Dissertations / Theses on the topic 'Models for competing specie'

Evaluation of a therapeutic strategy is complex when the course of a disease is characterized by the occurrence of different kinds of events. Competing risks arise when the occurrence of specific events prevents the observation of other events. Different survival or incidence functions can be defined in the presence of competing risks and a relevant issue is an adequate knowledge of the methodological background in order to apply a suitable statistical analysis for the study aims. This work aims at presenting different estimates of survival or incidence probabilities used in this framework. From clinical application, it emerges that crude cumulative incidence is widely diffuses both to estimate incidence probabilities and to evaluate covariate effects. On the contrary net survival functions, although of clinical interest, are not diffused because of more difficult model structure and lack of software availability. If the independence assumption between different events is tenable, Kaplan-Meier method can be used to estimate net survival. Otherwise multivariate distribution of times based on Copulas can be adopted. In the case of different causes of death, relative survival can be interpreted as net survival only under specific assumptions on the mortality pattern. A particular case on competing risks arises when only fatal events can prevent the observation of the non fatal ones, but not vice versa (semi-competing risks). The estimate of an interpretable measure of association between times to non fatal and fatal event is often of biological interest, in order to understand the disease progression. In the statistical literature some approaches have been proposed to estimate the association between two independently doubly censored failure times, but more specific approach have to be applied in the presence of semi-competing risks. After estimating the association parameter, the survival function of a non terminal event can be estimated after fixing a copula structure by means of the semi parametric methods proposed by Fine, Jiang and Chappell or the copula graphic estimator. Furthermore when the interest is to evaluate the effect of different therapeutic strategies or covariates on the occurrence of a non terminal event in a semi-competing risks setting, specific regression model have to be adopted. I propose here to adopt the methodology based on pseudo-observations, having the advantage to be implemented by generalized linear models approaches. Simulation studies are performed to compare the performances of methods to estimate the association between events, of methods based on Copulas models to estimate net survival and of regression method for net survival in the presence of semi-competing risks. A case series of breast cancer patients is used to illustrate different methods of estimating net survival functions on the causes of deaths and on the severe non fatal events in the presence of competing and semi-competing risks framework.

This research focuses on two theories: (i) competing risks and (ii) random eect (frailty) models. The theory of competing risks provides a structure for inference in problems where cases are subject to several types of failure. Random eects in competing risk models consist of two underlying distributions: the conditional distribution of the response variables, given the random eect, depending on the explanatory variables each with a failure type specic random eect; and the distribution of the random eect. In this situation, the distribution of interest is the unconditional distribution of the response variable, which may or may not have a tractable form. The parametric competing risk model, in which it is assumed that the failure times are coming from a known distribution, is widely used such as Weibull, Gamma and other distributions. The Gamma distribution has been widely used as a frailty distribution, perhaps due to its simplicity since it has a closed form expression of the unconditional hazard function. However, it is unrealistic to believe that a few parametric models are suitable for all types of failure time. This research focuses on a distribution free of the multivariate frailty models. Another approach used to overcome this problem is using nite mixture of parametric frailty especially those who have a closed form of unconditional survival function. In addition, the advantages and disadvantages of a parametric competing risk models with multivariate parametric and/or non-parametric frailty (correlated random eects) are investigated. In this research, four main models are proposed: rst, an application of a new computation and analysis of a multivariate frailty with competing risk model using Cholesky decomposition of the Lognormal frailty. Second, a correlated Inverse Gaussian frailty in the presence of competing risks model. Third, a non-parametric multivariate frailty with parametric competing risk model is proposed. Finally, a simulation study of nite mixture of Inverse Gaussian frailty showed the ability of this model to t dierent frailty distribution. One main issue in multivariate analysis is the time it needs to t the model. The proposed non-parametric model showed a signicant time decrease in estimating the model parameters (about 80% less time compared the Log-Normal frailty with nested loops). A real data of recurrence of breast cancer is used as the applications of these models.

The thesis concerns regression models related to the competing risks setting in survival analysis and deals with both the case of known specific causes and the case of unknown (even if present) specific causes of the event of interest. In the first part, dealing with events whose specific cause is known, competing risks modelling has been applied to a breast cancer study and some of the dynamic aspects such as time-dependent variables are tackled within the context of the application. The aim of the application was to detect an optimal chemotherapy dosage for different typologies of patients with advanced breast cancer in order to control the risk of cardiotoxicity. The attention was concentrated on the cumulative incidence probability of getting cardiotoxicity in a well-defined time period, conditional on risk factors. This probability was estimated as a function of the time-dependent covariate dosage. Within the context of the application, some problems of goodness-of-fit related to time-dependent covariates are discussed. The previous application gave rise to investigating the role of time-dependent covariates in competing risks regression models. There exist various types of time-dependent covariates, which differ in their random or deterministic development in time. For so-called internal covariates, predictions based on the model are not allowed, or they meet with difficulties. We describe a general overview of the state of the art, problems and future directions. Moreover, a possible extension of the competing risks model, that allows us to include a simple random binary time-dependent variable, in a multi-state framework, is presented. Inclusion of the sojourn time of an individual in a certain state as a time-dependent covariate into the model, is also studied. In the second part of the thesis, dealing with events whose specific cause is unavailable, regression models for relative survival are discussed. We study the nonparametric additive excess hazards models, where the excess hazard is on additive form. We show how recent developments can be used to make inferential statements about this models, and especially to test the hypothesis that an excess risk effect is time-varying in contrast to being constant over time. We also show how a semiparametric additive risk model can be considered in the excess risk setting. These two additive models are easy to fit with estimators on explicit form and inference including tests for time-constant effects can be carried out based on a resampling scheme. We analyze a real dataset using different approaches and show the need for more flexible models in relative survival. Finally, we describe a new suggestion for goodness-of-fit of the additive and proportional models for relative survival, which avoids some disadvantages of recent proposals in the literature. The method consists of statistical and graphical tests based on cumulative martingale residuals and it is illustrated for testing the proportional hazards assumption in the semiparametric proportional excess hazards model.

