Strategies for investigation of etiologic hypotheses, including
study design, data collection, quantitative assessment and
control of confounding and other biases, evaluation of effect
modification, and interpretation and reporting of study results
will be covered in detail. Topics will include analysis of data
from cross-sectional, case-control, and cohort study designs.
Athena Title
Intermediate Epidemiology
Prerequisite
EPID 7010
Semester Course Offered
Offered spring
Grading System
A - F (Traditional)
Student learning Outcomes
Students will formulate and justify an etiologic hypothesis or research question based on the current literature.
Students will identify the most appropriate study design for addressing an etiologic hypothesis.
Students will identify the most appropriate approaches to data collection, considering issues of accuracy, participant burden, and resource requirements (cost and personnel).
Students will identify potentially important sources of bias and design the study to avoid these and/or enable quantitative estimation of the potential impact of the bias.
Students will identify and conduct appropriate statistical analyses to address the specific bias of confounding.
Students will describe how to formulate questions of effect modification (i.e., interaction) and how to analyze, report, and interpret tests of effect modification.
Students will summarize key approaches, advantages, and disadvantages of data analysis for cross-sectional, case-control, and cohort designs.
Students will conduct a multi-faceted analysis of a research question addressing an etiologic hypothesis which integrates the concepts taught in this course in a practical experience.
Topical Outline
- Review study designs and Hybrid designs
- Project overview and SAS workshop
- Measures of disease occurrence
- Measures of association (1) cohort
- Measures of association (2) case-control
- Measures of association (3) case-control and cross-sectional
- Measures of impact
- Confounding
- Frequency tables, stratification and Intro to Regression
- SAS procedures for stratification and adjustment
