IL28B polymorphisms leading to poor response to treatment are associated with low necroinflammatory activity and slow fibrosis progression in HCV genotype non-1-infected patients

Background and Aims: Genetic polymorphisms near IL28Bhave been associated with spontaneous and treatment-inducedclearance of hepatitis C virus (HCV). This is believed to proceed viathe appropriate activation of innate and adaptive immune responsestargeting infected hepatocytes. Intrahepatic inflammation is thereflection of the host cell immune response, but its relationshipwith IL28B polymorphisms has yet to be fully appreciated.Methods: We analyzed the association of IL28B polymorphismswith Metavir activity (≥1) and fibrosis scores (≥2) in 1114 HCVinfectedCaucasian patients enrolled in the Swiss Hepatitis C CohortStudy (629, 127, 268 and 110 infected with HCV genotype 1, 2, 3and 4, respectively). In a subgroup of 915 patients with an estimateddate of infection, the association between IL28B polymorphismsand fibrosis progression rate (FPR > median) was assessed. Singlenucleotide polymorphisms (SNPs) of interest were extracted froma dataset generated in a genome-wide association study and/orgenotyped by TaqMan assay. Associations of alleles with differentdegrees of activity and fibrosis were evaluated using an additivemodel of inheritance by multivariate logistic regression, accountingfor all relevant covariates.Results: The rare G allele at marker rs8099917 was associated withlower activity (P = 0.008) and fibrosis (P = 0.01), as well as slower FPR(P = 0.02). Most striking associations were observed among patientsinfected with non-1 genotypes (P = 0.002 for activity, P = 0.002 forfibrosis and P = 0.005 for FPR). In genotype 1-infected patients, theassociation with activity was observed only in the recessive model(P = 0.04), whereas other associations were not significant (P = 0.7for fibrosis and P = 0.4 for FPR).Conclusions: In chronic hepatitis C, IL28B polymorphisms linkedwith a poor virological response to therapy are also associated withreduced intrahepatic necroinflammation and slower liver diseaseprogression. These observations underscore the role played by thehost immune response in clearing HCV, especially in patients withHCV genotypes non-1.

The relationship between body mass index and breast cancer recurrence/progression and breast cancer-specific death

The aim of this study was to describe the nonlinear association between body mass index (BMI) and breast cancer outcomes and to determine whether BMI improves prediction of outcomes. A cohort of906 breast cancer patients diagnosed at Henry Ford Health System, Detroit (1985-1990) were studied. The median follow-up was 10 years. Multivariate logistic regression was used to model breast cancer recurrence/progression and breast cancer-specific death. Restricted cubic splines were used to model nonlinear effects. Receiver operator characteristic areas under the curves (ROC AUC) were used to evaluate prediction. BMI was nonlinearly associated with recurrence/progression and death (p= 0.0230 and 0.0101). Probability of outcomes increased with increase or decrease ofBMI away from 25. BMI splines were suggestive of improved prediction of death. The ROC AUCs for nested models with and without BMI were 0.8424 and 0.8331 (p= 0.08). I f causally associated, modifying patients BMI towards 25 may improve outcomes.

Simulation-Based Finite and Large Sample Tests in Multivariate Regressions.

In the context of multivariate linear regression (MLR) models, it is well known that commonly employed asymptotic test criteria are seriously biased towards overrejection. In this paper, we propose a general method for constructing exact tests of possibly nonlinear hypotheses on the coefficients of MLR systems. For the case of uniform linear hypotheses, we present exact distributional invariance results concerning several standard test criteria. These include Wilks' likelihood ratio (LR) criterion as well as trace and maximum root criteria. The normality assumption is not necessary for most of the results to hold. Implications for inference are two-fold. First, invariance to nuisance parameters entails that the technique of Monte Carlo tests can be applied on all these statistics to obtain exact tests of uniform linear hypotheses. Second, the invariance property of the latter statistic is exploited to derive general nuisance-parameter-free bounds on the distribution of the LR statistic for arbitrary hypotheses. Even though it may be difficult to compute these bounds analytically, they can easily be simulated, hence yielding exact bounds Monte Carlo tests. Illustrative simulation experiments show that the bounds are sufficiently tight to provide conclusive results with a high probability. Our findings illustrate the value of the bounds as a tool to be used in conjunction with more traditional simulation-based test methods (e.g., the parametric bootstrap) which may be applied when the bounds are not conclusive.

