Evaluating the impact of vertical integration on health system peformance

Vertical integration is grounded in economic theory as a corporate strategy for reducing cost and enhancing efficiency. There were three purposes for this dissertation. The first was to describe and understand vertical integration theory. The review of the economic theory established vertical integration as a corporate cost reduction strategy in response to environmental, structural and performance dimensions of the market. The second purpose was to examine vertical integration in the context of the health care industry, which has greater complexity, higher instability, and more unstable demand than other industries, although many of the same dimensions of the market supported a vertical integration strategy. Evidence on the performance of health systems after integration revealed mixed results. Because the market continues to be turbulent, hybrid non-owned integration in the form of alliances have increased to over 40% of urban hospitals. The third purpose of the study was to examine the application of vertical integration in health care and evaluate the effects. The case studied was an alliance formed between a community hospital and a tertiary medical center to facilitate vertical integration of oncology services while maintaining effectiveness and preserving access. The economic benefits for 1934 patients were evaluated in the delivery system before and after integration with a more detailed economic analysis of breast, lung, colon/rectal, and non-malignant cases. A regression analysis confirmed the relationship between the independent variables of age, sex, location of services, race, stage of disease, and diagnosis, and the dependent variable, cost. The results of the basic regression model, as well as the regression with first-order interaction terms, were statistically significant. The study shows that vertical integration at an intermediate health care system level has economic benefits. If the pre-integration oncology group had been treated in the post-integration model, the expected cost savings from integration would be 31.5%. Quality indicators used were access to health care services and research treatment protocols, and access was preserved in the integrated model. Using survival as a direct quality outcome measure, the survival of lung cancer patients was statistically the same before and after integration. ^

Smoke free restaurant ordinance, compliance with the regulation to reduce exposure to environmental tobacco smoke while dining at local restaurants in San Antonio, Texas

This study is a secondary analysis of a survey developed by Dr. Jimmy Perkins and administered by San Antonio/Bexar County Metropolitan Health District. The survey was developed subsequent to the implementation of the city smoking ordinance effective January 1, 2004. The survey had a multi-purpose plan to establish the number of restaurants having smoke free status prior to and following the ordinance, determine compliance as it relates to a necessary smoking section and proper signage, and expose the rationale for restaurants to become smoke free. The data resulting from the survey was presented to the San Antonio/Bexar County Metropolitan Health District. The summary presented the types of establishments surveyed, smoking status of the establishment, reasons for the establishment becoming smoke free, compliance with smoking sections, compliance with signage requirements, awareness of ordinance, and chain status of the establishment. ^ The results of this study display the relationships among the variables previously mentioned. The following relationships have been examined and the outcomes have determined whether each is significant. After careful analysis, knowledge translates into compliance with signage regulations, which then translate into ordinance compliance. Size does matter as it relates to an establishment's number of employees and seating capacity. The smaller the establishment the more likely the establishment is to have become smoke free before the ordinance went into effect. Restaurants, rather than fast food establishments most commonly cited their reason for becoming smoke free was to comply with the ordinance and only ten percent of restaurants gave policy as the main reason for becoming smoke free. ^ This study is important for public health because the negative health effects of environmental tobacco smoke (ETS) are still an overwhelming problem in the United States (3). ETS is a Known Human Group A Carcinogen (5). The Environmental Protection Agency (EPA) has estimated that around 3,000 non-smoking Americans die every year from lung cancer caused by ETS (6). This information illustrates the importance of providing smoke free establishments, especially to non-smoking patrons. ^

Environmental tobacco smoke exposure, respiratory and cardiovascular health in restaurant and bar workers in Mexico

