Em análise de sobrevivência, usualmente o interesse esté em estudar o tempo até a ocorrência de um evento. Quando as observações estão sujeitas a mais de um tipo de evento (por exemplo, diferentes causas de óbito) e a ocorrência de um evento impede a ocorrência dos demais, tem-se uma estrutura de riscos competitivos. Em algumas situações, no entanto, o interesse está em estudar dois eventos, sendo que um deles (evento terminal) impede a ocorrência do outro (evento intermediário), mas não vice-versa. Essa estrutura é conhecida como riscos semicompetitivos e foi definida por Fine et al.(2001). Neste trabalho são consideradas duas abordagens para análise de dados com essa estrutura. Uma delas é baseada na construção da função de sobrevivência bivariada por meio de cópulas da família Arquimediana e estimadores para funções de sobrevivência são obtidos. A segunda abordagem é baseada em um processo de três estados, conhecido como processo doença-morte, que pode ser especificado pelas funções de intensidade de transição ou funções de risco. Neste caso, considera-se a inclusão de covariáveis e a possível dependência entre os dois tempos observados é incorporada por meio de uma fragilidade compartilhada. Estas metodologias são aplicadas a dois conjuntos de dados reais: um de 137 pacientes com leucemia, observados no máximo sete anos após transplante de medula óssea, e outro de 1253 pacientes com doença renal crônica submetidos a diálise, que foram observados entre os anos 2009-2011. / In survival analysis, usually the interest is to study the time until the occurrence of an event. When observations are subject to more than one type of event (e.g, different causes of death) and the occurrence of an event prevents the occurrence of the other, there is a competing risks structure. In some situations, nevertheless, the main interest is to study two events, one of which (terminal event) prevents the occurrence of the other (nonterminal event) but not vice versa. This structure is known as semicompeting risks, defined initially by Fine et al. (2001). In this work, we consider two approaches for analyzing data with this structure. One approach is based on the bivariate survival function through Archimedean copulas and estimators for the survival functions are obtained. The second approach is based on a process with three states, known as Illness-Death process, which can be specified by the transition intensity functions or risk functions. In this case, the inclusion of covariates and a possible dependence between the two times is taken into account by a shared frailty. These methodologies are applied to two data sets: the first one is a study with 137 patients with leukemia that received an allogeneic marrow transplant, with maximum follow up of 7 years; the second is a data set of 1253 patientswith chronic kidney disease on dialysis treatment, followed from 2009 until 2011.

Le stade terminal de l’insuffisance cardiaque peut nécessiter l’inscription en liste d’attente pour une greffe cardiaque. L’offre en greffon étant faible, l’accès à cette thérapeutique est priorisé pour les malades les plus graves dans le système d’allocation actuel, faisant de l’équité un enjeu éthique et sociétal important. L’objectif de cette thèse, grâce à des méthodes originales et de nouvelles sources de données, était d’analyser les déterminants d’accès à la greffe cardiaque liés aux candidats et aux équipes de greffe en France sur une période récente et d’apporter des éléments nouveaux sur le parcours de soin de ces malades. L’analyse du devenir des candidats à une greffe cardiaque inscrits entre 2010 et 2013 en intégrant la méthode de risques compétitifs a permis de dissocier le risque médical du risque induit par le système d’allocation actuel. Nous avons ainsi trouvé 7 facteurs relatifs au candidat associés à un risque de mortalité élevé dont 4 reliés à la sévérité de l’insuffisance cardiaque et 3 non spécifiques de l’insuffisance cardiaque mais associés à un faible accès à la greffe. La prise en compte de l’effet centre sur les inscrits entre 2010 et 2014 grâce à un modèle mixte de survie a permis de déterminer les facteurs équipe associés à l’accès à la greffe. Parmi les 23 équipes de greffe en France, l’ajustement sur les facteurs candidat et équipe, permettait d’observer que 5 équipes avaient des résultats différents des autres dont 3 avec un accès défavorisé. La mise en évidence de disparités médicales, géographiques et structurelles, révélatrices de failles dans le système actuel d’allocation des greffons cardiaques, nous a permis d’étayer la discussion sur la mise en place d’un nouveau système d’attribution en France. L’attribution au patient plutôt qu’à l’équipe de greffe devrait être plus pertinente. Les méthodologies utilisées permettront d’évaluer précisément ces répercussions. Enfin, l’étude du parcours hospitalier un an avant greffe (2010-2015) à l’aide des données du PMSI a permis de constater un nombre élevé d’hospitalisations pré-inscription majoritairement liées à leur défaillance cardiaque et de longue durée, confirmant le caractère réfractaire de ces insuffisants cardiaques. Ce travail sera poursuivi par une étude approfondie de la consommation de soins de ces malades grâce aux données du SNDS, indispensable étape pour évaluer la prise en charge et estimer les besoins en greffe cardiaque. / Heart transplantation (HTx) is the preferred option for medically refractory advanced heart failure. Due to the small number of available grafts, current allocation policy in France, as in many other countries, is based on the severity of the candidate’s heart disease. This Ph. D thesis was designed to determine candidate and center factors associated with access to heart transplantation in France and in-hospital care pathways one year before heart transplantation using appropriate methodologies and the national hospital database. We first analyzed 1-year mortality in patients listed for HTx in France from 2010 to 2013 using competing risk models in order to distinguish patient-related predictors and the influence of allocation policy. We then distinguished the proportions explained by candidate characteristics and center factors with the persistent between-center variability on 1-year access to transplantation (2010-2014). These disparities are mediated by the severity of the candidate’s heart disease, the allocation system and listing practices rather than by transplant activity. These findings provide a new contribution to improve the heart transplant allocation scoring system in France. The study based on the nationwide administrative database overcomes a major limitation of the national transplantation registry by shedding light on the healthcare pathway of heart transplanted recipients (2010-2015) during the year prior to transplantation. These findings will be useful to assess the medical benefits and criteria for registration on the heart transplant waiting list. This study will be continued by a detailed analysis of the healthcare consumption of these patients based on French national health insurance (SNDS) data.

This dissertation aims to develop statistical methodologies for estimating the effects of time-fixed and time-varying factors in recurrent events modeling context. The research is motivated by the traffic safety research question of evaluating the influence of crash on driving risk and driver behavior. The methodologies developed, however, are general and can be applied to other fields. Four alternative approaches based on various data settings are elaborated and applied to 100-Car Naturalistic Driving Study in the following Chapters. Chapter 1 provides a general introduction and background of each method, with a sketch of 100-Car Naturalistic Driving Study. In Chapter 2, I assessed the impact of crash on driving behavior by comparing the frequency of distraction events in per-defined windows. A count-based approach based on mixed-effect binomial regression models was used. In Chapter 3, I introduced intensity-based recurrent event models by treating number of Safety Critical Incidents and Near Crash over time as a counting process. Recurrent event models fit the natural generation scheme of the data in this study. Four semi-parametric models are explored: Andersen-Gill model, Andersen-Gill model with stratified baseline functions, frailty model, and frailty model with stratified baseline functions. I derived model estimation procedure and and conducted model comparison via simulation and application. The recurrent event models in Chapter 3 are all based on proportional assumption, where effects are constant. However, the change of effects over time is often of primary interest. In Chapter 4, I developed time-varying coefficient model using penalized B-spline function to approximate varying coefficients. Shared frailty terms was used to incorporate correlation within subjects. Inference and statistical test are also provided. Frailty representation was proposed to link time-varying coefficient model with regular frailty model. In Chapter 5, I further extended framework to accommodate multi-type recurrent events with time-varying coefficient. Two types of recurrent-event models were developed. These models incorporate correlation among intensity functions from different type of events by correlated frailty terms. Chapter 6 gives a general review on the contributions of this dissertation and discussion of future research directions. / Ph. D.

