# Epidemiology & Biostatistics Concentration Specific Courses

### MPH2000 – Epidemiology II (Formerly MPH0412) <a href="#mph2000" id="mph2000"></a>

**Credits: 3                                                                                                                  Offered: Spring 1 & Spring 2**\
Epidemiology is the study of the distribution and determinants of health-related states and events in specified populations, and the application of this knowledge to control health problems. This course will introduce students to concepts that guide the design and analysis of various epidemiologic study designs, including counterfactuals, confounding, effect measure modification, measurement error and bias, as well as practical considerations. In parallel with lectures and assigned readings, lab sessions will guide students through applications of these concepts, including constructing causal diagrams and using R software for epidemiologic analysis. Prior R knowledge is not needed

**Pre-requisites:**\
MPH1004 (formerly MPH0400) Introduction to Epidemiology,\
MPH1002 (formerly MPH0300) Introduction to Biostatistics Basic SAS proficiency

### MPH2001 – Epidemiology III (Formerly MPH0420) <a href="#mph2001" id="mph2001"></a>

**Credits: 3                                                                                                                        Offered: Spring 2**   \
Building upon the foundations of epidemiologic methods and design introduced in previous courses, Epidemiology III will cover the theoretical and practical considerations of analysis and interpretation of data generated from epidemiologic studies. Through lectures and guided analysis of epidemiologic datasets, students will learn the analytic approaches and modelling techniques used to investigate exposure-disease relationships within various epidemiologic study designs. This course will also include more advanced topics such as mediation analysis and the use of sensitivity analyses to quantify the impact of potential biases. As part of this course, students will perform an independent analysis of epidemiologic data to demonstrate mastery of the presented content. Students can use any statistical software they prefer for assignments, but all course examples, sample code and programming support will be provided using SAS only.

**Pre-requisite:** MPH2000 (formerly MPH0412) Epidemiology II

### MPH2002 Applied Linear Models I (Formerly MPH0812) <a href="#mph2002" id="mph2002"></a>

**Credits: 3                                                                                                                    Offered: Spring 1**                                                                                                                                           \
Regression analysis is a widely used set of methods for exploring the relationships between response variables and one or more explanatory variables. This course provides an introduction to regression methods for a single continuous response variable. Both linear and curvilinear regression models are considered. Model assumptions, and regression diagnostics for assessing those assumptions, are explored in detail. Strategies for model selection are presented. The emphasis is on concepts and application rather than on underlying theory. As mathematical results are presented without proof, students are not required to be proficient in calculus or matrix algebra. \
\
**Pre-requisite:** MPH1002 Introduction to Biostatistics

### MPH2003 Applied Linear Models II <a href="#mph2003" id="mph2003"></a>

**Credits: 3                                                                                                                   Offered: Spring 2**                                                                                                                                         &#x20;

This course provides a comprehensive overview of regression methods for analysis of categorical (binary and count) data and survival data, with applications to epidemiological and clinical studies. Topics discussed include logistic regression analysis, log linear model for contingency tables, Poisson regression, and survival regression. The emphasis is on concepts and application rather than on underlying theory. As mathematical results are presented without proof, students are not required to be proficient in calculus or matrix algebra.

**Pre-requisite:** MPH2002 Applied Linear Models I


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