Prerequisite

HPG 6103

Course Objective

The primary objective is to provide learners with the basic tools necessary to conceptualize the design of, and interpret the results from, observational epidemiologic studies. Learners will be able to calculate basic measures of association between exposures and disease, identify major epidemiologic study designs, and define confounding, selection bias and information bias.

Learning Outcomes

• Articulate the relationship between association and causation.
• Apply causal concepts to the design and interpretation of epidemiologic studies.
• Calculate and interpret basic measures of association.
• Develop testable research hypotheses from a causal theory.
• Recognize and explain the effects of non-exchangeability.
• Distinguish among the sources of non-exchangeability.
• Choose study designs appropriate for specific research questions.
• Identify sources of, and methods to avoid, invalidity in epidemiologic research.
• Relate these sources of invalidity to the definition of a cause.
• Test research hypotheses using stratification, standardization and logistic regression.
• Interpret logistic regression output to address causal questions.
• Critically evaluate the limitations of current epidemiologic methods.
• Work efficiently and productively in a team setting.
• Understand dangers of substituting one model for another; distinguish prediction vs causal models.
• Use modern methods (including ML) appropriately for large health datasets in public health research.

Course Content

The course will use examples and case studies that draw on data science methods, large datasets, and complex study designs, emphasizing integration of these approaches into public health practice.