The typical research of Alzheimer's disease includes a series of cognitive states. Multi-state models are often used to describe the history of disease evolvement. Competing risks models are a sub-category of multi-state models with one starting state and several absorbing states. Analyses for competing risks data in medical papers frequently assume independent risks and evaluate covariate effects on these events by modeling distinct proportional hazards regression models for each event. Jeong and Fine (2007) proposed a parametric proportional sub-distribution hazard (SH) model for cumulative incidence functions (CIF) without assumptions about the dependence among the risks. We modified their model to assure that the sum of the underlying CIFs never exceeds one, by assuming a proportional SH model for dementia only in the Nun study. To accommodate left censored data, we computed non-parametric MLE of CIF based on Expectation-Maximization algorithm. Our proposed parametric model was applied to the Nun Study to investigate the effect of genetics and education on the occurrence of dementia. After including left censored dementia subjects, the incidence rate of dementia becomes larger than that of death for age < 90, education becomes significant factor for incidence of dementia and standard errors for estimates are smaller. Multi-state Markov model is often used to analyze the evolution of cognitive states by assuming time independent transition intensities. We consider both constant and duration time dependent transition intensities in BRAiNS data, leading to a mixture of Markov and semi-Markov processes. The joint probability of observing a sequence of same state until transition in a semi-Markov process was expressed as a product of the overall transition probability and survival probability, which were simultaneously modeled. Such modeling leads to different interpretations in BRAiNS study, i.e., family history, APOE4, and sex by head injury interaction are significant factors for transition intensities in traditional Markov model. While in our semi-Markov model, these factors are significant in predicting the overall transition probabilities, but none of these factors are significant for duration time distribution.

The purpose of my research is to contribute to the field of forecasting from a methodological perspective as well as to the field of crude oil as an application area to test the performance of my methodological contributions and assess their merits. In sum, two main methodological contributions are presented. The first contribution consists of proposing a mathematical programming based approach, commonly referred to as Data Envelopment Analysis (DEA), as a multidimensional framework for relative performance evaluation of competing forecasting models or methods. As opposed to other performance measurement and evaluation frameworks, DEA allows one to identify the weaknesses of each model, as compared to the best one(s), and suggests ways to improve their overall performance. DEA is a generic framework and as such its implementation for a specific relative performance evaluation exercise requires a number of decisions to be made such as the choice of the units to be assessed, the choice of the relevant inputs and outputs to be used, and the choice of the appropriate models. In order to present and discuss how one might adapt this framework to measure and evaluate the relative performance of competing forecasting models, we first survey and classify the literature on performance criteria and their measures – including statistical tests – commonly used in evaluating and selecting forecasting models or methods. In sum, our classification will serve as a basis for the operationalisation of DEA. Finally, we test DEA performance in evaluating and selecting models to forecast crude oil prices. The second contribution consists of proposing a Multi-Criteria Decision Analysis (MCDA) based approach as a multidimensional framework for relative performance evaluation of the competing forecasting models or methods. In order to present and discuss how one might adapt such framework, we first revisit MCDA methodology, propose a revised methodological framework that consists of a sequential decision making process with feedback adjustment mechanisms, and provide guidelines as to how to operationalise it. Finally, we adapt such a methodological framework to address the problem of performance evaluation of competing forecasting models. For illustration purposes, we have chosen the forecasting of crude oil prices as an application area.

Doctor of Philosophy<br>Department of Physics<br>Bharat Ratra<br>The last decade of the 20th century was marked by the discovery of the accelerated expansion of the universe. This discovery puzzles physicists and has yet to be fully understood. It contradicts the conventional theory of gravity, i.e. Einstein’s General Relativity (GR). According to GR, a universe filled with dark matter and ordinary matter, i.e. baryons, leptons, and photons, can only expand with deceleration. Two approaches have been developed to study this phenomenon. One attempt is to assume that GR might not be the correct description of gravity, hence a modified theory of gravity has to be developed to account for the observed acceleration of the universe’s expansion. This approach is known as the ”Modified Gravity Theory”. The other way is to assume that the energy budget of the universe has one more component which causes expansion of space with acceleration on large scales. Dark Energy (DE) was introduced as a hypothetical type of energy homogeneously filling the entire universe and very weakly or not at all interacting with ordinary and dark matter. Observational data suggest that if DE is assumed then its contribution to the energy budget of the universe at the current epoch should be about 70% of the total energy density of the universe. In the standard cosmological model a DE term is introduced into the Einstein GR equations through the cosmological constant, a constant in time and space, and proportional to the metric tensor g[subscript]mu[subscript]nu. While this model so far fits most available observational data, it has some significant conceptual shortcomings. Hence there are a number of alternative cosmological models of DE in which the dark energy density is allowed to vary in time and space.