Finite-Sample Diagnostics for Multivariate Regressions with Applications to Linear Asset Pricing Models

In this paper, we propose several finite-sample specification tests for multivariate linear regressions (MLR) with applications to asset pricing models. We focus on departures from the assumption of i.i.d. errors assumption, at univariate and multivariate levels, with Gaussian and non-Gaussian (including Student t) errors. The univariate tests studied extend existing exact procedures by allowing for unspecified parameters in the error distributions (e.g., the degrees of freedom in the case of the Student t distribution). The multivariate tests are based on properly standardized multivariate residuals to ensure invariance to MLR coefficients and error covariances. We consider tests for serial correlation, tests for multivariate GARCH and sign-type tests against general dependencies and asymmetries. The procedures proposed provide exact versions of those applied in Shanken (1990) which consist in combining univariate specification tests. Specifically, we combine tests across equations using the MC test procedure to avoid Bonferroni-type bounds. Since non-Gaussian based tests are not pivotal, we apply the “maximized MC” (MMC) test method [Dufour (2002)], where the MC p-value for the tested hypothesis (which depends on nuisance parameters) is maximized (with respect to these nuisance parameters) to control the test’s significance level. The tests proposed are applied to an asset pricing model with observable risk-free rates, using monthly returns on New York Stock Exchange (NYSE) portfolios over five-year subperiods from 1926-1995. Our empirical results reveal the following. Whereas univariate exact tests indicate significant serial correlation, asymmetries and GARCH in some equations, such effects are much less prevalent once error cross-equation covariances are accounted for. In addition, significant departures from the i.i.d. hypothesis are less evident once we allow for non-Gaussian errors.

Exact Skewness-Kurtosis Tests for Multivariate Normality and Goodness-of-fit in Multivariate Regressions with Application to Asset Pricing Models

We study the problem of testing the error distribution in a multivariate linear regression (MLR) model. The tests are functions of appropriately standardized multivariate least squares residuals whose distribution is invariant to the unknown cross-equation error covariance matrix. Empirical multivariate skewness and kurtosis criteria are then compared to simulation-based estimate of their expected value under the hypothesized distribution. Special cases considered include testing multivariate normal, Student t; normal mixtures and stable error models. In the Gaussian case, finite-sample versions of the standard multivariate skewness and kurtosis tests are derived. To do this, we exploit simple, double and multi-stage Monte Carlo test methods. For non-Gaussian distribution families involving nuisance parameters, confidence sets are derived for the the nuisance parameters and the error distribution. The procedures considered are evaluated in a small simulation experi-ment. Finally, the tests are applied to an asset pricing model with observable risk-free rates, using monthly returns on New York Stock Exchange (NYSE) portfolios over five-year subperiods from 1926-1995.

Exact Multivariate Tests of Asset Pricing Models with Stable Asymmetric Distributions

In this paper, we propose exact inference procedures for asset pricing models that can be formulated in the framework of a multivariate linear regression (CAPM), allowing for stable error distributions. The normality assumption on the distribution of stock returns is usually rejected in empirical studies, due to excess kurtosis and asymmetry. To model such data, we propose a comprehensive statistical approach which allows for alternative - possibly asymmetric - heavy tailed distributions without the use of large-sample approximations. The methods suggested are based on Monte Carlo test techniques. Goodness-of-fit tests are formally incorporated to ensure that the error distributions considered are empirically sustainable, from which exact confidence sets for the unknown tail area and asymmetry parameters of the stable error distribution are derived. Tests for the efficiency of the market portfolio (zero intercepts) which explicitly allow for the presence of (unknown) nuisance parameter in the stable error distribution are derived. The methods proposed are applied to monthly returns on 12 portfolios of the New York Stock Exchange over the period 1926-1995 (5 year subperiods). We find that stable possibly skewed distributions provide statistically significant improvement in goodness-of-fit and lead to fewer rejections of the efficiency hypothesis.

