FALL 2011: BIOS613 Introduction to Statistical Genetics

Introduction to genetic epidemiology. Linkage analysis (parametric and non-parametric). Quantitative trait analysis. Linkage disequilibrium. Association analysis (candidate gene and genomewide). eQTL studies.

Prerequisite: Permission of instructor. Undergraduate course in mathematical statistics at level of MATH 324.

Academic Credits: 4

 

WINTER 2011: EPIB621 Data Analysis In The Health Sciences

Univariate and multivariate statistical techniques for continuous and dichotomous outcomes. Focus is on multiple linear and logistic regression models. Additional topics will include adjusting for missing data, measurement error, and hierarchical (random effects) models. All material will be taught from both Bayesian and frequentist viewpoints. R and WinBUGS software will be used throughout the course.
 

Prerequisites: Previous courses in differential and integral calculus, and EPIB 607 or equivalent

Academic Credits: 4
 

 

WINTER 2011: EPIB668 Genetic Epidemiology: an overview

The goal of the course is to introduce students to general notions of genetic epidemiology (GE) in order to enhance their ability to read the GE literature. It is aimed at providing a map of the different issues and study designs in GE through specific examples of studies, and not at teaching the fine details of each.  At the end of this course, the student will mostly have an overview of the different study design and analysis approaches used in genetic epidemiology studies currently appearing in the literature. It is not expected this course will fully enable students to carry out a genetic epidemiology study on their own.

Prerequisites: None
Academic Credits: 2

 

FALL 2009: EPIB607 Principles of inferential statistics in medicine

The aim of this course is to provide students with basic principles of statistical inference applicable to clinical and epidemiologic research so that they can: i) understand how statistical methods are used by others; (ii) apply statistical methods in their own research; (iii) use the methods learned in this course as a foundation for more advanced biostatistics courses. Topics include sampling, methods of describing data, introduction to probability, introduction to statistical inference, correlation and an introduction to regression.

Prerequisites: At least one course which includes differential and integral calculus.

Academic Credits: 4