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Course Catalog

140.754.01 Advanced Methods in Biostatistics IV

Department:
Biostatistics
Term:
4th term
Credits:
4 credits
Academic Year:
2019 - 2020
Location:
East Baltimore
Class Times:
  • Tu Th,  10:30 - 11:50am
Lab Times:
  • Tuesday,  9:00 - 10:20am
Auditors Allowed:
Yes, with instructor consent
Grading Restriction:
Letter Grade or Pass/Fail
Contact:
Hong Kai Ji
Course Instructor:
Resources:
Prerequisite:

140.751-753

Description:

Extends topics in 140.753 to encompass generalized linear mixed effects models. Introduces expectation-maximization and Markov Chain Monte Carlo. Introduces functional data analysis. Foundational topics include: linear mixed model, generalized linear mixed model, EM, MCMC, models for longitudinal data, and functional data analysis. Emphasizes both rigorous methodological development and practical data analytic strategies. Discusses the role of quantitative methods and sciences in public health, including how to use them to describe and assess population health, and the critical importance of evidence in advancing public health knowledge.

Learning Objectives:

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

  1. Use modern statistical concepts such as linear mixed model (LMM) and generalized linear mixed models (GLMM) for inference
  2. Describe the relationship between LMM and GLMM
  3. Extend models to account for clustering and correlation
  4. Understand and use EM and MCMC
  5. Learn techniques for solving prediction problems
  6. Describe modern statistical methods for complex datasets
  7. Improve computational and analytic skills through analysis of simulated and real data sets
  8. Explain the role of quantitative methods and sciences in describing and assessing a population’s health
  9. Explain the critical importance of evidence in advancing public health knowledge
Methods of Assessment:

Homework (60%) and a final exam (40%)

Instructor Consent:

No consent required