Modélisation bayésienne avec des splines du comportement moyen d'un échantillon de courbes

Cette thèse porte sur l'analyse bayésienne de données fonctionnelles dans un contexte hydrologique. L'objectif principal est de modéliser des données d'écoulements d'eau d'une manière parcimonieuse tout en reproduisant adéquatement les caractéristiques statistiques de celles-ci. L'analyse de données fonctionnelles nous amène à considérer les séries chronologiques d'écoulements d'eau comme des fonctions à modéliser avec une méthode non paramétrique. Dans un premier temps, les fonctions sont rendues plus homogènes en les synchronisant. Ensuite, disposant d'un échantillon de courbes homogènes, nous procédons à la modélisation de leurs caractéristiques statistiques en faisant appel aux splines de régression bayésiennes dans un cadre probabiliste assez général. Plus spécifiquement, nous étudions une famille de distributions continues, qui inclut celles de la famille exponentielle, de laquelle les observations peuvent provenir. De plus, afin d'avoir un outil de modélisation non paramétrique flexible, nous traitons les noeuds intérieurs, qui définissent les éléments de la base des splines de régression, comme des quantités aléatoires. Nous utilisons alors le MCMC avec sauts réversibles afin d'explorer la distribution a posteriori des noeuds intérieurs. Afin de simplifier cette procédure dans notre contexte général de modélisation, nous considérons des approximations de la distribution marginale des observations, nommément une approximation basée sur le critère d'information de Schwarz et une autre qui fait appel à l'approximation de Laplace. En plus de modéliser la tendance centrale d'un échantillon de courbes, nous proposons aussi une méthodologie pour modéliser simultanément la tendance centrale et la dispersion de ces courbes, et ce dans notre cadre probabiliste général. Finalement, puisque nous étudions une diversité de distributions statistiques au niveau des observations, nous mettons de l'avant une approche afin de déterminer les distributions les plus adéquates pour un échantillon de courbes donné.

Identification de facteurs de risque d'insuffisance rénale en trauma

Contexte: la survenue d’IRA chez les patients ayant subi un traumatisme est une problématique qui a été peu étudiée jusqu’à ce jour. La présence de cette atteinte rénale a été démontrée comme étant associée à un risque accru de morbidités et de mortalité chez les sujets atteints. Objectifs: identifier les facteurs prédictifs d’insuffisance rénale ou plus récemment appelée atteinte rénale dans cette population particulière et tenter de trouver des facteurs qui peuvent être mesurés dans les premières heures de la prise en charge du patient. Aussi, nous avons cherché à savoir si l’injection de produit de contraste est associée à un risque accru d’insuffisance rénale aiguë dans cette population. Méthodes et résultats: la recherche a eu lieu à l’Hôpital du Sacré-Coeur de Montréal, un centre de traumatologie tertiaire en milieu urbain. Nous avons utilisé le registre des patients hospitalisés en traumatologie dans notre centre hospitalier entre 2002 et mars 2007 de même que les banques de données de laboratoire et de radiologie pour obtenir les données sur la créatinine et les examens avec produits de contraste. Finalement, une revue de dossiers structurée fut conduite pour recueillir le reste de l’information requise. L’incidence d’IRA dans la population étudiée est estimée à environ 5 %. Une analyse cas témoins fut conduite pour identifier les facteurs prédictifs d’IRA. Quarante-neuf cas d’IRA diagnostiqués par le médecin traitant et 101 témoins sélectionnés au hasard ont été analysés. Les facteurs prédictifs suivants ont été identifiés à l’analyse univariée : la première valeur de créatinine obtenue (p<0,001), l’instabilité hémodynamique (p<0,001), les antécédents d’insuffisance rénale chronique tels que notés dans le dossier par le médecin traitant (p=0,009), une maladie cardiaque (p=0,007), une chirurgie dans les 48 premières heures suivant le traumatisme (p=0,053), le niveau de gravité du traumatisme (Injury Severity Score) (p=0,046) et l’injection de produit de contraste au cours des 48 heures suivant le trauma (p=0,077). Parmi ces facteurs, deux ont été identifiés comme prédicteurs indépendants d’IRA à l’analyse multivariée. Une des valeurs était la première valeur de créatinine obtenue RC = 6,17 (p<0,001, IC95 % 2,81 – 13,53) pour chaque augmentation de 0.5mg/dL de créatinine. L’autre facteur était la présence d’instabilité hémodynamique RC 11,61 (p<0,001, IC95 % 3,71 – 36,29). Conclusion: des informations obtenues tôt dans la prise en charge du patient permettent de prédire le risque d’IRA chez ces patients. L’administration de contraste (intraveineuse ou intra-artérielle) ne s’est pas avérée un facteur indépendant de prédiction d’insuffisance rénale aiguë dans cette population dans le modèle multivarié.

