O câncer de mama é a principal causa de mortalidade por câncer entre as mulheres. Alguns biomarcadores e características clínicas são utilizados para avaliar o prognóstico e prever a resposta a uma série de abordagens terapêuticas. A Homocisteína é conhecida como um fator de risco para doença vascular aterosclerótica, mas sua participação na biologia do câncer ainda é incerta. Cisteína é o aminoácido sulfurado derivado da Homocisteína no ciclo da Metionina. Este ciclo metabólico origina as bases nitrogenadas e também determina o nível de metilação da molécula de DNA. É atualmente reconhecido que a hipometilação global do genoma é um evento chave na transformação maligna das células. O objetivo deste estudo foi avaliar os níveis séricos de homocisteína e cisteína como biomarcadores de sobrevida e de progressão da doença em câncer de mama. Também foi avaliado o efeito de um curso de curta duração (um mês) de tratamento hormonal sobre os níveis de Homocisteína, Cisteína e metilação do DNA. Amostras de sangue foram obtidos por ocasião da biópsia inicial (pré-tratamento) em todas as pacientes e, de tumor e de tecido normal adjacente, ao diagnóstico eem um mês após, para as pacientes que receberam o regime hormonal neo-adjuvante (pré-operatório). Todas as pacientes eram mulheres na pós-menopausa, com tumores de mama ressecáveis, acompanhadas em dois hospitais públicos, que consentiram em participar de outros dois protocolos de pesquisa prévios. Homocisteína e Cisteína foram analisadas por HPLC e a metilação global do DNA do tecido foi determinada por meio da técnica de MSRE (Methylation-Sensitive Restriction Enzyme). Foi observada uma diferença significativa entre os níveis pré e póstratamento de Homocisteína e Cisteína em tumores avançados, sugerindo um papel prognóstico em pacientes com características clínicas reservadas. As variações nos níveis de Homocisteína se mostraram significativamente correlacionadas com a sobrevida livre de doença. O modelo de risco proporcional de Cox demonstrou que os níveis de homocisteína e o status dos linfonodos representaram fatores prognósticos independentes em termos de sobrevida livre de doença. Embora mais estudos sejam necessários para confirmar estes resultados, nossa pesquisa sugere que a Homocisteína pode ser usada como um biomarcador de prognóstico para câncer de mama / Breast cancer is the leading cause of cancer mortality among women. Some biomarkers and clinical features are used to evaluate prognosis and to predict response to a range of therapeutic approaches. Homocysteine is well known as a risk factor in atherosclerotic vascular diseases, but its participation in cancer biology is still unclear. Cysteine is a sulfur amino acid derived from Homocysteine in the Methionine cycle. This metabolic cycle originates the nitrogenous bases and determines the methylation level of the DNA molecule as well. It is currently recognized that the global hipomethylation of the genome is a key event in the malign transformation of cells. The aim of this study was to evaluate serum Homocysteine and Cysteine as biomarkers of survival and disease progression in breast tumor, as well as the methylation status of tumor and normal tissues. The effect of a short course (one month) of hormonal treatment on Homocysteine, Cysteine and DNA methylation levels was also evaluated. Blood samples were collected during the initial biopsy (pretreatment) in all patients and, tumor samples and normal adjacent tissue, at diagnosis and one month after, for the patients that received neo-adjuvant hormonal regimen (pre-treatment). All patients were post-menopausal women, with resectable breast tumors, followed at two public hospitals, and that had consented to participate in two previous research protocols related to their disease. Serum Homocysteine and Cysteine were analyzed by HPLC and tissue global DNA methylation was determined by the MSRE (Methylation- Sensitive Restriction Enzyme) technique. A significant difference was observed between pre- and post-treatment levels of Homocysteine and Cysteine in advanced tumors, suggesting a prognostic role in patients with poor clinical characteristics. Variations in Homocysteine levels were significantly correlated with disease free survival. Cox proportional risk model demonstrated that nodal status and Homocysteine levels were independent prognostic factors for Disease Free Survival. Although more studies are needed to confirm these results, our research suggests that Homocysteine might be used as a prognostic biomarker for breast cancer

