Binary classification of dyslipidemia from the waist-to-hip ratio and body mass index: a comparison of linear, logistic, and CART models.

BACKGROUND: We sought to improve upon previously published statistical modeling strategies for binary classification of dyslipidemia for general population screening purposes based on the waist-to-hip circumference ratio and body mass index anthropometric measurements. METHODS: Study subjects were participants in WHO-MONICA population-based surveys conducted in two Swiss regions. Outcome variables were based on the total serum cholesterol to high density lipoprotein cholesterol ratio. The other potential predictor variables were gender, age, current cigarette smoking, and hypertension. The models investigated were: (i) linear regression; (ii) logistic classification; (iii) regression trees; (iv) classification trees (iii and iv are collectively known as "CART"). Binary classification performance of the region-specific models was externally validated by classifying the subjects from the other region. RESULTS: Waist-to-hip circumference ratio and body mass index remained modest predictors of dyslipidemia. Correct classification rates for all models were 60-80%, with marked gender differences. Gender-specific models provided only small gains in classification. The external validations provided assurance about the stability of the models. CONCLUSIONS: There were no striking differences between either the algebraic (i, ii) vs. non-algebraic (iii, iv), or the regression (i, iii) vs. classification (ii, iv) modeling approaches. Anticipated advantages of the CART vs. simple additive linear and logistic models were less than expected in this particular application with a relatively small set of predictor variables. CART models may be more useful when considering main effects and interactions between larger sets of predictor variables.

Generalized linear and generalized additive models in studies of species distributions: setting the scene

An important statistical development of the last 30 years has been the advance in regression analysis provided by generalized linear models (GLMs) and generalized additive models (GAMs). Here we introduce a series of papers prepared within the framework of an international workshop entitled: Advances in GLMs/GAMs modeling: from species distribution to environmental management, held in Riederalp, Switzerland, 6-11 August 2001.We first discuss some general uses of statistical models in ecology, as well as provide a short review of several key examples of the use of GLMs and GAMs in ecological modeling efforts. We next present an overview of GLMs and GAMs, and discuss some of their related statistics used for predictor selection, model diagnostics, and evaluation. Included is a discussion of several new approaches applicable to GLMs and GAMs, such as ridge regression, an alternative to stepwise selection of predictors, and methods for the identification of interactions by a combined use of regression trees and several other approaches. We close with an overview of the papers and how we feel they advance our understanding of their application to ecological modeling.

Cursos diurnos e noturnos: fatores de aprovação no vestibular da UFMG

Esse estudo analisa dados do vestibular da Universidade Federal de Minas Gerais de 2004, mediante um modelo de regressão não paramétrico, o Classification and Regression Trees. Seu objetivo foi identificar os principais fatores de aprovação e, também, verificar se esses fatores eram os mesmos para os cursos diurnos e noturnos. A resposta a essas questões permitiria verificar se a expansão do turno noturno feita por essa universidade vinha promovendo maior inserção social. Observou-se que, em geral, a conclusão do ensino médio em escolas públicas federais ou particulares, o conhecimento de língua estrangeira e o pertencimento a um grupo socioeconômico alto são fatores fortemente associados à aprovação do candidato. Verificou-se, ainda, que nos cursos noturnos as variáveis socioeconômicas têm maior relevância, enquanto nos cursos diurnos a formação do candidato adquire maior peso. Finalmente, o fator socioeconômico médio tende a ser maior para os candidatos aprovados.