The attainment of high grades on the Victorian Certificate of Education (VCE) is critical to the future study and employment prospects of many Australian adolescents. Thus it is important to understand the factors that contribute to performance in the VCE. The aims of this study were twofold: the main aim was to test competing models of academic performance, subsuming a range of situational and dispositional variables based on a) self-efficacy theory, b) target and purpose goals, c) cognitive skills and self-regulatory strategies, and d) positive psychology. These models were each tested in terms of English performance and mathematics performance as these units contribute proportionally the most to overall VCE scores. In order to study whether pressures peculiar to the VCE impact on performance, the competing models were tested in a sample of Victorian students prior to the VCE (year 10) and then during the VCE (year 11). A preliminary study was conducted in order to develop and test four scales required for use in the major study, using an independent sample of 302 year nine students. The results indicated that these new scales were psychometrically reliable and valid. Three-hundred and seven Australian students participated in the year 10 and 11 study. These students were successively asked to provide their final years 9, 10 and 11 English and mathematics grades at times one, three and five and to complete a series of questionnaires at times two and four. Results of the year 10 study indicated that models based on self-efficacy theory were the best predictors of both English and mathematics performance, with high past grades, high self-efficacy and low anxiety contributing most to performance. While the year 10 self-efficacy models, target goal models, positive psychology models, self-regulatory models and cognitive skill based models were each robust in the sample in year 11, a substantial increase in explained variance was observed from year 10 to year 11 in the purpose goal models. Results indicated that students’ mastery goals and their performance-approach goals became substantially more predictive in the VCE than they were prior to the VCE. This result can be taken to suggest that these students responded in very instrumental ways to the pressures, and importance, of their VCE. An integrated model based on a combination of the variables from the competing models was also tested in the VCE. Results showed that these models were comparable, both in English and mathematics, to the self-efficacy models, but explained less variance than the purpose goal models. Thus in terms of parsimony the integrated models were not preferred. The implications of these results in terms of teaching practices and school counseling practices are discussed. It is recommended that students be encouraged to maintain a positive outlook in relation to their schoolwork and that they be encouraged to set their VCE goals in terms of a combination of self-referenced (mastery) and other-referenced (performance-approach) goals.

Multi-state models are often used to evaluate the effect of death as a competing event to the development of dementia in a longitudinal study of the cognitive status of elderly subjects. In this dissertation, both multi-state Markov model and semi-Markov model are used to characterize the flow of subjects from intact cognition to dementia with mild cognitive impairment and global impairment as intervening transient, cognitive states and death as a competing risk. Firstly, a multi-state Markov model with three transient states: intact cognition, mild cognitive impairment (M.C.I.) and global impairment (G.I.) and one absorbing state: dementia is used to model the cognitive panel data. A Weibull model and a Cox proportional hazards (Cox PH) model are used to fit the time to death based on age at entry and the APOE4 status. A shared random effect correlates this survival time with the transition model. Secondly, we further apply a Semi-Markov process in which we assume that the wait- ing times are Weibull distributed except for transitions from the baseline state, which are exponentially distributed and we assume no additional changes in cognition occur between two assessments. We implement a quasi-Monte Carlo (QMC) method to calculate the higher order integration needed for the likelihood based estimation. At the end of this dissertation we extend a non-parametric “local EM algorithm” to obtain a smooth estimator of the cause-specific hazard function (CSH) in the presence of competing risk. All the proposed methods are justified by simulation studies and applications to the Nun Study data, a longitudinal study of late life cognition in a cohort of 461 subjects.

Thesis (Ph. D.)--West Virginia University, 2002.<br>Title from document title page. Document formatted into pages; contains viii, 225 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 210-225).

Practical intelligence that accounts for people's performance on real-life problem solving is not related to intelligence in the traditional theories. The primary purpose of this research was to investigate the role of two competing cognitive models in explaining practical intelligence. The author extracted from the literature four cognitive processes and two types of knowledge that significantly accounted for performance on real-life problem solving. The cognitive processes model included (a) metacognition, (b) defining a problem, (c) flexibility of thinking, and (d) selecting a solution strategy. The types of knowledge model included (a) structural knowledge, and (b) tacit knowledge. The secondary purpose of this research was to determine the contribution of some non-cognitive factors to practical intelligence. These factors included (a) self-efficacy, and (b) motivation. These processes and constructs were derived from contemporary theories of intelligence including the Triarchic Theory of Sternberg (1985a), the Bioecological Treatise of Ceci (1996), and theories of expertise.The author developed a Practical Intelligence Instrument (PII) battery based on components of the cognitive processes model, the types of knowledge model, and non-cognitive factors. The PII battery consisted of several subscales to measure components mentioned above. The PII also included items to measure familiarity with problems. The PII was administered to 116 volunteer participants. The validity of the PII subscales was derived from three sources: content, face, and construct validity, including convergent and discriminant. The reliability of the subscales in the PII battery ranged from .63 to .93. The PII also included four scenarios that are real-life problems. Participants were asked to provide solutions for these problems. Three experts from the social science field evaluated participants' strategies based on four criteria. Several statistical procedures were used to analyze the data including a hierarchal multiple regression model, ANOVA, and the Pearson Product-Moment correlation.The results showed that around 54% of the variance in practical intelligence was explained by the cognitive processes model, the types of knowledge model, and self-efficacy and motivation. The cognitive model explained around 42%. The types of knowledge model explained around 15%. The non-cognitive factors explained around 20 % of the variance in practical intelligence.

