Magnetization reversal in amorphous Fe/Dy multilayers: a Monte Carlo study

The Monte Carlo method in the canonical ensemble is used to investigate magnetization reversal in amorphous transition metal - rare earth multilayers. Our study is based on a model containing diluted clusters which exhibit an effective uniaxial anisotropy in competition with random magnetic anisotropy in the matrix. We simulate hysteresis loops for an abrupt profile and a diffuse one obtained from atom probe tomography analyses. Our results evidence that the atom probe tomography profile favors perpendicular magnetic anisotropy in agreement with magnetic measurements. Moreover, the hysteresis loops calculated at several temperatures qualitatively agree with the experimental ones

A descriptive review of variable selection methods in four epidemiologic journals : there is still room for improvement

Background : A review of epidemiological papers conducted in 2009 concluded that several studies employed variable selection methods susceptible to introduce bias and yield inadequate inferences. Many new confounder selection methods have been developed since then. Methods: The goal of the study was to provide an updated descriptive portrait of which variable selection methods are used by epidemiologists for analyzing observational data. Studies published in four major epidemiological journals in 2015 were reviewed. Only articles concerned with a predictive or explicative objective and reporting on the analysis of individual data were included. Method(s) employed for selecting variables were extracted from retained articles. Results : A total of 975 articles were retrieved and 299 met eligibility criteria, 292 of which pursued an explicative objective. Among those, 146 studies (50%) reported using prior knowledge or causal graphs for selecting variables, 34 (12%) used change in effect estimate methods, 26 (9%) used stepwise approaches, 16 (5%) employed univariate analyses, 5 (2%) used various other methods and 107 (37%) did not provide sufficient details to allow classification (more than one method could be employed in a single article). Conclusions : Despite being less frequent than in the previous review, stepwise and univariable analyses, which are susceptible to introduce bias and produce inadequate inferences, were still prevalent. Moreover, 37% studies did not provide sufficient details to assess how variables were selected. We thus believe there is still room for improvement in variable selection methods used by epidemiologists and in their reporting

Estimation de la variance et construction d'intervalles de confiance pour le ratio standardisé de mortalité avec application à l'évaluation d'un programme de dépistage du cancer

Tableau d’honneur de la Faculté des études supérieures et postdoctorales, 2010-2011L'effet d'un programme de dépistage de cancer peut être évalué par le biais d'un ratio standardisé de mortalité (SMR). Dans ce mémoire, nous proposons un estimateur de la variance du dénominateur du SMR, c'est-à-dire du nombre de décès attendus, dans le cas où ce dernier est calculé selon la méthode de Sasieni. Nous donnons d'abord une expression générale pour la variance, puis développons des cas particuliers où des estimateurs spécifiques de l'incidence de la maladie et de son temps de survie sont utilisés. Nous montrons comment ce nouvel estimateur de la variance peut être utilisé dans la construction d'intervalles de confiance pour le SMR. Nous étudions la couverture de différents types d'intervalles de confiance par le biais de simulations et montrons que les intervalles utilisant l'estimateur de variance proposé disposent des meilleures propriétés. Nous appliquons la méthode suggérée sur les données du Programme québécois de dépistage du cancer du sein

A test for the correct specification of marginal structural models

Marginal structural models (MSMs) allow estimating the causal effect of a time‐varying exposure on an outcome in the presence of time‐dependent confounding. The parameters of MSMs can be estimated utilizing an inverse probability of treatment weight estimator under certain assumptions. One of these assumptions is that the proposed causal model relating the outcome to exposure history is correctly specified. However, in practice, the true model is unknown. We propose a test that employs the observed data to attempt validating the assumption that the model is correctly specified. The performance of the proposed test is investigated with a simulation study. We illustrate our approach by estimating the effect of repeated exposure to psychosocial stressors at work on ambulatory blood pressure in a large cohort of white‐collar workers in Québec City, Canada. Code examples in SAS and R are provided to facilitate the implementation of the test