Survival analytic methods help to analyze lifetime data arising from medical and reliability experiments. The popular proportional hazards model, proposed by Cox (1972), is widely used in survival analysis to study the effect of risk factors on lifetimes. An important assumption in regression type analysis is that all relative risk factors should be included in the model. However, not all relative risk factors are observed due to measurement difficulty, inaccessibility, cost considerations, and so on. These unobservable risk factors can be modelled by the so-called frailty model, originally introduced by Vaupel et al. (1979). Furthermore, the frailty model is also applicable to clustered data. Cluster data possesses the feature that observations within the same cluster share similar conditions and environment, which are sometimes difficult to observe. For example, patients from the same family share similar genetics, and patients treated in the same hospital share the same group of profes- sionals and same environmental conditions. These factors are indeed hard to quantify or measure. In addition, this type of similarity introduces correlation among subjects within clusters. In this thesis, a semi-parametric frailty model is proposed to address aforementioned issues. The baseline hazards function is approximated by a piecewise constant function and the frailty distribution is assumed to be a Birnbaum-Saunders distribution. Due to the advancement in modern medical sciences, many diseases are curable, which in turn leads to the need of incorporating cure proportion in the survival model. The frailty model discussed here is further extended to a mixture cure rate frailty model by integrating the frailty model into the mixture cure rate model proposed originally by Boag (1949) and Berkson and Gage (1952). By linking the covariates to the cure proportion through logistic/logit link function and linking observable covariates and unobservable covariates to the lifetime of the uncured population through the frailty model, we obtain a flexible model to study the effect of risk factors on lifetimes. The mixture cure frailty model can be reduced to a mixture cure model if the effect of frailty term is negligible (i.e., the variance of the frailty distribution is close to 0). On the other hand, it also reduces to the usual frailty model if the cure proportion is 0. Therefore, we can use a likelihood ratio test to test whether the reduced model is adequate to model the given data. We assume the baseline hazard to be that of Weibull distribution since Weibull distribution possesses increasing, constant or decreasing hazard rate, and the frailty distribution to be Birnbaum-Saunders distribution. D ́ıaz-Garc ́ıa and Leiva-Sa ́nchez (2005) proposed a new family of life distributions, called generalized Birnbaum-Saunders distribution, including Birnbaum-Saunders distribution as a special case. It allows for various degrees of kurtosis and skewness, and also permits unimodality as well as bimodality. Therefore, integration of a generalized Birnbaum-Saunders distribution as the frailty distribution in the mixture cure frailty model results in a very flexible model. For this general model, parameter estimation is carried out using a marginal likelihood approach. One of the difficulties in the parameter estimation is that the likelihood function is intractable. The current technology in computation enables us to develop a numerical method through Monte Carlo simulation, and in this approach, the likelihood function is approximated by the Monte Carlo method and the maximum likelihood estimates and standard errors of the model parameters are then obtained numerically by maximizing this approximate likelihood function. An EM algorithm is also developed for the Birnbaum-Saunders mixture cure frailty model. The performance of this estimation method is then assessed by simulation studies for each proposed model. Model discriminations is also performed between the Birnbaum-Saunders frailty model and the generalized Birnbaum-Saunders mixture cure frailty model. Some illustrative real life examples are presented to illustrate the models and inferential methods developed here. / Thesis / Doctor of Science (PhD)

Made available in DSpace on 2016-06-02T20:06:04Z (GMT). No. of bitstreams: 1 3451.pdf: 871063 bytes, checksum: 8af58118f0d60c000ca46f5d8bfda544 (MD5) Previous issue date: 2011-02-24 / In survival analysis, some studies are characterized by having a significant fraction of units that will never suffer the event of interest, even if accompanied by a long period of time. For the analysis of long-term data, we approach the standard mixture model by Berkson & Gage, where we assume the generalized modified Weibull distribution for the lifetime of individuals at risk. This model includes several classes of models as special cases, allowing its use to discriminate models. The standard mixture model implicitly assume that those individuals experiencing the event of interest possess homogeneous risk. Alternatively, we consider the standard mixture model with a frailty term in order to quantify the unobservable heterogeneity among individuals. This model is characterized by the inclusion of a unobservable random variable, which represents information that can not or have not been observed. We assume multiplicative frailty with a gamma distribution. For the lifetime of individuals at risk, we assume the Weibull distribution, obtaining the frailty Weibull standard mixture model. For both models, we realized simulation studies with the purpose of analyzing the frequentists properties of estimation procedures. Applications to real data set showed the applicability of the proposed models in which parameter estimates were determined using the approaches of maximum likelihood and Bayesian. / Em análise de sobrevivência determinados estudos caracterizam-se por apresentar uma fração significativa de unidades que nunca apresentarão o evento de interesse, mesmo se acompanhados por um longo período de tempo. Para a análise de dados com longa duração, abordamos o modelo de mistura padrão de Berkson & Gage supondo que os tempos de vida dos indivíduos em risco seguem distribuição Weibull modificada generalizada. Este modelo engloba diversas classes de modelos como casos particulares, propiciando o uso deste para discriminar modelos. O modelo abordado assume implicitamente que todos os indivíduos que falharam possuem risco homogêneo. Alternativamente, consideramos o modelo de mistura padrão com um termo de fragilidade com o objetivo de quantificar a heterogeneidade não observável entre os indivíduos. Este modelo é caracterizado pela inclusão de uma variável aleatória não observável, que representa as informações que não podem ou que não foram observadas. Assumimos que a fragilidade atua de forma multiplicativa com distribuição gama. Para os tempos de vida dos indivíduos em risco consideramos a distribuição Weibull, obtendo o modelo de mistura padrão Weibull com fragilidade. Para os dois modelos realizamos estudos de simulação com o objetivo de analisar as propriedades frequentistas dos processos de estimação. Aplicações a conjunto de dados reais mostraram a aplicabilidade dos modelos propostos, em que a estimação dos parâmetros foram determinadas através das abordagens de máxima verossimilhança e Bayesiana.