Utilisation de splines monotones afin de condenser des tables de mortalité dans un contexte bayésien

Dans ce mémoire, nous cherchons à modéliser des tables à deux entrées monotones en lignes et/ou en colonnes, pour une éventuelle application sur les tables de mortalité. Nous adoptons une approche bayésienne non paramétrique et représentons la forme fonctionnelle des données par splines bidimensionnelles. L’objectif consiste à condenser une table de mortalité, c’est-à-dire de réduire l’espace d’entreposage de la table en minimisant la perte d’information. De même, nous désirons étudier le temps nécessaire pour reconstituer la table. L’approximation doit conserver les mêmes propriétés que la table de référence, en particulier la monotonie des données. Nous travaillons avec une base de fonctions splines monotones afin d’imposer plus facilement la monotonie au modèle. En effet, la structure flexible des splines et leurs dérivées faciles à manipuler favorisent l’imposition de contraintes sur le modèle désiré. Après un rappel sur la modélisation unidimensionnelle de fonctions monotones, nous généralisons l’approche au cas bidimensionnel. Nous décrivons l’intégration des contraintes de monotonie dans le modèle a priori sous l’approche hiérarchique bayésienne. Ensuite, nous indiquons comment obtenir un estimateur a posteriori à l’aide des méthodes de Monte Carlo par chaînes de Markov. Finalement, nous étudions le comportement de notre estimateur en modélisant une table de la loi normale ainsi qu’une table t de distribution de Student. L’estimation de nos données d’intérêt, soit la table de mortalité, s’ensuit afin d’évaluer l’amélioration de leur accessibilité.

L’association entre les divers types de services de santé et l’initiation du traitement de l’hépatite C chez les utilisateurs de drogues par injection.

