Predicting smoking cessation and its relapse in HIV-infected patients: the Swiss HIV Cohort Study.

OBJECTIVES: The aim of the study was to assess whether prospective follow-up data within the Swiss HIV Cohort Study can be used to predict patients who stop smoking; or among smokers who stop, those who start smoking again. METHODS: We built prediction models first using clinical reasoning ('clinical models') and then by selecting from numerous candidate predictors using advanced statistical methods ('statistical models'). Our clinical models were based on literature that suggests that motivation drives smoking cessation, while dependence drives relapse in those attempting to stop. Our statistical models were based on automatic variable selection using additive logistic regression with component-wise gradient boosting. RESULTS: Of 4833 smokers, 26% stopped smoking, at least temporarily; because among those who stopped, 48% started smoking again. The predictive performance of our clinical and statistical models was modest. A basic clinical model for cessation, with patients classified into three motivational groups, was nearly as discriminatory as a constrained statistical model with just the most important predictors (the ratio of nonsmoking visits to total visits, alcohol or drug dependence, psychiatric comorbidities, recent hospitalization and age). A basic clinical model for relapse, based on the maximum number of cigarettes per day prior to stopping, was not as discriminatory as a constrained statistical model with just the ratio of nonsmoking visits to total visits. CONCLUSIONS: Predicting smoking cessation and relapse is difficult, so that simple models are nearly as discriminatory as complex ones. Patients with a history of attempting to stop and those known to have stopped recently are the best candidates for an intervention.

Extremal properties of certain risk models

Cette thèse s'intéresse à étudier les propriétés extrémales de certains modèles de risque d'intérêt dans diverses applications de l'assurance, de la finance et des statistiques. Cette thèse se développe selon deux axes principaux, à savoir: Dans la première partie, nous nous concentrons sur deux modèles de risques univariés, c'est-à- dire, un modèle de risque de déflation et un modèle de risque de réassurance. Nous étudions le développement des queues de distribution sous certaines conditions des risques commun¬s. Les principaux résultats sont ainsi illustrés par des exemples typiques et des simulations numériques. Enfin, les résultats sont appliqués aux domaines des assurances, par exemple, les approximations de Value-at-Risk, d'espérance conditionnelle unilatérale etc. La deuxième partie de cette thèse est consacrée à trois modèles à deux variables: Le premier modèle concerne la censure à deux variables des événements extrême. Pour ce modèle, nous proposons tout d'abord une classe d'estimateurs pour les coefficients de dépendance et la probabilité des queues de distributions. Ces estimateurs sont flexibles en raison d'un paramètre de réglage. Leurs distributions asymptotiques sont obtenues sous certaines condi¬tions lentes bivariées de second ordre. Ensuite, nous donnons quelques exemples et présentons une petite étude de simulations de Monte Carlo, suivie par une application sur un ensemble de données réelles d'assurance. L'objectif de notre deuxième modèle de risque à deux variables est l'étude de coefficients de dépendance des queues de distributions obliques et asymétriques à deux variables. Ces distri¬butions obliques et asymétriques sont largement utiles dans les applications statistiques. Elles sont générées principalement par le mélange moyenne-variance de lois normales et le mélange de lois normales asymétriques d'échelles, qui distinguent la structure de dépendance de queue comme indiqué par nos principaux résultats. Le troisième modèle de risque à deux variables concerne le rapprochement des maxima de séries triangulaires elliptiques obliques. Les résultats théoriques sont fondés sur certaines hypothèses concernant le périmètre aléatoire sous-jacent des queues de distributions. -- This thesis aims to investigate the extremal properties of certain risk models of interest in vari¬ous applications from insurance, finance and statistics. This thesis develops along two principal lines, namely: In the first part, we focus on two univariate risk models, i.e., deflated risk and reinsurance risk models. Therein we investigate their tail expansions under certain tail conditions of the common risks. Our main results are illustrated by some typical examples and numerical simu¬lations as well. Finally, the findings are formulated into some applications in insurance fields, for instance, the approximations of Value-at-Risk, conditional tail expectations etc. The second part of this thesis is devoted to the following three bivariate models: The first model is concerned with bivariate censoring of extreme events. For this model, we first propose a class of estimators for both tail dependence coefficient and tail probability. These estimators are flexible due to a tuning parameter and their asymptotic distributions are obtained under some second order bivariate slowly varying conditions of the model. Then, we give some examples and present a small Monte Carlo simulation study followed by an application on a real-data set from insurance. The objective of our second bivariate risk model is the investigation of tail dependence coefficient of bivariate skew slash distributions. Such skew slash distributions are extensively useful in statistical applications and they are generated mainly by normal mean-variance mixture and scaled skew-normal mixture, which distinguish the tail dependence structure as shown by our principle results. The third bivariate risk model is concerned with the approximation of the component-wise maxima of skew elliptical triangular arrays. The theoretical results are based on certain tail assumptions on the underlying random radius.

