Syftet med denna uppsats är att undersöka hur olika aspekter av arbetslivet påverkar individen. Vår utgångspunkt i denna uppsats är att vi ställer oss kritiska till arbeten med högt självbestämmande som blir allt mer vanligt. Till vår hjälp har vi en mängd tidigare forskning samt kritisk teori och Foucaults teori om makt och övervakning som guidar oss i genom forskningsprocessen. Urvalet är baserat på tidigare statistik framtaget av ESS, som består av ett samarbete mellan 24 europeiska länder. Uppsatsen testar hur olika aspekter av arbetslivet påverkar gränsdragningen mellan arbete och fritid. Resultaten visar att ju högre grad av självbestämmande individen har i sitt arbete, desto mindre missnöjd är hon med tidsfördelning mellan arbete och fritid samt att en högre grad av självbestämmande även tenderar att skapa en oro hos individen på hennes fritid över arbetsrelaterade problem. Vår slutsats är att det finns goda skäl att vara uppmärksam på vad detta alltmer vanliga arbetssätt kan ställa till med för individen. / The purpose of this paper is to explore how different aspects of working life affect the individual. Our starting point in this paper is that we are critical of jobs with high autonomy that is becoming increasingly common. To assist us, we have a lot of previous research as well as critical theory and Foucault’s theory about power and surveillance that guides us through the research process. The selection is based on previous statistics produced by the ESS, which consists of a collaboration between 24 European countries. The essay test different aspects of paid work and its affect on work-life balance. The results show that the higher the degree of autonomy the individual has in his/her work, the less dissatisfied he/she is with time spent between work and leisure. A higher degree of autonomy does in fact lead to the individual worrying about work-related problems in his/her leisure time. Our conclusion is that there are good reasons to pay attention to what this increasingly common tendency can affect the individual.

This thesis examined the factors correlated with rapid and benign progression of disease in a group of 1452 Parkinsons disease (PD) patients. The data were collected in a movement disorders clinic at the Royal University Hospital, University of Saskatchewan run by Dr. Alex Rajput and Dr. Ali Rajput. This data is a clinical dataset of PD patients collected from 1970 through to February, 2005. This was a retrospective cases-only study, with anticipated analytical follow-up if any correlations were detected between progression type of PD and the many independent variables available in the dataset. <p>Rapid progression was defined as those subjects who reached Hoehn and Yahr stage 3 within three years or H&Y stage 4 or 5 within five years. Subjects who remained in Hoehn and Yahr stage 1 or 2, ten years after onset of disease, were defined as having benign progression. The study analyzed demographic and clinical findings at first visit to this clinic associated with rapid and benign progression of PD. <p> Analysis revealed that, at first clinic visit, benign progression was positively associated with disease duration (OR=1.41; 95% CI 1.27, 1.57), male sex (OR=3.23; 95% CI 1.70, 6.16), and current smoking habit (OR=2.33; 95% CI 0.67, 8.11). Benign progression was negatively associated with older age of onset (OR=0.36; 95% CI 0.25, 0.50), past history of smoking (OR=0.46; 95% CI 0.24, 0.89), current or past use of levodopa (OR=0.45; 95% CI 0.21, 0.98), and mild to severe rigidity (OR=0.43; 95% CI 0.23, 0.80). <p>Analysis also revealed that, at first clinic visit, rapid progression was positively associated with older age of onset (OR=2.45; 95% CI 1.80, 3.33) and mild to severe rigidity (OR=1.73; 95% CI 1.02, 2.94). Rapid progression was negatively associated with disease duration (OR=0.52; 95% CI 0.44, 0.62), male sex (OR=0.58; CI 0.35, 0.95), and mild to severe resting tremor (OR=0.47; CI 0.28, 0.77). <p>The results of this study indicate that age of onset, disease duration, male sex, and rigidity are good potential predictors of disease progression in PD because they have opposite associations with rapid and benign progression. History of levodopa use was negatively associated with benign progression and as such may be good indicator of non-benign progression. Although previous studies found no predictive value for smoking history, the current study reported a unique association between smoking history and benign progression. Past smoking history was negatively associated with benign progression. While there was a positive association with current smoking history, the result was not statistically significant. Resting tremor was negatively associated with rapid progression and as such may be a good indicator of non-rapid progression. <p> Disease characteristics collected at first clinic visit are useful in predicting the course of progression of PD. With more rapid progression of PD closer and more frequent follow-up of patients may be necessary.