Environmental tobacco smoke (ETS) is a well established health hazard, being causally associated to lung cancer and cardiovascular disease. ETS regulations have been developed worldwide to reduce or eliminate exposure in most public places. Restaurants and bars constitute an exception. Restaurants and bar workers experience the highest ETS exposure levels across several occupations, with correspondingly increased health risks. In Mexico, previous exposure assessment in restaurants and bars showed concentrations in bars and restaurants to be the highest across different public and workplaces. Recently, Mexico developed at the federal level the General Law for Tobacco Control restricting indoors smoking to separated areas. AT the local level Mexico City developed the Law for the Protection of Non-smokers Health, completely banning smoking in restaurants and bars. Studies to assess ETS exposure in restaurants and bars, along with potential health effects were required to evaluate the impact of these legislative changes and to set a baseline measurement for future evaluations.^ A large cross-sectional study conducted in restaurants and bars from four Mexican cities was conducted from July to October 2008, to evaluate the following aims: Aim 1) Explore the potential impact of the Mexico City ban on ETS concentrations through comparison of Mexico City with other cities. Aim 2). Explore the association between ETS exposure, respiratory function indicators and respiratory symptoms. Aim 3). Explore the association between ETS exposure and blood pressure and heart rate.^ Three cities with no smoking ban were selected: Colima (11.5% smoking prevalence), Cuernavaca (21.5% smoking prevalence) and Toluca (27.8% smoking prevalence). Mexico City (27.9% smoking prevalence), the only city with a ban at the time of the study, was also selected. Restaurants and bars were randomly selected from municipal records. A goal of 26 restaurants and 26 bars per city was set, 50% of them under 100 m2. Each establishment was visited during the highest occupancy shift, and managers and workers answered to a questionnaire. Vapor-phase nicotine was measured using passive monitors, that were activated at the beginning and deactivated at the end of the shift. Also, workers participated at the beginning and end of the shift in a short physical evaluation, comprising the measurement of Forced Expiratory Volume in the first second (FEV1) and Peak Expiratory Flow (PEF), as well as blood pressure and heart rate.^ A total of 371 establishments were invited, 219 agreed to participate for a 60.1% participation rate. In them, 828 workers were invited, 633 agreed to participate for a 76% participation rate. Mexico City had at least 4 times less nicotine compared to any of the other cities. Differences between Mexico City and other cities were not explained by establishment characteristics, such as ventilation or air extraction. However, differences between cities disappeared when ban mechanisms, such as policy towards costumer's smoking, were considered in the models. An association between ETS exposure and respiratory symptoms (cough OR=1.27, 95%CI=1.04, 1.55) and respiratory illness (asthma OR=1.97, 95%CI=1.20, 3.24; respiratory illness OR=1.79, 95%CI=1.10, 2.94) was observed. No association between ETS and phlegm, wheezing or respiratory infections was observed. No association between ETS and any of the spirometric indicators was observed. An association between ETS exposure and increased systolic and diastolic blood pressure at the end of the shift was observed among non-smokers (systolic blood pressure beta=1.51, 95%CI=0.44, 2.58; diastolic blood pressure beta=1.50, 95%CI=0.72, 2.28). The opposite effect was observed in heavy smokers, were increased ETS exposure was associated with lower blood pressure at the end of the shift (systolic blood pressure beta=1.90, 95%CI=-3.57, -0.23; diastolic blood pressure beta=-1.46, 95%CI=-2.72, -0.02). No association in light smokers was observed. No association for heart rate was observed. ^ Results from this dissertation suggest Mexico City's smoking ban has had a larger impact on ETS exposure. Ventilation or air extraction, mechanisms of ETS control suggested frequently by tobacco companies to avoid smoking bans were not associated with ETS exposure. This dissertation suggests ETS exposure could be linked to changes in blood pressure and to increased respiratory symptoms. Evidence derived from this dissertation points to the potential negative health effects of ETS exposure in restaurants and bars, and provides support for the development of total smoking bans in this economic sector. ^

A cohort study of occupational asbestos-exposure related neoplasms in Texas Gulf Coast area

A cohort study was conducted in Texas and Louisiana Gulf Coast area on individual workers who have been exposed to asbestos for 15 years or more. Most of these workers were employed in petrochemical industries. Of the 15,742 subjects initially selected for the cohort study, 3,258 had positive chest X-ray findings believed to be related to prolonged asbestos exposure. These subjects were further investigated. Their work out included detailed medical and occupational history, laboratory tests and spirometry. One thousand eight-hundred and three cases with positive chest X-ray findings whose data files were considered complete at the end of May 1986 were analyzed and their findings included in this report.^ The prevalence of lung cancer and cancer of the following sights: skin, stomach, oropharyngeal, pancreas and kidneys were significantly increased when compared to data from Connecticut Tumor Registry. The prevalence of other chronic conditions such as hypertension, emphysema, heart disease and peptic ulcer was also significantly high when compared to data for the U.S. and general population furnished by the National Center for Health Statistics (NCHS). In most instances the occurrence of cancer and the chronic ailment previously mentioned appeared to follow 15-25 years of exposure to asbestos. ^

