Réalisé en cotutelle avec l'Université de Paris-Sud / La thèse a pour objectif d’étudier les rapports entre les besoins typiquement hétérogènes des personnes âgées vivant dans la communauté et l’utilisation conséquente des services médicaux et sociaux. Inspirée du concept de la fragilité, l’approche a d’abord consisté à modéliser l’hétérogénéité des besoins en classant les individus – par l’analyse de classes latentes (ACL) – dans des profils-types, chacun représentant une constellation particulière de problèmes de santé. Appliquée aux données recueillies dans le cadre du projet de démonstration du SIPA (Système de services intégrés pour personnes âgées en perte d’autonomie), l’ACL a révélé 4 profils qui se distinguent sur le plan qualitatif par les dimensions physique et cognitive, et sur le plan quantitatif par la gravité des incapacités que les individus manifestent (Article 1). L’analyse des transitions entre les profils sur 1 an et 22 mois montre des changements différenciés selon les profils et le sexe, et confirme la stabilité de la classification et sa sensibilité aux changements d’état de santé. En outre, les profils sont robustes aux effets de la mortalité et de l’attrition due aux sorties d’étude. Les rapports entre les profils, les variables sociodémographiques et les indicateurs de résultats tel l’hébergement et la mortalité appuient la validité de la classification. Les profils ont ensuite servi à modéliser les besoins dans des modèles économétriques ajustés pour étudier l’utilisation et les coûts par profil d’état de santé pour une gamme de services financés par le système public (Article 2). Ces analyses montrent que les profils permettent d’identifier des configurations distinctes d’utilisation et de coûts pour des sous-groupes représentatifs de la population âgée fragile. La sensibilité des profils aux différences d’accès et aux différences d’intensité d’utilisation révèle les choix de ces groupes cibles par les autorités locales dans la prestation des services institutionnels et des services de proximité. Enfin, une analyse fine de ces relations a été menée pour la couteuse période précédant le décès dans le but d’établir si l’intensification des coûts en fin de vie concerne les personnes âgées de manière homogène quelque soit la gravité de leur état de santé (Article 3). Les analyses comparatives des survivants et des décédés montrent une tendance inverse dans les coûts des services selon l’état de santé. Les personnes qui survivent dans des états de santé compromis coûtent significativement plus que leurs homologues relativement sains. En revanche, c’est chez les individus relativement sains, indépendamment de l’âge, qu’on retrouve les coûts de fin de vie les plus élevés – liés principalement aux hospitalisations de courte durée. Combiné aux résultats qui montrent que les coûts sont significativement moindres chez les sujets de plus de 85 ans, ce travail confirme l’hypothèse d’une compression des coûts attribuables à la morbidité chez les décédés les plus âgés. L’originalité de cette thèse tient au fait que très peu de travaux ont tenté de modéliser l’hétérogénéité de l’état de santé dans le but d’étudier ses conséquences sur l’accès, l’intensité et les coûts des services sociaux et médicaux pour notre population de référence. En outre, alors que la pertinence des soins de fin de vie et le maintien à domicile sont au cœur des préoccupations, ce travail est le premier à décrire les rapports entre l’état de santé et les coûts en fin de vie pour les personnes âgées fragiles vivant dans la communauté. / Abstract This thesis aims to study the relationship between the typically heterogeneous needs of community-living elderly and their consequent utilization of health and social services. Inspired by the concept of frailty, the approach consisted in modeling the heterogeneity – by latent class analysis (LCA) – to group individuals into homogenous categories of health status, each representing a constellation of health problems. Applied to the data collected for the demonstration project of the system of integrated services for frail elderly (SIPA), LCA revealed four health state profiles that distinguish the physical and cognitive dimensions of health and capture severity along the disability dimension (Article 1). Transition analyses over 1 year and 22 months showed differentiated and gender-specific patterns of transition probabilities, confirming the sensitivity of the profiles to change in health status. The profiles are stable over time and robust to mortality and lost to follow-up attrition. Relationships between the profiles, sociodemographic characteristics and distal outcomes, such as mortality and institutionalization, confirm the classification’s validity. These profiles were then used in two-part econometric models to study access and costs of several measures of publicly funded services (Article 2). Our results show the profiles are able to identify distinct configurations of service utilization and costs in substantially meaningful subgroups of the frail elderly population. What is important here is that the health state profiles are sensitive to differences, and changes, in available patterns of care in a specific milieu. These differences reveal choices of target groups by local authorities in delivering institutional and community-based services. Finally, a fine analysis of these differences was performed for the costly period preceding death to establish whether the high end-of-life costs affect all elderly homogenously irrespective of the severity of their health state (Article 3). A comparative analysis revealed that survivors’ and decedents’ costs of care trends according to health status go in opposite directions. Severely disabled survivors cost significantly more compared to their relatively healthy counterparts. In contrast, the highest end-of-life costs – driven by acute hospitalizations – belong to the relatively healthy, independent of age. Among survivors and among decedents, the oldest old (85+) cost significantly less compared to younger age groups. Combined, these results support the notion of a compression of costs due to morbidity in the oldest old decedents. The originality of this thesis rests on the fact that few very few studies attempted to model heterogeneity in health status with the goal of estimating its effect on patterns of service utilization. Moreover, in light of the shift towards community-based care as a response to economic pressures and population aging, our work is the first to describe the relationship between health status and end-of-life care in frail community-living elderly.

