The construction industry's two main certifications are Leadership in Energy and Environmental Design (LEED) and Energy Star. To achieve the triple bottom line of sustainability for these certifications, both certifications should make a positive impact individually as well as mutually, with their impact extending to the surrounding neighborhood. This research examined the spillover effect of LEED and/or Energy Star certified office buildings on the property values of other buildings in their neighborhood in Manhattan, New York City from an economic standpoint. The spatial analysis approach using the Geographic Information System and the statistical analysis approach based on the Hedonic Price Model and the Linear Mixed Effect Model were applied to identify the geographical distribution of LEED and/or Energy Star certified office buildings and their other buildings in their neighborhoods and analyze the impact of the former on the latter. The results were as follows: 1) There was a significant correlation between a LEED and/or Energy Star certified office building and the unit market values of its adjoining buildings through the unit market values of the certified office building, the LEED and/or Energy Star certification achievement, and the major features of LEED certification; 2) There was a varying spillover effect of the certified office building on the median unit market value of buildings depending on their proximities to a LEED and/or Energy Star certified office building. This research provides a firm foundation for further efforts to quantify the spillover effect of LEED and/or Energy Star certification on a neighborhood from an economic standpoint, thus supporting and encouraging growth in the local real estate market and benefitting not only the owners, developers, and investors of the certified office building but also the owners of neighboring buildings. / Ph. D.

The distribution of mountain permafrost is generally modeled using a combination of statistical techniques and empirical variables. Such models, based on topographic, climatic and geomorphological predictors of permafrost, have been widely used to estimate the spatial distribution of mountain permafrost in North America and Europe. However at present, little knowledge about the distribution and characteristics of mountain permafrost is available for the Andes. In addition, the effects of climate change on slope stability and the hydrological system, and the pressure of mining activities have increased concerns about the knowledge of mountain permafrost in the Andes. In order to model permafrost distribution in the semi-arid Chilean Andes between ~29°S and 32°S, an inventory of rock glaciers is carried out to obtain a variable indicative of the presence and absence of permafrost conditions. Then a Linear Mixed-Effects Model (LMEM) is used to determine the spatial distribution of Mean Annual Air Temperature (MAATs), which is then used as one of the predictors of permafrost occurrence. Later, a Generalized Additive Model (GAM) with a logistic link function is used to predict permafrost occurrence in debris surfaces within the study area. Within the study area, 3575 rock glaciers were inventoried. Of these, 1075 were classified as active, 493 as inactive, 343 as intact and 1664 as relict forms, based on visual interpretation of satellite imagery. Many of the rock glaciers (~60-80%) are situated at positive MAAT, and the number of rock glaciers at negative MAAT greatly decreases from north to south. The results of spatial temperature distribution modeling indicated that the temperature changes by -0.71°C per each 100 m increase in altitude, and that there is a 4°C temperature difference between the northern and southern part of the study area. The altitudinal position of the 0°C MAAT isotherm is situated at ~4250 m a.s.l. in the northern (29°S) section and drops latitudinally to ~4000 m a.s.l. in the southern section (32°S) of the study area. For permafrost modeling purposes, 1911 rock glaciers (active, inactive and intact forms) were categorized into the class indicative of permafrost presence and 1664 (relict forms) as non-permafrost. The predictors MAAT and Potential Incoming Solar Radiation (PISR) and their nonlinear interaction were modeled by the GAM using LOESS smoothing function. A temperature offset term was applied to reduce the overestimation of permafrost occurrence in debris surface areas due to the use of rock glaciers as permafrost proxies. The dependency between the predictor variables shows that a high amount of PISR has a greater effect at positive MAAT levels than in negative ones. The GAM for permafrost distribution achieved an acceptable discrimination capability between permafrost classes (area under the ROC curve ~0.76). Considering a permafrost probability score (PPS) ≥ 0.5 and excluding steep bedrock and glacier surfaces, mountain permafrost can be potentially present in up to about 6.8% (2636 km2) of the study area, whereas with a PPS ≥ 0.75, the potential permafrost area decreases to 2.7% (1051 km2). Areas with the highest PPS are spatially concentrated in the north section of the study area where altitude rises considerably (the Huasco and Elqui watersheds), while permafrost is almost absent in the southern section where the topography is considerably lower (Limarí and Choapa watersheds). This research shows that the potential mountain permafrost distribution can be spatially modeled using topoclimatic information and rock glacier inventories. Furthermore, the results have provided the first local estimation of permafrost distribution in the semi-arid Chilean Andes. The results obtained can be used for local environmental planning and to aid future research in periglacial topics.

