Il est déjà temps de s’inscrire afin de ne pas manquer l’activité par excellence de votre association: l’Assemblée générale annuelle qui aura lieu le 12 juin 2009 au Manoir du Mont Saint-Castin. Cette année, nous retrouvons la formule habituelle qui inclut trois conférences, un coquetel et un souper gastronomique. La fiche d’inscription se trouve ici.

CONFERENCIER : Constantine Frangakis (Johns Hopkins University)

TITRE : The role of principal stratification in instrumental variables in case-control designs – an application to Mendelian randomization

DATE : 1 mai 2009

HEURE : 15:30

SALLE : Salle 1B36, McGill University, Burnside Hall, 805 Sherbrooke O.

RÉSUME :
We are motivated by studying the effect that the expression of inflammatory genes has on the risk of colorectal cancer. Gene expression, however, is likely confounded with other risk factors for cancer. But because meiosis within families is considered a random process, the genotypes can potentially be used as instruments for the actual inflammation levels. The problem we address here is that designs are typically based on case-control sampling for these settings. We show first that, in contrast to settings with no confounding, modeled with conditional logistic regression, instrumental variables causal effects are generally incorrectly estimated if the design effect is ignored, as they are not invariant under such designs. We show, second, how in general the framework of principal stratification is useful to validly estimate the causal effects under such designs. We demonstrate these results with the effect of inflammation on colorectal cancer.

Le dernier Convergence (Volume XIV, Numéro 1) est maintenant disponible ici. Comme c’est maintenant coutume, le mot de passe habituel est requis pour accéder au dernier numéro.

CONFERENCIER : Jinko Graham (Simon Fraser University)

TITRE : Graphical displays to uncover gene-environment interaction from data on case-parent trios

DATE : 24 avril 2009

HEURE : 15:30

SALLE : Salle PK-5115, UQÀM, Pavillon Président-Kennedy, 201, av. Président-Kennedy
RÉSUME :
In genetic association studies of complex diseases, case-parent-trio designs involve the collection of data from affected offspring and their parents. This design is well-suited to diseases of early onset, such as type 1 diabetes and childhood leukemia. Unlike the case-control design, the case-parent design is robust to bias from ethnic differences between cases and controls and it enables investigation of parent-of-origin effects for genetic risk factors. While the use of the case-parent design for finding genetic associations has been well studied, its use for uncovering gene-environment interactions is less well-understood. We review two existing ad-hoc approaches to explore gene-environment interaction from case-parent trios and illustrate their potential bias. We propose an alternate penalized likelihood approach that does not suffer from such bias and illustrate its use on simulated data. We conclude with some directions for future research. This is joint work with Ji-Hyung Shin and Brad McNeney.

CONFERENCIER : David Dunson (Duke university)

TITRE : Bayesian density regression with epidemiology applications

DATE : 17 avril 2009

HEURE : 15:30

SALLE : Salle 1B24, McGill University, Burnside Hall, 805 Sherbrooke O.

RÉSUME :
In assessing relationships between a response and multiple predictors, it is appealing to allow the conditional response distribution to vary flexibly, allowing non-linear and varying relationships with the different quantiles and predictors. Such flexibility is of critical importance in applications in which the tails of the distribution are of primary interest. For example, in epidemiology studies of continuous health responses, the tails of the distribution typically correspond to those individuals having the most adverse health conditions. We would like a method that can allow an environmental exposure, genetic factor or demographic covariate to flexibly impact risk of an adverse response, with adverse corresponding to values in the tails of the distribution. Values further in the tails vary in their severity, so it is important to avoid categorization or grouping. Motivated by studies of pregnancy outcomes and premature delivery, this talk proposes Bayesian nonparametric methods for density regression. I will also describe applications to molecular epidemiology studies. The talk is designed to be accessible to a general audience of biostatisticians and epidemiologists, so technical details will kept to a minimum.

Bonjour à tous,

Le Conseil d’administration de l’ASSQ vous convie au prochain jeudi de l’ASSQ qui aura lieu le 16 avril prochain à l’Université Laval, Faculté des sciences et de génie, Pavillon Alexandre-Vachon, salle 2820, 1045, avenue de la Médecine, Québec

La première partie du 5 à 7 sera consacrée à une présentation de M. Daniel Lemire intitulée « Les modèles prédictifs en géomarketing ». Dans la deuxième partie, vous êtes invité(e)s à échanger avec l’ensemble des participants en dégustant quelques canapés et bons vins.

Ce document vous fournit toutes les informations pour l’inscription. Veuillez noter que la date limite d’inscription est le 13 avril 2009.