<p>Commercial and regional brewers are increasingly investing in sustainability equipment that reduces input use, operating costs, and environmental impact. These technologies often require significant upfront costs that can limit market access to microbreweries. One potential solution for these brewers is to market their product as sustainable and charge a premium for their product to offset some of the costs. A stated preference choice experiment of a nationally-representative sample is undertaken to elicit consumer willingness-to-pay (WTP) for sustainability attributes in beer, thus determining whether a market for sustainably-made beer exists. The facets of sustainability, including water reduction, energy reduction, and landfill diversion, are portrayed through eco-labels affixed the front of the primary packaging (aluminum can or glass bottle). Multiple specifications are employed to handle model shortcomings and incorporate discrete heterogeneity. Across all model specifications, <a>consumers show a positive and statistically significant marginal WTP for landfill diversion practices and carbon reduction practices, ranging from $0.40 to $1.37 per six-pack and $0.67 to $1.21 per six-pack, respectively. </a>These results indicate consumers do in fact place value on beer produced using sustainable practices, and the demographics of consumers with the greatest WTP are similar to that of craft beer consumer.</p>

The Water Framework Directive requires reduced environmental impacts from human activities and for the assessment of the non-market benefits of pollution remediation schemes. This policy shift has exacerbated the research problems surrounding the physical, social and economic consequences of the relationship between land use and water quality. This research seeks to quantify the major socio-economic and environmental benefits for people which may arise as riverine pollution is reduced. To achieve these aims this research integrates primary data analyses combining choice experiment techniques with geographical information system based analyses of secondary data concerning the spatial distributions of riverine pollution. Current knowledge on the microbial quality of river water, measured by faecal indicator organism (FIO) concentrations and assessed at catchment scale, is inadequate. This research develops generic regression models to predict base- and high-flow faecal coliform (FC) and enterococci (EN) concentrations, using land cover and population (human and livestock) variables. The resulting models are then used both to predict FIO concentrations in unmonitored watercourses and to evaluate the likely impacts of different land use scenarios, enabling insights into the optimal locations and cost-effective mix of implementation strategies. Valuation experiments frequently conflate respondents’ preferences for different aspects of water quality. This analysis uses stated preference techniques to disaggregate the values of recreation and ecological attributes of water quality, thereby allowing decision makers to better understand the consequences of adopting alternative investment strategies which favour either ecological, recreational or a mix of benefits. The results reveal heterogeneous preferences across society; specifically, latent class analysis identifies three distinct groups, holding significantly different preferences for water quality. From a methodological perspective this research greatly enhances the ongoing synthesis of geographic and economic social sciences and addresses important policy questions which are of interest to a variety of stakeholders, including government departments and the water industry.

Background: Rates of accidental overdose mortality from substance use disorder (SUD) have risen dramatically in the United States since 1990. Between 1999 and 2004 alone rates increased 62% nationwide, with rural overdose mortality increasing at a rate 3 times that seen in urban populations. Cultural differences between rural and urban populations (e.g., educational attainment, unemployment rates, social characteristics, etc.) affect the nature of SUD, leading to disparate risk of overdose across these communities. Methods: Multiple-groups latent class analysis with covariates was applied to data from the 2011 and 2012 National Survey on Drug Use and Health (n=12.140) to examine potential differences in latent classifications of SUD between rural and urban adult (aged 18 years and older) populations. Nine drug categories were used to identify latent classes of SUD defined by probability of diagnosis within these categories. Once the class structures were established for rural and urban samples, posterior membership probabilities were entered into a multinomial regression analysis of socio-demographic predictors' association with the likelihood of SUD latent class membership. Results: Latent class structures differed across the sub-groups, with the rural sample fitting a 3-class structure (Bootstrap Likelihood Ratio Test P value=0.03) and the urban fitting a 6-class model (Bootstrap Likelihood Ratio Test P valueThis result supports the hypothesis that different underlying elements exist in the two populations that affect SUD patterns, and thus can inform the development of surveillance instruments, clinical services, and prevention programming tailored to specific communities.

