This study investigated international medical graduates (IMGs), registered between January 1, 2007 and April 14, 2011, at the Access Centre for Internationally Educated Health Professionals in Ontario. By way of logistic regression in a cross-sectional design, it was found that permanent residents who were recent immigrants had lesser chances of being employed full-time at registration (baseline). By way of survival analysis in a cohort design, it was found that younger IMGs who have been in Canada less than 5 years and who have taken the Medical Council of Canada Evaluating Exam (MCCEE) have the greatest chances of securing residency positions in Canada or the US, whereas IMGs from Eastern Europe, South Asia and Africa have lesser chances. It was revealed that registered IMGs are a vulnerable population, and certain groups may be disadvantaged due to underlying characteristics. These groups can be targeted for specific interventions.

This study investigated international medical graduates (IMGs), registered between January 1, 2007 and April 14, 2011, at the Access Centre for Internationally Educated Health Professionals in Ontario. By way of logistic regression in a cross-sectional design, it was found that permanent residents who were recent immigrants had lesser chances of being employed full-time at registration (baseline). By way of survival analysis in a cohort design, it was found that younger IMGs who have been in Canada less than 5 years and who have taken the Medical Council of Canada Evaluating Exam (MCCEE) have the greatest chances of securing residency positions in Canada or the US, whereas IMGs from Eastern Europe, South Asia and Africa have lesser chances. It was revealed that registered IMGs are a vulnerable population, and certain groups may be disadvantaged due to underlying characteristics. These groups can be targeted for specific interventions.

Selection of signi?cant genes via expression patterns is important in a microarray problem. Owing to small sample size and large number of variables (genes), the selection process can be unstable. This research proposes a hierarchical Bayesian model for gene (variable) selection. We employ latent variables in a regression setting and use a Bayesian mixture prior to perform the variable selection. Due to the binary nature of the data, the posterior distributions of the parameters are not in explicit form, and we need to use a combination of truncated sampling and Markov Chain Monte Carlo (MCMC) based computation techniques to simulate the posterior distributions. The Bayesian model is ?exible enough to identify the signi?cant genes as well as to perform future predictions. The method is applied to cancer classi?cation via cDNA microarrays. In particular, the genes BRCA1 and BRCA2 are associated with a hereditary disposition to breast cancer, and the method is used to identify the set of signi?cant genes to classify BRCA1 and others. Microarray data can also be applied to survival models. We address the issue of how to reduce the dimension in building model by selecting signi?cant genes as well as assessing the estimated survival curves. Additionally, we consider the wellknown Weibull regression and semiparametric proportional hazards (PH) models for survival analysis. With microarray data, we need to consider the case where the number of covariates p exceeds the number of samples n. Speci?cally, for a given vector of response values, which are times to event (death or censored times) and p gene expressions (covariates), we address the issue of how to reduce the dimension by selecting the responsible genes, which are controlling the survival time. This approach enables us to estimate the survival curve when n << p. In our approach, rather than ?xing the number of selected genes, we will assign a prior distribution to this number. The approach creates additional ?exibility by allowing the imposition of constraints, such as bounding the dimension via a prior, which in e?ect works as a penalty. To implement our methodology, we use a Markov Chain Monte Carlo (MCMC) method. We demonstrate the use of the methodology with (a) di?use large B??cell lymphoma (DLBCL) complementary DNA (cDNA) data and (b) Breast Carcinoma data. Lastly, we propose a mixture of Dirichlet process models using discrete wavelet transform for a curve clustering. In order to characterize these time??course gene expresssions, we consider them as trajectory functions of time and gene??speci?c parameters and obtain their wavelet coe?cients by a discrete wavelet transform. We then build cluster curves using a mixture of Dirichlet process priors.