Predictive analysis and mapping of indoor radon concentrations in Switzerland

It is estimated that around 230 people die each year due to radon (222Rn) exposure in Switzerland. 222Rn occurs mainly in closed environments like buildings and originates primarily from the subjacent ground. Therefore it depends strongly on geology and shows substantial regional variations. Correct identification of these regional variations would lead to substantial reduction of 222Rn exposure of the population based on appropriate construction of new and mitigation of already existing buildings. Prediction of indoor 222Rn concentrations (IRC) and identification of 222Rn prone areas is however difficult since IRC depend on a variety of different variables like building characteristics, meteorology, geology and anthropogenic factors. The present work aims at the development of predictive models and the understanding of IRC in Switzerland, taking into account a maximum of information in order to minimize the prediction uncertainty. The predictive maps will be used as a decision-support tool for 222Rn risk management. The construction of these models is based on different data-driven statistical methods, in combination with geographical information systems (GIS). In a first phase we performed univariate analysis of IRC for different variables, namely the detector type, building category, foundation, year of construction, the average outdoor temperature during measurement, altitude and lithology. All variables showed significant associations to IRC. Buildings constructed after 1900 showed significantly lower IRC compared to earlier constructions. We observed a further drop of IRC after 1970. In addition to that, we found an association of IRC with altitude. With regard to lithology, we observed the lowest IRC in sedimentary rocks (excluding carbonates) and sediments and the highest IRC in the Jura carbonates and igneous rock. The IRC data was systematically analyzed for potential bias due to spatially unbalanced sampling of measurements. In order to facilitate the modeling and the interpretation of the influence of geology on IRC, we developed an algorithm based on k-medoids clustering which permits to define coherent geological classes in terms of IRC. We performed a soil gas 222Rn concentration (SRC) measurement campaign in order to determine the predictive power of SRC with respect to IRC. We found that the use of SRC is limited for IRC prediction. The second part of the project was dedicated to predictive mapping of IRC using models which take into account the multidimensionality of the process of 222Rn entry into buildings. We used kernel regression and ensemble regression tree for this purpose. We could explain up to 33% of the variance of the log transformed IRC all over Switzerland. This is a good performance compared to former attempts of IRC modeling in Switzerland. As predictor variables we considered geographical coordinates, altitude, outdoor temperature, building type, foundation, year of construction and detector type. Ensemble regression trees like random forests allow to determine the role of each IRC predictor in a multidimensional setting. We found spatial information like geology, altitude and coordinates to have stronger influences on IRC than building related variables like foundation type, building type and year of construction. Based on kernel estimation we developed an approach to determine the local probability of IRC to exceed 300 Bq/m3. In addition to that we developed a confidence index in order to provide an estimate of uncertainty of the map. All methods allow an easy creation of tailor-made maps for different building characteristics. Our work is an essential step towards a 222Rn risk assessment which accounts at the same time for different architectural situations as well as geological and geographical conditions. For the communication of 222Rn hazard to the population we recommend to make use of the probability map based on kernel estimation. The communication of 222Rn hazard could for example be implemented via a web interface where the users specify the characteristics and coordinates of their home in order to obtain the probability to be above a given IRC with a corresponding index of confidence. Taking into account the health effects of 222Rn, our results have the potential to substantially improve the estimation of the effective dose from 222Rn delivered to the Swiss population.

Classification models for neurocognitive impairment in HIV infection based on demographic and clinical variables

Objective: We used demographic and clinical data to design practical classification models for prediction of neurocognitive impairment (NCI) in people with HIV infection. Methods: The study population comprised 331 HIV-infected patients with available demographic, clinical, and neurocognitive data collected using a comprehensive battery of neuropsychological tests. Classification and regression trees (CART) were developed to btain detailed and reliable models to predict NCI. Following a practical clinical approach, NCI was considered the main variable for study outcomes, and analyses were performed separately in treatment-naïve and treatment-experienced patients. Results: The study sample comprised 52 treatment-naïve and 279 experienced patients. In the first group, the variables identified as better predictors of NCI were CD4 cell count and age (correct classification [CC]: 79.6%, 3 final nodes). In treatment-experienced patients, the variables most closely related to NCI were years of education, nadir CD4 cell count, central nervous system penetration-effectiveness score, age, employment status, and confounding comorbidities (CC: 82.1%, 7 final nodes). In patients with an undetectable viral load and no comorbidities, we obtained a fairly accurate model in which the main variables were nadir CD4 cell count, current CD4 cell count, time on current treatment, and past highest viral load (CC: 88%, 6 final nodes). Conclusion: Practical classification models to predict NCI in HIV infection can be obtained using demographic and clinical variables. An approach based on CART analyses may facilitate screening for HIV-associated neurocognitive disorders and complement clinical information about risk and protective factors for NCI in HIV-infected patients.