The question of adverbs and the meaning of the present perfect across varieties of English is central to sociolinguistic variationist methodologies that have approached the study of the present perfect (Winford, 1993; Tagliamonte, 1997; van Herk, 2008, 2010; Davydova, 2010; Tagliamonte, 2013). This dissertation attempts to disentangle the effect of adverbial support from the three canonical readings of the present perfect (Resultative, Experiential and Continuative). Canadian English, an understudied variety of English, is used to situate the results seen in the Early Modern English data. Early Modern English reflects the time period in which English has acquired the full modern use of the present perfect with the three readings. In order to address both these questions and current controversies over statistical models in sociolinguistics, different statistical models are used: both the traditional Goldvarb X (Sankoff, Tagliamonte and Smith, 2005) and the newer mixed-effects logistic regression (Johnson, 2009). What is missing from the previous literature in sociolinguistics that advocates logistic mixed-effects models, and provided in this dissertation, is a clear statement of where they are inappropriate to use and their limitations. The rate of adverbial marking of the present perfect in Canadian English falls between rates reported for US and British English in previous studies. The data show in both time periods that while adverbs are highly favored in continuative contexts, they are strongly disfavored in experiential and resultative contexts. In Early Modern English, adverbial support functions statistically differently for resultatives and experientials, but that difference collapses in the Canadian English sample. Both this and the other linguistic contexts support a different analysis for each set of data with respect to adverbial independence from the meaning of the present perfect form. Finally, when the focus of the analysis is on linguistic rather than social factors, both the traditional and newer models provide similar results. Where there are differences, however, these can be accounted for by the number of tokens and different estimation techniques for each model.

Master of Science<br>Department of Agricultural Economics<br>Glynn T. Tonsor<br>The objective of this analysis is to develop econometric models for forecasting fed cattle basis as well as compare these models with historic averaging methods of forecasting basis popular in existing literature. The econometric analysis also aims to identify important determinants of fed cattle basis. Both monthly and weekly models were assessed with data provided by the Livestock Marketing Information Center. All models analyzed the three regions of Nebraska, Kansas, and Texas. Monthly historic average approaches utilized historic fed cattle futures and fed cattle cash price series from January of 1995 through December of 2010. Weekly historic average approaches utilized historic fed cattle futures and fed cattle cash prices series from June of 2001 through December 2010. Data collected post mandatory price reporting implementation in 2001 was used in all econometric models. Overall lags of fed cattle basis, the spread between the nearby live cattle futures contract and the next deferred futures contract, and seasonality regularly proved to explain much of the variation in fed cattle basis in the econometric modeling. Multiple historic average based models were examined on both monthly and weekly frequencies. Once all competing models were estimated in-sample, out-of sample testing was conducted. The forecasting errors of all weekly models were compared to determine which methods prove to be dominant forecasters of fed cattle basis. This testing suggests historic averaging methods outperform the alternate econometric models in out-of-sample work. The econometric models helped to reveal some of the important factors determining fed cattle basis, however lags in collecting data on these factors may inhibit the forecaster’s ability to use these techniques in real time. One interesting revelation in regards to historic averages is the potential of Olympic averages as forecasters. These methods have not been explored in previous academic literature but tend to perform quite well in comparison with other methods explored.

This thesis examines the descriptions of Royalist and Parliamentarian masculinity in English Civil War memoirs by women through a close reading of three biographical memoirs written by Margaret Cavendish, duchess of Newcastle; Lady Ann Fanshawe; and Lucy Hutchinson. Descriptions of masculinity are evaluated through the lens of Raewyn Connell's theory of hegemonic masculinity to understand the impact two competing models of masculinity had on the social and political culture of the period. The prevailing Parliamentarian hegemonic masculinity in English Civil War memoirs is traced to its origins before the English Civil War to demonstrate how hegemonic masculinity changes over time. The thesis argues that these memoirs provide evidence of two competing models of Royalist and Parliamentarian masculinities during the Civil War that date back to changes in the Puritan meaning of the phrase “man of merit”, which influenced the development of a Parliamentarian model of masculinity.

This thesis focuses on two fundamental topics, specifically in medical statistics: the modelling of correlated survival datasets and the variable selection of the significant covariates and random effects. In particular, two types of survival data are considered: the classical survival datasets, where subjects are likely to experience only one type of event and the competing risks datasets, where subjects are likely to experience one of several types of event. In Chapter 2, among other topics, we highlight the importance of adding frailty terms on the proposed models in order to account for the association between the survival time and characteristics of subjects/groups. The main novelty of this thesis is to simultaneously select fixed effects and frailty terms through the proposed statistical models for each survival dataset. Chapter 3 covers the analysis of the classical survival dataset through the proposed Cox Proportional Hazard (PH) model. Utilizing a Cox PH frailty model, may increase the dimension of variable components and estimation of the unknown coefficients becomes very challenging. The method proposed for the analysis of classical survival datasets involves simultaneous variable selection on both fixed effects and frailty terms through penalty functions. The benefit of penalty functions is that they identify the non-significant parameters and set them to have a zero effect in the model. Hence, the idea is to 'doubly-penalize' the partial likelihood of the Cox PH frailty model; one penalty for each term. Estimation and selection implemented through Newton-Raphson algorithms, whereas closed iterative forms for the estimation and selection of fixed effects and prediction of frailty terms were obtained. For the selection of frailty terms, penalties imposed on their variances since frailties are random effects. Based on the same idea, we further extend the simultaneous variable selection in the competing risks datasets in Chapter 4, using extended cause-specific frailty models. Two different scenarios are considered for frailty terms; in the first case we consider that frailty terms vary among different types of events (similar to the fixed effects) whereas in the second case we consider shared frailties over all the types of events. Moreover, our 'individual penalization' approach allows for one covariate to be significant for some types of events, in contrast to the frequently used 'group-penalization' where a covariate is entirely removed when it is not significant over all the events. For both proposed methods, simulation studies were conduced and showed that the proposed procedure followed for each analysis works well in simultaneously selecting and estimating significant fixed effects and frailty terms. The proposed methods are also applied to real datasets analysis; Kidney catheter infections, Diabetes Type 2 and Breast Cancer datasets. Association of the survival times and unmeasured characteristics of the subjects was studied as well as a variable selection for fixed effects and frailties implemented successfully.