Cette thèse est consacrée aux méthodes statistiques d’extrapolation dans les essais de recherche de dose en pédiatrie. Dans un premier temps, nous réalisons une revue systématique de la littérature sur le sujet. Elle met en évidence la nécessité de proposer de nouvelles méthodes pour la conception des études d’escalade de dose chez l’enfant. Nous apportons des réponses à cette problématique en exploitant l’information disponible chez l’adulte. Dans une première série de travaux, nous étudions l’intérêt de la prédiction des paramètres pharmacocinétiques (PK) en pédiatrie à l’aide de méthodes d’extrapolation : l’allométrie et la maturation. Cette évaluation est réalisée à partir de données PK chez l’adulte et l’enfant pour la méfloquine. Faisant appel aux paramètres prédits, nous développons une approche pour choisir les temps de prélèvements (design) d’une étude PK. Nous recommandons un design obtenu par optimisation grâce à la méthode de D-optimalité en utilisant le logiciel PFIM. Ce design est ensuite validé à l’aide de simulations sur différents modèles. Une seconde série de travaux nous amène à proposer des recommandations pour la planification d’un essai de recherche de dose. Nous avançons d’abord des techniques pour choisir les doses à tester grâce à l’utilisation des données adultes et de l’extrapolation. Nous proposons ensuite une méthode proche de la méta-analyse pour prédire les probabilités de toxicités pour chaque dose. Enfin, nous employons la méthode de l’Effective sample size afin de construire une loi a priori lors de l’utilisation d’une estimation bayésienne. Nous validons ces recommandations sur une étude de cas en utilisant une méthode d’escalade de dose, la méthode de réévaluation séquentielle bivariée, pour laquelle nous évaluons à la fois la toxicité et l’efficacité. A partir de l’exemple de la molécule erlotinib, nous effectuons une série de simulations sur plusieurs scénarios afin d’illustrer les performances de la planification. / This thesis addresses extrapolation techniques for statistical models for dose-finding studies in pediatrics. After a litterature review on these clinical trials, we observed the need of methodological propositions for the planification of dose- finding studies in pediatrics. We deal with this issue using information from the adult population. In a first research, the objectives are to design a pharmacokinetic (PK) study by using information from adults and evaluate the robustness of the recommended design through a case study of mefloquine. Pediatric PK parameters are predicted from adult PK using extrapolation functions such as allometry and maturation. A D-optimal design for children is obtained with PFIM by assuming the extrapolated design. The robustness of the recommended design is evaluated in a simulation study with four different models and is compared to the empirical design used for the pediatric data. In a second research, we propose a global approach to conduct a pediatric dose-finding clinical trial using extrapolation from adult information. First, we extrapolate the dose-range from adults using allometry and maturation. Then, using an approach to meta-analysis, we choose the initial probabilities of toxicity for each dose. Finally, we use the effective sample size method to choose the prior distribution of parameters in a Bayesian setting. We perform a simulation study based on the molecule erlotinib to evaluate the performances of this global approach.