Many patient-level barriers to more effective post-fracture osteoporosis (OP) management are associated with patients’ perceptions of their bone health and the link between their fracture and OP. These barriers could be addressed by interventions which account for the role of patients’ perceptions, such as those based on theories of behaviour change. This thesis had two objectives: to review the literature and determine whether these theories have been integrated in post-fracture OP management and to examine patients’ awareness of the OP-fracture link. The results showed that theories of behaviour change have not been integrated into post-fracture interventions, that most patients do not perceive the OP-fracture link even after a baseline intervention and that two baseline factors predict who is less likely to make the OP-fracture link at follow up. Based on these findings, modifications to current OP interventions were proposed and guidance that could help create new, patient-centered interventions was provided.

Many patient-level barriers to more effective post-fracture osteoporosis (OP) management are associated with patients’ perceptions of their bone health and the link between their fracture and OP. These barriers could be addressed by interventions which account for the role of patients’ perceptions, such as those based on theories of behaviour change. This thesis had two objectives: to review the literature and determine whether these theories have been integrated in post-fracture OP management and to examine patients’ awareness of the OP-fracture link. The results showed that theories of behaviour change have not been integrated into post-fracture interventions, that most patients do not perceive the OP-fracture link even after a baseline intervention and that two baseline factors predict who is less likely to make the OP-fracture link at follow up. Based on these findings, modifications to current OP interventions were proposed and guidance that could help create new, patient-centered interventions was provided.

With the development of DNA microarray technology, scientists can now measure the expression levels of thousands of genes (features or genomic biomarkers) simultaneously in one single experiment. Robust and accurate gene selection methods are required to identify differentially expressed genes across different samples for disease diagnosis or prognosis. The problem of identifying significantly differentially expressed genes can be stated as follows: Given gene expression measurements from an experiment of two (or more)conditions, find a subset of all genes having significantly different expression levels across these two (or more) conditions. Analysis of genomic data is challenging due to high dimensionality of data and low sample size. Currently several mathematical and statistical methods exist to identify significantly differentially expressed genes. The methods typically focus on gene by gene analysis within a parametric hypothesis testing framework. In this study, we propose three flexible procedures for analyzing microarray data. In the first method we propose a parametric method which is based on a flexible distribution, Generalized Logistic Distribution of Type II (GLDII), and an approximate likelihood ratio test (ALRT) is developed. Though the method considers gene-by-gene analysis, the ALRT method with distributional assumption GLDII appears to provide a favourable fit to microarray data. In the second method we propose a test statistic for testing whether area under receiver operating characteristic curve (AUC) for each gene is greater than 0.5 allowing different variances for each gene. This proposed method is computationally less intensive and can identify genes that are reasonably stable with satisfactory prediction performance. The third method is based on comparing two AUCs for a pair of genes that is designed for selecting highly correlated genes in the microarray datasets. We propose a nonparametric procedure for selecting genes with expression levels correlated with that of a ``seed" gene in microarray experiments. The test proposed by DeLong et al. (1988) is the conventional nonparametric procedure for comparing correlated AUCs. It uses a consistent variance estimator and relies on asymptotic normality of the AUC estimator. Our proposed method includes DeLong's variance estimation technique in comparing pair of genes and can identify genes with biologically sound implications. In this thesis, we focus on the primary step in the gene selection process, namely, the ranking of genes with respect to a statistical measure of differential expression. We assess the proposed approaches by extensive simulation studies and demonstrate the methods on real datasets. The simulation study indicates that the parametric method performs favorably well at any settings of variance, sample size and treatment effects. Importantly, the method is found less sensitive to contaminated by noise. The proposed nonparametric methods do not involve complicated formulas and do not require advanced programming skills. Again both methods can identify a large fraction of truly differentially expressed (DE) genes, especially if the data consists of large sample sizes or the presence of outliers. We conclude that the proposed methods offer good choices of analytical tools to identify DE genes for further biological and clinical analysis.

