Thesis advisor: Laura O'Dwyer / In recent decades, the plight of early career teacher turnover has had significant financial ramifications for our nation’s schools and has posed a serious threat to achieving educational equity, with the most disadvantaged schools experiencing the highest rates of turnover. Using data collected from the Beginning Teacher Longitudinal Survey, this study employed discrete-time competing risks survival analysis to explore the first-year experiences of public middle and high school teachers as predictors of their career decisions to stay in their current school, move to a new school, or leave the profession across the first five years of their career. Four facets were conceived as characterizing teachers’ first-year experiences: 1) policies and programs for first-year teachers provided by the administration including mentoring and induction, 2) perceptions of their preparedness to teach, 3) perceptions of school climate and workplace conditions, and 4) satisfaction with teaching. The research questions are: 1. What are the first-year experiences for teachers in the sample and how do they compare between teachers who are retained in their first school placements and teachers who voluntarily or involuntarily turn over in later years? 2. What first-year teacher experiences predict voluntary and involuntary turnover at the end of years 1, 2, 3, and 4? And, how does satisfaction with teaching in the first year interact with the three other facets of the first-year experience to predict voluntary and involuntary turnover across the early career window? Findings suggest there may be differences in the mechanisms that drive the moving and leaving phenomena, suggesting that policymakers treat the two turnover pathways as separate problems requiring separate solutions. Furthermore, findings suggest there may be more policy-amendable variables that can be manipulated in the first year of teaching to prevent leaving than there are to prevent moving, implying that curbing rates of moving to minimize the localized impacts of teacher migration to other schools may be more challenging than reducing rates of leaving the profession. / Thesis (PhD) — Boston College, 2019. / Submitted to: Boston College. Lynch School of Education. / Discipline: Educational Research, Measurement and Evaluation.

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.

Li, Li. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (leaves 34-36). / Abstracts in English and Chinese. / Chapter Chapter One --- Introduction --- p.1 / Chapter Chapter Two --- Methodology --- p.5 / Chapter Chapter Three --- Data --- p.9 / Chapter 3.1 --- Data Description --- p.9 / Chapter 3.2 --- Selection of Covariate --- p.13 / Chapter Chapter Four --- Empirical Analysis --- p.20 / Chapter 4.1 --- General Survival Analysis by Cox PH Model --- p.20 / Chapter 4.2 --- Competing Risk Analysis of Listed Firms --- p.24 / Chapter 4.3 --- Robustness Check --- p.28 / Chapter Chapter Five --- Conclusion --- p.30 / Appendix 1 --- p.32 / Appendix II --- p.33 / Reference --- p.34 / Tables --- p.37 / Figures --- p.58

In clustered survival data, unobservable cluster effects may exert powerful influences on the outcomes and thus induce correlation among subjects within the same cluster. The ordinary partial likelihood approach does not account for this dependence. Frailty models, as an extension to Cox regression, incorporate multiplicative random effects, called frailties, into the hazard model and have become a very popular way to account for the dependence within clusters. We particularly study the two-level nested lognormal frailty model and propose an estimation approach based on the complete data likelihood with frailty terms integrated out. We adopt B-splines to model the baseline hazards and adaptive Gauss-Hermite quadrature to approximate the integrals efficiently. Furthermore, in finding the maximum likelihood estimators, instead of the Newton-Raphson iterative algorithm, Gauss-Seidel and BFGS methods are used to improve the stability and efficiency of the estimation procedure. We also study competing risks models with missing cause of failure in the context of Cox proportional hazards models. For competing risks data, there exists more than one cause of failure and each observed failure is exclusively linked to one cause. Conceptually, the causes are interpreted as competing risks before the failure is observed. Competing risks models are constructed based on the proportional hazards model specified for each cause of failure respectively, which can be estimated using partial likelihood approach. However, the ordinary partial likelihood is not applicable when the cause of failure could be missing for some reason. We propose a weighted partial likelihood approach based on complete-case data, where weights are computed as the inverse of selection probability and the selection probability is estimated by a logistic regression model. The asymptotic properties of the regression coefficient estimators are investigated by applying counting process and martingale theory. We further develop a double robust approach based on the full data to improve the efficiency as well as the robustness.

It is of interest that researchers study competing risks in which subjects may fail from any one of K causes. Comparing any two competing risks with covariate effects is very important in medical studies. This thesis develops omnibus tests for comparing cause-specific hazard rates and cumulative incidence functions at specified covariate levels. In the thesis, the omnibus tests are derived under the additive risk model, that is an alternative to the proportional hazard model, with by a weighted difference of estimates of cumulative cause-specific hazard rates. Simultaneous confidence bands for the difference of two conditional cumulative incidence functions are also constructed. A simulation procedure is used to sample from the null distribution of the test process in which the graphical and numerical techniques are used to detect the significant difference in the risks. A melanoma data set is used for the purpose of illustration.

