140.623.01
Statistical Methods in Public Health III

Location
East Baltimore
Term
3rd Term
Department
Biostatistics
Credit(s)
4
Academic Year
2024 - 2025
Instruction Method
In-person
Class Time(s)
Tu, Th, 10:30 - 11:50am
Lab Note
After the course opens, students will sign up in the CoursePlus Sign-up Sheets for a weekly Lab Session with review of a structured Lab Exercise. The format is onsite. The data analysis tool is either Stata or R.
Auditors Allowed
Yes, with instructor consent
Available to Undergraduate
Yes
Grading Restriction
Letter Grade or Pass/Fail
Course Instructor(s)
Marie Diener-West
Daniel Obeng
Contact Name
Marie Diener-West
Frequency Schedule
Every Year
Prerequisite

140.622

Description
Introduces the basic concepts and steps associated with multivariable statistical modeling. It integrates methods with performing the steps using data analysis tools through the Stata statistical analysis package or the R software.
Presents use of generalized linear models for quantitative analysis of data encountered in public health and medicine. Includes specific models of analysis of variance, analysis of covariance, multiple linear regression, logistic regression, and Cox regression.
Learning Objectives
Upon successfully completing this course, students will be able to:
  1. Recognize the influence of sample size on statistical inferences
  2. Appreciate the importance of relying upon many regression models to capture the relationships among a response and predictor in observational studies
  3. Critique a proposed public health hypothesis to determine its suitability for testing using regression methods and the available data
  4. Formulate and correctly interpret a multivariable linear, logistic or survival regression model to estimate a health effect while minimizing confounding and identifying possible effect modification
  5. Distinguish between the underlying probability distributions for modeling time-to-event data
  6. Employ Kaplan-Meier and Cox proportional hazards regression models to describe associations between risk factors and time to event data and employ life-table methods and Poisson regression models to describe associations between risk factors and grouped survival data
  7. Conduct a survival regression and correctly interpret the regression coefficients and their confidence intervals and use statistical methods for interference in order to draw valid public health inferences from data
  8. Create and interpret tables of regression results including unadjusted and adjusted estimates of coefficients with confidence intervals from many models
  9. Recognize the key assumptions underlying a multivariable regression model and judge whether departures in a particular application warrant consultation with a statistical expert
  10. Use the statistical analysis packages Stata or R to perform univariate, bivariate and multivariable regression models and to document and archive the steps of the statistical analysis
Methods of Assessment
This course is evaluated as follows:
  • 20% Problem sets
  • 10% Quizzes
  • 35% Midterm
  • 35% Final Exam
Enrollment Restriction
For onsite students only
Instructor Consent
Consent required for some students
Consent Note
Consent required for any non-SPH person who is not an undergraduate PH major
Consent Contact
mdiener@jhsph.edu
Special Comments

Restricted to 350 students. Lectures are synchronous onsite. After the term starts and the course opens, each student signs up in CoursePlus for one 90-minute onsite Lab Session.