While extensive research has been conducted on the causes of intimate partner violence in the community, very little is known about rates and predictors of domestic violence perpetrated by offenders who have recently been incarcerated. Some evidence suggests that formerly incarcerated individuals may be at an increased risk to perpetrate intimate partner violence during the transition from prison to the community (e.g., Hairston & Oliver 2006; Hilton, Harris, Popham, & Lang, 2010; Oliver & Hairston, 2008). The primary goal of this dissertation was to examine the extent to which former inmates engage in domestic violence during the transition from prison to the community. A second goal of this dissertation was to determine the independent and interactive effects of selected individual, situational, and social-structural factors on post-prison domestic violence. The current dissertation project involved a retrospective study of data collected from n = 1,137 formerly-incarcerated male offenders who were released from state prison between 2004 and 2009. Data regarding individual-level factors of borderline and antisocial personality characteristics and exposure to family-of-origin violence were extracted from institutional records. Additional individual-level demographic characteristics including offenders' age, ethnicity, education need, marital status, number of children, crime of conviction, length of incarceration, and participation in correctional rehabilitation programs extracted from institutional records were also considered. The situational-level factor of offenders' employment after prison release was also collected from institutional records; and the social-structural factor of neighborhood disadvantage was collected from information available in offenders' community supervision records and Census tract-level data. The outcome measure of post-prison domestic violence was gathered from local law enforcement records. Data were entered into statistical models to predict post-prison domestic violence. Main effects on post-prison domestic violence were examined for each of the individual-level demographic characteristics, borderline and antisocial personality features, exposure to family-of-origin violence, employment, and neighborhood disadvantage. Interactive effects on post-prison domestic violence were examined between borderline and antisocial personality characteristics, exposure to family-of-origin violence, employment, and neighborhood disadvantage. Significant predicted main effects on post-prison domestic violence included age, ethnicity, education need, number of children, violent criminal history, attendance of substance abuse treatment in prison, witnessing interparental violence as a child, and neighborhood disadvantage. Significant predicted interaction effects on post-prison domestic violence included the interaction between physical abuse as a child and neighborhood disadvantage. Implications for policies regarding post-prison supervision sentencing, housing, and the advancement of programming to prevent intimate partner violence during the transition from prison to the community are discussed. Contributions to the literature on intimate partner violence, environmental transition theory, and ecological theoretical frameworks are also addressed.

When the delicate hormonal balance in early pregnancy is disrupted, the consequences can be significant. We have a poor understanding of the "cross-talk" in the fetal/placental/ovarian axis that occurs throughout pregnancy and is essential for normal fetal development. This lack of knowledge challenges our ability to recognize disruptions in this axis that may be a signal for future disease. As a result, our ability to apply preventive measures against adverse obstetric outcomes, such as preterm birth (PTB), are quite limited. Attempts to predict PTB using biomarkers of feto-placental health have been largely unsuccessful, but no one has considered the inclusion of ovarian biomarkers in these models. Anti-Mullerian hormone (AMH) is a biomarker of ovarian activity that has recently been found to decline in early pregnancy at a time that corresponds to the involution of the corpus luteum (CL). The signal for CL involution is believed to originate from the placenta; therefore, the AMH levels in pregnancy may reflect the degree of ovarian up or down-regulation based on feto-placental needs. As the major function of the CL in pregnancy is the production of progesterone, which acts as an anti-inflammatory agent in the placental bed, changes in CL-derived progesterone could result in higher or lower degrees of placental inflammation. Therefore, monitoring the changes in AMH levels may provide insight into the inflammatory state of the placenta which could then be used as a signal for possible adverse obstetric outcomes resulting from a pro-inflammatory state, such as PTB. The first aim of this project was to test the hypothesis of an association between AMH levels in early pregnancy and PTB risk. When the differences in AMH levels between the 1st and 2nd trimesters of pregnancy were stratified by the level of maternal serum alpha-fetoprotein (MSAFP) and controlled for maternal weight gain between trimesters, small or absent decreases in AMH levels were associated with a higher probability of preterm birth. However, when AMH was modeled alone, no significant associations were found. The need for changes in multiple biomarkers in the fetal/placental/ovarian axis suggests that a change is only significant if it can impact multiple axis points. Therefore, models that included two biomarkers from different part of the axis would find stronger associations than two biomarkers from a single point (e.g. two feto-placental biomarkers), and monitoring these changes may help identify women at risk for PTB. The strategy of the second aim was to determine if the changes in AMH levels in early pregnancy could be used to predict time to delivery. Again, only when the risks of AMH and MSAFP were combined was a significant, dose-dependent relationship found with time to delivery. In women with an MSAFP of >1 multiple of the median (MoM), smaller declines and/or elevations in AMH levels were significantly associated with shorter times to delivery. In fact, 19% of women in the highest risk group delivered prior to 32 weeks gestation compared to 7% in the lowest risk group, and all infants who delivered prior to 24 weeks gestation were in the highest risk category. Thus, the amount of change in the AMH level when MSAFP is elevated may reflect the level of disruption in the fetal/placental/ovarian axis, which can then be used to predict time to delivery. Finally, the third aim of this study was to determine if AMH levels were associated with a pro-inflammatory placental state other than PTB. The degree of placental inflammation is known to vary by fetal gender, with male placentas having higher levels of inflammation compared to female placentas. When AMH levels were compared between women with male vs. female fetuses in early pregnancy, 1st trimester AMH levels were found to be lower when carrying a male fetus. Further, sexually-dimorphic patterns in AMH levels were seen between genders when stratified by birth outcome (term vs. preterm delivery). The stronger ovarian response seen in women with female fetuses suggests a better survival function and may account for the discrepancies between PTB rates in males and females. This also strengthens our hypothesis that the dynamic changes in AMH levels reflect the degree of placental inflammation and the need for CL-derived progesterone. This project demonstrates that the changes in AMH levels may be representative of the cross-talk occurring in the fetal/placental/ovarian axis in early pregnancy. Further, changes in AMH levels may be an indication of the amount of inflammation in the placenta and the physiologic need for higher levels of progesterone to control this inflammatory state when considered along with MSAFP. Therefore, the consideration of AMH levels as a biomarker of ovarian activity along with biomarkers of feto-placental health may provide clinically useful information about the development of future diseases such as preterm birth.