Genetic epidemiologic methods in risk determination in Li-Fraumeni syndrome

Li-Fraumeni syndrome (LFS) is characterized by a variety of neoplasms occurring at a young age with an apparent autosomal dominant transmission. Individuals in pedigrees with LFS have high incidence of second malignancies. Recently LFS has been found to be associated with germline mutations of a tumor-suppressor gene, p53. Because LFS is rare and indeed not a clear-cut disease, it is not known whether all cases of LFS are attributable to p53 germline mutations and how p53 plays in cancer occurrence in such cancer syndrome families. In the present study, DNAs from constitutive cells of two-hundred and thirty-three family members from ten extended pedigrees were screened for p53 mutations. Six out of the ten LFS families had germline mutations at the p53 locus, including point and deletion mutations. In these six families, 55 out of 146 members were carriers of p53 mutations. Except one, all mutations occurred in exons 5 to 8 (i.e., the "hot spot" region) of the p53 gene. The age-specific penetrance of cancer was estimated after the genotype for each family member at risk was determined. The penetrance was 0.15, 0.29, 0.35, 0.77, and 0.91 by 20, 30, 40, 50 and 60 year-old, respectively, in male carriers; 0.19, 0.44, 0.76, and 0.90 by 20, 30, 40, and 50 year-old, respectively, in female carriers. These results indicated that one cannot escape from tumorigenesis if one inherits a p53 mutant allele; at least ninety percent of p53 carriers will develop cancer by the age of 60. To evaluate the possible bias due to the unexamined blood-relatives in LFS families, I performed a simulation analysis in which a p53 genotype was assigned to each unexamined person based on his cancer status and liability to cancer. The results showed that the penetrance estimates were not biased by the unexamined relatives. I also determined the sex, site, and age-specific penetrance of breast cancer in female carriers and lung cancer in male carriers. The penetrance of breast cancer in female carriers was 0.81 by age 45; the penetrance of lung cancer in male carriers was 0.78 by age 60, indicating that p53 play a key role for tumorigenesis in common cancers. ^

A comparative analysis of Texas and Louisiana Oil-field NORM Waste Policy

NORM (Naturally Occurring Radioactive Material) Waste Policies for the nation's oil and gas producing states have been in existence since the 1980's, when Louisiana was the first state to develop a NORM regulatory program in 1989. Since that time, expectations for NORM Waste Policies have evolved, as Health, Safety, Environment, and Social responsibility (HSE & SR) grows increasingly important to the public. Therefore, the oil and gas industry's safety and environmental performance record will face challenges in the future, about its best practices for managing the co-production of NORM wastes. ^ Within the United States, NORM is not federally regulated. The U.S. EPA claims it regulates NORM under CERCLA (superfund) and the Clean Water Act. Though, there are no universally applicable regulations for radium-based NORM waste. Therefore, individual states have taken responsibility for developing NORM regulatory programs, because of the potential radiological risk it can pose to man (bone and lung cancer) and his environment. This has led to inconsistencies in NORM Waste Policies as well as a NORM management gap in both state and federal regulatory structures. ^ Fourteen different NORM regulations and guidelines were compared between Louisiana and Texas, the nation's top two petroleum producing states. Louisiana is the country's top crude oil producer when production from its Federal offshore waters are included, and fourth in crude oil production, behind Texas, Alaska, and California when Federal offshore areas are excluded. Louisiana produces more petroleum products than any state but Texas. For these reasons, a comparative analysis between Louisiana and Texas was undertaken to identify differences in their NORM regulations and guidelines for managing, handling and disposing NORM wastes. Moreover, this analysis was undertaken because Texas is the most explored and drilled worldwide and yet appears to lag behind its neighboring state in terms of its NORM Waste Policy and developing an industry standard for handling, managing and disposing NORM. As a result of this analysis, fourteen recommendations were identified.^

Logistic regression with Markov chains as covariates

The tobacco-specific nitrosamine 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK) is an obvious carcinogen for lung cancer. Since CBMN (Cytokinesis-blocked micronucleus) has been found to be extremely sensitive to NNK-induced genetic damage, it is a potential important factor to predict the lung cancer risk. However, the association between lung cancer and NNK-induced genetic damage measured by CBMN assay has not been rigorously examined. ^ This research develops a methodology to model the chromosomal changes under NNK-induced genetic damage in a logistic regression framework in order to predict the occurrence of lung cancer. Since these chromosomal changes were usually not observed very long due to laboratory cost and time, a resampling technique was applied to generate the Markov chain of the normal and the damaged cell for each individual. A joint likelihood between the resampled Markov chains and the logistic regression model including transition probabilities of this chain as covariates was established. The Maximum likelihood estimation was applied to carry on the statistical test for comparison. The ability of this approach to increase discriminating power to predict lung cancer was compared to a baseline "non-genetic" model. ^ Our method offered an option to understand the association between the dynamic cell information and lung cancer. Our study indicated the extent of DNA damage/non-damage using the CBMN assay provides critical information that impacts public health studies of lung cancer risk. This novel statistical method could simultaneously estimate the process of DNA damage/non-damage and its relationship with lung cancer for each individual.^

