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:
 Description:

Introduces generalized linear model (GLM). Foundational topics include: contingency tables, logistic regression for binary and binomial data, models for polytomous data, Poisson loglinear 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, quasilikelihood 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:
 Explain the role of quantitative methods and sciences in public health
 Explain the critical importance of evidence in advancing public health knowledge
 Construct, fit and interpret different types of linear model (LM) and generalized linear model (GLM) in the context of scientific and public health applications
 Conduct statistical inference in these models
 Use generalized linear model (GLM) to analyze continuous, categorical and count data
 Explain connections and differences between logistic regression, Poisson loglinear regression and linear regression
 Diagnose model assumptions
 Deal with overdispersion in GLM
 Expand the model and inference tools with quasilikelihood and conditional likelihood
 Extend linear model to account for clustering using random effects
 Apply theoretical concepts to scientific data using R software
 Improve computational and analytic skills through analysis of simulated and real data sets
 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.