Ce travail de thèse porte sur l'estimation paramétrique du maximum de vraisemblance pour des données de survie tronquées à droite, lorsque les délais de troncature sont considérés déterministes. Il a été motivé par le problème de la modélisation des délais de survenue des effets indésirables médicamenteux à partir des bases de données de pharmacovigilance, constituées des cas notifiés. Les distributions exponentielle, de Weibull et log-logistique ont été explorées.Parfois le caractère tronqué à droite des données est ignoré et un estimateur naïf est utilisé à la place de l'estimateur pertinent. Une première étude de simulations a montré que, bien que ces deux estimateurs - naïf et basé sur la troncature à droite - puissent être positivement biaisés, le biais de l'estimateur basé sur la troncature est bien moindre que celui de l'estimateur naïf et il en va de même pour l'erreur quadratique moyenne. De plus, le biais et l'erreur quadratique moyenne de l'estimateur basé sur la troncature à droite diminuent nettement avec l'augmentation de la taille d'échantillon, ce qui n'est pas le cas de l'estimateur naïf. Les propriétés asymptotiques de l'estimateur paramétrique du maximum de vraisemblance ont été étudiées. Sous certaines conditions, suffisantes, cet estimateur est consistant et asymptotiquement normal. La matrice de covariance asymptotique a été détaillée. Quand le délai de survenue est modélisé par la loi exponentielle, une condition d'existence de l'estimation du maximum de vraisemblance, assurant ces conditions suffisantes, a été obtenue. Pour les deux autres lois, une condition d'existence de l'estimation du maximum de vraisemblance a été conjecturée.A partir des propriétés asymptotiques de cet estimateur paramétrique, les intervalles de confiance de type Wald et de la vraisemblance profilée ont été calculés. Une seconde étude de simulations a montré que la couverture des intervalles de confiance de type Wald pouvait être bien moindre que le niveau attendu en raison du biais de l'estimateur du paramètre de la distribution, d'un écart à la normalité et d'un biais de l'estimateur de la variance asymptotique. Dans ces cas-là, la couverture des intervalles de la vraisemblance profilée est meilleure.Quelques procédures d'adéquation adaptées aux données tronquées à droite ont été présentées. On distingue des procédures graphiques et des tests d'adéquation. Ces procédures permettent de vérifier l'adéquation des données aux différents modèles envisagés.Enfin, un jeu de données réelles constitué de 64 cas de lymphomes consécutifs à un traitement anti TNF-α issus de la base de pharmacovigilance française a été analysé, illustrant ainsi l'intérêt des méthodes développées. Bien que ces travaux aient été menés dans le cadre de la pharmacovigilance, les développements théoriques et les résultats des simulations peuvent être utilisés pour toute analyse rétrospective réalisée à partir d'un registre de cas, où les données sur un délai de survenue sont aussi tronquées à droite. / This work investigates the parametric maximum likelihood estimation for right-truncated survival data when the truncation times are considered deterministic. It was motivated by the modeling problem of the adverse drug reactions time-to-onset from spontaneous reporting databases. The families of the exponential, Weibull and log-logistic distributions were explored.Sometimes, right-truncation features of spontaneous reports are not taken into account and a naive estimator is used instead of the truncation-based estimator. Even if the naive and truncation-based estimators may be positively biased, a first simulation study showed that the bias of the truncation-based estimator is always smaller than the naive one and this is also true for the mean squared error. Furthermore, when the sample size increases, the bias and the mean squared error are almost constant for the naive estimator while they decrease clearly for the truncation-based estimator.Asymptotic properties of the truncation-based estimator were studied. Under sufficient conditions, this parametric truncation-based estimator is consistent and asymptotically normally distributed. The covariance matrix was detailed. When the time-to-onset is exponentially distributed, these sufficient conditions are checked as soon as a condition for the maximum likelihood estimation existence is satisfied. When the time-to-onset is Weibull or log-logistic distributed, a condition for the maximum likelihood estimation existence was conjectured.The asymptotic distribution of the maximum likelihood estimator makes it possible to derive Wald-type and profile likelihood confidence intervals for the distribution parameters. A second simulation study showed that the estimated coverage probability of the Wald-type confidence intervals could be far from the expected level because of a bias of the parametric maximum likelihood estimator, a gap from the gaussian distribution and a bias of the asymptotic variance estimator. In these cases, the profile likelihood confidence intervals perform better.Some goodness-of-fit procedures adapted to right-truncated data are presented. Graphical procedures and goodness-of-fit tests may be distinguished. These procedures make it possible to check the fit of different parametric families to the data.Illustrating the developed methods, a real dataset of 64 cases of lymphoma, that occurred after anti TNF-α treatment and that were reported to the French pharmacovigilance, was finally analyzed. Whilst an application to pharmacovigilance was led, the theoretical developments and the results of the simulation study may be used for any retrospective analysis from case registries where data are right-truncated.