Post-transcriptional Regulation of Mammalian Gene Expression in Non-coding Region of Target RNA

Tumor Suppressor Candidate 2 (TUSC2) is a novel tumor suppressor gene located in the human chromosome 3p21.3 region. TUSC2 mRNA transcripts could be detected on Northern blots in both normal lung and some lung cancer cell lines, but no endogenous TUSC2 protein could be detected in a majority of lung cancer cell lines. Mechanisms regulating TUSC2 protein expression and its inactivation in primary lung cancer cells are largely unknown. We investigated the role of the 5’- and 3’-untranslated regions (UTRs) of the TUSC2 gene in the regulation of TUSC2 protein expression. We found that two small upstream open-reading frames (uORFs) in the 5’UTR of TUSC2 could markedly inhibit the translational initiation of TUSC2 protein by interfering with the “scanning” of the ribosome initiation complexes. Site-specific stem-loop array reverse transcription-polymerase chain reaction (SLA-RT-PCR) verified several micoRNAs (miRNAs) targeted at 3’UTR and directed TUSC2 cleavage and degradation. In addition, we used the established let-7-targeted high mobility group A2 (Hmga2) mRNA as a model system to study the mechanism of regulation of target mRNA by miRNAs in mammalian cells under physiological conditions. There have been no evidence of direct link between mRNA downregulation and mRNA cleavages mediated by miRNAs. Here we showed that the endonucleolytic cleavages on mRNAs were initiated by mammalian miRNA in seed pairing style. Let-7 directed cleavage activities among the eight predicted potential target sites have varied efficiency, which are influenced by the positional and the structural contexts in the UTR. The 5’ cleaved RNA fragments were mostly oligouridylated at their 3’-termini and accumulated for delayed 5’–3’ degradation. RNA fragment oligouridylation played important roles in marking RNA fragments for delayed bulk degradation and in converting RNA degradation mode from 3’–5’ to 5’–3’ with cooperative efforts from both endonucleolytic and non-catalytic miRNA-induced silencing complex (miRISC). Our findings point to a mammalian miRNA-mediated mechanism for the regulation of mRNA that miRNA can decrease target mRNA through target mRNA cleavage and uridine addition

Cellular Uptake of Neutrohpil Elastase Links Inflammation to Adaptive Immunity

Many tumors arise from sites of inflammation providing evidence that innate immunity is a critical component in the development and progression of cancer. Neutrophils are primary mediators of the innate immune response. Upon activation, an important function of neutrophils is release of an assortment of proteins from their granules including the serine protease neutrophil elastase (NE). The effect of NE on cancer has been attributed primarily to its ability to degrade the extracellular matrix thereby promoting invasion and metastasis. Recently, it was shown that NE could be taken up by lung cancer cells leading to degradation of insulin receptor substrate-1 thereby promoting hyperactivity of the phosphatidylinositol-3 kinase (PI3K) pathway and tumor cell proliferation. To our knowledge, nobody has investigated uptake of NE by other tumor types. In addition, NE has broad substrate specificity suggesting that uptake of NE by tumor cells could impact processes regulating tumorigenensis other than activation of the PI3K pathway. Neutrophil elastase has been identified in breast cancer specimens where high levels of NE have prognostic significance. These studies have assessed NE levels in whole tumor lysates. Because the major source of NE is from activated neutrophils, we hypothesized that breast cancer cells do not have endogenous NE but may take up NE released by tumor associated neutrophils in the tumor microenvironment and that this could provide a link between the innate immune response to tumors and specific adaptive immune responses. In this thesis, we show that breast cancer cells lack endogenous NE expression and that they are able to take up NE resulting in increased generation of low molecular weight cyclin E (CCNE) and enhanced susceptibility to lysis by CCNE-specific cytotoxic T lymphocytes. We also show that after taking up NE and proteinase 3 (PR3), a second primary granule protease with significant homology to NE, breast cancer cells cross-present the NE- and PR3-derived peptide PR1 rendering them susceptible to PR1-targeted therapies. Taken together, our data support a role for NE uptake in modulating adaptive immune responses against breast cancer.

