140.755.01 | AY 2013-2014 - 1st Term | 4 Credit(s)
TTh 10:30:00 AM
  • Contact Information
    Vadim Zipunnikov
    Lei Huang
  • Course Learning Objectives

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

    • Give examples of different types of data arising in public health studies
    • Use modern statistical concepts such as Generalized Linear Models for inference
    • Describe models for polytomous outcomes
    • Apply theoretical concepts to scientific data using R and WinBUGS software
    • Conduct and interpret logistic, conditional logistic, and probit regression inference
    • Extend models to account for clustering and correlation
    • Introduce the mixed effects framework and describe its relationship to multilevel models
    • Introduce models that account for measurement error in the covariates
    • Provide new computational tools for complex models including Markov Chain Monte Carlo (MCMC) and Expectation Maximization (EM) algorithms
    • Improve computational and analytic skills through analysis of simulated data sets
  • Course Description
    Reviews the extension of linear models to generalized linear models. Includes exponential family models, link functions, and over-dispersion. Also introduces models and inferential methods for polytomous outcomes. Describes extension of models to account for clustering using explicit modeling via mixed effects framework and generalized estimating equations (GEE). Introduces methods and models for regression with covariates subject to measurement error. Describes and implements advanced computational algorithms, such as Markov Chain Monte Carlo (MCMC) and expectation maximization (EM).
  • Intended Audience
    Biostatistics PhD students
  • Methods of Assessment

    Homework (30%), final in-class exam (30%), and final project (40%).

    Additional Faculty Notes:


  • Prerequisites
  • Required Text(s)

    Additional Faculty Notes:


     Recommended textbooks:
     Categorical Data Analysis by Agresti
     Generalized, Linear, and Mixed Models by McCulloch, Searle, Neuhaus
     Measurement Error in Nonlinear Models: A Modern Perspective by Carroll, Ruppert, Stefanski and Crainiceanu
     Linear and Generalized Linear Mixed Models and Their Applications by Jiming Jiang 
     Mixed Models: Theory and Applications by Eugene Demidenko
  • Course Schedule

    Please see the course Session for a full list of dates and items for this course.

  • Academic Ethics Code

    Students enrolled in the Bloomberg School of Public Health of The Johns Hopkins University assume an obligation to conduct themselves in a manner appropriate to the University's mission as an institution of higher education. A student is obligated to refrain from acts which he or she knows, or under the circumstances has reason to know, impair the academic integrity of the University. Violations of academic integrity include, but are not limited to: cheating; plagiarism; knowingly furnishing false information to any agent of the University for inclusion in the academic record; violation of the rights and welfare of animal or human subjects in research; and misconduct as a member of either School or University committees or recognized groups or organizations.

  • Course topics


    1. Models for polytomous outcomes 
    2. Analysis of count data 
    3. Quasi-likelihood
    4. Mixed effects models
    5. Multilevel models
    6. MCMC, Gibbs sampling, Metropolis Hastings 
    7. Expectation Maximization 
    8. Project presentations
  • Disability Support Services
    If you are a student with a documented disability who requires an academic accommodation, please contact the Office of Student Life Services at 410-955-3034 or via email at