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Course Catalog


Department: Biostatistics
Term: 3rd term
Credits: 3 credits
Contact: Jeffrey Leek
Academic Year: 2012 - 2013
Course Instructor:

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.

Learning Objective(s):
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

Methods of Assessment: Student evaluation based on homework and a final exam.
Location: East Baltimore
Class Times:
  • Tuesday 10:30 - 11:50
  • Thursday 10:30 - 11:50
Enrollment Minimum: 2
Instructor Consent: No consent required

140.751-752; Students must also register for 140.754

Auditors Allowed: Yes, with instructor consent
Grading Restriction: Letter Grade or Pass/Fail
Special Comments: Grade for 140.753 and 754 given at completion of 140.754