The mortality of thoracic aortic diseases (mainly aneurysms and dissections) is high, even with surgical treatment. Epidemiology and long-term outcomes are incompletely investigated. Stroke is a major complication contributing to mortality, morbidity, and possibly to reduced quality of life. Study I Increasing incidence of thoracic aortic diseases 1987 – 2002 was demonstrated (n=14229). Annual number of operations increased eight-fold. Overall long-time survival was 92%, 77%, and 57% at 1, 5, and 10 years. Risk of operative and long-term mortality was reduced across time. Study II 2634 patients operated on the proximal thoracic aorta (Swedish Heart Surgery register) were examined. Aortic valve replacement, coronary revascularization, emergency operation, and age were independently associated with surgical death. Long-term mortality was similar for aneurysms and dissections. Operative mortality was reduced (13.7% vs 7.2%) for aneurysms but remained unchanged (22.3% vs 22.4%) for dissections across time. Study III 65 patients underwent selective antegrade cerebral perfusion (SACP) uni- or bilaterally. Stroke was significantly more common after unilateral SACP (29% vs 8%, p=0.045), confirmed by propensity score-matched analysis. Subclavian artery cannulation with Seldinger-technique entailed vascular complication in one case (1.5%). Study IV Near-infrared spectroscopy (NIRS) was used to monitor cerebral tissue saturation (rSO2) during SACP in 46 patients. Lower rSO2 were encountered (1) in patients suffering a stroke (2) with unilateral SACP, and (3) in the affected hemisphere of stroke victims. A decrease of rSO2 by 14 – 21% from baseline increased the risk of stroke significantly. Study V Quality of life (QoL) in 76 survivors of thoracic aortic surgery was examined with the SF-36 health questionnaire. Except for pain, QoL was reduced in all dimensions. QoL was not affected by acuity of operation. Tendencies of lower QoL after descending aortic operations, after major complications, and with persistent dysfunction were non-significant.

L’intérêt principal de cette recherche porte sur la validation d’une méthode statistique en pharmaco-épidémiologie. Plus précisément, nous allons comparer les résultats d’une étude précédente réalisée avec un devis cas-témoins niché dans la cohorte utilisé pour tenir compte de l’exposition moyenne au traitement : – aux résultats obtenus dans un devis cohorte, en utilisant la variable exposition variant dans le temps, sans faire d’ajustement pour le temps passé depuis l’exposition ; – aux résultats obtenus en utilisant l’exposition cumulative pondérée par le passé récent ; – aux résultats obtenus selon la méthode bayésienne. Les covariables seront estimées par l’approche classique ainsi qu’en utilisant l’approche non paramétrique bayésienne. Pour la deuxième le moyennage bayésien des modèles sera utilisé pour modéliser l’incertitude face au choix des modèles. La technique utilisée dans l’approche bayésienne a été proposée en 1997 mais selon notre connaissance elle n’a pas été utilisée avec une variable dépendante du temps. Afin de modéliser l’effet cumulatif de l’exposition variant dans le temps, dans l’approche classique la fonction assignant les poids selon le passé récent sera estimée en utilisant des splines de régression. Afin de pouvoir comparer les résultats avec une étude précédemment réalisée, une cohorte de personnes ayant un diagnostique d’hypertension sera construite en utilisant les bases des données de la RAMQ et de Med-Echo. Le modèle de Cox incluant deux variables qui varient dans le temps sera utilisé. Les variables qui varient dans le temps considérées dans ce mémoire sont iv la variable dépendante (premier évènement cérébrovasculaire) et une des variables indépendantes, notamment l’exposition / The main interest of this research is the validation of a statistical method in pharmacoepidemiology. Specifically, we will compare the results of a previous study performed with a nested case-control which took into account the average exposure to treatment to : – results obtained in a cohort study, using the time-dependent exposure, with no adjustment for time since exposure ; – results obtained using the cumulative exposure weighted by the recent past ; – results obtained using the Bayesian model averaging. Covariates are estimated by the classical approach and by using a nonparametric Bayesian approach. In the later, the Bayesian model averaging will be used to model the uncertainty in the choice of models. To model the cumulative effect of exposure which varies over time, in the classical approach the function assigning weights according to recency will be estimated using regression splines. In order to compare the results with previous studies, a cohort of people diagnosed with hypertension will be constructed using the databases of the RAMQ and Med-Echo. The Cox model including two variables which vary in time will be used. The time-dependent variables considered in this paper are the dependent variable (first stroke event) and one of the independent variables, namely the exposure.