Submitted by Aelson Maciera (aelsoncm@terra.com.br) on 2017-04-24T18:48:10Z No. of bitstreams: 1 TeseJSL.pdf: 1918523 bytes, checksum: 4d551d58b97032091209f65b7428e992 (MD5) / Approved for entry into archive by Ronildo Prado (ronisp@ufscar.br) on 2017-04-25T18:50:15Z (GMT) No. of bitstreams: 1 TeseJSL.pdf: 1918523 bytes, checksum: 4d551d58b97032091209f65b7428e992 (MD5) / Approved for entry into archive by Ronildo Prado (ronisp@ufscar.br) on 2017-04-25T18:50:23Z (GMT) No. of bitstreams: 1 TeseJSL.pdf: 1918523 bytes, checksum: 4d551d58b97032091209f65b7428e992 (MD5) / Made available in DSpace on 2017-04-25T18:59:25Z (GMT). No. of bitstreams: 1 TeseJSL.pdf: 1918523 bytes, checksum: 4d551d58b97032091209f65b7428e992 (MD5) Previous issue date: 2017-01-09 / Não recebi financiamento / In this thesis we propose models based on a reparameterized Birnbaum-Saunder (BS) distribution introduced by Santos-Neto et al. (2012) and Santos-Neto et al. (2014), to analyze survival data. Initially we introduce the Birnbaum-Saunders frailty model where we analyze the cases (i) with (ii) without covariates. Survival models with frailty are used when further information is nonavailable to explain the occurrence time of a medical event. The random effect is the “frailty”, which is introduced on the baseline hazard rate to control the unobservable heterogeneity of the patients. We use the maximum likelihood method to estimate the model parameters. We evaluate the performance of the estimators under different percentage of censured observations by a Monte Carlo study. Furthermore, we introduce a Birnbaum-Saunders regression frailty model where the maximum likelihood estimation of the model parameters with censored data as well as influence diagnostics for the new regression model are investigated. In the following we propose a cure rate Birnbaum-Saunders frailty model. An important advantage of this proposed model is the possibility to jointly consider the heterogeneity among patients by their frailties and the presence of a cured fraction of them. We consider likelihood-based methods to estimate the model parameters and to derive influence diagnostics for the model. In addition, we introduce a bivariate Birnbaum-Saunders distribution based on a parameterization of the Birnbaum-Saunders which has the mean as one of its parameters. We discuss the maximum likelihood estimation of the model parameters and show that these estimators can be obtained by solving non-linear equations. We then derive a regression model based on the proposed bivariate Birnbaum-Saunders distribution, which permits us to model data in their original scale. A simulation study is carried out to evaluate the performance of the maximum likelihood estimators. Finally, examples with real-data are performed to illustrate all the models proposed here. / Nesta tese propomos modelos baseados na distribuição Birnbaum-Saunders reparametrizada introduzida por Santos-Neto et al. (2012) e Santos-Neto et al. (2014), para análise dados de sobrevivência. Incialmente propomos o modelo de fragilidade Birnbaum-Saunders sem e com covariáveis observáveis. O modelo de fragilidade é caracterizado pela utilização de um efeito aleatório, ou seja, de uma variável aleatória não observável, que representa as informações que não podem ou não foram observadas tais como fatores ambientais ou genéticos, como também, informações que, por algum motivo, não foram consideradas no planejamento do estudo. O efeito aleatório (a “fragilidade”) é introduzido na função de risco de base para controlar a heterogeneidade não observável. Usamos o método de máxima verossimilhança para estimar os parâmetros do modelo. Avaliamos o desempenho dos estimadores sob diferentes percentuais de censura via estudo de simulações de Monte Carlo. Considerando variáveis regressoras, derivamos medidas de diagnóstico de influência. Os métodos de diagnóstico têm sido ferramentas importantes na análise de regressão para detectar anomalias, tais como quebra das pressuposições nos erros, presença de outliers e observações influentes. Em seguida propomos o modelo de fração de cura com fragilidade Birnbaum-Saunders. Os modelos para dados de sobrevivência com proporção de curados (também conhecidos como modelos