Skip Navigation

Course Catalog

600.712.86 Public Health Statistics II

Department:
Online Programs for Applied Learning
Term:
1st term
Credits:
4 credits
Academic Year:
2019 - 2020
Location:
Internet
Auditors Allowed:
No
Grading Restriction:
Letter Grade or Pass/Fail
Contact:
John McGready
Course Instructor s:
Resources:
Prerequisite:

Public Health Statistics I (600.711)

Description:

Employs a conceptual framework to highlight the similarities and differences between linear, logistic and Cox Proportional Hazards methods, in terms of usage and the interpretations of results from such models. Provides details for these regression approaches in the “simple” scenario, involving relating an outcome to single predictor. Following this overview of simple regression, explores the use of multiple regression models to compare and contrast confounding and effect modification, produce adjusted and stratum-specific estimates, and allow for better prediction of an outcome via the use of multiple predictors. Offers a brief introduction to linear spline models and propensity score methods for adjustment.

Learning Objectives:

Upon successfully completing this course, students will be able to:

  1. Interpret the results from simple and multiple linear, logistic and Cox regression models
  2. Illustrate how hypotheses tests can be expressed as simple regression models
  3. Explain the assumption of proportional hazards, and what this means regarding the interpretation of hazard (incidence rate) ratios from Cox regression models
  4. Describe the conditions necessary for an exposure/outcome relationship to be confounded by one or more other variables
  5. Explain the concept of effect modification, and how it differs from confounding
  6. Use the results from all regression types covered (linear, logistic and Cox) to assess confounding and effect modification
Methods of Assessment:

Homework assignments: 80%, Quizzes: 20%

Enrollment Restriction:

Restricted to students enrolled in MAS in Spatial Analysis for Public Health

Instructor Consent:

No consent required