Silicon on Nothing Mems Electromechanical Resonator

The very significant growth of the wireless communication industry has spawned tremendous interest in the development of high performances radio frequencies (RF) components. Micro Electro Mechanical Systems (MEMS) are good candidates to allow reconfigurable RF functions such as filters, oscillators or antennas. This paper will focus on the MEMS electromechanical resonators which show interesting performances to replace SAW filters or quartz reference oscillators, allowing smaller integrated functions with lower power consumption. The resonant frequency depends on the material properties, such as Young's modulus and density, and on the movable mechanical structure dimensions (beam length defined by photolithography). Thus, it is possible to obtain multi frequencies resonators on a wafer. The resonator performance (frequency, quality factor) strongly depends on the environment, like moisture or pressure, which imply the need for a vacuum package. This paper will present first resonator mechanisms and mechanical behaviors followed by state of the art descriptions with applications and specifications overview. Then MEMS resonator developments at STMicroelectronics including FEM analysis, technological developments and characterization are detailed.Comment: Submitted on behalf of EDA Publishing Association (http://irevues.inist.fr/EDA-Publishing

A graphical perspective of marginal structural models : an application for the estimation of the effect of physical activity on blood pressure

Estimating causal effects requires important prior subject-matter knowledge and, sometimes, sophisticated statistical tools. The latter is especially true when targeting the causal effect of a time-varying exposure in a longitudinal study. Marginal structural models (MSMs) are a relatively new class of causal models which effectively deal with the estimation of the effects of time-varying exposures. MSMs have traditionally been embedded in the counterfactual framework to causal inference. In this paper, we use the causal graph framework to enhance the implementation of MSMs. We illustrate our approach using data from a prospective cohort study, the Honolulu Heart Program. These data consist of 8006 men at baseline. To illustrate our approach, we focused on the estimation of the causal effect of physical activity on blood pressure, which were measured at three time-points. First, a causal graph is built to encompass prior knowledge. This graph is then validated and improved utilizing structural equation models. We estimated the aforementioned causal effect using MSMs for repeated measures and guided the implementation of the models with the causal graph. Employing the causal graph framework, we also show the validity of fitting conditional MSMs for repeated measures in the context implied by our data

A cautionary note concerning the use of stabilized weights in marginal structural models

Marginal structural models (MSMs) are commonly used to estimate the causal effect of a time-varying treatment in presence of time-dependent confounding. When fitting a MSM to data, the analyst must specify both the structural model for the outcome and the treatment models for the inverse-probability-of-treatment weights. The use of stabilized weights is recommended since they are generally less variable than the standard weights. In this paper, we are concerned with the use of the common stabilized weights when the structural model is specified to only consider partial treatment history, such as the current or most recent treatments. We present various examples of settings where these stabilized weights yield biased inferences while the standard weights do not. These issues are first investigated on the basis of simulated data and subsequently exemplified using data from the Honolulu Heart Program. Unlike common stabilized weights, we find that basic stabilized weights offer some protection against bias in structural models designed to estimate current or most recent treatment effects. Copyright © 2010 John Wiley & Sons, Ltd

Importance of the lipid-related pathways in the association between statins, mortality and cardiovascular disease risk : the multi-ethnic study of atherosclerosis

PURPOSE: Estimating how much of the impact of statins on coronary heart diseases (CHD), cardiovascular disease (CVD), and mortality risk is attributable to their effect on low-density lipoprotein cholesterol (LDL), high-density lipoprotein cholesterol (HDL), and triglycerides. METHODS: A semi-parametric g-formula estimator together with data from the Multi-Ethnic Study of Atherosclerosis (a prospective multi-center cohort study) was utilized to perform a mediation analysis. A total of 5280 participants, men and women of various race/ethnicities from multiple sites across the United States, were considered in the current study. RESULTS: The adherence adjusted total relative risk reduction (RRR) estimate (95% confidence interval) of statins on CHD was 14% (-16%, 37%), and the indirect component through LDL was 23% (-4%, 58%). For CVD, the total RRR was 23% (2%, 40%), and the indirect component through LDL was 5% (-13%, 25%). The total RRR of mortality was 18% (-1%, 35%), and the indirect component through LDL was -4% (-17%, 12%). The estimated indirect components through HDL and triglycerides were close to zero with narrow confidence intervals for all 3 outcomes. CONCLUSIONS: The estimated effect of statins on mortality, CVD, and CHD appeared to be independent of their estimated effect on HDL and triglycerides. Our study provides evidence that the preventive effect of statins on CHD could be attributed in large part to their effect on LDL. Our g-formula estimator is a promising approach to elucidate pathways, even if it is hard to make firm conclusions for the LDL pathway on mortality and CV