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340.722.01
Epidemiologic Inference in Public Health II

Location
East Baltimore
Term
2nd Term
Department
Epidemiology
Credit(s)
4
Academic Year
2023 - 2024
Instruction Method
In-person
Class Time(s)
M, F, 9:00 - 10:20am
Auditors Allowed
Yes, with instructor consent
Available to Undergraduate
No
Grading Restriction
Letter Grade or Pass/Fail
Course Instructor(s)
Contact Name
Frequency Schedule
Every Year
Prerequisite

340.601 or 340.721 or 340.751; 140.621 or equivalent.

Description
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