140.623.81
Statistical Methods in Public Health III
 Location:
 Internet
 Term:
 3rd term
 Department:
 Biostatistics
 Credits:
 4 credits
 Academic Year:
 2022  2023
 Instruction Method:
 Asynchronous Online with Some Synchronous Online
 Auditors Allowed:
 Yes, with instructor consent
 Undergrads Allowed:
 Yes
 Grading Restriction:
 Letter Grade or Pass/Fail
 Course Instructors:

 Marie DienerWest
 Leah R. Jager
 Contact:
 Marie DienerWest
 Resources:
 Prerequisite:
Introduction to Online Learning is required prior to participating in any of the School's Internetbased courses. 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. Specific models include 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:
 Recognize the influence of sample size on statistical inferences
 Appreciate the importance of relying upon many regression models to capture the relationships among a response and predictor in observational studies
 Critique a proposed public health hypothesis to determine its suitability for testing using regression methods and the available data
 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
 Distinguish between the underlying probability distributions for modeling timetoevent data
 Employ KaplanMeier and Cox proportional hazards regression models to describe associations between risk factors and time to event data
 Employ lifetable methods and Poisson regression models to describe associations between risk factors and grouped survival data
 Conduct a survival regression and correctly interpret the regression coefficients and their confidence intervals
 Use statistical methods for inference to correctly interpret regression coefficients and their confidence intervals in order to draw valid public health inferences from data
 Create and interpret tables of regression results including unadjusted and adjusted estimates of coefficients with confidence intervals from many models
 Recognize the key assumptions underlying a multivariable regression model and judge whether departures in a particular application warrant consultation with a statistical expert
 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
 Instructor Consent:
Consent required for some students
 Consent Note:
Consent required for any nonSPH person who is not an undergraduate PH major
 For consent, contact: