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Course Catalog

140.753.01 Advanced Methods in Biostatistics III

Department:
Biostatistics
Term:
3rd term
Credits:
4 credits
Academic Year:
2015 - 2016
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:

140.751-752; Students must also register for 140.754

Description:

Introduces generalized linear model (GLM). Foundational topics include: contingency tables, logistic regression for binary and binomial data, models for polytomous data, Poisson log-linear model for count data, and GLM for exponential family. Methods for model fitting, diagnosis, interpretation and inference will be introduced and expanded with techniques for handling overdispersion, quasi-likelihood and conditional likelihood. Also introduces the concept of fixed effects and random effects for modeling clustered data. Related computing techniques including expectation-maximization (EM) and Markov Chain Monte Carlo are covered by an independent computing/data lab series running concurrently with the course.

Learning Objectives:

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

  1. Use generalized linear model (GLM) to analyze continuous, categorical and count data,
  2. Know how to construct, fit and interpret different types of GLM in the context of scientific and public health applications
  3. Understand connections and differences between logistic regression, Poisson log-linear regression and linear regression,
  4. Conduct statistical inference in these models,
  5. Diagnose model assumptions,
  6. Know how to deal with overdispersion in GLM,
  7. Expand the model and inference tools with quasi-likelihood and conditional likelihood,
  8. Extend linear model to account for clustering using random effects,
  9. Apply theoretical concepts to scientific data using R software,
  10. Improve computational and analytic skills through analysis of simulated and real data sets,
Methods of Assessment:

Homework (70%) and final exam (30%)

Instructor Consent:

No consent required