de taxa de cura ou modelos de sobrevivência com longa duração) têm sido amplamente estudados. Uma vantagem importante do modelo proposto é a possibilidade de considerar conjuntamente a heterogeneidade entre os pacientes por suas fragilidades e a presença de uma fração curada. As estimativas dos parâmetros do modelo foram obtidas via máxima verossimilhança, medidas de influência e diagnóstico foram desenvolvidas para o modelo proposto. Por fim, avaliamos a distribuição bivariada Birnbaum-Saunders baseada na média, como também introduzimos um modelo de regressão para o modelo proposto. Utilizamos os métodos de máxima verossimilhança e método dos momentos modificados, para estimar os parâmetros do modelo. Avaliamos o desempenho dos estimadores via estudo de simulações de Monte Carlo. Aplicações a conjuntos de dados reais ilustram as potencialidades dos modelos abordados.

De nombreuses études cliniques ou interventions de lutte ont été faites ou sont en cours en Afrique pour la lutte contre le fléau du paludisme. En zone d'endémie, le paludisme est une maladie récurrente. La revue de littérature indique une application limitée des outils statistiques appropriés existants pour l'analyse des données récurrentes de paludisme. Nous avons mis en oeuvre des méthodes statistiques appropriées pour l'analyse des données répétées d'essais thérapeutiques de paludisme. Nous avons également étudié les mesures répétées d'hémoglobine lors du suivi de traitements antipaludiques en vue d'évaluer la tolérance ou sécurité des médicaments en regroupant les données de 13 essais cliniques.Pour l'analyse du nombre d'épisodes de paludisme, la régression binomiale négative a été mise en oeuvre. Pour modéliser la récurrence des épisodes de paludisme, quatre modèles ont été utilisés : i) Les équations d'estimation généralisées (GEE) utilisant la distribution de Poisson; et trois modèles qui sont une extension du modèle Cox: ii) le modèle de processus de comptage d'Andersen-Gill (AG-CP), iii) le modèle de processus de comptage de Prentice-Williams-Peterson (PWP-CP); et iv) le modèle de Fragilité partagée de distribution gamma. Pour l'analyse de sécurité, c'est-à-dire l'évaluation de l'impact de traitements antipaludiques sur le taux d'hémoglobine ou la survenue de l'anémie, les modèles linéaires et latents généralisés mixtes (« GLLAMM : generalized linear and latent mixed models ») ont été mis en oeuvre. Les perspectives sont l'élaboration de guides de bonnes pratiques de préparation et d'analyse ainsi que la création d'un entrepôt des données de paludisme. / Numerous clinical studies or control interventions were done or are ongoing in Africa for malaria control. For an efficient control of this disease, the strategies should be closer to the reality of the field and the data should be analyzed appropriately. In endemic areas, malaria is a recurrent disease. Repeated malaria episodes are common in African. However, the literature review indicates a limited application of appropriate statistical tools for the analysis of recurrent malaria data. We implemented appropriate statistical methods for the analysis of these data We have also studied the repeated measurements of hemoglobin during malaria treatments follow-up in order to assess the safety of the study drugs by pooling data from 13 clinical trials.For the analysis of the number of malaria episodes, the negative binomial regression has been implemented. To model the recurrence of malaria episodes, four models were used: i) the generalized estimating equations (GEE) using the Poisson distribution; and three models that are an extension of the Cox model: ii) Andersen-Gill counting process (AG-CP), iii) Prentice-Williams-Peterson counting process (PWP-CP); and (iv) the shared gamma frailty model. For the safety analysis, i.e. the assessment of the impact of malaria treatment on hemoglobin levels or the onset of anemia, the generalized linear and latent mixed models (GLLAMM) has been implemented. We have shown how to properly apply the existing statistical tools in the analysis of these data. The prospects of this work remain in the development of guides on good practices on the methodology of the preparation and analysis and storage network for malaria data.