Introduction: Malgré des taux d’efficacité comparable du traitement antiviral de l’hépatite C (VHC) entre utilisateurs de drogues par injection (UDIs) et non-UDIs, il y a encore d’importantes barrières à l’accessibilité au traitement pour cette population vulnérable. La méfiance des UDIs à l’égard des autorités médicales, ainsi que leur mode de vie souvent désorganisé ont un impact sur l’initiation du traitement. L’objectif de cette étude est d’examiner les liens entre l’initiation du traitement du VHC et l’utilisation des services de santé chez les UDIs actifs. Methode: 758 UDIs actifs et séropositifs aux anticorps anti-VHC ont été interrogés durant la période de novembre 2004 à mars 2011, dans la région de Montréal. Des questionnaires administrés par des intervieweurs ont fourni des informations sur les caractéristiques socio-économiques, ainsi que sur les variables relatives à l’usage de drogues et à l’utilisation des services de santé. Des échantillons sanguins ont été prélevés et testés pour les anticorps anti-VHC. Une régression logistique multivariée a permis de générer des associations entre les facteurs relatifs aux services de santé et l’initiation du traitement contre le VHC. Resultats: Parmi les 758 sujets, 55 (7,3%) avaient initié un traitement du VHC avant leur inclusion dans l’étude. Selon les analyses multivariées, les variables significativement associées à l’initiation du traitement sont les suivantes: avoir vu un médecin de famille dans les derniers 6 mois (Ratio de Cote ajusté (RCa): 1,96; Intervalle de Confiance à 95% (IC): 1,04-3,69); plus de 2 ans sous traitement de la dépendance à vie, sans usage actuel de méthadone (RCa: 2,25; IC: 1,12-4,51); plus de 2 ans sous traitement de la dépendance à vie, avec usage actuel de méthadone (RCa: 3,78; IC: 1,85-7,71); et avoir déjà séjourné en prison (RCa: 0,44; IC: 0,22-0,87). Conclusion: L’exposition à des services d’aide à la dépendance et aux services médicaux est associée à l’initiation du traitement du VHC. Ces résultats suggèrent que ces services jouent leur rôle de point d’entrée au traitement. Alternativement, les UDIs ayant initié un traitement du VHC, auraient possiblement adopté une attitude proactive quant à l'amélioration de leur santé globale. D’autre part, l’incarcération ressort comme un obstacle à la gestion de l’infection au VHC.

Improved maximum-likelihood estimation in a regression model with general parametrization

We analyse the finite-sample behaviour of two second-order bias-corrected alternatives to the maximum-likelihood estimator of the parameters in a multivariate normal regression model with general parametrization proposed by Patriota and Lemonte [A. G. Patriota and A. J. Lemonte, Bias correction in a multivariate regression model with genereal parameterization, Stat. Prob. Lett. 79 (2009), pp. 1655-1662]. The two finite-sample corrections we consider are the conventional second-order bias-corrected estimator and the bootstrap bias correction. We present the numerical results comparing the performance of these estimators. Our results reveal that analytical bias correction outperforms numerical bias corrections obtained from bootstrapping schemes.

Blood lead level association with lower body weight in NHANES 1999-2006

BACKGROUND Lead exposure is associated with low birth-weight. The objective of this study is to determine whether lead exposure is associated with lower body weight in children, adolescents and adults. METHODS We analyzed data from NHANES 1999-2006 for participants aged ≥3 using multiple logistic and multivariate linear regression. Using age- and sex-standardized BMI Z-scores, overweight and obese children (ages 3-19) were classified by BMI ≥85 th and ≥95 th percentiles, respectively. The adult population (age ≥20) was classified as overweight and obese with BMI measures of 25-29.9 and ≥30, respectively. Blood lead level (BLL) was categorized by weighted quartiles. RESULTS Multivariate linear regressions revealed a lower BMI Z-score in children and adolescents when the highest lead quartile was compared to the lowest lead quartile (β (SE)=-0.33 (0.07), p<0.001), and a decreased BMI in adults (β (SE)=-2.58 (0.25), p<0.001). Multiple logistic analyses in children and adolescents found a negative association between BLL and the percentage of obese and overweight with BLL in the highest quartile compared to the lowest quartile (OR=0.42, 95% CI: 0.30-0.59; and OR=0.67, 95% CI: 0.52-0.88, respectively). Adults in the highest lead quartile were less likely to be obese (OR=0.42, 95% CI: 0.35-0.50) compared to those in the lowest lead quartile. Further analyses with blood lead as restricted cubic splines, confirmed the dose-relationship between blood lead and body weight outcomes. CONCLUSIONS BLLs are associated with lower body mass index and obesity in children, adolescents and adults.