This research focuses on developing a comprehensive framework for designing and modeling experiments in the presence of multiple sources of competing prior knowledge. In particular, methodology is proposed for process optimization in high-cost, low-resource experimental settings where the underlying response function can be highly non-linear. In the first part of this research, an initial experimental design criteria is proposed for optimization problems by combining multiple, potentially competing, sources of prior information--engineering models, expert opinion, and data from past experimentation on similar, non-identical systems. New methodology is provided for incorporating and combining conjectured models and data into both the initial modeling and design stages. The second part of this research focuses on the development of a batch sequential design procedure for optimizing high-cost, low-resource experiments with complicated response surfaces. The success in the proposed approach lies in melding a flexible, sequential design algorithm with a powerful local modeling approach. Batch experiments are designed sequentially to adapt to balance space-filling properties and the search for the optimal operating condition. Local model calibration and averaging techniques are introduced to easily allow incorporation of statistical models and engineering knowledge, even if such knowledge pertains to only subregions of the complete design space. The overall process iterates between adapting designs, adapting models, and updating engineering knowledge over time. Applications to nanomanufacturing are provided throughout.

This study develops a framework for pricing output traits derived from agriculture biotechnology and the effects on competing technologies post-introduction of the genetically modified (GM) variety. The price impact model determines processor or consumer adoption rates and changes in processor, farmer, and tech firm surplus as a result of the release of the new GM variety. Several implications result from this research. First, adoption of the GM variety may not be as high as expected due to the lower cost of using conventional varieties for processing or consumption inputs. Second, both processors who adopt the GM variety and those who continue to use conventional varieties will have an increase in surplus as a result of the introduction of the GM variety. Lower costs of conventional varieties will also result in new entrants into the market.

Thesis (Ph.D. in Biostatistics) -- University of Colorado Denver, 2008.<br>Typescript. Includes bibliographical references (leaves 117-119). Free to UCD Anschutz Medical Campus. Online version available via ProQuest Digital Dissertations;

The thesis looks at the reluctance of French political parties to select more women candidates in legislative elections despite a legal obligation to do so (the 'parity' law). The introductory first chapter sets up the questions and approaches of the thesis. The second chapter reviews the literature on women in politics and on political parties, arguing that there are insufficient linkages between these works. The third chapter explores some initial hypotheses regarding sex and candidate selection, looking at supply and demand explanations and considering whether parties with a high degree of internal democracy will find it easier to implement a quota. The next three chapters explore three main theories of party organisation in turn. Chapter four evaluates the electoral competition approach and asks whether parties prefer men candidates because they are more likely to win. This argument is tested empirically through a longitudinal study of candidate performance over four elections. Chapter five examines the institutional approach and considers how France's political institutions frame the choices available to parties when selecting their candidates. It reveals how some parties have considerably more room for manoeuvre than others. Chapter six looks at party ideology to explain why some parties might be motivated by their ideology to implement the parity law. Using a rational choice approach, the model argues that parties will prioritise the different choices available to them according to their underlying needs and motivations, and this will affect the order in which choices are made.

There is limited research on the relationship between trauma exposure, depression, and suicidality, particularly in high-risk primary care samples. The present study aims to: 1) characterize the prevalence of suicidality, depression, and trauma exposure in this sample; 2)develop and test models of the relationships between suicidality, depression, and trauma exposure in this sample; 3) augment the selected model with key protective factors, including social support and religiosity; and 4) further elaborate the nature of religiosity as a potential protective factor using the subscales of the Attitudes toward God scale, Anger toward God and Comfort with God. Patients (n=207) in a safety net primary care clinic waiting room completed measures assessing childhood and adult trauma, depression, and suicidal ideation. Approximately half of patients in this sample report having experienced at least four traumatic events, 82.13% of individuals were in the clinical range for depression, and nearly half of the sample endorsed some level of suicidality. In the first model, trauma exposure moderated the relationship between depression and in the second model, suicidality and depression mediated the relationship between trauma exposure and suicidality. Both models were then augmented with social support, religiosity, Anger toward God, and Comfort with God. All moderated moderational and moderated mediational models were nonsignificant, though certain paths were found to be significant. Social support had some protective effects, but all three aspects of religiosity exacerbated the models tested, in contrast to existing literature. These findings may be used to encourage more extensive and nuanced assessment for suicidality.

Thesis (Ph. D.)--University of Oregon, 1999.<br>Typescript. Includes vita and abstract. Includes bibliographical references (leaves 101-107). Also available for download via the World Wide Web; free to University of Oregon users. Address: http://wwwlib.umi.com/cr/uoregon/fullcit?p9955922.

