Biostatistics

Program Description

Biostatistics is the study of statistical estimation and inference in the context of biology and the health sciences. The domain encompasses a wide range of topics, such as statistical genetics, design and analysis of randomized control trials, modelling time-to-event data. Research in biostatistics is often motivated by particular biomedical questions or applications which raise methodological investigations into a general mathematical or statistical framework of the problem. Thus, biostatistics is a discipline which requires both a solid foundation in inference and asymptotic theory mixed with versatility and interdisciplinary collaborative skills. A sample of areas of interest of the group members includes the following topics:

  •  survival analyses
  •  statistical genetics
  •  causal inference
  •  methodology for longitudinal data

Specific techniques developed or extended in the group include semi- and non-parametric response modelling, data quality in genomic studies, Bayesian techniques for diagnostic testing, g-estimation and inference of dynamic treatment regimes, and high-dimensional propensity scores.

Program Members

2017-18 Course Listings

Fall

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: McGill University

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: McGill University

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: McGill University

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: McGill University

Winter

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: McGill University

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: McGill University

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: McGill University

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: McGill University

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: McGill University