Widely distributed species are experiencing a continual pattern of range shifts due to anthropogenic expansion and climate change, forcing these species into novel environments and out of critical habitat. The ability to estimate current and forecasted states of demographic parameters of species distributed along a gradient of environments is becoming increasingly important in a time of large-scale environmental change. Consulting models that provide temporally relevant estimates of population dynamics based on the latest realizations of environmental conditions can allow for informed, quick and decisive conservation and management actions. Modelling the drivers of demography across a wide range of environmental conditions will provide a more comprehensive understanding of how species will respond to novel environments. In this study we provide an example of relating seasonal-environmental variables to survival in a widely distributed ungulate species. We used a mule deer (Odocoileus hemionus) survival dataset collected in Utah with seven sites distributed across the multiple ecoregions of the state, allowing for the elucidation of relationships across a variety of environmental conditions. Multivariate analyses predicting survival of young and adult females were performed using geographic location, elevation, and seasonal satellite-derived primary productivity data and weather variables. We developed frameworks for estimating past and current states of survival and predicting short-term (sub-year) forecasts of survival. Furthermore, we investigated adaptive modelling techniques for increasing the certainty of the forecasted predictions of survival. We found that increased winter precipitation had a negative effect on survival across the state. Survival was lower in the northern region of the state and in higher elevations. Furthermore, measures of summer primary productivity had a positive relationship with survival. Lastly, our adaptive modelling demonstration shows that uncertainty of forecasted survival predictions can be reduced with the addition of data. This study provides a framework for developing models that will provide invaluable information to managers in a time of large-scale environmental change.

The objective of the present study is to investigate key aspects of ovarian and breast cancers, which are two main causes of mortality among women. Identification of the true behavior of survivorship and influential risk factors is essential in designing treatment protocols, increasing disease awareness and preventing possible causes of disease. There is a commonly held belief that African Americans have a higher risk of cancer mortality. We studied racial disparities of women diagnosed with ovarian cancer on overall and disease-free survival and found out that there is no significant difference in the survival experience among the three races: Whites, African Americans and Other races. Tumor sizes at diagnosis among the races were significantly different, as African American women tend to have larger ovarian tumor sizes at the diagnosis. Prognostic models play a major role in health data research. They can be used to estimate adjusted survival probabilities and absolute and relative risks, and to determine significantly contributing risk factors. A prognostic model will be a valuable tool only if it is developed carefully, evaluating the underlying model assumptions and inadequacies and determining if the most relevant model to address the study objectives is selected. In the present study we developed such statistical models for survival data of ovarian and breast cancers. We found that the histology of ovarian cancer had risk ratios that vary over time. We built two types of parametric models to estimate absolute risks and survival probabilities and to adjust the time dependency of the relative risk of Histology. One parametric model is based on classical probability distributions and the other is a more flexible parametric model that estimates the baseline cumulative hazard function using spline functions. In contrast to women diagnosed with ovarian cancer, women with breast cancer showed significantly different survivorship among races where Whites had a poorer overall survival rate compared to African Americans and Other races. In the breast cancer study, we identified that age and progesterone receptor status have time dependent hazard ratios and age and tumor size display non-linear effects on the hazard. We adjusted those non-proportional hazards and non-linear effects by using an extended Cox regression model in order to generate more meaningful interpretations of the data.

