Analyse des facteurs biodémographiques, sociéconomiques et familiaux de la longévité exceptionnelle

La recherche des facteurs de longévité gagne en intérêt dans le contexte actuel du vieillissement de la population. De la littérature portant sur la longévité et la mortalité aux grands âges, un constat émerge : bien que les déterminants associés à la survie humaine soient multiples, l'environnement familial aurait un rôle déterminant sur la mortalité et sur l'atteinte des âges avancés. Dès lors, l'objectif de cette thèse est d'évaluer les déterminants de la survie exceptionnelle et d'examiner le rôle des aspects familiaux, en début de vie et à l'âge adulte, dans les différentiels de durée de vie. Plus spécifiquement, elle vise à : (1) examiner la similarité des âges au décès entre frères, soeurs et conjoints afin d'apprécier l'ampleur de la composante familiale de la longévité; (2) explorer, d'un point de vue intrafamilial, les conséquences à long terme sur la survie des variables non partagées issues de la petite enfance tels l'âge maternel à la reproduction, le rang de naissance et la saison de naissance; et (3) s'interroger sur le rôle protecteur ou délétère de l’environnement et du milieu familial d'origine dans l’enfance sur l'atteinte des grands âges et dans quelle mesure le statut socioéconomique parvient à médiatiser la relation. Cette analyse s'appuie sur le jumelage des recensements canadiens et des actes de décès de l’état civil québécois et emploie des données québécoises du 20e siècle issues de deux échantillons distincts : un échantillon aléatoire représentatif de la population provenant du recensement canadien de 1901 ainsi qu’un échantillon de frères et soeurs de centenaires québécois appartenant à la même cohorte. Les résultats, présentés sous forme d'articles scientifiques, ont montré, en outre, que les frères et soeurs de centenaires vivent plus longtemps que les individus appartenant aux mêmes cohortes de naissance, reflétant la contribution d'une robustesse commune, mais également celle de l'environnement partagé durant la petite enfance. Ces analyses ont également témoigné d'un avantage de survie des conjoints des centenaires, soulignant l'importance d'un même environnement à l'âge adulte (1er article). De plus, nos travaux ont mis de l'avant la contribution aux inégalités de longévité des variables biodémographiques issues de l'environnement non partagé telles que l'âge maternel à la reproduction, le rang de naissance et la saison de naissance, qui agissent et interagissent entre elles pour créer des vulnérabilités et influer sur l'atteinte des âges exceptionnels (2e article). Enfin, une approche longitudinale a permis de souligner la contribution du milieu social d'origine sur la longévité, alors que les individus issus d’un milieu socioéconomique défavorisé pour l'époque (milieu urbain, père ouvrier) vivent moins longtemps que ceux ayant vécu dans un environnement socioéconomique favorable (milieu rural, fermier), résultat d'une potentielle accumulation des avantages liée à la reproduction du statut social ou d'une programmation précoce des trajectoires de santé. L’influence est toutefois moindre pour les femmes et pour les frères de centenaires et s'exprime, dans ce cas, en partie par l'effet de la profession à l'âge adulte (3e article).

A multivariate frailty model for disease recurrences and survival

A multivariate frailty hazard model is developed for joint-modeling of three correlated time-to-event outcomes: (1) local recurrence, (2) distant recurrence, and (3) overall survival. The term frailty is introduced to model population heterogeneity. The dependence is modeled by conditioning on a shared frailty that is included in the three hazard functions. Independent variables can be included in the model as covariates. The Markov chain Monte Carlo methods are used to estimate the posterior distributions of model parameters. The algorithm used in present application is the hybrid Metropolis-Hastings algorithm, which simultaneously updates all parameters with evaluations of gradient of log posterior density. The performance of this approach is examined based on simulation studies using Exponential and Weibull distributions. We apply the proposed methods to a study of patients with soft tissue sarcoma, which motivated this research. Our results indicate that patients with chemotherapy had better overall survival with hazard ratio of 0.242 (95% CI: 0.094 - 0.564) and lower risk of distant recurrence with hazard ratio of 0.636 (95% CI: 0.487 - 0.860), but not significantly better in local recurrence with hazard ratio of 0.799 (95% CI: 0.575 - 1.054). The advantages and limitations of the proposed models, and future research directions are discussed. ^