Design and Optimization of Four-dimensional Cone-beam Computed Tomography in Image-guided Radiation Therapy

The influence of respiratory motion on patient anatomy poses a challenge to accurate radiation therapy, especially in lung cancer treatment. Modern radiation therapy planning uses models of tumor respiratory motion to account for target motion in targeting. The tumor motion model can be verified on a per-treatment session basis with four-dimensional cone-beam computed tomography (4D-CBCT), which acquires an image set of the dynamic target throughout the respiratory cycle during the therapy session. 4D-CBCT is undersampled if the scan time is too short. However, short scan time is desirable in clinical practice to reduce patient setup time. This dissertation presents the design and optimization of 4D-CBCT to reduce the impact of undersampling artifacts with short scan times. This work measures the impact of undersampling artifacts on the accuracy of target motion measurement under different sampling conditions and for various object sizes and motions. The results provide a minimum scan time such that the target tracking error is less than a specified tolerance. This work also presents new image reconstruction algorithms for reducing undersampling artifacts in undersampled datasets by taking advantage of the assumption that the relevant motion of interest is contained within a volume-of-interest (VOI). It is shown that the VOI-based reconstruction provides more accurate image intensity than standard reconstruction. The VOI-based reconstruction produced 43% fewer least-squares error inside the VOI and 84% fewer error throughout the image in a study designed to simulate target motion. The VOI-based reconstruction approach can reduce acquisition time and improve image quality in 4D-CBCT.

DEVELOPMENT OF A BEAM-SPECIFIC PLANNING TARGET VOLUME AND A ROBUST PLAN ANALYSIS TOOL FOR PROTON THERAPY

Proton therapy is growing increasingly popular due to its superior dose characteristics compared to conventional photon therapy. Protons travel a finite range in the patient body and stop, thereby delivering no dose beyond their range. However, because the range of a proton beam is heavily dependent on the tissue density along its beam path, uncertainties in patient setup position and inherent range calculation can degrade thedose distribution significantly. Despite these challenges that are unique to proton therapy, current management of the uncertainties during treatment planning of proton therapy has been similar to that of conventional photon therapy. The goal of this dissertation research was to develop a treatment planning method and a planevaluation method that address proton-specific issues regarding setup and range uncertainties. Treatment plan designing method adapted to proton therapy: Currently, for proton therapy using a scanning beam delivery system, setup uncertainties are largely accounted for by geometrically expanding a clinical target volume (CTV) to a planning target volume (PTV). However, a PTV alone cannot adequately account for range uncertainties coupled to misaligned patient anatomy in the beam path since it does not account for the change in tissue density. In order to remedy this problem, we proposed a beam-specific PTV (bsPTV) that accounts for the change in tissue density along the beam path due to the uncertainties. Our proposed method was successfully implemented, and its superiority over the conventional PTV was shown through a controlled experiment.. Furthermore, we have shown that the bsPTV concept can be incorporated into beam angle optimization for better target coverage and normal tissue sparing for a selected lung cancer patient. Treatment plan evaluation method adapted to proton therapy: The dose-volume histogram of the clinical target volume (CTV) or any other volumes of interest at the time of planning does not represent the most probable dosimetric outcome of a given plan as it does not include the uncertainties mentioned earlier. Currently, the PTV is used as a surrogate of the CTV’s worst case scenario for target dose estimation. However, because proton dose distributions are subject to change under these uncertainties, the validity of the PTV analysis method is questionable. In order to remedy this problem, we proposed the use of statistical parameters to quantify uncertainties on both the dose-volume histogram and dose distribution directly. The robust plan analysis tool was successfully implemented to compute both the expectation value and its standard deviation of dosimetric parameters of a treatment plan under the uncertainties. For 15 lung cancer patients, the proposed method was used to quantify the dosimetric difference between the nominal situation and its expected value under the uncertainties.