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本篇論文主要考慮成群資料的存活分析，其特點為群內個體間具有相關性，並假定群內個體具有相同但無法觀測到的事故傾向。首先，探討事故傾向服從任一連續分配時混合Weibull迴歸模式的特性，接著，推導出事故傾向服從血Inverse Gaussian吧時之混合Weibull模式，並介紹參數的估計問題。然後，推導出群內個體是否獨立之分數檢定統計量，以分別就兩種最常見的存活資料型態一完整型態與右設限型態：檢定模式中事故傾向的效應是否存在。最後，並以實例說明分數檢定之程序。 / In this paper, we study survival analysis for grouped data, where the within group correlations are considered. It is also assumed that individuals within the same group share a common but unobservable random frailty. First, we discuss the properties of the Weibull regression model mixed by any continuous distribution. Next, we derive an Inverse Gaussan mixture of Weibull regression model, and discuss the estimation problem. Then, we derive the score test for testing independence between components within the same group, where the two most common cases are discussed the complete data case and the right censoring case. Finally, the testing procedures are illustrated by two examples.

要創造顧客價值，首先需瞭解顧客。顧客是企業最重要的資產，企業應掌握該資產之性質與經濟意涵。本研究自此觀點出發，對個案公司之顧客資產提出四大問題：1.可能長期/短期往來客戶分辨及探討可能長期往來客戶是否一定是好客戶2.各區隔客戶的主要消費型態3.各區隔顧客與企業之獲利關係4.各區隔顧客之理財型態。本研究藉由此四大問題，串連時間、獲利與經濟行為構面進行分析與探討。本研究之資料取自某銀行信用卡部門二年期之月顧客資料。 　　本研究之發現說明如下： 　　1.以獲利與時間兩構面為區隔，發現長期往來之客戶不一定是好客戶。可能短期往來客戶亦有極佳之獲利潛能，但可能因為企業未能滿足需求或客戶有交換使用各家信用卡之行為、預算分配情況的影響，致使此類客戶未能成為忠實客戶，管理當局應深入調查這些顧客的行為，以利策略之擬定。 　　2.各區隔間有鮮明之行為特質。各區隔之所注重的消費層次不同，關心的議題可能不同，因此企業在行銷上注重的層面理應不同。 　　3.大多數之客群對獲利有顯著貢獻，顯示出大多數客戶是具有潛在利潤的客戶。 　　4.「一般交易需求者」為個案銀行之主要客戶，屬於極少拖欠帳款，或者是有支出預算之消費者，銀行只能自手續費獲利，因此，日常營運成本可能是關心重點，企業應力求成為此類顧客的主要銀行。 / Customer valuation is becoming a critical element in strategy development. It is built on the notation that the customer is the primary asset of the firm. The firm has a protfolio of customer assets that should be analyzed economically to determine their value to the firm. The four issues in this study relate to (1) identifying possible long-lived or short-lived customers and if those possible long-lived customers are necessarily profitable customers or not; (2) investigating consuming behavior of each segment; (3) understanding the profit relationship between the firm and customers; (4) establising financing behavior of each segment. This study is based on a monthly data from a large retail bank for two years. 　　Bellows are the illustation of the empirical findings in this study： 　　 1. Duration and customer profitability are two good dimensions of segmentation. Long-lived customers are not necessarily profitable customers. Short-lived customers have great potential in profitability, too. The firm may not fulfill their needs. Those customers may be butterflies, i.e., they like to use different banks’credit cards. On the other hand, some customers may have budget for their spending. The firm must investigate this phenomenon deeply, so as to plan their marketing strategy. 　　 2. Each segment concerns about different issues because they have distintive consuming behavior. So, the firm must have different marketing strategies for each segment. 　　 3. There is positive association between customers and the firm's profitability in most of the segments. This reveals that most customers are profitable. 　　 4. Tansistors are the domain customer type of the firm. They usually pay bills in time. Maybe, they have spending budget. The firm receives transation fees only. Under this condition, operation excellence is the key point. The firm must pay great efforts to become the domain bank of their customers.