L’arbre de régression multivariable et les modèles linéaires généralisés revisités : applications à l’étude de la diversité bêta et à l’estimation de la biomasse d’arbres tropicaux

En écologie, dans le cadre par exemple d’études des services fournis par les écosystèmes, les modélisations descriptive, explicative et prédictive ont toutes trois leur place distincte. Certaines situations bien précises requièrent soit l’un soit l’autre de ces types de modélisation ; le bon choix s’impose afin de pouvoir faire du modèle un usage conforme aux objectifs de l’étude. Dans le cadre de ce travail, nous explorons dans un premier temps le pouvoir explicatif de l’arbre de régression multivariable (ARM). Cette méthode de modélisation est basée sur un algorithme récursif de bipartition et une méthode de rééchantillonage permettant l’élagage du modèle final, qui est un arbre, afin d’obtenir le modèle produisant les meilleures prédictions. Cette analyse asymétrique à deux tableaux permet l’obtention de groupes homogènes d’objets du tableau réponse, les divisions entre les groupes correspondant à des points de coupure des variables du tableau explicatif marquant les changements les plus abrupts de la réponse. Nous démontrons qu’afin de calculer le pouvoir explicatif de l’ARM, on doit définir un coefficient de détermination ajusté dans lequel les degrés de liberté du modèle sont estimés à l’aide d’un algorithme. Cette estimation du coefficient de détermination de la population est pratiquement non biaisée. Puisque l’ARM sous-tend des prémisses de discontinuité alors que l’analyse canonique de redondance (ACR) modélise des gradients linéaires continus, la comparaison de leur pouvoir explicatif respectif permet entre autres de distinguer quel type de patron la réponse suit en fonction des variables explicatives. La comparaison du pouvoir explicatif entre l’ACR et l’ARM a été motivée par l’utilisation extensive de l’ACR afin d’étudier la diversité bêta. Toujours dans une optique explicative, nous définissons une nouvelle procédure appelée l’arbre de régression multivariable en cascade (ARMC) qui permet de construire un modèle tout en imposant un ordre hiérarchique aux hypothèses à l’étude. Cette nouvelle procédure permet d’entreprendre l’étude de l’effet hiérarchisé de deux jeux de variables explicatives, principal et subordonné, puis de calculer leur pouvoir explicatif. L’interprétation du modèle final se fait comme dans une MANOVA hiérarchique. On peut trouver dans les résultats de cette analyse des informations supplémentaires quant aux liens qui existent entre la réponse et les variables explicatives, par exemple des interactions entres les deux jeux explicatifs qui n’étaient pas mises en évidence par l’analyse ARM usuelle. D’autre part, on étudie le pouvoir prédictif des modèles linéaires généralisés en modélisant la biomasse de différentes espèces d’arbre tropicaux en fonction de certaines de leurs mesures allométriques. Plus particulièrement, nous examinons la capacité des structures d’erreur gaussienne et gamma à fournir les prédictions les plus précises. Nous montrons que pour une espèce en particulier, le pouvoir prédictif d’un modèle faisant usage de la structure d’erreur gamma est supérieur. Cette étude s’insère dans un cadre pratique et se veut un exemple pour les gestionnaires voulant estimer précisément la capture du carbone par des plantations d’arbres tropicaux. Nos conclusions pourraient faire partie intégrante d’un programme de réduction des émissions de carbone par les changements d’utilisation des terres.