Le concept d’habitat, qui définit le lieu de vie des organismes par des conditions abiotiques et biotiques, est déterminant pour étudier les relations entre les organismes et leur environnement. La sélection d’habitat est le processus à travers lequel l’organisme va choisir l’habitat où il se trouve en fonction des différents habitats disponibles autour de lui. Cette sélection va dépendre d’un choix individuel, qui est propre à l’organisme (ex. son comportement), et d’un choix commun, qui est observable chez des organismes qui partagent des traits communs (ex. les individus d’une même espèce). Les modèles spécifiques de sélection d’habitat cherchent à expliquer et prédire ce choix commun, et sont notamment utilisés pour les cours d’eau dans les outils d'aide à la définition de débits écologiques. Pour les poissons de rivière, la plupart des modèles spécifiques à l’échelle du microhabitat sont peu transférables à d’autres rivières. En effet, ils sont construits à partir de données d’abondance échantillonnées dans le même site pendant quelques campagnes. Afin d’améliorer la qualité prédictive de ces modèles, j’ai développé une approche prometteuse de modélisation multi-sites et multi-campagnes permettant à la fois de considérer la réponse non linéaire de la sélection et la surdispersion des données d’abondance. A partir de suivis individuels par télémétrie, j’ai montré la pertinence des modèles de sélection spécifiques malgré la forte variabilité individuelle observée. Finalement, la sélection d’habitat étant dépendante de processus structurant les communautés et agissant à l’échelle du paysage, telle que la dispersion des individus, j’ai mis en évidence l’intérêt d’utiliser des techniques légères d’échantillonnage comme les observations par plongée pour caractériser les structures des communautés et leurs répartitions spatiales. Ces techniques permettront alors d’étudier l’influence des processus du paysage sur les modèles de sélection d’habitat / The habitat concept, which defines the place where organisms live, is composed by abiotic and biotic conditions and differs for examples between species or activities. The habitat selection is the process where organisms choose the habitat to live in function of all habitats available around them. This habitat selection depends on an individual choice related to the organism, for example its behavior and a common choice related to organisms sharing common traits as individuals from the same species. Specific habitat selection models are developed to understand and represent this common choice and used to build ecological flow tools. For freshwater fish, most of specific habitat selection models have low transferability between reaches and rivers. Indeed, they are built from abundance data and sampled in the same study reach during few numbers of surveys. In order to improve predictive quality of models, I developed an attractive modelling approach, both multi-reach and multi-survey, involving the non-linear response of habitat selection and abundance data overdispersion. Then, despite the high individual variability of habitat selection, I showed, from telemetry data, the relevance of developing specific habitat selection models. Finally, as the habitat selection is also depending on processes which influence community structures at the landscape scale (e.g. dispersal), I demonstrate the benefits of sampling methods such as snorkeling to characterize community structures and their longitudinal distributions at a large spatial scale. These techniques will allow studying the influence of landscape processes on habitat selection models.

The migratory birds stop at different stopover sites during migration. The presence of resources in these stopover sites is essential to regain the energy of these birds. This thesis aims to compare the resource abundance and intake at the two stopover sites: Reda and Wisla river estuaries. How a bird's mass changes during its stay at an estuary is considered as a proxy for the resource abundance of a site. The comparison is made on different subsets, including those which has incomplete data, i.e. next day is not exactly one day after the previous capture. Multiple linear regression, Generalized additive model and Linear mixed effect model are used for analysis. Expectation maximization and an iterative predictive process are implemented to deal with incomplete data. We found that Reda has higher resource abundance and intake as compared to that of Wisla river estuary.

