Epidemiology of masked and white-coat hypertension : the family-based SKIPOGH study

Un enregistrement de la tension artérielle ambulatoire (couvrant 24 heures) ainsi que plusieurs mesures en cabinet ont permis de classer chaque participant dans 4 catégories : normotension (tension artérielle normale au cabinet et en ambulatoire), hypertension artérielle soutenue (tension artérielle élevée au cabinet et en ambulatoire), hypertension de la blouse blanche (tension artérielle élevée au cabinet mais normale en ambulatoire) et hypertension artérielle masquée (tension artérielle élevée en ambulatoire mais normale au cabinet). Dans la littérature, la prévalence de l'hypertension artérielle masquée varie entre 8% et 48% selon la méthodologie utilisée et la population étudiée. Les personnes présentant une hypertension artérielle masquée ou une hypertension de la blouse blanche pourraient avoir un risque cardiovasculaire plus élevé que des personnes normotendues. Il est utile de déterminer les facteurs cliniques associés à l'hypertension artérielle masquée et à l'hypertension de la blouse blanche afin d'identifier les personnes à risque de développer ces conditions. Peu d'études ont examiné la proportion et les facteurs associés à l'hypertension artérielle masquée et à l'hypertension de la blouse blanche en Suisse, et aucune étude n'a été faite au niveau populationnel. Dans cette étude, nous investiguons les facteurs associés à l'hypertension masquée et à l'hypertension de la blouse blanche dans une étude populationnelle Suisse. Le Swiss Kidney Project on Genes in Hypertension (SKIPOGH) est une étude familiale transversale. La tension artérielle au cabinet et la tension artérielle ambulatoire sont mesurées par des appareils validés. Dans cette étude, nous avons défini l'hypertension artérielle masquée comme une tension artérielle au cabinet < 140/90 mmHg et une tension ambulatoire (jour) s 135/85 mmHg ; l'hypertension de la blouse blanche comme une tension artérielle au cabinet s 140/90 mmHg et une tension ambulatoire < 135/85 mmHg ; et enfin la tension artérielle à la limite supérieure de la norme au cabinet comme une tension systolique entre 130 et 139 mmHg et/ou une tension artérielle diastolique entre 85 et 89 mmHg lors de la mesure au cabinet. Nous avons utilisé une régression logistique multiple pour examiner la relation entre l'hypertension masquée et l'hypertension de la blouse blanche, d'une part, et les facteurs associés, d'autre part, en prenant en compte les corrélations familiales. Parmi les 652 participants inclus dans cette analyse, 51% sont des femmes. L'âge moyen (± écart type) est de 48 ans (± 18 ans). Les proportions de participants avec une hypertension masquée et une hypertension de la blouse blanche sont de 15.8% et de 2.6% respectivement. L'hypertension masquée est associée à l'âge (odds ratio (OR) = 1.02, p = 0.012), à une tension artérielle au cabinet à la limite supérieure de la norme (OR = 6.68, p, 0.001) et à l'obésité (OR = 3.63, p = 0.001). L'hypertension de la blouse blanche est associée à l'âge (OR = 1.07, p, 0.001) mais pas au niveau d'éducation, à l'anamnése familiale d'hypertension ou à l'activité physique. Nos données suggèrent que les médecins doivent envisager d'effectuer un enregistrement de la tension artérielle ambulatoire chez les personnes âgées avec une tension au cabinet à la limite supérieure de la norme et/ou chez les patients obèses afin de déterminer si ces individus présentent une hypertension artérielle en ambulatoire.

