140.608.11
Analysis of Longitudinal Data

Prerequisite:

Intermediate level biostatistics and epidemiology

Description:

Covers statistical models for drawing scientific inferences from longitudinal data. Topics include longitudinal study design; exploring longitudinal data; linear and generalized linear regression models for correlated data, including marginal, random effects, and transition models; and handling missing data.

Learning Objectives:

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

1. Prepare graphical or tabular displays of longitudinal data that effectively communicate the patterns of scientific interest
2. Use a general linear model to make scientific inferences about the relationship between response and explanatory variables while accounting for the correlation among repeated responses for an individual
3. Use marginal, random effects, or transitional generalized linear models to make scientific inferences when the repeated observations are binary, counts, or non-Gaussian continuous observations
4. Use SAS or STATA to conduct the appropriate longitudinal data analyses

Methods of Assessment:

This course is evaluated as follows:

• 15% Participation
• 15% Lab Assignments
• 15% Lab Assignments
• 15% Lab Assignments
• 40% Written Assignment(s)

Instructor Consent:

No consent required

Special Comments:

Students are required to have Stata. Course will be taught online, via Zoom, on the scheduled dates and times. All in-person classes will be taught online via Zoom, on the dates and times the course is scheduled. For further information, please see the Institute website jhsph.edu/summerepi