La thèse a pour objectif d’étudier les rapports entre les besoins typiquement hétérogènes des personnes âgées vivant dans la communauté et l’utilisation conséquente des services médicaux et sociaux. Inspirée du concept de la fragilité, l’approche a d’abord consisté à modéliser l’hétérogénéité des besoins en classant les individus – par l’analyse de classes latentes (ACL) – dans des profils-types, chacun représentant une constellation particulière de problèmes de santé. Appliquée aux données recueillies dans le cadre du projet de démonstration du SIPA (Système de services intégrés pour personnes âgées en perte d’autonomie), l’ACL a révélé 4 profils qui se distinguent sur le plan qualitatif par les dimensions physique et cognitive, et sur le plan quantitatif par la gravité des incapacités que les individus manifestent (Article 1). L’analyse des transitions entre les profils sur 1 an et 22 mois montre des changements différenciés selon les profils et le sexe, et confirme la stabilité de la classification et sa sensibilité aux changements d’état de santé. En outre, les profils sont robustes aux effets de la mortalité et de l’attrition due aux sorties d’étude. Les rapports entre les profils, les variables sociodémographiques et les indicateurs de résultats tel l’hébergement et la mortalité appuient la validité de la classification. Les profils ont ensuite servi à modéliser les besoins dans des modèles économétriques ajustés pour étudier l’utilisation et les coûts par profil d’état de santé pour une gamme de services financés par le système public (Article 2). Ces analyses montrent que les profils permettent d’identifier des configurations distinctes d’utilisation et de coûts pour des sous-groupes représentatifs de la population âgée fragile. La sensibilité des profils aux différences d’accès et aux différences d’intensité d’utilisation révèle les choix de ces groupes cibles par les autorités locales dans la prestation des services institutionnels et des services de proximité. Enfin, une analyse fine de ces relations a été menée pour la couteuse période précédant le décès dans le but d’établir si l’intensification des coûts en fin de vie concerne les personnes âgées de manière homogène quelque soit la gravité de leur état de santé (Article 3). Les analyses comparatives des survivants et des décédés montrent une tendance inverse dans les coûts des services selon l’état de santé. Les personnes qui survivent dans des états de santé compromis coûtent significativement plus que leurs homologues relativement sains. En revanche, c’est chez les individus relativement sains, indépendamment de l’âge, qu’on retrouve les coûts de fin de vie les plus élevés – liés principalement aux hospitalisations de courte durée. Combiné aux résultats qui montrent que les coûts sont significativement moindres chez les sujets de plus de 85 ans, ce travail confirme l’hypothèse d’une compression des coûts attribuables à la morbidité chez les décédés les plus âgés. L’originalité de cette thèse tient au fait que très peu de travaux ont tenté de modéliser l’hétérogénéité de l’état de santé dans le but d’étudier ses conséquences sur l’accès, l’intensité et les coûts des services sociaux et médicaux pour notre population de référence. En outre, alors que la pertinence des soins de fin de vie et le maintien à domicile sont au cœur des préoccupations, ce travail est le premier à décrire les rapports entre l’état de santé et les coûts en fin de vie pour les personnes âgées fragiles vivant dans la communauté. / Abstract This thesis aims to study the relationship between the typically heterogeneous needs of community-living elderly and their consequent utilization of health and social services. Inspired by the concept of frailty, the approach consisted in modeling the heterogeneity – by latent class analysis (LCA) – to group individuals into homogenous categories of health status, each representing a constellation of health problems. Applied to the data collected for the demonstration project of the system of integrated services for frail elderly (SIPA), LCA revealed four health state profiles that distinguish the physical and cognitive dimensions of health and capture severity along the disability dimension (Article 1). Transition analyses over 1 year and 22 months showed differentiated and gender-specific patterns of transition probabilities, confirming the sensitivity of the profiles to change in health status. The profiles are stable over time and robust to mortality and lost to follow-up attrition. Relationships between the profiles, sociodemographic characteristics and distal outcomes, such as mortality and institutionalization, confirm the classification’s validity. These profiles were then used in two-part econometric models to study access and costs of several measures of publicly funded services (Article 2). Our results show the profiles are able to identify distinct configurations of service utilization and costs in substantially meaningful subgroups of the frail elderly population. What is important here is that the health state