Radiomics of NSCLC: Quantitative CT Image Feature Characterization and Tumor Shrinkage Prediction

Radiomics is the high-throughput extraction and analysis of quantitative image features. For non-small cell lung cancer (NSCLC) patients, radiomics can be applied to standard of care computed tomography (CT) images to improve tumor diagnosis, staging, and response assessment. The first objective of this work was to show that CT image features extracted from pre-treatment NSCLC tumors could be used to predict tumor shrinkage in response to therapy. This is important since tumor shrinkage is an important cancer treatment endpoint that is correlated with probability of disease progression and overall survival. Accurate prediction of tumor shrinkage could also lead to individually customized treatment plans. To accomplish this objective, 64 stage NSCLC patients with similar treatments were all imaged using the same CT scanner and protocol. Quantitative image features were extracted and principal component regression with simulated annealing subset selection was used to predict shrinkage. Cross validation and permutation tests were used to validate the results. The optimal model gave a strong correlation between the observed and predicted shrinkages with . The second objective of this work was to identify sets of NSCLC CT image features that are reproducible, non-redundant, and informative across multiple machines. Feature sets with these qualities are needed for NSCLC radiomics models to be robust to machine variation and spurious correlation. To accomplish this objective, test-retest CT image pairs were obtained from 56 NSCLC patients imaged on three CT machines from two institutions. For each machine, quantitative image features with concordance correlation coefficient values greater than 0.90 were considered reproducible. Multi-machine reproducible feature sets were created by taking the intersection of individual machine reproducible feature sets. Redundant features were removed through hierarchical clustering. The findings showed that image feature reproducibility and redundancy depended on both the CT machine and the CT image type (average cine 4D-CT imaging vs. end-exhale cine 4D-CT imaging vs. helical inspiratory breath-hold 3D CT). For each image type, a set of cross-machine reproducible, non-redundant, and informative image features was identified. Compared to end-exhale 4D-CT and breath-hold 3D-CT, average 4D-CT derived image features showed superior multi-machine reproducibility and are the best candidates for clinical correlation.

THE NATURAL AND ORTHOGONAL INTERACTION (NOIA) MODELS FOR QUANTITATIVE TRAITS (QTs) AND COMPLEX DISEASES

My dissertation focuses on developing methods for gene-gene/environment interactions and imprinting effect detections for human complex diseases and quantitative traits. It includes three sections: (1) generalizing the Natural and Orthogonal interaction (NOIA) model for the coding technique originally developed for gene-gene (GxG) interaction and also to reduced models; (2) developing a novel statistical approach that allows for modeling gene-environment (GxE) interactions influencing disease risk, and (3) developing a statistical approach for modeling genetic variants displaying parent-of-origin effects (POEs), such as imprinting. In the past decade, genetic researchers have identified a large number of causal variants for human genetic diseases and traits by single-locus analysis, and interaction has now become a hot topic in the effort to search for the complex network between multiple genes or environmental exposures contributing to the outcome. Epistasis, also known as gene-gene interaction is the departure from additive genetic effects from several genes to a trait, which means that the same alleles of one gene could display different genetic effects under different genetic backgrounds. In this study, we propose to implement the NOIA model for association studies along with interaction for human complex traits and diseases. We compare the performance of the new statistical models we developed and the usual functional model by both simulation study and real data analysis. Both simulation and real data analysis revealed higher power of the NOIA GxG interaction model for detecting both main genetic effects and interaction effects. Through application on a melanoma dataset, we confirmed the previously identified significant regions for melanoma risk at 15q13.1, 16q24.3 and 9p21.3. We also identified potential interactions with these significant regions that contribute to melanoma risk. Based on the NOIA model, we developed a novel statistical approach that allows us to model effects from a genetic factor and binary environmental exposure that are jointly influencing disease risk. Both simulation and real data analyses revealed higher power of the NOIA model for detecting both main genetic effects and interaction effects for both quantitative and binary traits. We also found that estimates of the parameters from logistic regression for binary traits are no longer statistically uncorrelated under the alternative model when there is an association. Applying our novel approach to a lung cancer dataset, we confirmed four SNPs in 5p15 and 15q25 region to be significantly associated with lung cancer risk in Caucasians population: rs2736100, rs402710, rs16969968 and rs8034191. We also validated that rs16969968 and rs8034191 in 15q25 region are significantly interacting with smoking in Caucasian population. Our approach identified the potential interactions of SNP rs2256543 in 6p21 with smoking on contributing to lung cancer risk. Genetic imprinting is the most well-known cause for parent-of-origin effect (POE) whereby a gene is differentially expressed depending on the parental origin of the same alleles. Genetic imprinting affects several human disorders, including diabetes, breast cancer, alcoholism, and obesity. This phenomenon has been shown to be important for normal embryonic development in mammals. Traditional association approaches ignore this important genetic phenomenon. In this study, we propose a NOIA framework for a single locus association study that estimates both main allelic effects and POEs. We develop statistical (Stat-POE) and functional (Func-POE) models, and demonstrate conditions for orthogonality of the Stat-POE model. We conducted simulations for both quantitative and qualitative traits to evaluate the performance of the statistical and functional models with different levels of POEs. Our results showed that the newly proposed Stat-POE model, which ensures orthogonality of variance components if Hardy-Weinberg Equilibrium (HWE) or equal minor and major allele frequencies is satisfied, had greater power for detecting the main allelic additive effect than a Func-POE model, which codes according to allelic substitutions, for both quantitative and qualitative traits. The power for detecting the POE was the same for the Stat-POE and Func-POE models under HWE for quantitative traits.