Influence of prosthesis type and retention mechanism on complications with fixed implant-supported prostheses: a systematic review applying multivariate analyses

PURPOSE To identify the influence of fixed prosthesis type on biologic and technical complication rates in the context of screw versus cement retention. Furthermore, a multivariate analysis was conducted to determine which factors, when considered together, influence the complication and failure rates of fixed implant-supported prostheses. MATERIALS AND METHODS Electronic searches of MEDLINE (PubMed), EMBASE, and the Cochrane Library were conducted. Selected inclusion and exclusion criteria were used to limit the search. Data were analyzed statistically with simple and multivariate random-effects Poisson regressions. RESULTS Seventy-three articles qualified for inclusion in the study. Screw-retained prostheses showed a tendency toward and significantly more technical complications than cemented prostheses with single crowns and fixed partial prostheses, respectively. Resin chipping and ceramic veneer chipping had high mean event rates, at 10.04 and 8.95 per 100 years, respectively, for full-arch screwed prostheses. For "all fixed prostheses" (prosthesis type not reported or not known), significantly fewer biologic and technical complications were seen with screw retention. Multivariate analysis revealed a significantly greater incidence of technical complications with cemented prostheses. Full-arch prostheses, cantilevered prostheses, and "all fixed prostheses" had significantly higher complication rates than single crowns. A significantly greater incidence of technical and biologic complications was seen with cemented prostheses. CONCLUSION Screw-retained fixed partial prostheses demonstrated a significantly higher rate of technical complications and screw-retained full-arch prostheses demonstrated a notably high rate of veneer chipping. When "all fixed prostheses" were considered, significantly higher rates of technical and biologic complications were seen for cement-retained prostheses. Multivariate Poisson regression analysis failed to show a significant difference between screw- and cement-retained prostheses with respect to the incidence of failure but demonstrated a higher rate of technical and biologic complications for cement-retained prostheses. The incidence of technical complications was more dependent upon prosthesis and retention type than prosthesis or abutment material.

Coreferentiality: A New Method for the Hypothesis-Based Analysis of Phenotypes Characterized by Multivariate Data

Many multifactorial biologic effects, particularly in the context of complex human diseases, are still poorly understood. At the same time, the systematic acquisition of multivariate data has become increasingly easy. The use of such data to analyze and model complex phenotypes, however, remains a challenge. Here, a new analytic approach is described, termed coreferentiality, together with an appropriate statistical test. Coreferentiality is the indirect relation of two variables of functional interest in respect to whether they parallel each other in their respective relatedness to multivariate reference data, which can be informative for a complex effect or phenotype. It is shown that the power of coreferentiality testing is comparable to multiple regression analysis, sufficient even when reference data are informative only to a relatively small extent of 2.5%, and clearly exceeding the power of simple bivariate correlation testing. Thus, coreferentiality testing uses the increased power of multivariate analysis, however, in order to address a more straightforward interpretable bivariate relatedness. Systematic application of this approach could substantially improve the analysis and modeling of complex phenotypes, particularly in the context of human study where addressing functional hypotheses by direct experimentation is often difficult.

Seemingly unrelated linear regression for contaminated data based on Gaussian mixtures

In this thesis, new classes of models for multivariate linear regression defined by finite mixtures of seemingly unrelated contaminated normal regression models and seemingly unrelated contaminated normal cluster-weighted models are illustrated. The main difference between such families is that the covariates are treated as fixed in the former class of models and as random in the latter. Thus, in cluster-weighted models the assignment of the data points to the unknown groups of observations depends also by the covariates. These classes provide an extension to mixture-based regression analysis for modelling multivariate and correlated responses in the presence of mild outliers that allows to specify a different vector of regressors for the prediction of each response. Expectation-conditional maximisation algorithms for the calculation of the maximum likelihood estimate of the model parameters have been derived. As the number of free parameters incresases quadratically with the number of responses and the covariates, analyses based on the proposed models can become unfeasible in practical applications. These problems have been overcome by introducing constraints on the elements of the covariance matrices according to an approach based on the eigen-decomposition of the covariance matrices. The performances of the new models have been studied by simulations and using real datasets in comparison with other models. In order to gain additional flexibility, mixtures of seemingly unrelated contaminated normal regressions models have also been specified so as to allow mixing proportions to be expressed as functions of concomitant covariates. An illustration of the new models with concomitant variables and a study on housing tension in the municipalities of the Emilia-Romagna region based on different types of multivariate linear regression models have been performed.