profiles are sensitive to differences, and changes, in available patterns of care in a specific milieu. These differences reveal choices of target groups by local authorities in delivering institutional and community-based services. Finally, a fine analysis of these differences was performed for the costly period preceding death to establish whether the high end-of-life costs affect all elderly homogenously irrespective of the severity of their health state (Article 3). A comparative analysis revealed that survivors’ and decedents’ costs of care trends according to health status go in opposite directions. Severely disabled survivors cost significantly more compared to their relatively healthy counterparts. In contrast, the highest end-of-life costs – driven by acute hospitalizations – belong to the relatively healthy, independent of age. Among survivors and among decedents, the oldest old (85+) cost significantly less compared to younger age groups. Combined, these results support the notion of a compression of costs due to morbidity in the oldest old decedents. The originality of this thesis rests on the fact that few very few studies attempted to model heterogeneity in health status with the goal of estimating its effect on patterns of service utilization. Moreover, in light of the shift towards community-based care as a response to economic pressures and population aging, our work is the first to describe the relationship between health status and end-of-life care in frail community-living elderly. / Réalisé en cotutelle avec l'Université de Paris-Sud

No description available.

In this research consumers attitudes towards general food safety and their perceptions of the safety of beef in Japan and Canada are examined. Risk perceptions, the willingness to pay for beef traceability from farm to final consumer and the willingness to pay for animal testing for bovine spongifrom encephalopothy (BSE) are measured through a stated preference exercise, provided as part of national surveys in each country. Japanese respondents continue to have higher risk attitudes and perceptions about beef than Canadian respondents in 2009 as compared to 2006. In each country survey respondents strongly prefer domestic beef over imports from any other country. However, interest in beef from other countries increases as full traceability, or one hundred % animal testing for BSE or both attributes are incorporated into the markets. The willingness to pay increases at a diminishing rate, from either traceability or BSE animal testing to both attributes. In latent class models the Japanese data suggest that there are three distinct classes of survey respondents, where class 1 respondents are characterized as being more trusting and willing to pay for beef from different countries, class 2 respondents strongly prefer domestic beef and their willingness to pay for imported beef does not increase with traceability or animal testing and class 3 respondents would only be willing to pay for traceable and a combination of traceable and animal tested domestic beef. Similarly, Canadian survey respondents can be segregated into two classes. Class 1 consumers are more trusting and will be willing to pay for both domestic and imported beef. Class 2 consumers are more cautious. / Agricultural and Resource Economics

No description available.

Made available in DSpace on 2016-06-02T20:04:51Z (GMT). No. of bitstreams: 1 4040.pdf: 1510214 bytes, checksum: 7dfe4542c20ffa8a47309738bc22a922 (MD5) Previous issue date: 2011-12-16 / Financiadora de Estudos e Projetos / The application of a gold standard reference test in all or part of the sample under investigation is often not feasible for the majority of diseases affecting humans, either by a lack of consensus on which testing may be considered a gold standard, the high level of invasion of the gold standard technique, the high cost of financially large-scale application, or by ethical questions, so to know the performance of existing tests is essential for the process of diagnosis of these diseases. In statistical modeling aimed to obtain robust estimates of the prevalence of the disease (x ) and the performance parameters of diagnostic tests (sensitivity (Se) and specificity (Sp)), various strategies have been considered such as the stratification of the population, the relaxation of the assumption of conditional independence, the inclusion of covariates, the verification type (partial or total) and the techniques to replace the gold standard. In this thesis we propose a new structure of stratification of the population considering both the prevalence rates and the parameters of test performance among the different strata (EHW). A Bayesian latent class modeling to estimate these parameters was developed for the general case of K diagnostic tests under investigation, relaxation of the assumption of conditional independence according to the formulations of the fixed effect (FECD) and random (RECD) with dependent order (h _ k) and M covariates. The application of models to two data sets about the performance evaluation of diagnostic tests used in screening for Chagas disease in blood donors showed results consistent with the sensitivity studies. Overall, we observed for the structure of stratification proposal (EHW) superior performance and estimates closer to the nominal values when compared to the structure of stratification when only the prevalence rates are different between the strata (HW), even when we consider data set with rates of Se, Sp and x close among the strata. Generally, the structure of latent class, when we have low or high prevalence of the disease, estimates of sensitivity and specificity rates have higher standard errors. However, in these cases, when there is high concordance of positive or negative results of the tests, the error pattern of these estimates are reduced. Regardless of the structure of stratification (EHW, HW), sample size and the different scenarios used to model the prior information, the model of conditional dependency from the FECD and RECD had, from the information criteria (AIC, BIC and DIC), superior performance to the structure of conditional independence (CI) and to FECD with improved performance and estimates closer to the nominal values. Besides the connection logit, derived from the logistic distribution with symmetrical shape, find in the link GEV, derived from the generalized extreme value distribution which accommodates symmetric and asymmetric shapes, a interesting alternative to construct the conditional dependence structure from the RECD. As an alternative to the problem of identifiability, present in this type of model, the criteria adopted to elicit the informative priors by combining descriptive analysis of data, adjustment models from simpler structures, were able to produce estimates with low standard error and very close to the nominal values. / Na área da saúde a aplicação de teste de referência padrão ouro na totalidade ou parte da amostra sob investigação é, muitas vezes, impraticável devido à inexistência de consenso sobre o teste a ser considerado padrão ouro, ao elevado nível de invasão da técnica, ao alto custo da aplicação em grande escala ou por questões éticas. Contudo, conhecer o desempenho dos testes é fundamental no processo de diagnóstico. Na modelagem estatística voltada à estimação da taxa de prevalência da doença (x ) e dos parâmetros de desempenho de testes diagnósticos (sensibilidade (S) e especificidade (E)), a literatura tem explorado: estratificação da população, relaxamento da suposição de independência condicional, inclusão de covariáveis, tipo de verificação pelo teste padrão ouro e técnicas para substituir o teste padrão ouro inexistente ou inviável de ser aplicado em toda a amostra. Neste trabalho, propomos uma nova estrutura de estratificação da população considerando taxas de prevalências e parâmetros de desempenho diferentes entre os estratos (HWE). Apresentamos uma modelagem bayesiana de classe latente para o caso geral de K testes diagnósticos sob investigação, relaxamento da suposição de independência condicional segundo as formulações de efeito fixo (DCEF) e efeito aleatório (DCEA) com dependência de ordem (h _ K) e inclusão de M covariáveis. A aplicação dos modelos a dois conjuntos de dados sobre avaliação do desempenho de testes diagnósticos utilizados na triagem da doença de Chagas em doadores de sangue apresentou resultados coerentes com os estudos de sensibilidade. Observamos, para a estrutura de estratificação proposta, HWE, desempenho superior e estimativas muito próximas dos valores nominais quando comparados à estrutura de estratificação na qual somente as taxas de prevalências são diferentes entre os estratos (HW), mesmo quando consideramos dados com taxas de S, E e x muito próximas entre os estratos. Geralmente, na estrutura de classe latente, quando temos baixa ou alta prevalência da doença, as estimativas das sensibilidades e especificidades apresentam, respectivamente, erro padrão mais elevado. No entanto, quando há alta concordância de resultados positivos ou negativos, tal erro diminui. Independentemente da estrutura de estratificação (HWE, HW), do tamanho amostral e dos diferentes cenários utilizados para modelar o conhecimento a priori, os modelos de DCEF e de DCEA apresentaram, a partir dos critérios de informação (AIC, BIC e DIC), desempenhos superiores à estrutura de independência condicional (IC), sendo o de DCEF com melhor desempenho e estimativas mais próximas dos valores nominais. Além da ligação logito, derivada da distribuição logística com forma simétrica, encontramos na ligação VEG , derivada da distribuição de valor extremo generalizada a qual acomoda formas simétricas e assimétricas, interessante alternativa para construir a estrutura de DCEA. Como alternativa ao problema de identificabilidade, neste tipo de modelo, os critérios para elicitar as prioris informativas, combinando análise descritiva dos dados com ajuste de modelos de estruturas mais simples, contribuíram para produzir estimativas com baixo erro padrão e muito próximas dos valores nominais.