房屋貸款保證保險制度可移轉部分違約風險予保險公司。然而，保險公司與金融機構在共同承擔風險之際，因房貸保證保險制度之施行，於提高貸款成數後，產生違約風險提高之矛盾現象；而估計保險之預期損失時，以目前尚無此制度下之違約數據估計損失額，將有錯估之可能。 本研究以二元邏吉斯特迴歸模型(Binary Logistic Regression Model)與存活分析(Survival Analysis)估計違約行為，並比較各模型間資料適合度及預測能力，進而單獨分析變數-貸款成數對違約率之邊際機率影響。以探討房貸保證保險施行後，因其對借款者信用增強而提高之貸款成數，所增加之違約風險。並評估金融機構因提高貸款成數後可能之違約風險變動，據以推估違約率數據，並根據房貸保證保險費率結構模型，計算可能之預期損失額，估算變動的保險費率。 實證結果發現，貸款成數與違約風險呈現顯著正相關，貸款成數增加，邊際影響呈遞增情形，違約率隨之遞增，而違約預期損失額亦同時上升。保險公司因預期損失額增加，為維持保費收入得以支付預期損失，其保險費率將明顯提升。故實施房屋貸款保證保險，因借款者信用增強而提高之貸款成數，將增加違約機率並對保險費率產生直接變動。 / Mortgage insurance system may transfer part of the default risk to insurance companies. However, the implementation of mortgage insurance system, on increasing loan to value ratio, the resulting increase default risk. And literatures estimate the expected loss without the default data, there will be misjudge. Our study constructs the binary logistic regression model and survival analysis to estimate the mortgage default behavior, and compare the data between the model fit and the predictive power. Analyzes the effect of loan to value ratio on the marginal probability of default rate. Furthermore, assess the financial institutions in the risk of default due to loan to value ratio changes. According to the estimated default rate data, we employ the mortgage insurance rate structural model to calculate the expected amount of loss and the changes in premium rates. Empirical results found loan to value ratio have a significant positive effect on borrowers’ default. Loan to value ratio increase, the marginal effect progressively increase, along with increasing default rates and expected default losses. Due to the ascendant expected loss, insurance companies increase premiums to cover the expected loss, the premium rate will be significantly improved. Therefore, the implementation of mortgage insurance, credit enhancement for the borrower to improve loan to value ratio, will increase the probability of default and insurance rates.

Från 1900-talets början har banker och låneinstitut använt nyckeltal som hjälpmedel vid bedömning och kvantifiering av kreditrisk. För dagens investerare är den ekonomiska miljön mer komplicerad än för bara 40 år sedan då teknologin och datoriseringen öppnade upp världens marknader mot varandra. Bedömning av kreditrisk idag kräver effektiv analys av kvantitativa data och modeller som med god träffsäkerhet kan förutse risker. Under 1900-talets andra hälft skedde en snabb utveckling av de verktyg som används för konkursprediktion, från enkla univariata modeller till komplexa data mining-modeller med tusentals observationer. Denna studie undersöker om det är möjligt att prediktera att svenska företag kommer att gå i konkurs och vilka variabler som innehåller relevant information för detta. Metoderna som används är diskriminantanalys, logistisk regression och överlevnadsanalys på 50 aktiva och 50 företag försatta i konkurs. Resultaten visar på en träffsäkerhet mellan 67,5 % och 75 % beroende på vald statistisk metod. Oavsett vald statistisk metod är det möjligt att klassificera företag som konkursmässiga två år innan konkursens inträffande med hjälp av finansiella nyckeltal av typerna lönsamhetsmått och solvensmått. Samhällskostnader reduceras av bättre konkursprediktion med hjälp av finansiella nyckeltal vilka bidrar till ökad förmåga för företag att tillämpa ekonomistyrning med relevanta nyckeltal i form av lager, balanserad vinst, nettoresultat och rörelseresultat. / From the early 1900s, banks and lending institutions have used financial ratios as an aid in the assessment and quantification of credit risk. For today's investors the economic environment is far more complicated than 40 years ago when the technology and computerization opened up the world's markets. Credit risk assessment today requires effective analysis of quantitative data and models that can predict risks with good accuracy. During the second half of the 20th century there was a rapid development of the tools used for bankruptcy prediction. We moved from simple univariate models to complex data mining models with thousands of observations. This study investigates if it’s possible to predict bankruptcy in Swedish limited companies and which variables contain information relevant for this cause. The methods used in the study are discriminant analysis, logistic regression and survival analysis on 50 active and 50 failed companies. The results indicate accuracy between 67.5 % and 75 % depending on the choice of statistical method. Regardless of the selected statistical method used, it’s possible to classify companies as bankrupt two years before the bankruptcy occurs using financial ratios which measures profitability and solvency. Societal costs are reduced by better bankruptcy prediction using financial ratios which contribute to increasing the ability of companies to apply financial management with relevant key ratios in the form of stock , retained earnings , net income and operating income.

