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Department: Biostatistics
Term: 3rd term
Credits: 4 credits
Academic Year: 2013 - 2014
Course Instructor:
  • Elizabeth Colantuoni

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 Objective(s):
Upon successfully completing this course, students will be able to:
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: Student evaluation based on a multiple choice exam and an analysis of a longitudinal data set, presentation of the results, and a written scientific report of the analysis methods and results.
Location: East Baltimore
Class Times:
  • Monday 10:30 - 11:50
  • Wednesday 10:30 - 11:50
Lab Times:
  • Monday 9:00 - 10:20
  • Wednesday 9:00 - 10:20
Enrollment Minimum: 8
Instructor Consent: No consent required

140.621-624, former 140.601-604, or 140.651-654

Auditors Allowed: Yes, with instructor consent
Grading Restriction: Letter Grade or Pass/Fail
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.