Long-term survivor mixture model with random effects: application to a multi-centre clinical trial of carcinoma

A mixture model incorporating long-term survivors has been adopted in the field of biostatistics where some individuals may never experience the failure event under study. The surviving fractions may be considered as cured. In most applications, the survival times are assumed to be independent. However, when the survival data are obtained from a multi-centre clinical trial, it is conceived that the environ mental conditions and facilities shared within clinic affects the proportion cured as well as the failure risk for the uncured individuals. It necessitates a long-term survivor mixture model with random effects. In this paper, the long-term survivor mixture model is extended for the analysis of multivariate failure time data using the generalized linear mixed model (GLMM) approach. The proposed model is applied to analyse a numerical data set from a multi-centre clinical trial of carcinoma as an illustration. Some simulation experiments are performed to assess the applicability of the model based on the average biases of the estimates formed. Copyright (C) 2001 John Wiley & Sons, Ltd.

Explicit ruin formulas for models with dependence among risks

We show that a simple mixing idea allows one to establish a number of explicit formulas for ruin probabilities and related quantities in collective risk models with dependence among claim sizes and among claim inter-occurrence times. Examples include compound Poisson risk models with completely monotone marginal claim size distributions that are dependent according to Archimedean survival copulas as well as renewal risk models with dependent inter-occurrence times.

Modelling and Analysis of Recurrent Event Data with Multiple Causes

This thesis Entitled “modelling and analysis of recurrent event data with multiple causes.Survival data is a term used for describing data that measures the time to occurrence of an event.In survival studies, the time to occurrence of an event is generally referred to as lifetime.Recurrent event data are commonly encountered in longitudinal studies when individuals are followed to observe the repeated occurrences of certain events. In many practical situations, individuals under study are exposed to the failure due to more than one causes and the eventual failure can be attributed to exactly one of these causes.The proposed model was useful in real life situations to study the effect of covariates on recurrences of certain events due to different causes.In Chapter 3, an additive hazards model for gap time distributions of recurrent event data with multiple causes was introduced. The parameter estimation and asymptotic properties were discussed .In Chapter 4, a shared frailty model for the analysis of bivariate competing risks data was presented and the estimation procedures for shared gamma frailty model, without covariates and with covariates, using EM algorithm were discussed. In Chapter 6, two nonparametric estimators for bivariate survivor function of paired recurrent event data were developed. The asymptotic properties of the estimators were studied. The proposed estimators were applied to a real life data set. Simulation studies were carried out to find the efficiency of the proposed estimators.

Aspectes metodològics i aplicacions de la modelització del temps de supervivència multivariant mitjançant models mixtes

Els estudis de supervivència s'interessen pel temps que passa des de l'inici de l'estudi (diagnòstic de la malaltia, inici del tractament,...) fins que es produeix l'esdeveniment d'interès (mort, curació, millora,...). No obstant això, moltes vegades aquest esdeveniment s'observa més d'una vegada en un mateix individu durant el període de seguiment (dades de supervivència multivariant). En aquest cas, és necessari utilitzar una metodologia diferent a la utilitzada en l'anàlisi de supervivència estàndard. El principal problema que l'estudi d'aquest tipus de dades comporta és que les observacions poden no ser independents. Fins ara, aquest problema s'ha solucionat de dues maneres diferents en funció de la variable dependent. Si aquesta variable segueix una distribució de la família exponencial s'utilitzen els models lineals generalitzats mixtes (GLMM); i si aquesta variable és el temps, variable amb una distribució de probabilitat no pertanyent a aquesta família, s'utilitza l'anàlisi de supervivència multivariant. El que es pretén en aquesta tesis és unificar aquests dos enfocs, és a dir, utilitzar una variable dependent que sigui el temps amb agrupacions d'individus o d'observacions, a partir d'un GLMM, amb la finalitat d'introduir nous mètodes pel tractament d'aquest tipus de dades.

