# 140.753.01 Advanced Methods in Biostatistics III

Department:
Biostatistics
Term:
3rd term
Credits:
4 credits
2019 - 2020
Location:
East Baltimore
Class Times:
• Tu Th,  10:30 - 11:50am
Lab Times:
• Tuesday,  9:00 - 10:20am
Auditors Allowed:
Yes, with instructor consent
Contact:
Hong Kai Ji
Course Instructor :
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.

Learning Objectives:

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

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

Homework (60%) and final exam (40%)

Instructor Consent:

No consent required