NS 6580

Advanced Epidemiology: Theory and Practice

Course Description

Course information provided by the Courses of Study 2020-2021.

This course will use a combination of lectures and discussions with 'hands-on' laboratory sessions as a method to learn about nutritional epidemiology. Students should be able to apply the methods learned in this class and gain proficiency in designing, conducting, and analyzing nutritional epidemiology studies. Broadly, the topics that will be covered would guide the design of research projects, and include data management and data analysis as it pertains to nutritional data, errors in nutrition assessment, biomarkers of nutritional status or outcome, methods of energy adjustment, anthropometry, and body composition, CDC and WHO growth charts, propensity scores, genetics and gene-environment interactions in nutritional epidemiology, measurement and analysis of physical activity, translating conceptual models into statistical models and dealing with confounders, mediators, endogenous variables, and multilevel models, working with large samples, and longitudinal analysis for analyzing the relationship between diet and disease.

When Offered Fall (offered in even-numbered years only).

Permission Note Enrollment limited to: graduate students who have completed all requirements for obtaining a minor in Epidemiology.
Prerequisites/Corequisites Prerequisite: BTRY 4110 or equivalent, NS 6520, BTRY 6010, BTRY 6020 .

• Critically evaluate the nutrition epidemiology literature.
• Describe and compare common methods of dietary assessment, and understand the nature of nutrient variation in the diet.
• Understand the components of study design in nutrition epidemiology studies, particularly data analysis and interpretation.
• Analyze and interpret gene-environment interactions.
• Select appropriate physical activity indicators and describe common methods of anthropometric assessment.
• Independently construct a nutritional epidemiology question and conduct data analysis to address that question in a dataset (either open-source such as NHANES or a dataset related to their theses).

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