SURVIVAL PREDICTION FOR BRAIN TUMOR PATIENTS USING GENE EXPRESSION DATA

Brain tumor is one of the most aggressive types of cancer in humans, with an estimated median survival time of 12 months and only 4% of the patients surviving more than 5 years after disease diagnosis. Until recently, brain tumor prognosis has been based only on clinical information such as tumor grade and patient age, but there are reports indicating that molecular profiling of gliomas can reveal subgroups of patients with distinct survival rates. We hypothesize that coupling molecular profiling of brain tumors with clinical information might improve predictions of patient survival time and, consequently, better guide future treatment decisions. In order to evaluate this hypothesis, the general goal of this research is to build models for survival prediction of glioma patients using DNA molecular profiles (U133 Affymetrix gene expression microarrays) along with clinical information. First, a predictive Random Forest model is built for binary outcomes (i.e. short vs. long-term survival) and a small subset of genes whose expression values can be used to predict survival time is selected. Following, a new statistical methodology is developed for predicting time-to-death outcomes using Bayesian ensemble trees. Due to a large heterogeneity observed within prognostic classes obtained by the Random Forest model, prediction can be improved by relating time-to-death with gene expression profile directly. We propose a Bayesian ensemble model for survival prediction which is appropriate for high-dimensional data such as gene expression data. Our approach is based on the ensemble "sum-of-trees" model which is flexible to incorporate additive and interaction effects between genes. We specify a fully Bayesian hierarchical approach and illustrate our methodology for the CPH, Weibull, and AFT survival models. We overcome the lack of conjugacy using a latent variable formulation to model the covariate effects which decreases computation time for model fitting. Also, our proposed models provides a model-free way to select important predictive prognostic markers based on controlling false discovery rates. We compare the performance of our methods with baseline reference survival methods and apply our methodology to an unpublished data set of brain tumor survival times and gene expression data, selecting genes potentially related to the development of the disease under study. A closing discussion compares results obtained by Random Forest and Bayesian ensemble methods under the biological/clinical perspectives and highlights the statistical advantages and disadvantages of the new methodology in the context of DNA microarray data analysis.

Integrating sequence information in microarray data analysis by free energy modeling

Microarray technology is a high-throughput method for genotyping and gene expression profiling. Limited sensitivity and specificity are one of the essential problems for this technology. Most of existing methods of microarray data analysis have an apparent limitation for they merely deal with the numerical part of microarray data and have made little use of gene sequence information. Because it's the gene sequences that precisely define the physical objects being measured by a microarray, it is natural to make the gene sequences an essential part of the data analysis. This dissertation focused on the development of free energy models to integrate sequence information in microarray data analysis. The models were used to characterize the mechanism of hybridization on microarrays and enhance sensitivity and specificity of microarray measurements. ^ Cross-hybridization is a major obstacle factor for the sensitivity and specificity of microarray measurements. In this dissertation, we evaluated the scope of cross-hybridization problem on short-oligo microarrays. The results showed that cross hybridization on arrays is mostly caused by oligo fragments with a run of 10 to 16 nucleotides complementary to the probes. Furthermore, a free-energy based model was proposed to quantify the amount of cross-hybridization signal on each probe. This model treats cross-hybridization as an integral effect of the interactions between a probe and various off-target oligo fragments. Using public spike-in datasets, the model showed high accuracy in predicting the cross-hybridization signals on those probes whose intended targets are absent in the sample. ^ Several prospective models were proposed to improve Positional Dependent Nearest-Neighbor (PDNN) model for better quantification of gene expression and cross-hybridization. ^ The problem addressed in this dissertation is fundamental to the microarray technology. We expect that this study will help us to understand the detailed mechanism that determines sensitivity and specificity on the microarrays. Consequently, this research will have a wide impact on how microarrays are designed and how the data are interpreted. ^

Behavioral journalism newsletters to increase employee participation in a free worksite wellness center