We develop novel statistical methods for analysing biomedical survival data based on Gaussian process (GP) regression. GP regression provides a powerful non-parametric probabilistic method of relating inputs to outputs. We apply this to survival data which consist of time-to-event and covariate measurements. In the context of GP regression the covariates are regarded as `inputs' and the event times are the `outputs'. This allows for highly exible inference of non-linear relationships between covariates and event times. Many existing methods for analysing survival data, such as the ubiquitous Cox proportional hazards model, focus primarily on the hazard rate which is typically assumed to take some parametric or semi-parametric form. Our proposed model belongs to the class of accelerated failure time models and as such our focus is on directly characterising the relationship between the covariates and event times without any explicit assumptions on what form the hazard rates take. This provides a more direct route to connecting the covariates to survival outcomes with minimal assumptions. An application of our model to experimental data illustrates its usefulness. We then apply multiple output GP regression, which can handle multiple potentially correlated outputs for each input, to competing risks survival data where multiple event types can occur. In this case the multiple outputs correspond to the time-to-event for each risk. By tuning one of the model parameters we can control the extent to which the multiple outputs are dependent thus allowing the specication of correlated risks. However, the identiability problem, which states that it is not possible to infer whether risks are truly independent or otherwise on the basis of observed data, still holds. In spite of this fundamental limitation simulation studies suggest that in some cases assuming dependence can lead to more accurate predictions. The second part of this thesis is concerned with high dimensional survival data where there are a large number of covariates compared to relatively few individuals. This leads to the problem of overtting, where spurious relationships are inferred from the data. One strategy to tackle this problem is dimensionality reduction. The Gaussian process latent variable model (GPLVM) is a powerful method of extracting a low dimensional representation of high dimensional data. We extend the GPLVM to incorporate survival outcomes by combining the model with a Weibull proportional hazards model (WPHM). By reducing the ratio of covariates to samples we hope to diminish the eects of overtting. The combined GPLVM-WPHM model can also be used to combine several datasets by simultaneously expressing them in terms of the same low dimensional latent variables. We construct the Laplace approximation of the marginal likelihood and use this to determine the optimal number of latent variables, thereby allowing detection of intrinsic low dimensional structure. Results from both simulated and real data show a reduction in overtting and an increase in predictive accuracy after dimensionality reduction.

Patterns of resource allocation have been studied by a variety of researchers, but this study includes variables in ways that they have not been operationalized or featured in previous studies. The purpose of this study is to analyze patterns of resource allocation across different academic units within a major research university. Eighty-five departments were studied with data gathered for the years 1988-89 and 1992-93. Twenty-six independent variables were analyzed including rational/political and critical/political framework variables. The dependent variable was the amount of state allocations to each academic unit. Regression and correlation analyses indicated that grants/contracts, gender, and ethnicity were highly significant factors in determining the amount of state dollar allocations to a department. Departments generating more in external grants/contracts received more in terms of state allocated dollars to the unit. Departments with higher percentages of women faculty and minority faculty tended to receive less in terms of state allocations. The rational/political theory more adequately described graduate education, while the critical/political theory described undergraduate education. Future research may need to include the effect of complex missions and multiple labor markets on education.