Avaliação genética da eficiência reprodutiva em vacas pardo-suíças por meio da análise de sobrevivência

Among the traits of economic importance to dairy cattle livestock those related to sexual precocity and longevity of the herd are essential to the success of the activity, because the stayability time of a cow in a herd is determined by their productive and reproductive lives. In Brazil, there are few studies about the reproductive efficiency of Swiss-Brown cows and no study was found using the methodology of survival analysis applied to this breed. Thus, in the first chapter of this study, the age at first calving from Swiss-Brown heifers was analyzed as the time until the event by the nonparametric method of Kaplan-Meier and the gamma shared frailty model, under the survival analysis methodology. Survival and hazard rate curves associated with this event were estimated and identified the influence of covariates on such time. The mean and median times at the first calving were 987.77 and 1,003 days, respectively, and significant covariates by the Log-Rank test, through Kaplan-Meier analysis, were birth season, calving year, sire (cow s father) and calving season. In the analysis by frailty model, the breeding values and the frailties of the sires (fathers) for the calving were predicted modeling the risk function of each cow as a function of the birth season as fixed covariate and sire as random covariate. The frailty followed the gamma distribution. Sires with high and positive breeding values possess high frailties, what means shorter survival time of their daughters to the event, i.e., reduction in the age at first calving of them. The second chapter aimed to evaluate the longevity of dairy cows using the nonparametric Kaplan-Meier and the Cox and Weibull proportional hazards models. It were simulated 10,000 records of the longevity trait from Brown-Swiss cows involving their respective times until the occurrence of five consecutive calvings (event), considered here as typical of a long-lived cow. The covariates considered in the database were age at first calving, herd and sire (cow s father). All covariates had influence on the longevity of cows by Log-Rank and Wilcoxon tests. The mean and median times to the occurrence of the event were 2,436.285 and 2,437 days, respectively. Sires that have higher breeding values also have a greater risk of that their daughters reach the five consecutive calvings until 84 months

Bayesian and survival model for tumor relapse status and disease-specific survival, with applications to breast cancer