Determination of meropenem in plasma and filtrate-dialysate from patients under continuous veno-venous haemodiafiltration by SPE-LC.

Meropenem, a carbapenem antibiotic displaying a broad spectrum of antibacterial activity, is administered in Medical Intensive Care Unit to critically ill patients undergoing continuous veno-venous haemodiafiltration (CVVHDF). However, there are limited data available to substantial rational dosing decisions in this condition. In an attempt to refine our knowledge and propose a rationally designed dosage regimen, we have developed a HPLC method to determine meropenem after solid-phase extraction (SPE) of plasma and dialysate fluids obtained from patients under CVVHDF. The assay comprises the simultaneous measurement of meropenem's open-ring metabolite UK-1a, whose fate has never been studied in CVVHDF patients. The clean-up procedure involved a SPE on C18 cartridge. Matrix components were eliminated with phosphate buffer pH 7.4 followed by 15:85 MeOH-phosphate buffer pH 7.4. Meropenem and UK-1a were subsequently desorbed with MeOH. The eluates were evaporated under nitrogen at room temperature (RT) and reconstituted in phosphate buffer pH 7.4. Separation was performed at RT on a Nucleosil 100-5 microm C18 AB cartridge column (125 x 4 mm I.D.) equipped with a guard column (8 x 4 mm I.D.) with UV-DAD detection set at 208 nm. The mobile phase was 1 ml min(-1), using a step-wise gradient elution program: %MeOH/0.005 M tetrabutylammonium chloride pH 7.4; 10/90-50/50 in 27 min. Over the range of 5-100 microg ml(-1), the regression coefficient of the calibration curves (plasma and dialysate) were >0.998. The absolute extraction recoveries of meropenem and UK-1a in plasma and filtrate-dialysate were stable and ranged from 88-93 to 72-77% for meropenem, and from 95-104 to 75-82% for UK-1a. In plasma and filtrate-dialysate, respectively, the mean intra-assay precision was 4.1 and 2.6% for meropenem and 4.2 and 3.7% for UK-1a. The inter-assay variability was 2.8 and 3.6% for meropenem and 2.3 and 2.8% for UK-1a. The accuracy was satisfactory for both meropenem and UK-1a with deviation never exceeding 9.0% of the nominal concentrations. The stability of meropenem, studied in biological samples left at RT and at +4 degrees C, was satisfactory with < 5% degradation after 1.5 h in blood but reached 22% in filtrate-dialysate samples stored at RT for 8 h, precluding accurate measurements of meropenem excreted unchanged in the filtrate-dialysate left at RT during the CVVHDF procedure. The method reported here enables accurate measurements of meropenem in critically ill patients under CVVHDF, making dosage individualisation possible in such patients. The levels of the metabolite UK-1a encountered in this population of patients were higher than those observed in healthy volunteers but was similar to those observed in patients with renal impairment under hemodialysis.

Immunoglobulin classes in aquatic mammals. Characterization by serologic cross-reactivity, molecular size and binding of human free secretory component.

On the basis of serologic cross-reactivity, three immunoglobulin classes homologous to human IgG, IgM and IgA were identified in two species of acquatic mammal representing the orders Cetacea (dolphin) and Pinnipedea (sea lion). Molecular size was estimated by sucrose density gradient ultracentrifugation and Sephadex G-200 chromatography, indicating a 7S IgG, 19S IgM and heterogeneous serum IgA. Human secretory component was readily bound to the IgM of both species and to an apparently lesser extent to the larger molecular size populations of IgA. No binding was observed with IgG. Several antisera specific for human γ-chains gave a single precipitin line with the sea lion IgG but when made to react with dolphin serum produced two lines, suggesting the presence of two different subclasses of IgG in this species.