Biostatistique

Description du programme

La biostatistique est l’étude de l’estimation et de l’inférence statistiques en biologie et en sciences de la santé. Elle couvre un large éventail de sujets, tels la génétique statistique, la conception et l’analyse d’essais cliniques randomisés contrôlés, ainsi que la modélisation de durées de vie. La recherche en biostatistique est souvent motivée par des questions biomédicales précises ou par des applications qui requièrent des travaux méthodologiques s’inscrivant dans un cadre mathématique ou statistique plus général. La pratique de la biostatistique nécessite donc une bonne connaissance des techniques d’inférence et de statistique asymptotique, mais aussi de la polyvalence et des aptitudes pour le travail interdisciplinaire. Les membres du groupe de biostatistique de l’ISM s’intéressent entre autres à

  • l’analyse de survie
  • la génétique statistique
  • l’inférence causale
  • la méthodologie pour les données longitudinales

 Les membres du groupe ont notamment contribué à la conception ou à l’étude de modèles semi-paramétriques et non paramétriques pour des variables réponses, de techniques d’assurance de la qualité des données dans le cadre d’études génomiques, de méthodes bayésiennes pour des tests diagnostiques, d’estimateurs-g et autres techniques d’inférence pour des régimes de traitement dynamique, ainsi que d’outils d’analyse de scores de propensité en haute dimension.

Membres du programme

Cours 2017-18

Automne

Epidemiology: Introduction and Statistical Models

Examples of applications of statistics and probability in epidemiologic research. Sources of epidemiologic data (surveys, experimental and non-experimental studies). Elementary data analysis for single and comparative epidemiologic parameters.

Prof. James Hanley

BIOS 601

Institution: Université McGill

Advanced Generalized Linear Models

Statistical methods for multinomial outcomes, overdispersion, and continuous and categorical correlated data; approaches to inference (estimating equations, likelihood-based methods, semi-parametric methods); analysis of longitudinal data; theoretical content and applications.

Prof. Alexandra Schmidt

BIOS 612

Institution: Université McGill

Data Analysis and Report Writing

Common data-analytic problems. Practical approaches to complex data. Graphical and tabular presentation of results. Writing reports for scientific journals, research collaborators, consulting clients.

Prof. James Hanley

BIOS 624

Institution: Université McGill

Introduction to Bayesian Analysis in Health Sciences (2 credits)

Introduction to practical Bayesian methods. Topics will include Bayesian philosophy, simple Bayesian models including linear and logistic regression, hierarchical models, and numerical techniques, including an introduction to the Gibbs sampler. Programming in R and WinBUGS.

Prof. Lawrence Joseph

EPIB 668

Institution: Université McGill

Hiver

Epidemiology: Regression Models

Multivariable regression models for proportions, rates, and their differences/ratios; Conditional logistic regression; Proportional hazards and other parametric/semi-parametric models; unmatched, nested, and self-matched case-control studies; links to Cox's method; Rate ratio estimation when "time-dependent" membership in contrasted categories.

Prof. Erica Moodie

BIOS 602

Institution: Université McGill

Advanced Modelling: Survival and Other Multivariate Data

Advanced applied biostatistics course dealing with flexible modeling of non-linear effects of continuous covariates in multivariable analyses, and survival data, including e.g. time-varying covariates and time-dependent or cumulative effects. Focus on the concepts, limitations and advantages of specific methods, and interpretation of their results. In addition to 3 hours of weekly lectures, shared with epidemiology students, an additional hour/week focuses on statistical inference and complex simulation methods. Students get hands-on experience in designing and implementing simulations for survival analyses, through individual term projects.

Prof. Michal Abrahamowicz

BIOS 637

Institution: Université McGill

Intermediate Bayesian Analysis in Health Sciences (2 credits)

Bayesian design and analysis with applications specifically geared towards epidemiological research. Topics may include multi-leveled hierarchical models, diagnostic tests, Bayesian sample size methods, issues in clinical trials, measurement error and missing data problems. Programming in R and WinBUGS.

Prof. Lawrence Joseph

EPIB 669

Institution: Université McGill

Advanced Methods: Causal Inference

Foundations of causal inference in biostatistics. Statistical methods based on potential outcomes; propensity scores, marginal structural models, instrumental variables, structural nested models. Introduction to semiparametric theory.

Prof. Robert Platt

BIOS 610

Institution: Université McGill

Analysis of Correlated Data

This course will provide a basic introduction to methods for analysis of correlated, or dependent, data. These data arise when observations are not gathered independently; examples are longitudinal data, household data, cluster samples, etc. Basic descriptive methods and introduction to regression methods for both continuous and discrete outcomes.

Prof. P. Saha-Chauduri

EPIB 627

Institution: Université McGill