The main goal of this study is to seek the effects of some risk and prognostic factors contributing to survival of female invasive breast cancer in United States. The study presents the survival analysis for the adult female invasive breast cancer based on the datasets chosen from the Surveillance Epidemiology and End Results (SEER) program of National Cancer Institute (NCI). In this study, the Cox proportional hazard regression model and logistic regression model were employed for statistical analysis. The odds ratios (OR), hazard ratios (HR) and confidence interval (C.I.) were obtained for the risk and prognosis factors. The study results showed that some risk and prognosis factors, such as the demographic factors (race and age), social and family factor (marital status), biomedical factors (tumor size, disease stage, tumor markers and tumor cell differentiation level etc.) and type of treatment patients received had significant effects on survival of the female invasive breast cancer patients.

A truncated sample consists of realizations of a pair of random variables (L, T) subject to the constraint that L ≤T. The major study interest with a truncated sample is to find the marginal distributions of L and T. Many studies have been done with the assumption that L and T are independent. We introduce a new way to specify a Cox model for a truncated sample, assuming that the truncation time is a predictor of T, and this causes the dependence between L and T. We develop an algorithm to obtain the adjusted risk sets and use the Kaplan-Meier estimator to estimate the Marginal distribution of L. We further extend our method to more practical situation, in which the Cox model includes other covariates associated with T. Simulation studies have been conducted to investigate the performances of the Cox model and the new estimators.

Many statistical methods for truncated data rely on the assumption that the failure and truncation time are independent, which can be unrealistic in applications. The study cohorts obtained from bone marrow transplant (BMT) registry data are commonly recognized as truncated samples, the time-to-failure is truncated by the transplant time. There are clinical evidences that a longer transplant waiting time is a worse prognosis of survivorship. Therefore, it is reasonable to assume the dependence between transplant and failure time. To better analyze BMT registry data, we utilize a Cox analysis in which the transplant time is both a truncation variable and a predictor of the time-to-failure. An inverse-probability-weighted (IPW) estimator is proposed to estimate the distribution of transplant time. Usefulness of the IPW approach is demonstrated through a simulation study and a real application.

This paper analyzes the effect of political and economic factors on the risk of regime change in countries between 1975 and 2010, using survival analysis with time-dependent covariates. The findings show that negative economic growth increases the risk of regime change in the following year, and that a higher level of GDP per Capita, as well as international trade, has an inhibiting effect on the risk of regime change in democracies. The results also show that countries with young regimes are more likely to experience a regime change, and that countries with a long tradition of democratic governance suffer virtually no risk of experiencing a regime failure. These findings lend heavy support to the democratic consolidation theory, while giving mixed support to other theories of economic and political causes of regime change. The more generalized approach to regime change used in this paper provides a stepping stone for opening up a greater understanding of the mechanisms which cause regime change in all types of governments, and regardless of the direction of the change in relation to democracy.

xiv, 110 p. A print copy of this thesis is available through the UO Libraries. Search the library catalog for the location and call number. / The purpose of this study was to examine the influence that select demographic and employment factors have on the risk of attrition for beginning special and general educators. Data for this study came from the University of Oregon College of Education Student Follow-up Survey project. Employment outcomes were assessed at 1, 3, and 5-year intervals for a sample of early career special and general educators via a mailed survey. Cox regression analysis was used to estimate the risk of attrition during the study period. The findings suggest that overall special and general educators had low a risk of attrition, but risk varied by demographic and employment characteristics. Implications for practice and research are discussed. / Committee in charge: Michael Bullis, Chairperson, Special Education and Clinical Sciences; Christopher Murray, Member, Special Education and Clinical Sciences; Paul Yovanoff, Member, Educational Leadership; Susan Hardwick, Outside Member, Geography

This thesis deals with a classification of glioma grade in high and low aggressive tumours and overall survival prediction based on magnetic resonance imaging. Data used in this work is from BRATS challenge 2019 and each set contains information from 4 weighting sequences of MRI. Thesis is implemented in PYTHON programming language and Jupyter Notebooks environment. Software PyRadiomics is used for calculation of image features. Goal of this work is to determine best tumour region and weighting sequence for calculation of image features and consequently select set of features that are the best ones for classification of tumour grade and survival prediction. Part of thesis is dedicated to survival prediction using set of statistical tests, specifically Cox regression