# STATISTICAL METHODS IN PUBLIC HEALTH III Syllabus

140.623.01 | AY 2013-2014 - 3rd Term | 4 Credit(s)
TTh 10:30:00 AM No Classroom Set
• Contact Information
Faculty
Marie Diener-West
• Course Learning Objectives

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

• Use statistical reasoning to formulate public health questions in quantitative terms 1.1 Critique a proposed public health hypothesis to determine its suitability for testing using regression methods and the available data; 1.2 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; 1.3 Evaluate the limitations of observational data as evidence for a health effect; 1.4 Appreciate the importance of relying upon many regression models to capture the relationships among a response and predictor in observational studies
• Conduct statistical computations and construct graphical and tabular displays for regression analysis 2.1 Use the statistical analysis package Stata to perform multivariable regression models; 2.2 Document and archive the steps of your statistical analysis by creating a Stata do-file; 2.3 Create and interpret scatter-plots and adjusted variable plots that display the relationships among an outcome and multiple risk factors; 2.4 Create and interpret tables of regression results including unadjusted and adjusted estimates of coefficients with confidence intervals from many models
• Use probability models to describe trends and random variation in public health data 3.1 Distinguish between the underlying probability distributions for modeling continuous, categorical, binary and time-to-event data; 3.2 Recognize the key assumptions underlying a multivariable regression model and judge whether departures in a particular application warrant consultation with a statistical expert
• Use statistical methods for inference in multiple regression to draw valid public health inferences from data [4.1 Conduct a simple linear, logistic or survival regression and correctly interpret the regression coefficients and their confidence interval.
• Course Description
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.
• Intended Audience
Non-Biostatistics students
• Methods of Assessment
Student evaluation based on problem sets and exams.
• Prerequisites
140.622
• Required Text(s)

Recommended: Lawrence C. Hamilton. Statistics with Stata 10, Duxbury, Pacific Grove, California

Recommended: Bernard Rosner. Foundations of Biostatistics.

• Course Schedule

Please see the course Session for a full list of dates and items for this course.

Students enrolled in the Bloomberg School of Public Health of The Johns Hopkins University assume an obligation to conduct themselves in a manner appropriate to the University's mission as an institution of higher education. A student is obligated to refrain from acts which he or she knows, or under the circumstances has reason to know, impair the academic integrity of the University. Violations of academic integrity include, but are not limited to: cheating; plagiarism; knowingly furnishing false information to any agent of the University for inclusion in the academic record; violation of the rights and welfare of animal or human subjects in research; and misconduct as a member of either School or University committees or recognized groups or organizations.

• Welcome Message
The course web site for both sections of 140.623 may be accessed directly at http://www.biostat.jhsph.edu/courses/bio623/
• Course Objectives(from old syllabus)
To provide an introduction to the basic concepts and methods for regression models as applied to public health and medicine. Models emphasized include multiple linear regression, analysis of variance, analysis of covariance, logistic regression, and survival analysis. Emphasis on developing data analysis skills. Includes the analysis of several datasets throughout the course.
• Disability Support Services