Darbo metu buvo išnagrinėta įvairių faktorių (kovariančių) įtaka reumatoidiniu artritu sergančio 531 ligonio mirtingumui. Buvo taikomas vienas iš regresinių išgyvenamumo modelių – Cox’o modelis. Iš minėtos 531 ligonio imties mirę buvo 32 ligoniai. Iš pradžių buvo tiriama ligonių imtis laiko nuo ligos pradžios aspektu. Šiuo atveju prognozuojantys veiksniai buvo amžius, kada liga buvo diagnozuota (AMZDGN), lytis (LYTKOD), gydymas Metotreksatu (GYD_MTX) ir gydymas Azatriopinu/Imuranu (AZA_IMUR). Vėliau, tiriant ligonių mirtingumą kaip amžiaus funkciją, nustatyti svarbiausi lemiantys veiksniai buvo šie: ligonių lytis (LYTKOD) ir gydymas Azatriopinu/Imuranu (AZA_IMUR). Gauti rezultatai, t.y. ligonių išgyvenamumą lemiančios kovariantės (veiksniai), beveik visiškai sutampa su gydytojų nurodytais. Tai dar kartą patvirtina matematinių statistinių modelių, šiuo atveju nagrinėjamo Cox‘o modelio, taikymo realiame gyvenime, svarbą. Kitai duomenų imčiai, t.y. vėžiu sergančių ligonių duomenų aibei, buvo taikomas Persikertančių mirimų intensyvumų (SCE) modelis, t.y. tikrinama Cox‘o modelio adekvatumo duomenims hipotezė. Hipotezė buvo atmesta, nes minėtiems duomenims Cox‘o modelis negalioja. Pagrindinis darbo rezultatas yra šis: gautas kriterijus Cox‘o modelio adekvatumui tikrinti, naudojant nupjautus iš kairės ir cenzūruotus iš dešinės duomenis, sudarytos programos kriterijui realizuoti. Reumatoidinio artrito ligonių duomenų aibei, t.y. nupjautiems iš kairės ir cenzūruotiems iš dešinės... [toliau žr. visą tekstą] / In this work the Cox proportional hazards model was applied to investigate the influence of various factors (covariates) to mortality of rheumatoid arthritis patients of Vilnius. In the first case, the sample of 531 patients was analysed. Analysing survival of patients of the sample as function of time from the beginnig of the disease, the prognostic factors were LYTKOD (the sex of patients), AMZDGN (patients‘ age, when the rheumatoid arthritis was diagnosed), GYD_MTX (treatment with metotrexat) and AZA_IMUR (treatment with Azatriopin/Imuran). When survival was analysed as function of age then the prognostic factor were LYTKOD (the sex of patients) and AZA_IMUR (treatment with Azatriopin/Imuran). The results are almost identical to those, which doctors suggested. This fact confirms the importance of using mathematical statistical models to solve the problems of the real life. In this case, the importance of using the Cox model. On the other hand, Simple cross-effects (SCE) model was aplied for the sample of canser patients. In the case of this model the hypothesis of Cox model fiting for canser patients‘ data was rejected. The most important result of this work is that the criterion of Cox model fitting to left truncated and right censored data was constructed. Also a program of SAS for the criterion was created. The the hypothesis of Cox model fiting for the rheumatoid arthritis patients wasn‘t rejected, because Cox model fit for these data.