L'accident vasculaire cérébral et la sclérose en plaques figurent parmi les maladies neurologiques les plus destructrices du système nerveux central. L'accident vasculaire cérébral est la deuxième cause de décès et la principale cause de handicap chez l'adulte dans le monde alors que la sclérose en plaques est la maladie neurologique non traumatique la plus fréquente chez l'adulte jeune. L'imagerie par résonance magnétique est un outil important pour distinguer le tissu cérébral sain du tissu pathologique à des fins de diagnostic, de suivi de la maladie, et de prise de décision pour un traitement personnalisé des patients atteints d'accident vasculaire cérébral ou de sclérose en plaques. La prédiction de l'évolution individuelle de la maladie chez les patients atteints d'accident vasculaire cérébral ou de sclérose en plaques constitue un défi pour les cliniciens avant de donner un traitement individuel approprié. Cette prédiction est possible avec des approches statistiques appropriées basées sur des informations cliniques et d'imagerie. Toutefois, l'étiologie, la physiopathologie, les symptômes et l'évolution dans l'accident vasculaire cérébral et la sclérose en plaques sont très différents. Par conséquent, dans cette thèse, les méthodes statistiques utilisées pour ces deux maladies neurologiques sont différentes. Le premier objectif était l'identification du tissu à risque d'infarctus chez les patients atteints d'accident vasculaire cérébral. Pour cet objectif, les méthodes de classification (dont les méthodes de machine learning) ont été utilisées sur des données d'imagerie mesurées à l'admission pour prédire le risque d'infarctus à un mois. Les performances des méthodes de classification ont été ensuite comparées dans un contexte d'identification de tissu à haut risque d'infarctus à partir de données humaines codées voxel par voxel. Le deuxième objectif était de regrouper les patients atteints de sclérose en plaques avec une méthode non supervisée basée sur des trajectoires individuelles cliniques et d'imagerie tracées sur cinq ans. Les groupes de trajectoires aideraient à identifier les patients menacés d'importantes progressions et donc à leur donner des médicaments plus efficaces. Le troisième et dernier objectif de la thèse était de développer un modèle prédictif pour l'évolution du handicap individuel des patients atteints de sclérose en plaques sur la base de données démographiques, cliniques et d'imagerie obtenues a l'inclusion. L'hétérogénéité des évolutions du handicap chez les patients atteints de sclérose en plaques est un important défi pour les cliniciens qui cherchent à prévoir l'évolution individuelle du handicap. Le modèle mixte linéaire à classes latentes a été utilisé donc pour prendre en compte la variabilité individuelle et la variabilité inobservée entre sous-groupes de sclérose en plaques / Stroke and multiple sclerosis are two of the most destructive neurological diseases of the central nervous system. Stroke is the second most common cause of death and the major cause of disability worldwide whereas multiple sclerosis is the most common non-traumatic disabling neurological disease of adulthood. Magnetic resonance imaging is an important tool to distinguish healthy from pathological brain tissue in diagnosis, monitoring disease evolution, and decision-making in personalized treatment of patients with stroke or multiple sclerosis.Predicting disease evolution in patients with stroke or multiple sclerosis is a challenge for clinicians that are about to decide on an appropriate individual treatment. The etiology, pathophysiology, symptoms, and evolution of stroke and multiple sclerosis are highly different. Therefore, in this thesis, the statistical methods used for the study of the two neurological diseases are different.The first aim was the identification of the tissue at risk of infarction in patients with stroke. For this purpose, the classification methods (including machine learning methods) have been used on voxel-based imaging data. The data measured at hospital admission is performed to predict the infarction risk at one month. Next, the performances of the classification methods in identifying the tissue at a high risk of infarction were compared. The second aim was to cluster patients with multiple sclerosis using an unsupervised method based on individual clinical and imaging trajectories plotted over five 5 years. Clusters of trajectories would help identifying patients who may have an important progression; thus, to treat them with more effective drugs irrespective of the clinical subtypes. The third and final aim of this thesis was to develop a predictive model for individual evolution of patients with multiple sclerosis based on demographic, clinical, and imaging data taken at study onset. The heterogeneity of disease evolution in patients with multiple sclerosis is an important challenge for the clinicians who seek to predict the disease evolution and decide on an appropriate individual treatment. For this purpose, the latent class linear mixed model was used to predict disease evolution considering individual and unobserved subgroup' variability in multiple sclerosis