With the development of DNA microarray technology, scientists can now measure the expression levels of thousands of genes (features or genomic biomarkers) simultaneously in one single experiment. Robust and accurate gene selection methods are required to identify differentially expressed genes across different samples for disease diagnosis or prognosis. The problem of identifying significantly differentially expressed genes can be stated as follows: Given gene expression measurements from an experiment of two (or more)conditions, find a subset of all genes having significantly different expression levels across these two (or more) conditions. Analysis of genomic data is challenging due to high dimensionality of data and low sample size. Currently several mathematical and statistical methods exist to identify significantly differentially expressed genes. The methods typically focus on gene by gene analysis within a parametric hypothesis testing framework. In this study, we propose three flexible procedures for analyzing microarray data. In the first method we propose a parametric method which is based on a flexible distribution, Generalized Logistic Distribution of Type II (GLDII), and an approximate likelihood ratio test (ALRT) is developed. Though the method considers gene-by-gene analysis, the ALRT method with distributional assumption GLDII appears to provide a favourable fit to microarray data. In the second method we propose a test statistic for testing whether area under receiver operating characteristic curve (AUC) for each gene is greater than 0.5 allowing different variances for each gene. This proposed method is computationally less intensive and can identify genes that are reasonably stable with satisfactory prediction performance. The third method is based on comparing two AUCs for a pair of genes that is designed for selecting highly correlated genes in the microarray datasets. We propose a nonparametric procedure for selecting genes with expression levels correlated with that of a ``seed" gene in microarray experiments. The test proposed by DeLong et al. (1988) is the conventional nonparametric procedure for comparing correlated AUCs. It uses a consistent variance estimator and relies on asymptotic normality of the AUC estimator. Our proposed method includes DeLong's variance estimation technique in comparing pair of genes and can identify genes with biologically sound implications. In this thesis, we focus on the primary step in the gene selection process, namely, the ranking of genes with respect to a statistical measure of differential expression. We assess the proposed approaches by extensive simulation studies and demonstrate the methods on real datasets. The simulation study indicates that the parametric method performs favorably well at any settings of variance, sample size and treatment effects. Importantly, the method is found less sensitive to contaminated by noise. The proposed nonparametric methods do not involve complicated formulas and do not require advanced programming skills. Again both methods can identify a large fraction of truly differentially expressed (DE) genes, especially if the data consists of large sample sizes or the presence of outliers. We conclude that the proposed methods offer good choices of analytical tools to identify DE genes for further biological and clinical analysis.

The aim of this thesis is to develop discrete fractional models of tumor growth for a given data and to estimate parameters of these models in order to have better data tting. We use discrete nabla fractional calculus because we believe the discrete counterpart of this mathematical theory will give us a better and more accurate outcome. This thesis consists of ve chapters. In the rst chapter, we give the history of the fractional calculus, and we present some basic de nitions and properties that are used in this theory. We de ne nabla fractional exponential and then nabla fractional trigonometric functions. In the second chapter, we concentrate on completely monotonic functions on R, and we introduce completely monotonic functions on discrete domain. The third chapter presents discrete Laplace N-transform table which is a great tool to nd solutions of -th order nabla fractional di erence equations. Furthermore, we nd the solution of nonhomogeneous up to rst order nabla fractional di erence equation using N-transform. In the fourth chapter, rst we give the de nition of Casoration for the set of solutions up to n-th order nabla fractional equation. Then, we state and prove some basic theorems about linear independence of the set of solutions. We focus on the solutions of up to second order nabla fractional di erence equation. We examine these solutions case by case namely, for the real and distinct characteristic roots, real and same, and complex ones. The fth chapter emphasizes the aim of this thesis. First, we give a vi brief introduction to parameter estimation with Gomperts and Logistic curves. In addition, we recall a statistical method called cross-validation for prediction. We state continuous, discrete, continuous fractional and discrete fractional forms of Gompertz and Logistic curves. We use the tumor growth data for twenty-eight mice for the comparison. These control mice were inoculated with tumors but did not receive any succeeding treatment. We claim that the discrete fractional type of sigmoidal curves have the best data tting results when they are compared to the other types of models.

