Topic:
Epidemiology & Surveillance
Format:
Informational Brief
Time:
40 minutes
Level:
Introductory
University:
University of North Carolina
PERLC:
University of North Carolina PERLC
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Description:
This issue of FOCUS offered by the UNC Gillings School of Global Public Health will explore logistic regression and accounting for matched data. Logistic regression is an efficient way to control for many potential confounders at one time. Matching, if done correctly in the study design stage of the investigation, reduces confounding before the analysis even begins.
Learning Objectives:
- Define and describe confounders
- Discuss three ways to reduce the effects of confounders on your data: restriction, stratification, and logistic regression
- Learn how to identify and interpret effect measure modifiers
- Learn how to calculate matched odds ratios for case-control studies
PHEP Capabilities:
Public Health Surveillance and Epidemiological Investigation