Development and implementation of a dynamic heterogeneous proton equivalent anthropomorphic thorax phantom for the assessment of scanned proton beam therapy

DEVELOPMENT AND IMPLEMENTATION OF A DYNAMIC HETEROGENEOUS PROTON EQUIVALENT ANTHROPOMORPHIC THORAX PHANTOM FOR THE ASSESSMENT OF SCANNED PROTON BEAM THERAPY by James Leroy Neihart, B.S. APPROVED: ______________________________David Followill, Ph.D. ______________________________Peter Balter, Ph.D. ______________________________Narayan Sahoo, Ph.D. ______________________________Kenneth Hess, Ph.D. ______________________________Paige Summers, M.S. APPROVED: ____________________________ Dean, The University of Texas Graduate School of Biomedical Sciences at Houston DEVELOPMENT AND IMPLEMENTATION OF A DYNAMIC HETEROGENEOUS PROTON EQUIVALENT ANTHROPOMORPHIC THORAX PHANTOM FOR THE ASSESSMENT OF SCANNED PROTON BEAM THERAPY A THESIS Presented to the Faculty of The University of Texas Health Science Center at Houston andThe University of TexasMD Anderson Cancer CenterGraduate School of Biomedical Sciences in Partial Fulfillment of the Requirements for the Degree of MASTER OF SCIENCE by James Leroy Neihart, B.S. Houston, Texas Date of Graduation August, 2013 Acknowledgments I would like to acknowledge my advisory committee members, chair David Followill, Ph.D., Peter Balter, Ph.D, Narayan Sahoo, Ph.D., Kenneth Hess, Ph.D., Paige Summers M.S. and, for their time and effort contributed to this project. I would additionally like to thank the faculty and staff at the PTC-H and the RPC who assisted in many aspects of this project. Falk Pӧnisch, Ph.D. for his breath hold proton therapy treatment expertise, Matt Palmer and Jaques Bluett for proton dosimetry assistance, Matt Kerr for verification plan assistance, Carrie Amador, Nadia Hernandez, Trang Nguyen, Andrea Molineu, Lynda McDonald for TLD and film dosimetry assistance. Finally, I would like to thank my wife and family for their support and encouragement during my research and studies. Development and implementation of a dynamic heterogeneous proton equivalent anthropomorphic thorax phantom for the assessment of scanned proton beam therapy By: James Leroy Neihart, B.S. Chair of Advisory Committee: David Followill, Ph.D Proton therapy has been gaining ground recently in radiation oncology. To date, the most successful utilization of proton therapy is in head and neck cases as well as prostate cases. These tumor locations do not suffer from the resulting difficulties of treatment delivery as a result of respiratory motion. Lung tumors require either breath hold or motion tracking, neither of which have been assessed with an end-to-end phantom for proton treatments. Currently, the RPC does not have a dynamic thoracic phantom for proton therapy procedure assessment. Additionally, such a phantom could be an excellent means of assessing quality assurance of the procedures of proton therapy centers wishing to participate in clinical trials. An eventual goal of this phantom is to have a means of evaluating and auditing institutions for the ability to start clinical trials utilizing proton therapy procedures for lung cancers. Therefore, the hypothesis of this study is that a dynamic anthropomorphic thoracic phantom can be created to evaluate end-to-end proton therapy treatment procedures for lung cancer to assure agreement between the measured and calculated dose within 5% / 5 mm with a reproducibility of 2%. Multiple materials were assessed for thoracic heterogeneity equivalency. The phantom was designed from the materials found to be in greatest agreement. The phantom was treated in an end-to-end treatment four times, which included simulation, treatment planning and treatment delivery. Each treatment plan was delivered three times to assess reproducibility. The dose measured within the phantom was compared to that of the treatment plan. The hypothesis was fully supported for three of the treatment plans, but failed the reproducibility requirement for the most aggressive treatment plan.