Aquesta tesi tracta sobre tècniques d'anàlisi de supervivència en situacions amb múltiples esdeveniments i patrons complexes de censura. Proposem una nova metodologia per tractar la situació de riscos semi-competitius quan les dades estan censurades en un interval. La motivació del treball neix de la nostra col·laboració amb l'Estudi Espanyol del Càncer de Bufeta (SBC/EPICURO), el més gran estudi sobre càncer de bufeta realitzat fins ara a l'Estat Espanyol. La nostra contribució en el projecte es centra en la modelització i identificació de factors pronòstics de l'evolució de la malaltia.<br/>L'evolució de malalties complexes, com el càncer o la infecció VIH, es caracteritza per la ocurrència de múltiples esdeveniments en el mateix pacient: per exemple, la recaiguda de la malaltia o la mort. Aquests esdeveniments poden ser finals, quan el seguiment del pacient s'atura després de l'esdeveniment, o bé intermedis, quan l'individu continua sota observació. La presència d'esdeveniments finals complica l'anàlisi dels intermedis ja que n'impedeix la seva completa observació, induint una possible censura depenent.<br/>En aquest context, es requereixen metodologies apropiades. Els següents mètodes són emprats: riscos competitius, models multiestat i riscos semi-competitius. A resultes de l'aplicació de mètodes per riscos competitius i models multi-estat, proposem dues aportacions rellevants al coneixement de la malaltia: (1) la caracterització dels pacients amb un alt risc de progressió com a primer esdeveniment després de la diagnosi, i (2) la construcció d'un model pronòstic dinàmic per al risc de progressió.<br/>La situació de riscos competitius es dóna quan volem descriure el temps fins al primer entre K possibles esdeveniments, juntament amb un indicador del tipus d'esdeveniment observat. En l'estudi EPICURO, és rellevant estudiar el temps fins al primer entre recidiva, progressió o mort. La caracterització d'aquest primer esdeveniment permetria seleccionar el millor tractament d'acord amb el perfil de risc basal del pacient.<br/>Els models multi-estat descriuen les diferents evolucions que la malaltia pot seguir, establint relacions entre els esdeveniments d'interès: per exemple, un pacient pot experimentar una recidiva del tumor primari, i després morir, o bé pot morir sense haver tingut cap recaiguda de la malaltia. Una característica interessant d'aquests models és que permeten fer prediccions del risc de futurs esdeveniments per a un pacient, d'acord amb la història que hagi pogut tenir fins aquell moment. En el cas de càncer de bufeta podrem avaluar la influència que té en el risc de progressar haver patit o no una recidiva prèvia.<br/>Un cas especial de model multi-estat és aquell que conté un esdeveniment intermedi E1, i un esdeveniment final, E2. Siguin T1 i T2 els temps fins aquests esdeveniments, respectivament. Ni l'anàlisi de riscos competitius ni els models multi-estat permeten adreçar l'estudi de la distribució marginal de T1. En efecte, l'anàlisi de riscos competitius tracta amb la distribució del mínim entre els dos<br/>temps, T=min(T1,T2), mentre que els models multi-estat es centren en la distribució condicional de T2|T1, és a dir, en com la ocurrència de E1 modifica el risc de E2. En aquest cas, la distribució de T1 no és identificable a partir de les dades observades. La situació abans descrita, on la ocurrència d'un esdeveniment final impedeix l'observació de l'esdeveniment intermedi és coneguda com a riscos semi-competitius (Fine et al., 2001). L'estratègia d'aquests autors passà per assumir un model per a la distribució conjunta (T1, T2), i aleshores recuperar la distribució marginal de T1 derivada d'aquest model.<br/>Proposem una nova metodologia per tractar amb riscos semi-competitius quan el temps fins l'esdeveniment intermedi, T1, està censurat en un interval. En molts estudis mèdics longitudinals, la ocurrència de l'esdeveniment d'interès s'avalua en visites periòdiques del pacient, i per tant, T1 és desconegut, però es sap que pertany al interval comprès entre els temps de dues visites consecutives. Els mètodes per riscos semi-competitius en el context usual de censura per la dreta no són vàlids en aquest cas i és necessària una nova aproximació. En aquest treball ampliem la metodología semi-paramètrica proposada per Fine et al. (2001), que assumeix un model de còpula de Clayton (1978) per a descriure la dependència entre T1 i T2. Assumint el mateix model, desenvolupem un algoritme iteratiu que estima conjuntament el paràmetre d'associació del model de còpula, així com la funció de supervivència del temps intermedi T1.<br/>Fine, J. P.; Jiang, H. & Chappell, R. (2001), 'On Semi-Competing Risks Data', Biometrika 88(4), 907--919.<br/>Clayton, D. G. (1978), 'A Model for Association in Bivariate Life Tables and Its Application in Epidemiological Studies of Familial. Tendency in Chronic Disease Incidence', Biometrika 65(1), 141--151.<br>La presente tesis trata sobre técnicas de análisis de supervivencia en situaciones con múltiples eventos y patrones complejos de censura. Proponemos una nueva metodología para tratar el problema de riesgos semi-competitivos cuando los datos están censurados en un intervalo. La motivación de este trabajo nace de nuestra colaboración con el estudio Español de Cáncer de Vejiga (SBC/EPICURO), el más grande estudio sobre cáncer de vejiga realizado en España hasta el momento. Nuestra participación en el mismo se centra en la modelización e identificación de factores pronósticos en el curso de la enfermedad.<br/>El curso de enfermedades complejas tales como el cáncer o la infección por VIH, se caracteriza por la ocurrencia de múltiples eventos en el mismo paciente, como por ejemplo la recaída o la muerte. Estos eventos pueden ser finales, cuando el seguimiento del paciente termina con el evento, o bien intermedios, cuando el individuo sigue bajo observación. La presencia de eventos finales complica el análisis de los eventos intermedios, ya que impiden su completa observación, induciendo una posible censura dependiente.<br/>En este contexto, se requieren metodologías apropiadas. Se utilizan los siguientes métodos: riesgos competitivos, modelos multiestado y riesgos semi-competitivos. De la aplicación de métodos para riesgos competitivos y modelos multi-estado resultan dos aportaciones relevantes sobre el conocimiento de la enfermedad: (1) la caracterización de los pacientes con un alto riesgo de progresión como primer evento después del diagnóstico, y (2) la construcción de un modelo pronóstico y dinámico para el riesgo de progresión.<br/>El problema de riesgos competitivos aparece cuando queremos describir el tiempo hasta el primero de K posibles eventos, junto con un indicador del tipo de evento observado. En el estudio SBC/EPICURO es relevante estudiar el tiempo hasta el primero entre recidiva, progresión o muerte. La caracterización de este primer evento permitiría seleccionar el tratamiento más adecuado de acuerdo con el perfil de riesgo basal del paciente.<br/>Los modelos multi-estado describen las diferentes tipologías que el curso de la enfermedad puede seguir, estableciendo relaciones entre los eventos de interés. Por ejemplo, un paciente puede experimentar una recidiva y después morir, o bien puede morir sin haber tenido recaída alguna. El potencial interesante de los modelos multi-estado es que permiten realizar predicciones sobre el riesgo de futuros eventos dada la historia del paciente hasta ese momento. En el caso del cáncer de vejiga, podremos evaluar la influencia que tiene en el riesgo de progresar el haber tenido o no una recidiva previa.<br/>Un caso especial de modelo multi-estado es el que contiene un evento intermedio E1 y uno final, E2. Sean T1 y T2 los tiempos hasta tales eventos, respectivamente. Ni el análisis de riesgos competitivos ni los modelos multi-estado permiten estudiar la distribución marginal de T1. En efecto, el análisis de riesgos competitivos trata con la distribución del mínimo entre los dos tiempos, T=min(T1,T2), mientras que los modelos multi-estado se centran en la distribución condicional de T2 dado T1, T2|T1, en cómo la ocurrencia de E1 modifica el riesgo de E2. En ambos casos, la distribución de T1 no es identificable a partir de los datos observados.<br/>La situación anteriormente descrita donde un evento final impide la observación de un evento intermedio se conoce como riesgos semi-competitivos (Fine et al. 2001). La estrategia de estos autores asume un modelo para la distribución conjunta (T1,T2) para así recuperar la distribución de T1 derivada de ese modelo.<br/>Proponemos una nueva metodología para tratar con riesgos semi-competitivos cuando el tiempo hasta el evento intermedio, T1, esta censurado en un intervalo. En muchos estudios médicos longitudinales, la ocurrencia del evento de interés se evalúa en visitas periódicas al paciente, por lo que T1 es desconocido, aunque se conoce que pertenece al intervalo comprendido entre los tiempos de dos visitas consecutivas. Los métodos para riesgos semi-competitivos en el contexto usual de censura por la derecha no son válidos en este caso y se requiere una nueva aproximación. En este trabajo ampliamos la metodología semi-paramétrica propuesta por Fine et al. (2001), que asume una cópula de Clayton (1978) para describir la dependencia entre T1 y T2. Bajo el mismo modelo de asociación, desarrollamos un algoritmo iterativo que estima conjuntamente el parámetro de asociación del modelo de cópula, así como la función de supervivencia del tiempo al evento intermedio T1.<br/>Fine, J. P.; Jiang, H. & Chappell, R. (2001), 'On Semi-Competing Risks Data', Biometrika 88(4), 907--919. <br/>Clayton, D. G. (1978), 'A Model for Association in Bivariate Life Tables and Its Application in Epidemiological Studies of Familial. Tendency in Chronic Disease Incidence', Biometrika 65(1), 141--151.