C’est un événement à ne pas manquer! On vous attend en grand nombre.

Tony Labillois
Directeur des communications de l’ASSQ

CONFERENCIER : Mary Lesperance (Victoria)

TITRE : Testing for Benford’s Law and Possible Fraud Detection

DATE : 3 avril 2009

HEURE : 15:30

SALLE : Salle 6214, CRM, UdeM, Pav. André-Aisenstadt, 2920, ch. de la Tour

RÉSUMÉ :
Recent high profile accounting scandals have revealed the need for automated methods which can quickly analyze large amounts of financial data and signal when unusual observations are present. The literature suggests that many financial (and other) data sets conform to the first digit frequency distribution known as Benford’s Law. In this paper, various methods of testing whether observed frequencies of first significant digits agree with Benford’s law are presented and compared in terms of their power. Theoretical and empirical results are used to compare these methods. Some recommendations are given on how these procedures may be employed in the field of accounting to detect unusual observations, fraud or error.

CONFERENCIER : Lei SUN (University of Toronto)

TITRE : Unifying Stratified and Weighted FDR Methods with Applications to Large-Scale Genetic Studies

DATE : 27 mars 2009

HEURE : 15:30

SALLE : LB 921-04, Université Concordia

RÉSUMÉ :
A central issue in high-dimensional genetic studies is how to assess statistical significance taking into account the inherent large-scale multiple hypothesis testing. To improve power, a number of studies have investigated the benefits of utilizing available prior information, however, the relative merits of different methods remain unknown. We focus on the stratified FDR (Sun et al., 2006) and weighted FDR (Genovese et al., 2006; Roeder et al., 2006) control methods. The two approaches model the prior distinctively. Weighted FDR converts the available prior information to test-specific weighting factor and adjusts the p-values accordingly. In contrast, stratified FDR divides tests into several disjoint strata based on the prior information and applies FDR control separately in each stratum. We first unify the two approaches in one framework and we show the trade-off between power and robustness by theoretical, simulation, and application studies. Robustness is desirable to safeguard against potential uninformative or even misleading prior information. We demonstrate the practical relevance by applying the two methods to three genome-wide association studies on diabetes and diabetes-related complications using previous genome-wide linkage results as the available prior information. This is joint work with Yun Joo Yoo, Shelley Bull, Andrew Paterson and Daryl Waggott.

CONFERENCIER : Susan Shortreed (McGill University)

Titre: « Learning in Spectral Clustering »

DATE : 20 mars 2009

HEURE : 15:30

SALLE : McGill, Burnside Hall, 805 Sherbrooke O., salle 1B36

RÉSUME :
Spectral clustering is a pairwise clustering technique that uses the eigenvectors and eigenvalues of a normalized similarity matrix to cluster the data. While it is a popular clustering method, a limiting factor in spectral segmentation is that the similarity matrix is not usually known a priori. In this talk we will review spectral clustering and present our method for learning the similarity matrix. We introduce the idea of optimizing a cost function composed of clustering quality term, the gap, regularized by a clustering stability term, the eigengap. We will present our supervised learning methods in detail, which assumes that a training set with known clustering labels is available for learning the similarity matrix. We will also discuss how we can extend our methodology to the unsupervised and semi-supervised frameworks.

CONFERENCIER : Fernando Camacho (Damos Inc., Toronto)

TITRE : Statistical Analysis for Life Cycle Management of Steam Generators

DATE : Vendredi le 13 mars 2009

HEURE : 15:30

SALLE : LB-921.04, Library Bldg., Concordia University, 1400 de Maisonneuve West

RÉSUME :
As equipment and systems age, Life Cycle Management (LCM) analysis becomes an important tool in assessing and managing potentially life limiting degradation mechanisms. Adequate LCM analysis usually considers a range of inspection and mitigation strategies aiming to maintain or extend the technical and economic life of the equipment. The assessment of these strategies needs to reflect not only the deterioration rate of the equipment, but also the impact the mitigation strategies have on the equipment. Deterioration rates can be assessed based on historical inspection trends, but in general it is much harder to assess how different mitigation strategies may affect the deterioration. This talk will describe some of the statistical analyses carried out to develop models that could be used on the LCM of steam generators of nuclear reactors. In particular, we will discuss the data collection, parameter estimation and variable selection used to select a model suitable to assess the effect of different mitigation actions on the deterioration rate of tube pitting in the steam generators. (Joint work with Sandra Pagan, Ontario Power Generation Inc., Pickering, CANADA)