Cette etude pilote de six mois examinait si l'autogestion (AG) intensive par un agent secreteur d'insuline avait pour consequence une glycemie amelioree en comparaison avec une gestion standard (GS) chez les individus atteints de diabete de type 2. Des patients ont ete randomises soit a l'AG avec du repaglinide (n=8), ou du glyburide (n=6) ou a la GS (n=5). Des analyses biochimiques, alimentaires, comportementales, et d'activite physique ont ete effectuees. Les deux groupes de l'AG ont recu un enseignement d'autogestion en fonction du taux de glucose sanguin et une evaluation nutritionnelle qualitative. Le groupe AG (n=11) a suivi la cedule 65% du temps et a fait des ajustements 29% du temps. Une relation inverse significative a ete trouvee entre le changement de l'Alc et le pourcentage de temps d'ajustements accomplis correctement (r=0.64, p=0.035). La difference de masse corporelle entre l'AG et la GS n'etait pas significative, tout comme la masse corporelle moyenne a six mois. Une recherche plus approfondie avec un echantillon de plus grande taille serait necessaire afin d'explorer les avantages potentiels de la gestion du diabete via l'autogestion de medicaments oraux.

The objective of this study was to determine if teaching the new sugar guidelines, which now permit up to 10% of energy from added sugars, would modify dietary habits, metabolic control and perceived quality of life in free-living subjects with type 2 diabetes. In an eight month randomized controlled trial, 48 subjects with type 2 diabetes were taught, by a trained dietitian, either a conventional diabetic meal plan: conventional group (C) or one in which they could integrate the new sugar guidelines: sugar group (S). Patients were seen at the clinic every 2 months (total of 5 visits) by the dietitian and the endocrinologist. During the pre-randomization period (0 to 4 months) all subjects were taught the conventional diabetic diet and advised to avoid concentrated sugars. Randomization to the C or the S group took place at the 4 month visit (4 to 8 month period = post-randomization). The S group were taught how to use and integrate the Canadian Diabetes Association's new sugar choices (e.g. added sugar: honey, regular jam, white sugar, etc) into their daily diabetic meal plan. (Abstract shortened by UMI.)

It is estimated that approximately 80% of all persons with DM2 are obese, and the prevalence of diabetes increases With increasing age and body weight. Intensive control resulting in near-normal glycemia in obese DM2 holds great potential for reducing morbidity and mortality, but is associated with weight gain. In turn, modest weight loss improves glycemia considerably. Current weight loss programs, with decreasing frequency of interventions over time, result in a weight loss pattern of a U-shaped curve indicating an inability in maintaining the lower weight. We hypothesized that increasing vs decreasing frequency of treatment visits over time would result in better weight loss and metabolic control. Forty-eight DM2 subjects (28 F, 20 M; wt = 98 +/- 3 kg; BMI = 35 +/- 2 kg/m2; DM duration = 11 +/- 8 yrs) were stratified according to weight and level of glycated hemoglobin (HbA1c), and randomized to one of two protocols: decreasing frequency (DF) of visits, seen weekly (for 2 mos), bimonthly (for 3 mos) and monthly (for 3 mos) and increasing frequency (IF) of visits seen in the reverse order. Visits included 18 lifestyle, behaviour modification, group sessions and 5 individual evaluations at 0, 2, 4, 8 and 12 months. (Abstract shortened by UMI.)

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There are two sources of information available in empirical research in finance: one corresponding to historical data and the other to prices currently observed in the markets. When proposing a model, it is desirable to use information from both sources. However in modern finance, where stochastic differential equations have been one of the main modeling tools, the common models are typically different for historical data and for current market data. The former are usually assumed to be time homogeneous, while the latter are typically time in-homogeneous. This practice can be explained by the fact that a time-homogeneous model is stationary and easier to estimate, while time-inhomogeneous model are required in order to replicate market data sufficiently well without creating arbitrage opportunities. In this thesis, we study methods of statistical inference, both parametric and non-parametric, for stochastic differential equations with time-dependent parameters. In the first part, we propose a new class of stochastic differential equation with time-dependent drift and diffusion terms, where some of the parameters change according to a hidden Markov process. We show that under some technical conditions this innovative way of modeling switching times renders the resulting model stationary. We also explore different approaches to estimate parameters in our proposed model. Our simulation studies demonstrate that the parameters of the model can be efficiently estimated by using a version of the filtering method proposed in the literature. We illustrate our model and the proposed estimation method by applying them to interest rate data, and we detect significant time variations in early 1980s, when targets of the monetary policy in the United States were changed. One of the known drawbacks of parametric models is the risk of model misspecification. In the second part of the thesis, we allow the drift to be time-dependent and nonparametric, and our objective is to estimate it using a single trajectory of the process. The main idea underlying this method is to approximate the time-dependent function with a sequence of polynomials. Since we can estimate efficiently only a finite number of parameters for any finite length of data, in our method we propose to relate the number of parameters to the length of the observed trajectory. This idea is similar to the method of sieves proposed by Grenander (Abstract Inference, 1981). The asymptotic analysis that we present is based on the assumption that the length of available data $T$ increases to infinity. We investigate two cases, one is a Brownian motion with time-dependent drift and the other corresponds to a class of mean-reverting stochastic differential equations with time-dependent mean-reversion level. In both cases we prove asymptotic consistency and normality of a modified maximum likelihood estimator of the projected time-dependent component. The main challenge in proving our results in the second case stems from two features of the problem: one is due to the fact that coefficients of projections change with $T$ and the other is related to the confounding effect between the mean-reversion speed and the level function. By applying our method to the same interest rate data we use in the first part, we find another evidence of time-variation in the drift term.

Classifying pipe cracks by size from their pulse-echo ultrasonic signal is difficult but highly significant for the defect evaluation required in pipe testing and maintenance decision making. For this thesis, a binary Support Vector Machine (SVM) classifier, which divides pipe cracks into two categories: large and small, was developed using collected ultrasonic signals. To improve the performance of this SVM classifier in terms of reducing test errors, we first combined the Sequential Backward Selection and Sequential Forward Selection schemes for input feature reduction. Secondly, we used the data dependent kernel instead of the Gaussian kernel as the kernel function in the SVM classifier. Thirdly, as it is time-consuming to use the classic grid-search method for parameter selection of SVM, this work proposes a Kernel Fisher Discriminant Ratio (KFD Ratio) which makes it possible to more quickly select parameters for the SVM classifier.