BAYESIAN STATISTICAL METHODS IN GENE-ENVIRONMENT AND GENE-GENE INTERACTION STUDIES

Complex diseases such as cancer result from multiple genetic changes and environmental exposures. Due to the rapid development of genotyping and sequencing technologies, we are now able to more accurately assess causal effects of many genetic and environmental factors. Genome-wide association studies have been able to localize many causal genetic variants predisposing to certain diseases. However, these studies only explain a small portion of variations in the heritability of diseases. More advanced statistical models are urgently needed to identify and characterize some additional genetic and environmental factors and their interactions, which will enable us to better understand the causes of complex diseases. In the past decade, thanks to the increasing computational capabilities and novel statistical developments, Bayesian methods have been widely applied in the genetics/genomics researches and demonstrating superiority over some regular approaches in certain research areas. Gene-environment and gene-gene interaction studies are among the areas where Bayesian methods may fully exert its functionalities and advantages. This dissertation focuses on developing new Bayesian statistical methods for data analysis with complex gene-environment and gene-gene interactions, as well as extending some existing methods for gene-environment interactions to other related areas. It includes three sections: (1) Deriving the Bayesian variable selection framework for the hierarchical gene-environment and gene-gene interactions; (2) Developing the Bayesian Natural and Orthogonal Interaction (NOIA) models for gene-environment interactions; and (3) extending the applications of two Bayesian statistical methods which were developed for gene-environment interaction studies, to other related types of studies such as adaptive borrowing historical data. We propose a Bayesian hierarchical mixture model framework that allows us to investigate the genetic and environmental effects, gene by gene interactions (epistasis) and gene by environment interactions in the same model. It is well known that, in many practical situations, there exists a natural hierarchical structure between the main effects and interactions in the linear model. Here we propose a model that incorporates this hierarchical structure into the Bayesian mixture model, such that the irrelevant interaction effects can be removed more efficiently, resulting in more robust, parsimonious and powerful models. We evaluate both of the 'strong hierarchical' and 'weak hierarchical' models, which specify that both or one of the main effects between interacting factors must be present for the interactions to be included in the model. The extensive simulation results show that the proposed strong and weak hierarchical mixture models control the proportion of false positive discoveries and yield a powerful approach to identify the predisposing main effects and interactions in the studies with complex gene-environment and gene-gene interactions. We also compare these two models with the 'independent' model that does not impose this hierarchical constraint and observe their superior performances in most of the considered situations. The proposed models are implemented in the real data analysis of gene and environment interactions in the cases of lung cancer and cutaneous melanoma case-control studies. The Bayesian statistical models enjoy the properties of being allowed to incorporate useful prior information in the modeling process. Moreover, the Bayesian mixture model outperforms the multivariate logistic model in terms of the performances on the parameter estimation and variable selection in most cases. Our proposed models hold the hierarchical constraints, that further improve the Bayesian mixture model by reducing the proportion of false positive findings among the identified interactions and successfully identifying the reported associations. This is practically appealing for the study of investigating the causal factors from a moderate number of candidate genetic and environmental factors along with a relatively large number of interactions. The natural and orthogonal interaction (NOIA) models of genetic effects have previously been developed to provide an analysis framework, by which the estimates of effects for a quantitative trait are statistically orthogonal regardless of the existence of Hardy-Weinberg Equilibrium (HWE) within loci. Ma et al. (2012) recently developed a NOIA model for the gene-environment interaction studies and have shown the advantages of using the model for detecting the true main effects and interactions, compared with the usual functional model. In this project, we propose a novel Bayesian statistical model that combines the Bayesian hierarchical mixture model with the NOIA statistical model and the usual functional model. The proposed Bayesian NOIA model demonstrates more power at detecting the non-null effects with higher marginal posterior probabilities. Also, we review two Bayesian statistical models (Bayesian empirical shrinkage-type estimator and Bayesian model averaging), which were developed for the gene-environment interaction studies. Inspired by these Bayesian models, we develop two novel statistical methods that are able to handle the related problems such as borrowing data from historical studies. The proposed methods are analogous to the methods for the gene-environment interactions on behalf of the success on balancing the statistical efficiency and bias in a unified model. By extensive simulation studies, we compare the operating characteristics of the proposed models with the existing models including the hierarchical meta-analysis model. The results show that the proposed approaches adaptively borrow the historical data in a data-driven way. These novel models may have a broad range of statistical applications in both of genetic/genomic and clinical studies.