  # 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.

## 2023-24 Course Listings

### 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.

#### Institution: McGill University

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.

### Méthodes d’analyse biostatistique

Survol de méthodes d'analyse couramment utilisées en biostatistique (théorie et application). Modèles linéaires généralisés et équations d'estimation.

Analyse de survie paramétrique ou semiparamétrique. Introduction à l'inférence causale et la théorie semiparamétrique.

### 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.