Breast cancer is the most common non-skin cancer and the second leading cause of cancer-related death in women in the United States. Studies on ipsilateral breast tumor relapse (IBTR) status and disease-specific survival will help guide clinic treatment and predict patient prognosis.^ After breast conservation therapy, patients with breast cancer may experience breast tumor relapse. This relapse is classified into two distinct types: true local recurrence (TR) and new ipsilateral primary tumor (NP). However, the methods used to classify the relapse types are imperfect and are prone to misclassification. In addition, some observed survival data (e.g., time to relapse and time from relapse to death)are strongly correlated with relapse types. The first part of this dissertation presents a Bayesian approach to (1) modeling the potentially misclassified relapse status and the correlated survival information, (2) estimating the sensitivity and specificity of the diagnostic methods, and (3) quantify the covariate effects on event probabilities. A shared frailty was used to account for the within-subject correlation between survival times. The inference was conducted using a Bayesian framework via Markov Chain Monte Carlo simulation implemented in softwareWinBUGS. Simulation was used to validate the Bayesian method and assess its frequentist properties. The new model has two important innovations: (1) it utilizes the additional survival times correlated with the relapse status to improve the parameter estimation, and (2) it provides tools to address the correlation between the two diagnostic methods conditional to the true relapse types.^ Prediction of patients at highest risk for IBTR after local excision of ductal carcinoma in situ (DCIS) remains a clinical concern. The goals of the second part of this dissertation were to evaluate a published nomogram from Memorial Sloan-Kettering Cancer Center, to determine the risk of IBTR in patients with DCIS treated with local excision, and to determine whether there is a subset of patients at low risk of IBTR. Patients who had undergone local excision from 1990 through 2007 at MD Anderson Cancer Center with a final diagnosis of DCIS (n=794) were included in this part. Clinicopathologic factors and the performance of the Memorial Sloan-Kettering Cancer Center nomogram for prediction of IBTR were assessed for 734 patients with complete data. Nomogram for prediction of 5- and 10-year IBTR probabilities were found to demonstrate imperfect calibration and discrimination, with an area under the receiver operating characteristic curve of .63 and a concordance index of .63. In conclusion, predictive models for IBTR in DCIS patients treated with local excision are imperfect. Our current ability to accurately predict recurrence based on clinical parameters is limited.^ The American Joint Committee on Cancer (AJCC) staging of breast cancer is widely used to determine prognosis, yet survival within each AJCC stage shows wide variation and remains unpredictable. For the third part of this dissertation, biologic markers were hypothesized to be responsible for some of this variation, and the addition of biologic markers to current AJCC staging were examined for possibly provide improved prognostication. The initial cohort included patients treated with surgery as first intervention at MDACC from 1997 to 2006. Cox proportional hazards models were used to create prognostic scoring systems. AJCC pathologic staging parameters and biologic tumor markers were investigated to devise the scoring systems. Surveillance Epidemiology and End Results (SEER) data was used as the external cohort to validate the scoring systems. Binary indicators for pathologic stage (PS), estrogen receptor status (E), and tumor grade (G) were summed to create PS+EG scoring systems devised to predict 5-year patient outcomes. These scoring systems facilitated separation of the study population into more refined subgroups than the current AJCC staging system. The ability of the PS+EG score to stratify outcomes was confirmed in both internal and external validation cohorts. The current study proposes and validates a new staging system by incorporating tumor grade and ER status into current AJCC staging. We recommend that biologic markers be incorporating into revised versions of the AJCC staging system for patients receiving surgery as the first intervention.^ Chapter 1 focuses on developing a Bayesian method to solve misclassified relapse status and application to breast cancer data. Chapter 2 focuses on evaluation of a breast cancer nomogram for predicting risk of IBTR in patients with DCIS after local excision gives the statement of the problem in the clinical research. Chapter 3 focuses on validation of a novel staging system for disease-specific survival in patients with breast cancer treated with surgery as the first intervention. ^

Study on Robustness of Correlated Frailty Model

2000 Mathematics Subject Classifi cation: 62J12.

Gamma Knife Surgery versus Linac for Brain Metastasis: Which Technique for Which Patient? Analysis of an Historical Cohort of 63 Consecutive Patients Harboring 130 Lesions

Objectives: We present the retrospective analysis of a single-institution experience for radiosurgery (RS) in brain metastasis (BM) with Gamma Knife (GK) and Linac. Methods: From July 2010 to July 2012, 28 patients (with 83 lesions) had RS with GK and 35 patients (with 47 lesions) with Linac. The primary outcome was the local progression-free survival (LPFS). The secondary outcome was the overall survival (OS). Apart a standard statistical analysis, we included a Cox regression model with shared frailty, to modulate the within-patient correlation (preliminary evaluation showed a significant frailty effect, meaning that the correlation within patient could be ignored). Results: The mean follow-up period was 11.7 months (median 7.9, 1.7-22.7) for GK and 18.1 (median 17, 7.5-28.7) for Linac. The median number of lesions per patient was 2.5 (1-9) in GK compared with 1 (1-3) in Linac. There were more radioresistant lesions (melanoma) and more lesions located in functional areas for the GK group. The median dose was 24 Gy (GK) compared with 20 Gy (Linac). The LPFS actuarial rate was as follows: for GK at 3, 6, 9, 12, and 17 months: 96.96, 96.96, 96.96, 88.1, and 81.5%, and remained stable till 32 months; for Linac at 3, 6, 12, 17, 24, and 33 months, it was 91.5, 91.5, 91.5, 79.9, 55.5, and 17.1%, respectively (p = 0.03, chi-square test). After the Cox regression analysis with shared frailty, the p-value was not statistically significant between groups. The median overall survival was 9.7 months for GK and 23.6 months for Linac group. Uni- and multivariate analysis showed a lower GPA score and noncontrolled systemic status were associated with lower OS. Cox regression analysis adjusting for these two parameters showed comparable OS rate. Conclusions: In this comparative report between GK and Linac, preliminary analysis showed that more difficult cases are treated by GK, with patients harboring more lesions, radioresistant tumors, and highly functional located. The groups look, in this sense, very heterogeneous at baseline. After a Cox frailty model, the LPFS rates seemed very similar (p < 0.05). The OS was similar, after adjusting for systemic status and GPA score (p < 0.05). The technical reasons for choosing GK instead of Linac were the anatomical location related to highly functional areas, histology, technical limitations of Linac movements, especially lower posterior fossa locations, or closeness of multiple lesions to highly functional areas optimal dosimetry with Linac