Indiana University-Purdue University Indianapolis (IUPUI) / Useful medical indices pose important roles in predicting medical outcomes. Medical indices, such as the well-known Body Mass Index (BMI), Charleson Comorbidity Index, etc., have been used extensively in research and clinical practice, for the quantification of risks in individual patients. However, the development of these indices is challenged; and primarily based on heuristic arguments. Statistically, most medical indices can be expressed as a function of a linear combination of individual variables and fitted by single-index model. Single-index model represents a way to retain latent nonlinear features of the data without the usual complications that come with increased dimensionality. In my dissertation, I propose a single-index model approach to analytically derive indices from observed data; the resulted index inherently correlates with specific health outcomes of interest. The first part of this dissertation discusses the derivation of an index function for the prediction of one outcome using longitudinal data. A cubic-spline estimation scheme for partially linear single-index mixed effect model is proposed to incorporate the within-subject correlations among outcome measures contributed by the same subject. A recursive algorithm based on the optimization of penalized least square estimation equation is derived and is shown to work well in both simulated data and derivation of a new body mass measure for the assessment of hypertension risk in children. The second part of this dissertation extends the single-index model to a multivariate setting. Specifically, a multivariate version of single-index model for longitudinal data is presented. An important feature of the proposed model is the accommodation of both correlations among multivariate outcomes and among the repeated measurements from the same subject via random effects that link the outcomes in a unified modeling structure. A new body mass index measure that simultaneously predicts systolic and diastolic blood pressure in children is illustrated. The final part of this dissertation shows existence, root-n strong consistency and asymptotic normality of the estimators in multivariate single-index model under suitable conditions. These asymptotic results are assessed in finite sample simulation and permit joint inference for all parameters.

Objectifs Les études évaluant l’efficacité de l’entraînement physique chez les lombalgiques chroniques observent généralement des tailles d’effet faibles à modérer. Une possibilité est que les essais ont été trop courts pour observer le plein potentiel de l’exercice physique. Nous avons cherché à caractériser la cinétique de la douleur pendant un régime d’exercice multimodal afin d’explorer si les participants pourraient bénéficier de régimes d’exercice plus long. Méthodes Cinquante-sept participants souffrant de lombalgie chronique ont suivi un régime d’exercice multimodal (exercices aérobies et en résistance) ou une période d’attente. Les capacités physiques et fonctionnelles des participants ont été évaluées avant l’intervention et la semaine suivant la complétion du protocole. L’intensité de la douleur dans le bas du dos a été enregistrée au début de chaque séance d’entraînement afin de suivre l’évolution de la douleur pendant le programme d’entraînement. Une modélisation linéaire à effets mixtes a été réalisée pour décrire la cinétique de la douleur. Des analyses de modération ont été effectuées pour vérifier si le sexe, l’intensité de la douleur au début du programme et l’amélioration des capacités physiques pouvaient prédire une plus grande réduction de la douleur. Résultats Les participants du groupe d’entraînement, mais pas ceux du groupe contrôle, se sont améliorés aux tests de capacités physiques. Cependant, les participants du groupe d’entraînement ne semblent pas avoir amélioré leurs capacités fonctionnelles. La douleur a diminué tout au long du programme d’entraînement (terme linéaire : 𝛽 = −14.89, 𝑝 < .001). Cependant, la réduction de la douleur ralentissait à mesure que les participants progressaient dans le programme d’entraînement (𝛽 = −5.48, 𝑝 = .001). La réduction de la douleur était modérée par l’amélioration des capacités physiques et l’intensité de la douleur au début du programme, mais pas par le sexe. Conclusion Il semble que l’efficacité de l’entraînement physique atteigne un effet plancher, empêchant les participants d’atteindre un état sans douleur. Favoriser l’amélioration des capacités physiques semble aussi être une stratégie pour maximiser l’effet du programme d’entraînement. / Objectives Trials investigating the efficacy exercise therapy in low back pain (LBP) patients typically yield small to moderate effect size. One possibility is that trials have been too short to observe the full potential of physical exercise. We sought to characterize the kinetic of pain during a multimodal exercise regimen to explore if participants could benefit from longer exercise regimen. Methods Fifty-seven LBP patients completed a multimodal exercise regimen (aerobic and resistance exercises) or a waiting period. Physical and functional outcomes were measured prior to the intervention and one week after its completion. Pain intensity in the lower back was recorded at the beginning of each training sessions to monitor the evolution of pain during the exercise regimen. Linear mixed-effect modelling was performed to describe the kinetic of pain. Moderation analyses were performed to test whether sex, baseline pain intensity and improvement in physical capacities predicted greater pain reduction. Results Participants in the exercise group, but not the control group, improved in physical and functional outcomes. Pain decreased throughout the exercise regimen (linear trend: 𝛽 = −14.89, 𝑝 < .001). However, pain reduction slowed down as participants progressed in the training program (quadratic trend: 𝛽 = -5.48, 𝑝 = .001). Pain reduction was moderated by improvement in maximal aerobic capacities and baseline pain intensities, but not by sex. Conclusion It appears that the effect of exercise therapy reaches a floor effect, preventing participants from reaching a painless state. Designing physical training program maximizing improvements in physical capacities may be a good strategy to increase the efficacy of exercise therapy.

