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140.654.01 Methods in Biostatistics IV

4th term
4 credits
Academic Year:
2014 - 2015
East Baltimore
Class Times:
  • Tu Th,  10:30 - 11:50am
Auditors Allowed:
Yes, with instructor consent
Grading Restriction:
Letter Grade or Pass/Fail
Scott Zeger
Course Instructor:



Covers regression analysis for continuous and discrete outcome data using generalized linear models including: logistic models for binary responses, log-linear models for incidence rates and contingency tables; and survival analysis for time to event responses. Also covers strategies for formulating regression analyses that effectively address scientific questions. Methods are learned through lectures and multiple problem sets/data analyses abstracted from important public health studies.

Learning Objectives:

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

  1. Formulate a scientific question about the relationship of a response variable Y and predictor variables X in terms of the appropriate logistic, log-linear or survival regression model
  2. Interpret the meaning of regression coefficients in scientific terms as if for a substantive journal. For binary responses collected in clusters, distinguish between marginal and cluster-specific regression coefficients estimated by ordinary and conditional logistic regression
  3. Develop graphical and/or tabular displays of the data to show the evidence relevant to describing the relationship of Y with X. For survival data, produce Kaplan-Meier and complimentary log, log plots of survival functions with standard errors
  4. Estimate the model using a modern statistical package such as R and interpret the results for substantive colleagues. Derive the estimating equations for the maximum likelihood estimates for the class of generalized linear models and state the asymptotic distributions of the regression coefficients and linear combinations thereof
  5. Give a heuristic derivation of the Cox proportional hazards estimating function in terms of Poisson regression for grouped survival data
  6. Check the major assumptions of the model including independence and model form (mean, variance, proportional hazards) and make changes to the model or method of estimation and inference to appropriately handle violations. For example, use robust variance estimates for violations of independence or variance model
  7. Use regression diagnostics to determine whether a small fraction of observations is having undue influence on the results
  8. Correctly interpret the regression results to answer the specific substantive questions posed in terms that can be understood by substantive experts
  9. Write a methods and results section for a substantive journal, correctly describing the regression model in scientific terms and the method used to specify and estimate the model
  10. Critique the methods and results from the perspective of the statistical methods chosen and alternative approaches that might have been used
Methods of Assessment:

Method of student evaluation based on problem sets, an exam, and a data analysis project.

Instructor Consent:

No consent required