L’explication de la délinquance prolifique : l’importance de l’interaction entre le risque individuel et le risque contextuel

Le but de cette thèse est d’expliquer la délinquance prolifique de certains délinquants. Nous avançons la thèse que la délinquance prolifique s’explique par la formation plus fréquente de situations criminogènes. Ces situations réfèrent au moment où un délinquant entre en interaction avec une opportunité criminelle dans un contexte favorable au crime. Plus exactement, il s’agit du moment où le délinquant fait face à cette opportunité, mais où le crime n’a pas encore été commis. La formation de situations criminogènes est facilitée par l’interaction et l’interdépendance de trois éléments : la propension à la délinquance de la personne, son entourage criminalisé et son style de vie. Ainsi, la délinquance prolifique ne pourrait être expliquée adéquatement sans tenir compte de l’interaction entre le risque individuel et le risque contextuel. L’objectif général de la présente thèse est de faire la démonstration de l’importance d’une modélisation interactionnelle entre le risque individuel et le risque contextuel afin d’expliquer la délinquance plus prolifique de certains contrevenants. Pour ce faire, 155 contrevenants placés sous la responsabilité de deux établissements des Services correctionnels du Québec et de quatre centres jeunesse du Québec ont complété un protocole d’évaluation par questionnaires auto-administrés. Dans un premier temps (chapitre trois), nous avons décrit et comparé la nature de la délinquance autorévélée des contrevenants de notre échantillon. Ce premier chapitre de résultats a permis de mettre en valeur le fait que ce bassin de contrevenants est similaire à d’autres échantillons de délinquants en ce qui a trait à la nature de leur délinquance, plus particulièrement, au volume, à la variété et à la gravité de leurs crimes. En effet, la majorité des participants rapportent un volume faible de crimes contre la personne et contre les biens alors qu’un petit groupe se démarque par un lambda très élevé (13,1 % des délinquants de l’échantillon sont responsables de 60,3% de tous les crimes rapportés). Environ quatre délinquants sur cinq rapportent avoir commis au moins un crime contre la personne et un crime contre les biens. De plus, plus de 50% de ces derniers rapportent dans au moins quatre sous-catégories. Finalement, bien que les délinquants de notre échantillon aient un IGC (indice de gravité de la criminalité) moyen relativement faible (médiane = 77), près de 40% des contrevenants rapportent avoir commis au moins un des deux crimes les plus graves recensés dans cette étude (décharger une arme et vol qualifié). Le second objectif spécifique était d’explorer, au chapitre quatre, l’interaction entre les caractéristiques personnelles, l’entourage et le style de vie des délinquants dans la formation de situations criminogènes. Les personnes ayant une propension à la délinquance plus élevée semblent avoir tendance à être davantage entourées de personnes criminalisées et à avoir un style de vie plus oisif. L’entourage criminalisé semble également influencer le style de vie de ces délinquants. Ainsi, l’interdépendance entre ces trois éléments facilite la formation plus fréquente de situations criminogènes et crée une conjoncture propice à l’émergence de la délinquance prolifique. Le dernier objectif spécifique de la thèse, qui a été couvert dans le chapitre cinq, était d’analyser l’impact de la formation de situations criminogènes sur la nature de la délinquance. Les analyses de régression linéaires multiples et les arbres de régression ont permis de souligner la contribution des caractéristiques personnelles, de l’entourage et du style de vie dans l’explication de la nature de la délinquance. D’un côté, les analyses de régression (modèles additifs) suggèrent que l’ensemble des éléments favorisant la formation de situations criminogènes apporte une contribution unique à l’explication de la délinquance. D’un autre côté, les arbres de régression nous ont permis de mieux comprendre l’interaction entre les éléments dans l’explication de la délinquance prolifique. En effet, un positionnement plus faible sur certains éléments peut être compensé par un positionnement plus élevé sur d’autres. De plus, l’accumulation d’éléments favorisant la formation de situations criminogènes ne se fait pas de façon linéaire. Ces conclusions sont appuyées sur des proportions de variance expliquée plus élevées que celles des régressions linéaires multiples. En conclusion, mettre l’accent que sur un seul élément (la personne et sa propension à la délinquance ou le contexte et ses opportunités) ou leur combinaison de façon simplement additive ne permet pas de rendre justice à la complexité de l’émergence de la délinquance prolifique. En mettant à l’épreuve empiriquement cette idée généralement admise, cette thèse permet donc de souligner l’importance de considérer l’interaction entre le risque individuel et le risque contextuel dans l’explication de la délinquance prolifique.