During 2012, the employer of this essay carried out a telephone survey with 18000 participants and a web panel survey with 708 participants. Those who partook in the telephone survey were given a choice to join the web panel. The purpose of this work is to study the participants of the telephone survey and see if they reflect the Swedish population with regards to several socio-demographic factors. Also, we intend to investigate if the propensity to join the web panel differs for participants of the telephone survey with regards to various socio-demographic affiliations. It is also of interest to study if the response pattern is different for participants of the telephone survey that would like to join the web panel and those who reject. A comparison of response pattern between the telephone survey and web panel survey has also been done, to see if there exist any differences for these two groups of surveys. The statistical methods used in this essay are descriptive statistics, multiple logistic regression and decision trees. Conclusions to be drawn with result from these methods are that the participants from the telephone survey do reflect the Swedish population regarding certain socio-demographic factors and that there is a slight difference in propensity to join the web panel for people which have dissimilar socio-demographic affiliation. It has also been found that there is a slight difference in response pattern for participants who would or would not like to join the web panel, as well as differences in response pattern also exist between the telephone survey and the web panel survey.

The most common decisions to be taken in the design of logistic systems are related to the location of facilities and the management of vehicle fleets.In this thesis, we study three of the optimization problems arising around this kind of decisions; namely the LRP, the SGAP and the SLRP. The first problem analyzed in this work is a capacitated LRP with one single uncapacitated vehicle at each open plant. To model this problem we resort to an auxiliary network that allows us to represent feasible solutions as families of paths satisfying a series of side constraints.The solutions of a reinforced LP relaxation of this model are used as the basis of a rounding heuristic designed to build feasible solutions of the problem. Those solutions are then improved with a TS heuristic.Two lower bounds, distinct from that obtained with the LP relaxation of the model, are proposed for this problem. The first one is obtained by bound ing separately the two different parts of the cost of any feasible solution, namely the fixed costs for opening plants and the route costs. The second lower bound is the result of applying CG to the Lagrangian dual obtained by dualizing the assignment constraints. The pricing problem obtained from our formulation is an ESPPRC. The complexity of this problem, and the fact that optimality of the obtained solutions is not always necessary, have motivated us to develope a simple heuristic for it.The computational experiences show a very good behavior of the TS procedure both, for the computational effort required and the quality of the solutions. The first lower bound proposed gives satisfactory results in reasonable amounts of time. In the case of the CG approach, results are very encouraging. In some of the tested instances the program terminated because of the CPU time limit specification, before succeeding to find a valid lower bound.In those instances, the algorithm was always stalled in the exact resolution of an ESPPRC. The difficulties encountered to solve this problem represent a limitation of this approach and suggest the future study of alternative solution methods. In spite of this limitation, in a high proportion of the instances the algorithm succeeded, and the final gap between the upper and the lower bound was always 0. The success in these instances is partially due to the use of our heuristic to generate new columns whenever this is possible.The second problem studied in this thesis is a SGAP. In this assignment problem the jobs are interpreted as customers that can request a service with a given probability, and each agent can serve a limited number of customers. This uncertainty about the presence of each customer is represented by modelling the demands of the customers as Bernoulli distributed independent random variables. The problem consists of finding an a priori assignment of customers to agents. Once the actual requests for service are known, an adaptive action is taken to tackle violations of the capacity constraints. On the one hand, part of the customers assigned to overloaded agents can be reassigned. On the other hand, some of the service requests can be disregarded. Different penalties for reassignment and for unattended service requests are pre-specified. The problem is formulated as a recourse model, where the recourse function gives the expected penalties for reassignments and unattended service requests.Since