Prévention et dépistage du cancer en Suisse : fréquence et déterminants

Contexte : Après les maladies cardiovasculaires, le cancer est la deuxième cause de mortalité en Suisse. Les cancers de la prostate, du sein, du côlon-rectum, du col utérin et le mélanome cutané représentent, en termes d'incidence et de mortalité, la moitié du fardeau du cancer en Suisse. Des moyens de prévention primaire et/ou secondaire contribuent à réduire la fréquence et la mortalité due à ces cinq cancers. Cependant, l'attitude face à la prévention diffère selon les individus et dépend de multiples facteurs socio-économiques, environnementaux, culturels et comportementaux. Objectif : Évaluer la fréquence et identifier les déterminants des pratiques de dépistage des cancers de la prostate, du sein, du côlon-rectum, du col utérin et du mélanome cutané en Suisse. Matériel et méthode : Les données utilisées sont issues de l'Enquête suisse sur la santé 2007. Une pondération statistique permet d'extrapoler les résultats à la population générale. Des modèles de régression logistique multivariée ont été construits afin de décrire l'association entre pratique du dépistage et facteurs sociodémographiques, style de vie, état de santé, recours aux prestations de santé et soutien social. Résultats : En 2007, selon les méthodes et fréquences recommandées en Suisse et dans les tranches d'âge concernées, 49% des hommes ont effectué un dépistage du cancer prostatique, 13% du cancer colorectal et 33,7% du mélanome cutané. Chez les femmes, 17,9% ont réalisé un dépistage du cancer du sein, 8,7% du cancer colorectal, 36,8% du mélanome cutané et 50,2% du cancer du col utérin. Globalement et pour les deux sexes, l'âge, le lieu de résidence, le niveau de formation, la classe socioprofessionnelle, le revenu d'équivalence du ménage, la pratique d'autres dépistages des cancers, le nombre de visites médicales et de jours d'hospitalisation au cours des 12 mois précédents déterminent le recours au dépistage des cancers d'intérêt. Chez les hommes, la présence d'un médecin de famille et, chez les femmes, la franchise annuelle, influencent aussi la pratique du dépistage. Conclusion : Les prévalences du dépistage varient notablement selon le type de cancer. Le recours aux dépistages des cancers dépend de facteurs sociodémographiques, de l'utilisation des services de santé et de la pratique d'autres dépistages, mais peu, voire pas, du style de vie, de l'état de santé et de la sécurité et du soutien sociaux. Les facteurs identifiés sont souvent communs aux différents types de cancer et rendent possible l'établissement d'un profil général d'utilisateurs du dépistage des cancers. Les stratégies visant à améliorer la compliance aux examens de dépistage devraient considérer les facteurs qui en déterminent le recours et mieux cibler les segments de la population qui les sous-utilisent.

Vestibular schwannoma : the evolution of hearing and tumor size in natural course and after treatment by Linac stereotactic radiosurgery

Objectif : D'analyser l'évolution naturelle de la taille de la tumeur et de l'audition chez 151 patients avec schwannome vestibulaire (VS) en suivi et d'évaluer les mêmes paramètres pour une partie du group traité par Radiochirurgie Stéréotaxique Linac (SRS). Méthodes: Etude prospective des patients bilantés par IRM et tests audio-vestibulaires à l'inclusion, pendant la période du suivi et après SRS. L'audition a été gradé selon l'échelle de Gardner-Robertson (GR) et la taille tumorale selon l'échelle de Koos. L'analyse statistique inclut l'analyse de survie de Kaplan-Meier, analyse multivariée avec régression linéaire et logistique. Les patients avec une audition utile ont étés spécifiquement analysés. Résultats: Pendant la période du suivi (moyenne 24 mois, déviation 6-96), le risqué annuel de dégradation de la classe GR était 6% pour les patients GRI et 15% pour les GRII. La perte auditive comme symptôme initial était un facteur signifïcativement prédictif pour une aggravation auditive ultérieure (p=0.003). La croissance tumorale était de 25% à la dernière observation pendant le suivi. Pour les patients traités par Linac, la préservation d'une audition utile était 51% à 1 an et 36% à 3 ans. Le contrôle tumoral était 94 % and 91% respectivement. Conclusion: Chez les patients avec VS, la perte auditive déjà présente au diagnostique est un facteur prédictif négatif pour l'évolution de l'audition. La Radiochirurgie Stéréotaxique Linac est efficace pour le contrôle tumoral. Les patients ayant préservés leur status auditif prétraitement présentent un rythme annuel de perte auditive diminué après SRS compare à celle-ci avant le traitement. Cette constatation suggère un effet protectif potentiel de la SRS, à condition que la fonction cochléaire soit préservée.

Sharing a River

A group of agents located along a river have quasi-linear preferences over water and money. We ask how the water should be allocated and what money transfers should be performed. We are interested in efficiency, stability (in the sense of the core), and fairness (in a sense to be defined). We first show that the cooperative game associated with our problem is convex : its core is therefore large and easily described. Next, we propose the following fairness requirement : no group of agents should enjoy a welfare higher than what it could achieve in the absence of the remaining agents. We prove that only one welfare vector in the core satisfies this condition : it is the marginal contribution vector corresponding to the ordering of the agents along the river. We discuss how it could be decentralized or implemented.

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.