No description available.

Depuis 2013, le traitement « universel » est recommandé en France. Le moment de l’initiation thérapeutique est une question qui reste cependant d’actualité pour les patients se présentant en primo-infection. Cette thèse s’attache à étudier la prise en charge thérapeutique du VIH au stade de la primo-infection (PIV) sous différents angles :1) le suivi par les cliniciens des recommandations d’initiation des traitements antirétroviraux depuis 1996 en fonction de l’évolution de ces recommandations; 2) l’impact d’un traitement ARV transitoire en PIV sur la réponse immuno-virologique lors de la reprise du traitement et 3) l’identification de nouveaux biomarqueurs comme facteurs pronostiques de progression de l’infection VIH. La majorité des travaux présentés dans cette thèse repose sur les données de la cohorte ANRS PRIMO qui comporte environ 1 500 patients infectés par le VIH inclus en PIV entre juin 1996 et décembre 2013, dans 94 hôpitaux français. Tous les patients étaient naïfs de traitement antirétroviral à l'inclusion.La première partie de la thèse analyse la mise en œuvre des recommandations d’initiation du traitement ARV entre 1996 et 2010 par les médecins en France, dans deux situations distinctes : au stade chronique et lors de la primo-infection par le VIH-1. Nous avons montré que les recommandations d’initiation du traitement ARV étaient largement suivies. Néanmoins, il existe un effet d’inertie dans leurs applications lors des changements de recommandation. Il reste à améliorer le délai de mise sous traitement lorsque le taux de CD4 atteint le seuil recommandé. Au stade chronique, le traitement était plus fréquemment initié chez les patients présentant un critère d’initiation dès le diagnostic d’infection par le VIH (96%), que chez les patients qui atteignaient un critère d’initiation au cours du suivi (78%, p<0.001). Nous avons identifié comme facteurs de risque de ne pas être traité en phase chronique malgré une indication de traitement : une charge virale < 5log (versus >5), un plus faible niveau d’éducation et des conditions de vie précaires.L’impact de l’interruption d’un traitement antirétroviral initié en PIV sur la restauration des CD4 après reprise du traitement a été exploré en modélisant l’évolution des CD4 avec des modèles linéaires à effets mixtes avec intercept et pente aléatoires. Les patients qui avaient initié un traitement ARV pendant la phase chronique avaient une meilleure réponse immunologique que les patients reprenant un 2ème traitement après un traitement transitoire en PIV : à 36 mois, les gains en √CD4 cellules/mm3 et en pourcentage de CD4 étaient significativement plus élevés. Cependant, il s’agissait de différences modestes en termes cliniques, qui ne conduisent pas à recommander d’arrêter la recherche clinique sur les arrêts de traitement cherchant à induire des contrôleurs post traitement. Après un état des lieux des mécanismes complexes d’activation/inflammation du système immunitaire pendant la primo-infection nous avons cherché à identifier de nouveaux biomarqueurs prédictifs de l’évolution de l’infection. Le taux de sCD14 (marqueur d’activation monocyte/macrophage et marqueur indirect de translocation microbienne) au moment de la PIV a été identifié comme marqueur potentiel de prédiction du déclin des CD4 et du risque de mortalité d’origine cardio-vasculaire. En conclusion, bien que des progrès considérables aient été réalisés dans la prise en charge du VIH, d'autres études sont nécessaires pour optimiser et adapter le traitement au profil du patient dès les premiers stades de l’infection VIH. / In France, since 2013, HIV treatment has been recommended for all HIV-infected patients independently of their CD4 count. However, when to start anti-retroviral (ARV) treatment is still an issue. This thesis aims to explore the therapeutic management of HIV at the stage of PHI in different aspects: 1) we explored how physicians in France have applied the evolving guidelines for ART initiation since 1996 2) the impact of a transient ARV treatment at PHI on immuno-virological