this recourse function is defined as the expected value of an integer programming recourse model, it does not have the regularity properties characteristic of those defined by linear recourse models. To overcome the difficulties caused by this, we construct a convex approximation of the recourse function that is tight in all feasible points. Moreover, as illustrated in the computational experiences, the use of this approximation reduces the computational effort required to evaluate the recourse function is some orders of magnitude. The convex approximation of the recourse function allows us to adapt the well-known L-shaped method to our problem. Integrality of the first stage variables is tackled in three different ways, giving raise to three versions of the algorithm. The difference among them resides in the hierarchy between the branching and the addition of violated cuts. On the one hand, we present a version where cuts are only added when integer solutions are found. On the other hand, a version is proposed where branching is only performed when no more violated cuts can be identified. The remaining version is designed as a tradeoff of these two; at each node of the search tree, new optimality cuts are added, if needed, and branching is performed if the solution at hand is fractional. Computational experiences point out this last version as the best of the three, since the efforts devoted to obtain a rich approximation of the recourse function and to achieve integrality are more balanced.We have also derived both, lower and upper bounds for this specific SGAP. Upper bounds are obtained from three simple heuristics. All them are based on solving deterministic approximations of the SGAP and provide good quality solutions in small amounts of CPU time. A lower bound is derived from a family of linear stochastic subproblems. Althoug in some of the tested instances the gap between the bounds exceeded the 30%, in the general case we obtained small gaps.One of the heuristics was used in the exact algorithm to provide it with a good upper bound. The lower bound is also used in the three versions of the algorithm, as the basis of some of the optimality cuts and also to identify optimal solutions. The quality of these bounds is one of the factors that explain the success of the exact algorithm.The last problem studied in this thesis is a SLRP. The stochasticity considered here is of the same type as that considered for the SGAP. Again, customers may request a service with a given probability and this is modeled by introducing Bernoulli random variables to represent the demands. A two stage model is proposed for this problem. In a first stage, a set of plants to open has to be chosen together with a family of disjoint routes (one rooted at each open plant) that visit all the customers. In the second stage, once all the demands become available, the actual routes have to be designed. For plants whose number of service requests does not exceed the capacity, the actual route is derived from that designed a priori by skipping customers with no demand. When the requests for service allocated to a plant exceed its capacity, a subset of them is randomly chosen to be served, and they are visited in the order defined by the a priori route.Penalties are paid for the unattended service requests. The expected total cost of the actual routes and the expected penalties for unserviced customers are contained in the recourse function.We present a two phase heuristic to solve this problem. In the first phase, a series of subproblems are sequentially solved to build an initial solution. In the second phase, this solution is successively improved using LS. This improving phase requires a high number of evaluations of the recourse function. Although we have developed an analytical expression for this recourse function, the computational effort required for its evaluation is considerable due to its combinatorial nature. For this reason, we approximate it with a simpler auxiliary function that has allowed us to obtain solutions in small computational times.We also propose a lower bound obtained from bounding different parts of the objective function independently. Unfortunately, we only could find reasonable bounds for the sum of fixed costs for opening the plants plus the expected penalty paid for unserviced customers. Further research is intended to improve the bounding of the expected total cost of the routes.The evaluation of the quality of the solutions obtained with our heuristic is not easy due to the lack of a tight global lower bound. However, the partial bound on the costs relative to the plants allows to conclude that the heuristic makes in general a good choice of the set of plants. As for the allocation of customers to plants and the design of the routes we can only evaluate the evolution along the search. In the computational experiences reported it can be seen that this evolution is satisfactory.

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.