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Epidemiologic Inference in Public Health II


2nd term
4 credits
Academic Year:
2022 - 2023
Instruction Method:
Synchronous Online
Class Times:
Mon & Fri - synchronous virtual (Zoom) in course times; Wed - mix of synchronous activity (12/1, 8, 15, 22) or listening to asynchronous modules (10/27, 11/3, 10, 17, 24) by that Fri.
  • M W F,  9:00 - 10:20am
Auditors Allowed:
Undergrads Allowed:
Grading Restriction:
Letter Grade or Pass/Fail
Course Instructor:
Elizabeth Platz
Frequency Schedule:
One Year Only

340.601 or 340.721 or 340.751; 140.621 or equivalent.


Expands knowledge beyond introductory level epidemiologic concepts and methods material, using examples from the published literature. 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:

  1. Critically analyze public health literature and utilize a framework to illustrate strengths and limitations in the epidemiologic approach
  2. Compare and contrast advanced aspects of randomized clinical trials, cohort, and nested study designs, with an emphasis on methods for participant selection, data summarization and population comparisons based on these designs
  3. Identify and differentiate sources of bias resulting from participant selection, measurement and misallocation of person-time, describe the impact of these biases on epidemiologic inferences, and identify approaches for ameliorating their influence
  4. Articulate concepts and terminology used to define a ‘cause’ in epidemiology; utilize graphical tools (e.g., DAGs) to illustrate and explain causal inference concepts
  5. Distinguish and illustrate confounding, effect modification, and mediation, and contrast ‘classical’ (e.g., regression-based) and modern (e.g., propensity-score) approaches for addressing these phenomena
  6. Evaluate the strengths and weakness of epidemiological investigations with non-causal inferential goals, including ‘risk-factor’ studies and prediction
Methods of Assessment:

This course is evaluated as follows:

  • 60% Assignments
  • 20% Final Exam
  • 20% Group Project

Instructor Consent:

No consent required