response during 2nd treatment and 3) identification of new biomarkers prognostic of HIV progression.Most of the work presented in this thesis is based on data from the ongoing ANRS PRIMO cohort that enrolled more than 1 500 HIV infected patients enrolled at PHI since June 1996 in 94 French hospitals. All patients were antiretroviral therapy naive at baseline.The first part of the thesis analyzes the implementation of the recommendations of ARV treatment initiation between 1996 and 2010 by physicians in France, in two distinct situations: in the chronic HIV-1 infection and during primary HIV-1 infection. We have shown that the recommendations of ARV treatment initiation were widely followed. Nevertheless, there was inertia in guidelines application when changes in the recommendations took place. The time to treatment when CD4 cell counts reach the threshold to treat can be improved. 96% of the patients initiated ART when they had a CD4 cell count below the threshold to treat at entry, while treatment was less timely initiated when the CD4 threshold was reached during active follow-up (78%, p <0.001).We identified as risk factors for not being timely treated in chronic phase despite an indication for treatment: a viral load <5log (versus> 5), a lower education level and poor living conditions.The impact of ARV interruption after a first treatment initiated at PHI on the CD4 count restoration after resumption was explored by modeling the evolution of CD4 cells with linear mixed effects models with random intercept and slope. Patients who initiated ARV treatment during the chronic phase had a better immune response than patients who initiated a second course treatment after a transient ART at PHI: at 36 months, the gains in √CD4 cells / mm3 and CD4 percentage were significantly higher. However, this difference was clinically modest and further research on treatment interruptions seeking to induce post-treatment controllers is still an issue but only in research settings and under close medical surveillance. After an overview of the complex mechanisms of activation / inflammation of the immune system during primary infection we sought to identify new predictive biomarkers of disease progression. The level of sCD14 (marker of monocyte/macrophage activation and an indirect marker of microbial translocation) at the time of PHI was identified as predictive marker of CD4 decline and of risk of cardio-vascular mortality. In conclusion, although considerable progress has been made in the management of HIV, further studies are needed to optimize and adapt the treatment to the patient profile in the early stages of HIV infection.

Indiana University-Purdue University Indianapolis (IUPUI) / Joint models of longitudinal and survival outcomes have been used with increasing frequency in clinical investigations. Correct specification of fixed and random effects, as well as their functional forms is essential for practical data analysis. However, no existing methods have been developed to meet this need in a joint model setting. In this dissertation, I describe a penalized likelihood-based method with adaptive least absolute shrinkage and selection operator (ALASSO) penalty functions for model selection. By reparameterizing variance components through a Cholesky decomposition, I introduce a penalty function of group shrinkage; the penalized likelihood is approximated by Gaussian quadrature and optimized by an EM algorithm. The functional forms of the independent effects are determined through a procedure for structural discovery. Specifically, I first construct the model by penalized cubic B-spline and then decompose the B-spline to linear and nonlinear elements by spectral decomposition. The decomposition represents the model in a mixed-effects model format, and I then use the mixed-effects variable selection method to perform structural discovery. Simulation studies show excellent performance. A clinical application is described to illustrate the use of the proposed methods, and the analytical results demonstrate the usefulness of the methods.