140.655.01 Analysis of Longitudinal Data
- 3rd term
- 4 credits
- Academic Year:
- 2017 - 2018
- East Baltimore
- Class Times:
- M W, 10:30 - 11:50am
- Lab Times:
Wednesday, 9:00 - 10:20am
Explores 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. Intended for doctoral students in quantitative sciences.
- Learning Objectives:
- Prepare graphical or tabular displays of longitudinal data that effectively communicate the patterns of scientific interest
- Implement and interpret 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
- Implement and interpret marginal, random effects, or transitional generalized linear models to make scientific inferences when the repeated observations are binary, counts, or non-Gaussian continuous observations
- Implement analysis of longitudinal data within SAS or STATA
- Methods of Assessment:
The lecture notes will be posted as powerpoint and pdf files. The course faculty feel use of laptops during class time is fine for taking electronic notes.
- Instructor Consent:
No consent required
- Special Comments:
The Advanced Topics lab sequence (Monday 9:00 - 10:20 ) is required for Biostatistics students; interested non-Biostatistics students may attend. The Implementation and Interpretation of Analysis of Longitudinal Data (Wednesday 9:00 - 10:20) is highly recommended for all students. The course faculty request that all cell phones be silenced during class time out of respect for both the faculty and students.