340.608.81
Observational Epidemiology
Location
Internet
Term
4th Term
Department
Epidemiology
Credit(s)
4
Academic Year
2014 - 2015
Instruction Method
TBD
Auditors Allowed
No
Available to Undergraduate
No
Grading Restriction
Letter Grade or Pass/Fail
Course Instructor(s)
Contact Name
Frequency Schedule
Every Year
Resources
Prerequisite
Introduction to Online Learning; 340.601 or 550.694-695 or 340.751; prior or concurrent enrollment in 140.612 or equivalent.
Expands upon material presented in introductory level epidemiologic concepts and methods, using examples from the published literature through weekly online lectures. Emphasizes interpretation and the ability to critically evaluate issues related to populations / study design, measurement, population comparisons and inference, including: modern cohort study designs; advanced nested designs; novel techniques for exposure assessment; interpretation and utility of measures of impact; sources of bias and methods for their prevention; descriptive and analytical goals for observational study inference; the counterfactual model for defining exchangeability, cause, and confounding; and synthesis of inferences from observational studies.
Learning Objectives
Upon successfully completing this course, students will be able to:
- Describe modern design features for cohort studies, including use of existing clinical and administrative databases
- Compare and contrast various nested designs, including methods for participant selection and analysis
- Identify biases resulting from participant selection and misallocation of person-time and Identify and interpret epidemiologic measures of disease frequency
- Describe the issues underlying measurement of exposures and outcomes in observational research
- Identify the effect of measurement error and bias on epidemiologic inferences and calculate and contrast measures of association and measures of impact
- Utilize and illustrate a framework for distinguishing different inferential goals of an epidemiological study
- Define concepts and terminology in causal inference for epidemiology and develop graphical approaches (e.g., DAGs) for models that integrate confounding and mediation effects
- Illustrate, interpret, and contrast ‘classical’ (e.g., regression) approaches for addressing confounding with modern techniques (e.g., propensity-score and inverse-weighting methods)
- Identify and critically analyze contemporaneous articles that utilize epidemiologic designs and methods
- Articulate concerns about limitations in the epidemiological approach