Exact Tests for Contemporaneous Correlation of Disturbances in Seemingly Unrelated Regressions.

This paper proposes finite-sample procedures for testing the SURE specification in multi-equation regression models, i.e. whether the disturbances in different equations are contemporaneously uncorrelated or not. We apply the technique of Monte Carlo (MC) tests [Dwass (1957), Barnard (1963)] to obtain exact tests based on standard LR and LM zero correlation tests. We also suggest a MC quasi-LR (QLR) test based on feasible generalized least squares (FGLS). We show that the latter statistics are pivotal under the null, which provides the justification for applying MC tests. Furthermore, we extend the exact independence test proposed by Harvey and Phillips (1982) to the multi-equation framework. Specifically, we introduce several induced tests based on a set of simultaneous Harvey/Phillips-type tests and suggest a simulation-based solution to the associated combination problem. The properties of the proposed tests are studied in a Monte Carlo experiment which shows that standard asymptotic tests exhibit important size distortions, while MC tests achieve complete size control and display good power. Moreover, MC-QLR tests performed best in terms of power, a result of interest from the point of view of simulation-based tests. The power of the MC induced tests improves appreciably in comparison to standard Bonferroni tests and, in certain cases, outperforms the likelihood-based MC tests. The tests are applied to data used by Fischer (1993) to analyze the macroeconomic determinants of growth.

Markovian Processes, Two-Sided Autoregressions and Finite-Sample Inference for Stationary and Nonstationary Autoregressive Processes.

In this paper, we develop finite-sample inference procedures for stationary and nonstationary autoregressive (AR) models. The method is based on special properties of Markov processes and a split-sample technique. The results on Markovian processes (intercalary independence and truncation) only require the existence of conditional densities. They are proved for possibly nonstationary and/or non-Gaussian multivariate Markov processes. In the context of a linear regression model with AR(1) errors, we show how these results can be used to simplify the distributional properties of the model by conditioning a subset of the data on the remaining observations. This transformation leads to a new model which has the form of a two-sided autoregression to which standard classical linear regression inference techniques can be applied. We show how to derive tests and confidence sets for the mean and/or autoregressive parameters of the model. We also develop a test on the order of an autoregression. We show that a combination of subsample-based inferences can improve the performance of the procedure. An application to U.S. domestic investment data illustrates the method.

Simulation-Based Finite-Sample Tests for Heteroskedasticity and ARCH Effects.

A wide range of tests for heteroskedasticity have been proposed in the econometric and statistics literature. Although a few exact homoskedasticity tests are available, the commonly employed procedures are quite generally based on asymptotic approximations which may not provide good size control in finite samples. There has been a number of recent studies that seek to improve the reliability of common heteroskedasticity tests using Edgeworth, Bartlett, jackknife and bootstrap methods. Yet the latter remain approximate. In this paper, we describe a solution to the problem of controlling the size of homoskedasticity tests in linear regression contexts. We study procedures based on the standard test statistics [e.g., the Goldfeld-Quandt, Glejser, Bartlett, Cochran, Hartley, Breusch-Pagan-Godfrey, White and Szroeter criteria] as well as tests for autoregressive conditional heteroskedasticity (ARCH-type models). We also suggest several extensions of the existing procedures (sup-type of combined test statistics) to allow for unknown breakpoints in the error variance. We exploit the technique of Monte Carlo tests to obtain provably exact p-values, for both the standard and the new tests suggested. We show that the MC test procedure conveniently solves the intractable null distribution problem, in particular those raised by the sup-type and combined test statistics as well as (when relevant) unidentified nuisance parameter problems under the null hypothesis. The method proposed works in exactly the same way with both Gaussian and non-Gaussian disturbance distributions [such as heavy-tailed or stable distributions]. The performance of the procedures is examined by simulation. The Monte Carlo experiments conducted focus on : (1) ARCH, GARCH, and ARCH-in-mean alternatives; (2) the case where the variance increases monotonically with : (i) one exogenous variable, and (ii) the mean of the dependent variable; (3) grouped heteroskedasticity; (4) breaks in variance at unknown points. We find that the proposed tests achieve perfect size control and have good power.

Nonparametric Instrumental Regression

The focus of the paper is the nonparametric estimation of an instrumental regression function P defined by conditional moment restrictions stemming from a structural econometric model : E[Y-P(Z)|W]=0 and involving endogenous variables Y and Z and instruments W. The function P is the solution of an ill-posed inverse problem and we propose an estimation procedure based on Tikhonov regularization. The paper analyses identification and overidentification of this model and presents asymptotic properties of the estimated nonparametric instrumental regression function.