Models to predict the distribution and abundance of breeding ducks in Canada

Detailed knowledge of waterfowl abundance and distribution across Canada is lacking, which limits our ability to effectively conserve and manage their populations. We used 15 years of data from an aerial transect survey to model the abundance of 17 species or species groups of ducks within southern and boreal Canada. We included 78 climatic, hydrological, and landscape variables in Boosted Regression Tree models, allowing flexible response curves and multiway interactions among variables. We assessed predictive performance of the models using four metrics and calculated uncertainty as the coefficient of variation of predictions across 20 replicate models. Maps of predicted relative abundance were generated from resulting models, and they largely match spatial patterns evident in the transect data. We observed two main distribution patterns: a concentrated prairie-parkland distribution and a more dispersed pan-Canadian distribution. These patterns were congruent with the relative importance of predictor variables and model evaluation statistics among the two groups of distributions. Most species had a hydrological variable as the most important predictor, although the specific hydrological variable differed somewhat among species. In some cases, important variables had clear ecological interpretations, but in some instances, e.g., topographic roughness, they may simply reflect chance correlations between species distributions and environmental variables identified by the model-building process. Given the performance of our models, we suggest that the resulting prediction maps can be used in future research and to guide conservation activities, particularly within the bounds of the survey area.

Threshold detection: matching statistical methodology to ecological questions and conservation planning objectives

Two types of ecological thresholds are now being widely used to develop conservation targets: breakpoint-based thresholds represent tipping points where system properties change dramatically, whereas classification thresholds identify groups of data points with contrasting properties. Both breakpoint-based and classification thresholds are useful tools in evidence-based conservation. However, it is critical that the type of threshold to be estimated corresponds with the question of interest and that appropriate statistical procedures are used to determine its location. On the basis of their statistical properties, we recommend using piecewise regression methods to identify breakpoint-based thresholds and discriminant analysis or classification and regression trees to identify classification thresholds.

Tree-structured smooth transition models

The goal of this paper is to introduce a class of tree-structured models that combines aspects of regression trees and smooth transition regression models. The model is called the Smooth Transition Regression Tree (STR-Tree). The main idea relies on specifying a multiple-regime parametric model through a tree-growing procedure with smooth transitions among different regimes. Decisions about splits are entirely based on a sequence of Lagrange Multiplier (LM) tests of hypotheses.

Evidence for production of single top quarks

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Effects of basic fibroblast growth factor on density and morphology of fibroblasts grown on root surfaces with or without conditioning with tetracycline or EDTA.

A study was conducted to evaluate in vitro the effect of root surface conditioning with basic fibroblast growth factor (b-FGF) on morphology and proliferation of fibroblasts. Three experimental groups were used: non-treated, and treated with 50 microg or 125 microg b-FGF/ml. The dentin samples in each group were divided into subgroups according to the chemical treatment received before application of b-FGF: none, or conditioned with tetracycline-HCl or EDTA. After contact with b-FGF for 5 min, the samples were incubated for 24 h with 1 ml of culture medium containing 1 x 10(5) cells/ml plus 1 ml of culture medium alone. The samples were then subjected to routine preparation for SEM, and random fields were photographed. Three calibrated and blind examiners performed the assessment of morphology and density according to two index systems. Classification and regression trees indicated that the root surfaces treated with 125 microg b-FGF and previously conditioned with tetracycline-HCl or EDTA presented a morphology more suggestive of cellular adhesion and viability (P = 0.004). The density of fibroblasts on samples previously conditioned with EDTA, regardless of treatment with b-FGF, was significantly higher than in the other groups (P < 0.001). The present findings suggest that topical application of b-FGF has a positive influence on both the density and morphology of fibroblasts.

O efeito do fator de crescimento de fibroblastos básico aplicado em superfícies radiculares condicionadas com cloridrato de tetraciclina ou EDTA na morfologia e densidade de fibroblastos. Estudo in vitro

Pós-graduação em Odontologia - FOAR

Biodiversidade dos parasitas de peixes provenientes do rio Sapucaí-Mirim, Estado de São Paulo, Brasil

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)