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140.753.41
Advanced Methods in Biostatistics III

Location:
East Baltimore
Term:
3rd term
Department:
Biostatistics
Credits:
4 credits
Academic Year:
2022 - 2023
Instruction Method:
Synchronous Online
Class Times:
  • Tu Th,  10:30 - 11:50am
Lab Times:
  • Tuesday,  9:00 - 10:20am
Auditors Allowed:
No
Undergrads Allowed:
No
Grading Restriction:
Letter Grade or Pass/Fail
Course Instructor:
Contact:
Hongkai Ji
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. Introduces methods for model fitting, diagnosis, interpretation and inference and expands on those topics with techniques for handling overdispersion, quasi-likelihood and conditional likelihood. Introduces the role of quantitative methods and sciences in public health, including how to use them to describe and assess population health, and the critical importance of evidence in advancing public health knowledge. Introduces how to produce a complete data analysis to answer a targeted scientific or public health question.

Learning Objectives:

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

  1. Explain the role of quantitative methods and sciences in public health
  2. Explain the critical importance of evidence in advancing public health knowledge
  3. Construct, fit and interpret different types of linear model (LM) and generalized linear model (GLM) in the context of scientific and public health applications
  4. Conduct statistical inference in these models
  5. Use generalized linear model (GLM) to analyze continuous, categorical and count data
  6. Explain connections and differences between logistic regression, Poisson log-linear regression and linear regression
  7. Diagnose model assumptions
  8. Deal with overdispersion in GLM
  9. Expand the model and inference tools with quasi-likelihood and conditional likelihood
  10. Extend linear model to account for clustering using random effects
  11. Apply theoretical concepts to scientific data using R software
  12. Improve computational and analytic skills through analysis of simulated and real data sets
  13. Produce a complete data analysis to answer a targeted scientific or public health question
Methods of Assessment:

This course is evaluated as follows:

  • 40% Homework
  • 20% Project(s)
  • 40% Exam(s)

Instructor Consent:

No consent required

Special Comments:

Please note: This is the virtual/online section of a course that is also offered onsite. Students will need to commit to the modality for which they register.