De la diversité des équipes : étude du rôle des vecteurs de failles sur la performance.

Malgré que plus de 50 ans nous séparent des premières études empiriques s’attardant à la diversité dans les équipes de travail, il demeure difficile de tirer des conclusions claires et cohérentes quant à la nature et à la direction des relations qu’elle entretient avec la performance groupale. Ce constat a amené de nombreux auteurs à formuler diverses recommandations visant à sortir le domaine de recherche de l’impasse. Dans un contexte où, d’une part, les organisations tendent à s’appuyer de plus en plus sur des équipes afin d’assurer leur efficacité et, d’autre part, la diversité ne cesse de s’accroitre au rythme de l’immigration et de la spécialisation du savoir, il devient particulièrement pertinent de poursuivre les efforts de recherche en fonction de ces recommandations afin de clarifier les impacts de la diversité sur la performance. La présente thèse s’inscrit dans un courant de recherche en pleine croissance qui répond aux appels des chercheurs du domaine et qui vise à évaluer les effets de la structure de la diversité plutôt qu’uniquement ceux de la quantité de diversité dans les équipes. La théorie des vecteurs de failles (Lau & Murnighan, 1998), qui sont des lignes hypothétiques divisant les membres d’une équipe lorsque des caractéristiques de diversité concordent et créent des sous-groupes homogènes, constitue une avancée majeure à cet effet. Toutefois, certains résultats empiriques contradictoires à son sujet mettent en lumière l’importance de prendre en considération l’ensemble des recommandations qui ont été formulés à l’intention des chercheurs du domaine de la diversité. À travers la lentille des vecteurs de failles, la thèse vise à approfondir notre compréhension du rôle de la diversité sur la performance des équipes en mettant en pratique ces diverses recommandations, qui invitent à examiner le rôle des mécanismes médiateurs ainsi que des effets modérateurs pouvant intervenir dans cette relation, à préciser les typologies employées et à prendre en considération l’influence du contexte dans lequel évoluent les équipes de travail. Le premier article constitue un effort de synthèse empirique cherchant à préciser les effets différenciés que peuvent avoir divers types de failles sur divers types de performance et à évaluer le rôle modérateur que joue le type d’équipe étudié dans ces relations. Les résultats de la méta-analyse, menée à l’aide d’un échantillon de 38 études comprenant 3046 équipes, viennent nuancer ceux précédemment rapportés dans la documentation scientifique et montrent que les failles ont un effet négatif sur la performance comportementale mais pas sur la performance de résultats. De plus, le type d’équipe modère cette relation de sorte que celle-ci est plus fortement négative pour les équipes de projet et les équipes de gestion. Le deuxième article évalue empiriquement l’effet des vecteurs de faille informationnels sur une dimension spécifique de la performance, l’adaptabilité d’équipe, en examinant le rôle médiateur de la coordination implicite ainsi que l’effet modérateur de la clarté des rôles et responsabilités. Une analyse de médiation modérée auprès d’un échantillon de 14 équipes de projet révèle que la coordination implicite médie la relation négative entre les vecteurs de faille informationnels et l’adaptabilité d’équipe. De plus, cette relation est plus fortement négative lorsque les rôles et responsabilités des équipiers sont clairs ou très clairs. Les implications théoriques et pratiques des résultats obtenus sont discutées.

Modelling and Analysis of Bivariate Lifetime Data using Reversed Hazard Rates

Department of Statistics, Cochin University of Science and Technology