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Department: Biostatistics
Term: Summer Inst. term
Credits: 2 credits
Contact: Ayesha Khan
Academic Year: 2012 - 2013
Course Instructor:

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 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
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
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
Use SAS or STATA to conduct the appropriate longitudinal data analyses

Methods of Assessment: Student evaluation based on 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:
  • Mon 06/25/2012 - Fri 06/29/2012
  • Monday 1:30 - 5:00
  • Tuesday 1:30 - 5:00
  • Wednesday 1:30 - 5:00
  • Thursday 1:30 - 5:00
  • Friday 1:30 - 5:00
Enrollment Minimum: 10
Instructor Consent: No consent required

Intermediate level biostatistics and epidemiology

Auditors Allowed: No
Grading Restriction: Letter Grade or Pass/Fail