Background. Providing an worksite fitness center for employees is an effective setting to increase an adult’s levels of physical activity, demonstrably reducing health care costs and elevating employee productivity. Increasing compliance and adherence among employees to consistently utilize an onsite facility remains a challenge.^ Purpose. To illustrate if two electronic behavioral journalism newsletters can increase employee participation in a free worksite wellness center.^ Design. A randomized control trial was implemented. ^ Setting. A large petroleum-producing corporation located in Houston, Texas Subjects.70 full or part time employees that had checked into visited the wellness center during September, October, and November 2009. ^ Intervention. Two behavioral journalism newsletters were created based on authentic community member role model interviews. Newsletters were distributed to intervention group participants via company email. Intervention and control group participant’s visits to the wellness center were monitored via a key card check in system at the facility for two weeks following the newsletter intervention.^ Analysis. Count variable statistics were used to identify rate differences between the intervention and control group wellness center visits. A binomial test for equality of proportions was used to identify differences between participants that had at least one visit to the wellness center compared with those with none.^ Results. Both intervention and control groups had a baseline of zero wellness center visits. The control group had a mean visit rate 0.441 and the intervention group had 0.857. A 0.416, almost one half more visits were found among the intervention group.^ Conclusion. This study indicates that behavioral journalism newsletters featuring a role model from within the worksite population can be a cost effective communication method to improve participation in an onsite wellness center. ^

AN ECONOMIC ANALYSIS OF THE DEMAND FOR CARE IN A FREE-STANDING EMERGENCY CENTER

Free-standing emergency centers (FECs) represent a new approach to the delivery of health care which are competing for patients with more conventional forms of ambulatory care in many parts of the U.S. Currently, little is known about these centers and their patient populations. The purpose of this study, therefore, was to describe the patients who visited two commonly-owned FECs, and determine the reasons for their visits. An economic model of the demand for FEC care was developed to test its ability to predict the economic and sociodemographic factors of use. Demand analysis of other forms of ambulatory services, such as a regular source of care (RSOC), was also conducted to examine the issues of substitution and complementarity.^ A systematic random sample was chosen from all private patients who used the clinics between July 1 and December 31, 1981. Data were obtained by means of a telephone interview and from clinic records. Five hundred fifty-one patients participated in the study.^ The typical FEC patient was a 26 year old white male with a minimum of a high school education, and a family income exceeding $25,000 a year. He had lived in the area for at least twenty years, and was a professional or a clerical worker. The patients made an average of 1.26 visits to the FECs in 1981. The majority of the visits involved a medical complaint; injuries and preventive care were the next most common reasons for visits.^ The analytic results revealed that time played a relatively important role in the demand for FEC care. As waiting time at the patients' regular source of care increased, the demand for FEC care increased, indicating that the clinic serves as a substitute for the patients' usual means of care. Age and education were inversely related to the demand for FEC care, while those with a RSOC frequented the clinics less than those lacking such a source.^ The patients used the familiar forms of ambulatory care, such as a private physician or an emergency room in a more typical fashion. These visits were directly related to the age and education of the patients, existence of a regular source of care, and disability days, which is a measure of health status. ^

Breast cancer risk with aromatase inhibition and phytoestrogen-free diet

In this thesis a mouse model was used to examine the effect of pubertal estrogen inhibition and a phytoestrogen-free diet on the development of mammary glands. The study question was does treatment with aromatase inhibitor during puberty increase susceptibility to breast cancer among cohorts that consumed a diet free of phytoestrogens. The study design consisted of a cohort of mice treated with aromatase inhibitor, letrozole, during puberty and a vehicular group that was used as a control. Both groups were fed a diet free of phytoestrogens from the time of weaning until sacrifice during adulthood. The study aimed to assess mammary gland development in terms of breast cancer risk. The methods employed in this research included morphological and histological analysis of mammary glands, as well as estradiol, RNA and protein analysis. The main finding of the study was that mice exposed to aromatase inhibitor during puberty developed mammary glands with specific characteristics suggestive of vulnerability to oncogenesis such as increased lateral branching, increased number of glands, increase ductal hyperplasia, and diminished expression of TGFβ and p27 protein levels. The conclusions suggest that puberty is a critical period in which the mammary gland is susceptible to environmental threats that may result in deleterious epigenetic effects leading to an increased breast cancer risk in adulthood. This study has several public health implications; the most significant is that environmental threats during puberty may result in adverse mammary gland development and that phytoestrogen sources in the diet are necessary for normal maturation of the mammary glands.^