In this study we look at the problem of analysing survival data in the presence of longitudinally collected covariates. New methodology for analysing such data has been developed through the use of hidden Markov modeling. Special attention has been given to the case of large information volume, where a preliminary data reduction is necessary. Novel graphical diagnostics have been proposed to assess goodness of fit and significance of covariates. The methodology developed has been applied to the data collected on behaviors of Mexican fruit flies, which were monitored throughout their lives. It has been found that certain patterns in eating behavior may serve as an aging marker. In particular it has been established that the frequency of eating is positively correlated with survival times. / Thesis (Ph.D, Mathematics & Statistics) -- Queen's University, 2010-04-26 18:34:01.131

<p>This study extends the existing literature on how social networks enhance the performance and sustainability of small enterprises. More specifically, the study isolates and investigates the mechanisms through which social capital helps with the growth and survival of MSMEs. The evidence presented in this study strongly suggests that an indigenous social network widely practiced in Ethiopia, the &ldquo / iqqub&rdquo / , contributes significantly to the start-up, survival and development of urban MSMEs.</p>

Incomplete data often arise in the study of life history processes. Examples include missing responses, missing covariates, and unobservable latent processes in addition to right censoring. This thesis is on the development of statistical models and methods to address these problems as they arise in oncology and chronic disease. Methods of estimation and inference in parametric, weakly parametric and semiparametric settings are investigated. Studies of chronic diseases routinely sample individuals subject to conditions on an event time of interest. In epidemiology, for example, prevalent cohort studies aiming to evaluate risk factors for survival following onset of dementia require subjects to have survived to the point of screening. In clinical trials designed to assess the effect of experimental cancer treatments on survival, patients are required to survive from the time of cancer diagnosis to recruitment. Such conditions yield samples featuring left-truncated event time distributions. Incomplete covariate data often arise in such settings, but standard methods do not deal with the fact that the covariate distribution is also affected by left truncation. We develop a likelihood and algorithm for estimation for dealing with incomplete covariate data in such settings. An expectation-maximization algorithm deals with the left truncation by using the covariate distribution conditional on the selection criterion. An extension to deal with sub-group analyses in clinical trials is described for the case in which the stratification variable is incompletely observed. In studies of affective disorder, individuals are often observed to experience recurrent symptomatic exacerbations of symptoms warranting hospitalization. Interest lies in modeling the occurrence of such exacerbations over time and identifying associated risk factors to better understand the disease process. In some patients, recurrent exacerbations are temporally clustered following disease onset, but cease to occur after a period of time. We develop a dynamic mover-stayer model in which a canonical binary variable associated with each event indicates whether the underlying disease has resolved. An individual whose disease process has not resolved will experience events following a standard point process model governed by a latent intensity. If and when the disease process resolves, the complete data intensity becomes zero and no further events will arise. An expectation-maximization algorithm is developed for parametric and semiparametric model fitting based on a discrete time dynamic mover-stayer model and a latent intensity-based model of the underlying point process. The method is applied to a motivating dataset from a cohort of individuals with affective disorder experiencing recurrent hospitalization for their mental health disorder. Interval-censored recurrent event data arise when the event of interest is not readily observed but the cumulative event count can be recorded at periodic assessment times. Extensions on model fitting techniques for the dynamic mover-stayer model are discussed and incorporate interval censoring. The likelihood and algorithm for estimation are developed for piecewise constant baseline rate functions and are shown to yield estimators with small empirical bias in simulation studies. Data on the cumulative number of damaged joints in patients with psoriatic arthritis are analysed to provide an illustrative application.

This study investigated international medical graduates (IMGs), registered between January 1, 2007 and April 14, 2011, at the Access Centre for Internationally Educated Health Professionals in Ontario. By way of logistic regression in a cross-sectional design, it was found that permanent residents who were recent immigrants had lesser chances of being employed full-time at registration (baseline). By way of survival analysis in a cohort design, it was found that younger IMGs who have been in Canada less than 5 years and who have taken the Medical Council of Canada Evaluating Exam (MCCEE) have the greatest chances of securing residency positions in Canada or the US, whereas IMGs from Eastern Europe, South Asia and Africa have lesser chances. It was revealed that registered IMGs are a vulnerable population, and certain groups may be disadvantaged due to underlying characteristics. These groups can be targeted for specific interventions.