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L'objectif principal de ce travail est d’étudier en profondeur certaines techniques biostatistiques avancées en recherche évaluative en chirurgie cardiaque adulte. Les études ont été conçues pour intégrer les concepts d'analyse de survie, analyse de régression avec “propensity score”, et analyse de coûts. Le premier manuscrit évalue la survie après la réparation chirurgicale de la dissection aigüe de l’aorte ascendante. Les analyses statistiques utilisées comprennent : analyses de survie avec régression paramétrique des phases de risque et d'autres méthodes paramétriques (exponentielle, Weibull), semi-paramétriques (Cox) ou non-paramétriques (Kaplan-Meier) ; survie comparée à une cohorte appariée pour l’âge, le sexe et la race utilisant des tables de statistiques de survie gouvernementales ; modèles de régression avec “bootstrapping” et “multinomial logit model”. L'étude a démontrée que la survie s'est améliorée sur 25 ans en lien avec des changements dans les techniques chirurgicales et d’imagerie diagnostique. Le second manuscrit est axé sur les résultats des pontages coronariens isolés chez des patients ayant des antécédents d'intervention coronarienne percutanée. Les analyses statistiques utilisées comprennent : modèles de régression avec “propensity score” ; algorithme complexe d'appariement (1:3) ; analyses statistiques appropriées pour les groupes appariés (différences standardisées, “generalized estimating equations”, modèle de Cox stratifié). L'étude a démontrée que l’intervention coronarienne percutanée subie 14 jours ou plus avant la chirurgie de pontages coronariens n'est pas associée à des résultats négatifs à court ou long terme. Le troisième manuscrit évalue les conséquences financières et les changements démographiques survenant pour un centre hospitalier universitaire suite à la mise en place d'un programme de chirurgie cardiaque satellite. Les analyses statistiques utilisées comprennent : modèles de régression multivariée “two-way” ANOVA (logistique, linéaire ou ordinale) ; “propensity score” ; analyses de coûts avec modèles paramétriques Log-Normal. Des modèles d’analyse de « survie » ont également été explorés, utilisant les «coûts» au lieu du « temps » comme variable dépendante, et ont menés à des conclusions similaires. L'étude a démontrée que, après la mise en place du programme satellite, moins de patients de faible complexité étaient référés de la région du programme satellite au centre hospitalier universitaire, avec une augmentation de la charge de travail infirmier et des coûts. / The main objective of this work is to study in depth advanced biostatistical techniques in adult cardiac surgery outcome research. The studies were designed to incorporate the concepts of survival analysis, regression analysis with propensity score, and cost analysis. The first manuscript assessed survival, and cardiovascular specific mortality, following surgical repair of acute ascending aortic dissection. The statistical analyses included survival analyses with multiphase parametric hazard regression and other parametric (exponential, Weibull), semi-parametric (Cox) or non-parametric models (Kaplan Meier), comparison with the survival of a matched cohort for age, gender and race using State lifetables, and modelization with bootstrapping and multinomial logit models. The study showed that the early and late survival following surgical repair has improved progressively over 25 years in association with noticeable changes in surgical techniques and preoperative diagnostic testing. The second manuscript focused on outcomes following isolated coronary artery bypass grafting in patients with a history of percutaneous coronary intervention. The statistical analyses included multivariable regression models with propensity score, complex matching algorithm (1:3) and appropriate statistical analyses for matched groups (standardized differences, generalized estimating equations, and survival analyses with stratified proportional hazards models). The study showed that remote prior percutaneous coronary intervention more than 14 days before coronary artery bypass grafting surgery was not associated with adverse outcomes at short or long-term follow-up. The third manuscript evaluated the financial consequences and the changes in case mix that occurred at an academic medical center subsequent to the implementation of a satellite cardiac surgery program. The statistical analyses included two-way ANOVA multivariable regression models (logistic, linear or ordinal), propensity score, and cost analyses using Log-Normal parametric models. “Survival” analyses models were also explored, using “cost” instead of “time” as the outcome of interest, and led to similar conclusions. The study showed that, after implementation of the satellite cardiac surgery program, fewer patients of lower complexity came to the academic medical center from the satellite program area, with a significant increase in nursing workload and costs.