A device that is accompanied by an irreversible chemical reaction or physical destruction and could no longer function properly after performing its intended function is referred to as a one-shot device. One-shot device test data differ from typical data obtained by measuring lifetimes in standard life-tests. Due to the very nature of one-shot devices, actual lifetimes of one-shot devices under test cannot be observed, and they are either left- or right-censored. In addition, a one-shot device often has multiple components that could cause the failure of the device. The components are coupled together in the manufacturing process or assembly, resulting in the failure modes possessing latent heterogeneity and dependence. Frailty models enable us to describe the influence of common, but unobservable covariates, on the hazard function as a random effect in a model and also provide an easily understandable interpretation. In this thesis, we develop some inferential results for one-shot device testing data with gamma frailty model. We first develop an efficient expectation-maximization (EM) algorithm for determining the maximum likelihood estimates of model parameters of a gamma frailty model with exponential lifetime distributions for components based on one-shot device test data with multiple failure modes, wherein the data are obtained from a constant-stress accelerated life-test. The maximum likelihood estimate of the mean lifetime of $k$-out-of-$M$ structured one-shot devices under normal operating conditions is also presented. In addition, the asymptotic variance–covariance matrix of the maximum likelihood estimates is derived, which is then used to construct asymptotic confidence intervals for the model parameters. The performance of the proposed inferential methods is finally evaluated through Monte Carlo simulations and then illustrated with a numerical example. A gamma frailty model with Weibull baseline hazards is considered next for fitting one-shot device testing data. The Weibull baseline hazards enable us to analyze time-varying failure rates more accurately, allowing for a deeper understanding of the dynamic nature of system's reliability. We develop an EM algorithm for estimating the model parameters utilizing the complete likelihood function. A detailed simulation study evaluates the performance of the Weibull baseline hazard model with that of the exponential baseline hazard model. The introduction of shape parameters in the component's lifetime distribution within the Weibull baseline hazard model offers enhanced flexibility in model fitting. Finally, Bayesian inference is then developed for the gamma frailty model with exponential baseline hazard for one-shot device testing data. We introduce the Bayesian estimation procedure using Markov chain Monte Carlo (MCMC) technique for estimating the model parameters as well as for developing credible intervals for those parameters. The performance of the proposed method is evaluated in a simulation study. Model comparison between independence model and the frailty model is made using Bayesian model selection criterion. / Thesis / Candidate in Philosophy

IntroductionLes chutes et les blessures dues aux chutes représentent un véritable enjeu de santé publique. Les programmes d’exercices physiques axés sur l’équilibre permettent de réduire de 30 à 40% le taux de chutes chez les personnes âgées vivant à leur domicile. Cependant, leur efficacité sur la prévention des traumatismes dus aux chutes n’a pas été établie.Ce travail comporte 2 parties :- Une revue systématique de la littérature et méta-analyse des résultats d’essais contrôlés randomisés (ECR) qui évaluent l’efficacité de l’exercice sur différents types de chutes traumatiques chez les personnes âgées en milieu communautaire. - L’analyse des données de l’ECR multicentrique ‘Ossébo’, qui évalue l’efficacité d’un programme d’exercice physique de prévention de chutes traumatiques chez des femmes âgées.MéthodesRevue systématiqueDes recherches bibliographiques ont été effectuées pour repérer les ECR de prévention des chutes par l’exercice physique, réalisés chez des personnes âgées vivant à leur domicile, et présentant des données sur les chutes traumatiques.Ensuite, on a regroupé les définitions des chutes traumatiques trouvées dans les études sélectionnées en 4 catégories :A/ avec conséquence.B/ avec recours à des soins médicaux. C/ ayant entraîné