Impulsivity is a hallmark of two of the three DSM-IV ADHD subtypes and is associated with myriad adverse outcomes. Limited research, however, is available concerning the mechanisms and processes that contribute to impulsive responding by children with ADHD. The current study tested predictions from two competing models of ADHD--working memory (WM) and behavioral inhibition (BI)--to examine the extent to which ADHD-related impulsive responding was attributable to model-specific mechanisms and processes. Children with ADHD (n = 21) and typically developing children (n = 20) completed laboratory tasks that provided WM (domain-general central executive (CE), phonological/visuospatial storage/rehearsal) and BI indices (stop-signal reaction time (SSRT), stop-signal delay, mean reaction time). These indices were examined as potential mediators of ADHD-related impulsive responding on two diverse laboratory tasks used commonly to assess impulsive responding (CPT: continuous performance test; VMTS: visual match-to-sample). Bias-corrected, bootstrapped mediation analyses revealed that CE processes significantly attenuated between-group impulsivity differences, such that the initial large-magnitude impulsivity differences were no longer significant on either task after accounting for ADHD-related CE deficits. In contrast, SSRT partially mediated ADHD-related impulsive responding on the CPT but not VMTS. This partial attenuation was no longer significant after accounting for shared variance between CE and SSRT; CE continued to attenuate the ADHD-impulsivity relationship after accounting for SSRT. These findings add to the growing literature implicating CE deficits in core ADHD behavioral and functional impairments, and suggest that cognitive interventions targeting CE rather than storage/rehearsal or BI processes may hold greater promise for alleviating ADHD-related impairments.<br>ID: 030646239; System requirements: World Wide Web browser and PDF reader.; Mode of access: World Wide Web.; Thesis (M.S.)--University of Central Florida, 2011.; Includes bibliographical references (p. 41-55).<br>M.S.<br>Masters<br>Psychology<br>Sciences<br>Psychology Clinical

In population-based cancer studies, researchers are often only interested in cancer-specific survival to determine variations in the impact of cancer in different population groups. In such cases, the net survival measure is usually reported. However, this is of little relevance for patients as it does not consider the probability of dying from other causes before dying from cancer, otherwise known as competing risks. Therefore, alternative measures that take this into account are required for a better representation of cancer survival in the real-world. Measures estimated from within this framework provide a more meaningful interpretation for patients which can be communicated to facilitate treatment-related decisions. Differences in interpretation between various cancer survival measures, and when they are appropriate, has led to some confusion amongst non-statisticians. This motivates the development of publicly available tools to improve understanding and communication. Thus, an aim of this thesis is to develop an interactive web-tool to aid interpretation of various important cancer survival measures that are commonly reported. Although not a new concept, many often fail to account for competing risks when it is necessary for a study. Even when accounted for, many apply the theory, or report analyses incorrectly. Recently, efforts have been made to make competing risks methods more accessible for researchers from within the increasingly popular flexible parametric modelling framework. However, much work is yet to be done, especially as cancer registry datasets are becoming larger with more detailed covariate information. This means that models are increasing in complexity and more computationally efficient methods are required. With this in mind, the primary aim of this PhD is to further develop competing risks methods from within the flexible parametric modelling framework. Particular focus is on obtaining predictions with less computational effort that facilitate communication of risk when interest is in prognosis.

Thesis (M.S.)--University of North Carolina at Chapel Hill, 2009.<br>Title from electronic title page (viewed Mar. 10, 2010). "... in partial fulfillment of requirements for the degree of Master of Science in the Department of Geological Sciences." Discipline: Geology; Department/School: Geological Sciences.

Arbitration is of growing importance across the United Arab Emirate (UAE) and there are various arbitral regimes with different levels of international respectability within the federation. Despite the existence of a draft federal arbitration law there is no respective federal law in place. The economy of the UAE has expanded over the last 5 decades and with economic development has come an increase in commercial activities, which has led to higher incidence of commercial disputes on an equally high rate. Arbitration has not only become a favourite route for domestic commercial transactions but it also applies to international commercial transactions and investment disputes. With the increase of arbitration cases in the UAE, it has become necessary to pass a federal law for arbitration in general in order to fill the legal vacuum in the present civil and procedures law No.11/1992. Passing a federal law to regulate arbitral proceedings would enhance the legal position of the UAE and not just a part of it in attracting international investment. This will in turn lead to improved economic growth and the encouragement of foreign investments. The significance of enacting a new arbitration law is that it would also help to end the conflict between the existing nine arbitration centres in the UAE with regard to the enforcement of local and foreign judgments. The different regulations and laws for each centre furthermore creates uncertainties about how to enforce awards between these centres. This begs the question of when will this important Gulf Cooperation Council member state have a deserving and fit for purpose federal law on commercial arbitration. This thesis evaluates the draft federal law, investigates the socio-legal challenges preventing the achievement of the noble aim of implementation of a federal law and evaluates the existing regimes and bodies regulating arbitration in the federating parts of the UAE.