation procedure including the Empirical Logit model and the binning step was called Discrete Empirical Logit model. In the second part of the study, the Discrete Empirical Logit model was chosen to generate specified data sets under six different cross-cultural cases. After analyzing these data sets by ANOVA reduced and full models, the empirical power of ANOVA under different cases was calculated and compared. The results showed that both Beta-Hypergeometric and Discrete Empirical Logit were flexible on simulating sensory responses, but the Discrete Empirical Logit was relatively simple to use. Comparing with the ANOVA reduced model, the full model gave better information on evaluating the case that segments differ in product preferences. This suggested segmentation was very important in cross-cultural data analysis. Under the situations that sample sizes were equal and respondents performed consistently within segment (MSE ≈ 1), ANOVA was very robust to different scale usage, losing at worst 18% in power. From the scope of this study, we recommend using the ANOVA full model in the cross-cultural research. Results from different cultures could be combined when consistency within segments was high. / Graduation date: 2003

Indiana University-Purdue University Indianapolis (IUPUI) / With the availability of electronic medical records, medication dispensing data offers an unprecedented opportunity for researchers to explore complex relationships among longterm medication use, disease progression and potential side-effects in large patient populations. However, these data also pose challenges to existing statistical models because both medication exposure status and its intensity vary over time. This dissertation focused on flexible models to investigate the association between time-varying exposures and different types of outcomes. First, a penalized functional regression model was developed to estimate the effect of time-varying exposures on multivariate longitudinal outcomes. Second, for survival outcomes, a regression spline based model was proposed in the Cox proportional hazards (PH) framework to compare disease risk among different types of time-varying exposures. Finally, a penalized spline based Cox PH model with functional interaction terms was developed to estimate interaction effect between multiple medication classes. Data from a primary care patient cohort are used to illustrate the proposed approaches in determining the association between antidepressant use and various outcomes. / NIH grants, R01 AG019181 and P30 AG10133.

Indiana University-Purdue University Indianapolis (IUPUI) / A semi-parametric approach incorporates parametric and nonparametric functions in the model and is very useful in situations when a fully parametric model is inadequate. The objective of this dissertation is to extend statistical methodology employing the semi-parametric modeling approach to analyze data in health science research areas. This dissertation has three parts. The first part discusses the modeling of the dose-response relationship with correlated data by introducing overall drug effects in addition to the deviation of each subject-specific curve from the population average. Here, a penalized spline regression method that allows modeling of the smooth dose-response relationship is applied to data in studies monitoring malaria drug resistance through the ex-vivo assays.The second part of the dissertation extends the SiZer map, which is an exploratory and a powerful visualization tool, to detect underlying significant features (increase, decrease, or no change) of the curve at various smoothing levels. Here, Penalized Spline Significant Zero Crossings of Derivatives (PS-SiZer), using a penalized spline regression, is introduced to investigate significant features in correlated data arising from longitudinal settings. The third part of the dissertation applies the proposed PS-SiZer methodology to analyze HIV data. The durability of significant weight change over a period is explored from the PS-SiZer visualization. PS-SiZer is a graphical tool for exploring structures in curves by mapping areas where rate of change is significantly increasing, decreasing, or does not change. PS-SiZer maps provide information about the significant rate of weigh change that occurs in two ART regimens at various level of smoothing. A penalized spline regression model at an optimum smoothing level is applied to obtain an estimated first-time point where weight no longer increases for different treatment regimens.

Over the last 5 decades, the retrospective pretest has been used in behavioral science research to battle key threats to the internal validity of posttest-only control-group and pretest-posttest only designs. The purpose of this study was to compare outcome measures resulting from four research design implementations incorporating the retrospective pretest: (a) pre-post-then, (b) pre-post/then, (c) post-then, and (d) post/then. The study analyzed the interaction effect of pretest sensitization and post-intervention survey order on two subjective measures: (a) a control measure not related to the intervention and (b) an experimental measure consistent with the intervention. Validity of subjective measurement outcomes were assessed by correlating resulting to objective performance measurement outcomes. A Situational Leadership® II (SLII) training workshop served as the intervention. The Work Involvement Scale of the self version of the Survey of Management Practices Survey served as the subjective control measure. The Clarification of Goals and Objectives Scale of the self version of the Survey of Management Practices Survey served as the subjective experimental measure. The Effectiveness Scale of the self version of the Leader Behavior Analysis II® served as the objective performance measure. This study detected differences in measurement outcomes from SLII participant responses to an experimental and a control measure. In the case of the experimental measure, differences were found in the magnitude and direction of the validity coefficients. In the case of the control measure, differences were found in the magnitude of the treatment effect between groups. These differences indicate that, for this study, the pre-post-then design produced the most valid results for the experimental measure. For the control measure in this study, the pre-post/then design produced the most valid results. Across both measures, the post/then design produced the least valid results.