This thesis investigates the relationship between foster care placement settings and discharges. Placement settings are where foster children live: foster homes, group homes, etc. There may be one or several placements for any individual child. In the interest of stability, federal funding to states depends in part on low numbers of placement moves. Federal reviews, however, do not consider whether the placement settings resemble permanent family life (foster homes compared to congregate care) or the direction of placement moves. Competing risks regression was used to analyze time to discharge data of foster children in Georgia. Discharges (competing risks) were compared based on the number and the direction of placement moves. Children with movement patterns that favored placements similar to permanent family life were found to have higher probabilities of discharges to safe permanence. This thesis promotes “proximity to permanence” as an important, but often overlooked, consideration in foster care placements.

Thesis (Ph. D.)--State University of New York at Binghamton, Department of Mathematical Sciences, 2009. / Includes bibliographical references.

Orientador: Paulo Correa Lima / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecanica / Made available in DSpace on 2018-08-04T03:13:38Z (GMT). No. of bitstreams: 1 Marcondes_AndrezaBenatti_M.pdf: 5495313 bytes, checksum: a5a7651ef5e80d1a06695be92059dd0b (MD5) Previous issue date: 2003 / Resumo: As mudanças nos padrões de competitividade da indústria brasileira influenciaram os procedimentos na avaliação de investimentos em capital fixo das empresas. Neste sentido, a decisão de investir em determinado projeto não é baseada somente na relação entre uma taxa interna de retomo e uma taxa mínima de atratividade, mas deve-se levar em consideração fatores como tempo de resposta ao cliente, qualidade e custo. Para alcançar estes requisitos, a empresa deve reconfigurar seu processo de negócios e uma das áreas mais afetadas é o chão-de-fábrica. A abordagem da Toyota Motor Corporation para projeto de sistemas de manufatura, conhecida como manufatura enxuta, mostrou ser capaz de garantir resultados superiores. Os modelos de análise de investimento atuais, criados para atender a produção em massa, falham em apontar os resultados gerados pela produção enxuta. Este trabalho pretende entender o impacto financeiro de se converter uma fábrica do sistema de produção em massa para o sistema enxuto. Um caso de estudo de uma montadora que se tornou referência nas práticas enxutas é utilizado para verificar se ocorreram transformações na metodologia utilizada pela empresa / Abstract: The changes in the competitiveness standards in the Brazilian industry had influenced on the investment evaluation procedure of fixed capital in corporations. In this way, the decision to invest in a project must not be only based on a relation between the internal rate of return and attractiveness rate. It is necessary to consider factors as response time to customers orders, quality and cost. In order to achieve those requirements most companies need to reconfigure their business process and one of the most affected areas in the shop floor. Toyota Motor Corporation' s approach to manufacturing system design, a1so known as lean manufacturing, has been showing superior performance. The current investment ana1ysis models, created to support mass production, :fail in recognize the effectiveness of lean manufacturing. This work aims at understanding financial impact of restructuring a plant from mass to lean production. A case study of an automotive assembly company who become a benchmark on lean practices is used to check of investments methodologies had been reviewed to support manufacturing transformation / Mestrado / Materiais e Processos de Fabricação / Mestre em Engenharia Mecânica

This thesis first introduces the basic notions of univariate survival analysis. Then the survival analysis setting is extended to competing risk models, i.e. the cases considering several events of interest or several causes of one event. In the competing risk model, we discuss the problem of identification, which means that it is not possible to identify marginal distributions from observed competing risk data. Next, we present copula models, which are a suitable mathematical tool for modelling dependence structure between random variables. We explain their basic characteristics, present some useful copula families and the relationship of copula parameters with certain dependence (correlation) measures. Further, we show the utilization of copulas within competing risks models and how they can be helpful in the solution of identifiability problem. Finally, we apply the listed theoretical knowledge in a simulated example. Powered by TCPDF (www.tcpdf.org)

The thesis presents fundamental characteristics of survival analysis in the case of competing risks and their relationships. In the case without regression, basic nonparametric estimates and a logarithmic likelihood function for parameter estimates is given. The main focus is on Cox's proportional hazards model (PH), a model with accelerated time (AFT) and a flexible regression model (FG) are also mentioned. The identifiability of the associated survival function is solved using copulas. Basics of copula theory and the measurement of dependence by correlation coefficients (Pearson, Spearman and Kendal) are described in a separate chapter. A substantial part of the theory is practically used in a generated case without regression.