M.S. in Epidemiology

Foundational statistical methods used in clinical and public health research. Basic probability, samples and population, interval estimation, and more. How to present data in professional publications.

Introduces principles and practices of epidemiology, along with a population-based understanding of health and disease. Basic measurements and methods used in studying health in populations.

This course is a culminating experience designed to provide students with an opportunity to integrate knowledge and skills they have learned throughout their graduate education. Students will appropriately define an epidemiology problem, study design, data source, literature review, results interpretation, and scientific communication in written and oral form. This course will consist of lectures, group discussions and class presentations. This course is to be taken as the final requirement for the student to qualify for graduation with an M.S. degree in Epidemiology.

Prerequisites: Completion of all CORE and REQUIRED courses as well as completion of CHSM 7088 Practicum with a passing grade. Registration for Capstone requires approval from the Department Chair.

Building off of Intro to Epidemiology, expands on concepts of epidemiologic reasoning, and the design and interpretation of research. Topics include reliability and validity, confounding and effect modification, and more.

Continuing to build off previous epidemiology courses, covers advanced methods and special topics like power and sample size, regression techniques, epidemiologic consulting, and related topics.

First part of a two-semester sequence. Topics covered include descriptive statistics, probability, estimation, nonparametric methods.

Second part of a two-semester sequence. Topics covered include hypothesis testing with categorical data, multiple and logistic regression. Also, statistical methods often used in epidemiological studies and clinical trials such as life table analysis and logistic analysis.

Application of basic SAS programming for data management and analysis. Exposes students to a range of computing techniques used for health science data, foundational for later courses.

Builds on Introduction to Epidemiology and Introduction to Biostatistics. Teaches students to apply basic epidemiological reasoning and statistical methods and concepts using SAS statistical software. Acts as a bridge to more advanced courses. Students will conduct analysis on actual data sets and will learn to prepare and clean data for analysis, conduct descriptive analysis using SAS, Analysis of Variance (ANOVA) and more. Limited to epidemiology majors, other students may be permitted with instructor permission.

Advanced study or research endeavor in area of student’s choice, with consultation from the professor. Will be replaced with another Required Program Elective under some circumstances.

Basic grounding in biomedical and methodological issues associated with epidemiologic research on chronic disease, including applied examples such as diabetes and obesity.

Aims to help students understand the multiple influences – from social networks to veterinary practices to bioterrorism – that fuel infectious disease transmission dynamics.

Students will learn about issues in maternal and child health by assessing national and international epidemiologic data cross-sectionally and longitudinally, with results from ongoing research. Topics include pregnancy health and risk behaviors, injury trends in early to mid-childhood and more, all explored through a social-ecological lens.

Overview of surveillance systems as well as the issues involved in the design and execution of epidemiological surveys. Surveillance systems covered include: national and international reportable disease surveillance systems, systems designed to detect chronic disease and diseases related to behaviors and risk factors, surveillance for injuries, and more.

Provides an overview of major infectious diseases of public health interest, including vector borne diseases, sexually transmitted diseases, and HIV/AIDS. At the end of the course, participants will be able to describe the five types of microbial pathogens that cause infections, common diagnostic tests, issues related to control and prevention of infection, and more.

Intermediate-level quantitative course covering epidemiological methodologies applicable to the study of infectious diseases. Subjects covered include methods used in the study of respiratory, fecal-oral, vector borne, and sexually transmitted diseases, issues related to seroepidemiological studies, and differentiation between an infectious and a chronic event.

Comprehensive, individual study on a specific topic guided by a professor.

Introduces advanced methods to statistical modelling techniques – linear mixed effect models, generalized linear models for correlated data, missing data, proportional models, and repeated measure design.

Focuses on applications of the analysis of time to event data. Covers lifetime distribution, censoring, parametric models, among other topics.

Prerequisite: BISM 5001. Overview of randomized clinical trials. Topics include sample size and power, reliability of measurement, factorial design, stratification, and more.

Complex survey design analysis methods for large datasets such as the National Health and Nutrition Examination survey. Topics include but are not limited to: Practical data management skills, statistical programming and exploratory data analysis, statistical simulation, sensitivity analysis.

Examines the method employed in designing and analyzing complex surveys. Explores major sampling designs and estimation procedures like simple and stratified random sampling and variance estimation in complex sample surveys. Students will get hands-on experience analyzing existing data sets from complex surveys.