140.753.01
Advanced Methods in Biostatistics III
Location
East Baltimore
Term
3rd Term
Department
Biostatistics
Credit(s)
4
Academic Year
2024 - 2025
Instruction Method
In-person
Tu, Th, 10:30 - 11:50am
Lab Times
Tuesday, 3:30 - 4:50pm (01)
Auditors Allowed
Yes, with instructor consent
Available to Undergraduate
No
Grading Restriction
Letter Grade or Pass/Fail
Course Instructor(s)
Contact Name
Frequency Schedule
Every Year
Resources
Prerequisite
140.751-752; Students must also register for 140.754
Introduces generalized linear model (GLM). Includes foundational topics: 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:
- Explain the role of quantitative methods and sciences and critical importance of evidence in public health
- Apply quantitative methods to public health and scientific problems
- Construct, fit and interpret different types of linear model (LM) and generalized linear model (GLM) in the context of scientific and public health applications
- Apply foundational concepts of probability theory and statistical inference in the context of LM and GLM models
- Use GLM to analyze continuous, categorical and count data
- Explain connections and differences between logistic regression, Poisson log-linear regression and linear regression
- Diagnose model assumptions and conduct statistical inference in these models
- Expand the model and inference tools with quasi-likelihood and conditional likelihood
- 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)