Computation and Analysis of Multiple Structural-Change Models

In a recent paper, Bai and Perron (1998) considered theoretical issues related to the limiting distribution of estimators and test statistics in the linear model with multiple structural changes. In this companion paper, we consider practical issues for the empirical applications of the procedures. We first address the problem of estimation of the break dates and present an efficient algorithm to obtain global minimizers of the sum of squared residuals. This algorithm is based on the principle of dynamic programming and requires at most least-squares operations of order O(T 2) for any number of breaks. Our method can be applied to both pure and partial structural-change models. Secondly, we consider the problem of forming confidence intervals for the break dates under various hypotheses about the structure of the data and the errors across segments. Third, we address the issue of testing for structural changes under very general conditions on the data and the errors. Fourth, we address the issue of estimating the number of breaks. We present simulation results pertaining to the behavior of the estimators and tests in finite samples. Finally, a few empirical applications are presented to illustrate the usefulness of the procedures. All methods discussed are implemented in a GAUSS program available upon request for non-profit academic use.

Bootstrapping Autoregressions with Conditional Heteroskedasticity of Unknown Form

Conditional heteroskedasticity is an important feature of many macroeconomic and financial time series. Standard residual-based bootstrap procedures for dynamic regression models treat the regression error as i.i.d. These procedures are invalid in the presence of conditional heteroskedasticity. We establish the asymptotic validity of three easy-to-implement alternative bootstrap proposals for stationary autoregressive processes with m.d.s. errors subject to possible conditional heteroskedasticity of unknown form. These proposals are the fixed-design wild bootstrap, the recursive-design wild bootstrap and the pairwise bootstrap. In a simulation study all three procedures tend to be more accurate in small samples than the conventional large-sample approximation based on robust standard errors. In contrast, standard residual-based bootstrap methods for models with i.i.d. errors may be very inaccurate if the i.i.d. assumption is violated. We conclude that in many empirical applications the proposed robust bootstrap procedures should routinely replace conventional bootstrap procedures for autoregressions based on the i.i.d. error assumption.

Testing Mean-Variance Efficiency in CAPM with Possibly Non-Gaussian Errors : An Exact Simulation-Based Approach

In this paper we propose exact likelihood-based mean-variance efficiency tests of the market portfolio in the context of Capital Asset Pricing Model (CAPM), allowing for a wide class of error distributions which include normality as a special case. These tests are developed in the frame-work of multivariate linear regressions (MLR). It is well known however that despite their simple statistical structure, standard asymptotically justified MLR-based tests are unreliable. In financial econometrics, exact tests have been proposed for a few specific hypotheses [Jobson and Korkie (Journal of Financial Economics, 1982), MacKinlay (Journal of Financial Economics, 1987), Gib-bons, Ross and Shanken (Econometrica, 1989), Zhou (Journal of Finance 1993)], most of which depend on normality. For the gaussian model, our tests correspond to Gibbons, Ross and Shanken’s mean-variance efficiency tests. In non-gaussian contexts, we reconsider mean-variance efficiency tests allowing for multivariate Student-t and gaussian mixture errors. Our framework allows to cast more evidence on whether the normality assumption is too restrictive when testing the CAPM. We also propose exact multivariate diagnostic checks (including tests for multivariate GARCH and mul-tivariate generalization of the well known variance ratio tests) and goodness of fit tests as well as a set estimate for the intervening nuisance parameters. Our results [over five-year subperiods] show the following: (i) multivariate normality is rejected in most subperiods, (ii) residual checks reveal no significant departures from the multivariate i.i.d. assumption, and (iii) mean-variance efficiency tests of the market portfolio is not rejected as frequently once it is allowed for the possibility of non-normal errors.

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.

Short run and long run causality in time series: Inference

We propose methods for testing hypotheses of non-causality at various horizons, as defined in Dufour and Renault (1998, Econometrica). We study in detail the case of VAR models and we propose linear methods based on running vector autoregressions at different horizons. While the hypotheses considered are nonlinear, the proposed methods only require linear regression techniques as well as standard Gaussian asymptotic distributional theory. Bootstrap procedures are also considered. For the case of integrated processes, we propose extended regression methods that avoid nonstandard asymptotics. The methods are applied to a VAR model of the U.S. economy.