140.753.01 | AY 2012-2013 - 3rd Term | 3 Credit(s)
TTh 10:30:00 AM
  • Contact Information
  • 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
    • Discuss differences and similarities between standard linear regression and models for discrete outcomes
    • use modern statistical concepts such as generalized linear models for inference
    • 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
    • expand the set of biostatistical models with quasi-likelihood, beta-binomial and log-linear models
    • improve computational and analytic skills through analysis of simulated data sets
  • Course Description

    Introduces the General Linear Model and Generalized Least Squares. Develops the Generalized Likelihood Ratio Test (GLRT) and connects it to the Gaussian Linear Model. Defines Fisher Information and Observed Information. Compares methods of simultaneous inference and multiple comparisons. Covers robust variance estimation. Compares optimal statistical weights to optimal policy weights, and missing data theory and practice. Develops consequences of departures from assumptions, efficiency and robustness trade-offs in the context of missing data and correlated responses. Identifies implications for design, and outlines basic experimental designs, choice of design and analysis, fixed and random effects, Introduces shrinkage estimates. Covers study designs that account for uncertainty in input parameters. Introduces sample reuse via the jackknife and adds to criteria to use in evaluating a procedure and how to identify when a new method or adaptation is needed.

  • Intended Audience

    Biostatistics PhD students

  • Methods of Assessment

    Grading Policy: Student evaluation based on homework and a final exam.

    Grading Restrictions: Letter grade

  • Prerequisites

    140.751-752; Students must also register for 140.754

  • Academic Ethics Code

    The code, discussed in the Policy and Procedure Memorandum for Students, March 31, 2002, will be adhered to in this class:

    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.

  • Disability Support Services

    If you are a student with a documented disability who requires an academic accommodation, please contact Betty H. Addison in the Office of Student Life Services:, 410-955-3034, or 2017 E. Monument Street.