un traumatisme grave.D/ avec fracture.On a réalisé une méta-analyse (MA) pour chaque catégorie, donc on a calculé un effet global (effet poolé) de l’exercice correspondant au ratio des taux d’incidence dans les 2 groupes par un modèle à effet aléatoire.L’essai OsséboLes participantes à l’essai sont des femmes âgées de 75 à 85 ans, vivant à leur domicile, et ayant des capacités physiques diminuées. Au total, 706 femmes, dans 20 centres en France, ont été randomisées en 2 groupes : le groupe intervention (GI), et le groupe contrôle (GC).L’intervention comprend des ateliers hebdomadaires d’exercice en petits groupes pendant 2 ans, et des exercices au domicile.La survenue de chutes a été enregistrée à l’aide des cartes-calendriers. Les circonstances et les conséquences de la chute étaient demandées en cas de signalement d’une chute, afin de classer la chute (sans conséquence, traumatisme modéré, traumatisme grave).Des bilans ont été effectués à 1 an et 2 ans après l’inclusion, selon le même protocole que le bilan initial, qui comprenait notamment des tests fonctionnels simples.Le critère principal est le taux d’incidence des ‘chutes traumatiques’ (modérée et graves). Des modèles à fragilité (modèles de survie avec un effet aléatoire) ont été utilisés pour modéliser ce taux dans les 2 groupes.L’évolution au cours du temps des capacités physiques, et d’autres facteurs ont été comparés grâce à un modèle marginal avec un effet aléatoire au niveau du centre.RésultatsRevue systématique17 essais totalisant 4305 participants ont été sélectionnés. Toutes les interventions évaluées comprenaient des exercices de l’équilibre. Les résultats de la MA montrent que l’exercice est associé à une réduction du taux de chutes traumatiques dans chacune des catégories considérées, avec un effet poolé de 0.63 (IC95% : 0.51-0.77) pour la catégorie A(10 essais). Le RaR poolé était de 0.70 (0.54-0.92) pour la catégorie B (8 essais), de 0.57 (0.36-0.90) pour la catégorie C (7 essais), et de 0.39 (IC 95% : 0.22-0.66) pour la catégorie C (6 essais). L’essai OsséboOn a recensé 397 chutes traumatiques dans le GC, et 305 dans le GI, correspondant à une réduction significative de 19% du taux de chutes traumatiques (‘hazard ratios’ HR= 0.81 IC95% : (0.67 - 0.99). A 2 ans, les femmes du GI ont des performances significativement meilleures que les femmes du GC sur l’ensemble des tests physiquesDiscussionLes programmes d’exercice destinés à prévenir les chutes sont également efficaces pour réduire les traumatismes dus à la chute, y compris les plus graves. Aussi, il est possible de mettre en place à large échelle un programme efficace d’exercice de prévention des chutes traumatiques de longue durée chez des personnes âgées / Context: Exercise programmes can prevent falls in older community-dwellers. However, evidence that these programmes can also prevent injurious falls was poor.Objectives : Systematic review of evidence of the effect of exercise interventions on injurious fall prevention from randomised controlled trials (RCT).Evaluate the effectiveness of ‘Ossébo’, a multi-centre RCT assessing the effectiveness of a 2-year injurious fall prevention balance training programme.Methods:Systematic reviewThe definitions of injurious falls from included studies were classified into more homogeneous categories. This allowed the estimation of a pooled rate ratio for each injurious falls category based on random effects models. Ossébo trial706 women aged 75-85 years ; home-living with diminished functional capacities were included. The 2 groups were compared for rates of injurious falls with a frailty model. Other outcomes included physical functional capacities, and quality of life indicators. Results:Systematic review17 trials involving 4305 participants were included. Four categories were identified: all injurious falls, falls resulting in medical care, severe injurious falls, and falls resulting in fractures. Exercise had a significant preventive effect in all categories.OsséboThere were 305 injurious falls in the intervention group and 397 in the control group, for a HR of 0.81 (0.67 to 0.99). At 2 years, women in the intervention group had significantly better performances on all physical tests and a better perception of their overall physical function. Conclusion:Fall prevention exercise programmes are effective in preventing injurious falls, and are feasible for long-term, wide-spread dissemination