Publicly available datasets in health science are often large and observational, in contrast to experimental datasets where a small number of data are collected in controlled experiments. Variables' causal relationships in the observational dataset are yet to be determined. However, there is a significant interest in health science to discover and analyze causal relationships from health data since identified causal relationships will greatly facilitate medical professionals to prevent diseases or to mitigate the negative effects of the disease. Recent advances in Computer Science, particularly in Bayesian networks, has initiated a renewed interest for causality research. Causal relationships can be possibly discovered through learning the network structures from data. However, the number of candidate graphs grows in a more than exponential rate with the increase of variables. Exact learning for obtaining the optimal structure is thus computationally infeasible in practice. As a result, heuristic approaches are imperative to alleviate the difficulty of computations. This research provides effective and efficient learning tools for local causal discoveries and novel methods of learning causal structures with a combination of background knowledge. Specifically in the direction of constraint based structural learning, polynomial-time algorithms for constructing causal structures are designed with first-order conditional independence. Algorithms of efficiently discovering non-causal factors are developed and proved. In addition, when the background knowledge is partially known, methods of graph decomposition are provided so as to reduce the number of conditioned variables. Experiments on both synthetic data and real epidemiological data indicate the provided methods are applicable to large-scale datasets and scalable for causal analysis in health data. Followed by the research methods and experiments, this dissertation gives thoughtful discussions on the reliability of causal discoveries computational health science research, complexity, and implications in health science research.

Lam Yuk Fai. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2000. / Includes bibliographical references (leaves 41-43). / Abstracts in English and Chinese. / Chapter 1 --- Basic Concepts --- p.9 / Chapter 1.1 --- Survival data --- p.9 / Chapter 1.1.1 --- An example --- p.9 / Chapter 1.2 --- Some important functions --- p.11 / Chapter 1.2.1 --- Survival function --- p.12 / Chapter 1.2.2 --- Hazard function --- p.12 / Chapter 1.3 --- Cox Proportional Hazards Model --- p.13 / Chapter 1.3.1 --- A special case --- p.14 / Chapter 1.3.2 --- An example (continued) --- p.15 / Chapter 1.4 --- Extension of the Cox Proportional Hazards Model --- p.16 / Chapter 1.5 --- Bootstrap --- p.17 / Chapter 2 --- A New Method --- p.19 / Chapter 2.1 --- Introduction --- p.19 / Chapter 2.2 --- Definition of the test --- p.20 / Chapter 2.2.1 --- Our test statistic --- p.20 / Chapter 2.2.2 --- The alternative test statistic I --- p.22 / Chapter 2.2.3 --- The alternative test statistic II --- p.23 / Chapter 2.3 --- Variations of the test --- p.24 / Chapter 2.3.1 --- Restricted test --- p.24 / Chapter 2.3.2 --- Adjusting for other covariates --- p.26 / Chapter 2.4 --- Apply with bootstrap --- p.28 / Chapter 2.5 --- Examples --- p.29 / Chapter 2.5.1 --- Male mice data --- p.34 / Chapter 2.5.2 --- Stanford heart transplant data --- p.34 / Chapter 2.5.3 --- CGD data --- p.34 / Chapter 3 --- Large Sample Properties and Discussions --- p.35 / Chapter 3.1 --- Large sample properties and relationship to goodness of fit test --- p.35 / Chapter 3.1.1 --- Large sample properties of A and Ap --- p.35 / Chapter 3.1.2 --- Large sample properties of Ac and A --- p.36 / Chapter 3.2 --- Discussions --- p.37

Bibliography: leaves 250-264 / xx. 264, [47] leaves : ill ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / Thesis (Ph.D.)--University of Adelaide, Waite Agricultural Research Institute, 1984

Analytical problems caused by over-fitting, confounding and non-independence in the data is a major challenge for variable selection. As more variables are tested against a certain data set, there is a greater risk that some will explain the data merely by chance, but will fail to explain new data. The main aim of this study is to employ a systematic and practicable variable selection process for the spatial analysis and mapping of historical malaria risk in Botswana using data collected from the MARA (Mapping Malaria Risk in Africa) project and environmental and climatic datasets from various sources. Details of how a spatial database is compiled for a statistical analysis to proceed is provided. The automation of the entire process is also explored. The final bayesian spatial model derived from the non-spatial variable selection procedure using Markov Chain Monte Carlo simulation was fitted to the data. Winter temperature had the greatest effect of malaria prevalence in Botswana. Summer rainfall, maximum temperature of the warmest month, annual range of temperature, altitude and distance to closest water source were also significantly associated with malaria prevalence in the final spatial model after accounting for spatial correlation. Using this spatial model malaria prevalence at unobserved locations was predicted, producing a smooth risk map covering Botswana. The automation of both compiling the spatial database and the variable selection procedure proved challenging and could only be achieved in parts of the process. The non-spatial selection procedure proved practical and was able to identify stable explanatory variables and provide an objective means for selecting one variable over another, however ultimately it was not entirely successful due to the fact that a unique set of spatial variables could not be selected.

This study presents the methodology for estimating the incidence rate from the age specific prevalence data of three different eye diseases. We consider both situations where the mortality may differ from one person to another, with and without the disease. The method used was developed by Marvin J. Podgor for estimating incidence rate from prevalence data. It delves into the application of logistic regression to obtain the smoothed prevalence rates that helps in obtaining incidence rate. The study concluded that the use of logistic regression can produce a meaningful model, and the incidence rates of these diseases were not affected by the assumption of differential mortality.