Spring 2023 - HSCI 849 G100
Regression Modeling for Public Health (3)
Class Number: 5740
Delivery Method: In Person
Development of linear and logistic regression models from hypothesis to interpretation. Counter-factual framework for causal inference. Directed acyclic graphs for model building. This is a skills developing course in which students will use real data to create authentic models.
Course Description:Linear and logistic regression modeling methods are commonly used in public health epidemiology. Whether in academic or public health settings, this work is usually done in collaborative teams that include both research methodologists and content area experts. This course will give students a hands-on opportunity using real data to design, build and interpret a linear or logistic regression model at a foundational level using the counterfactual framework for causal inference. This course is foundational and would benefit any student who would like to develop skills for observational, quantitative research using linear or logistic regression.
COURSE-LEVEL EDUCATIONAL GOALS:
Course Goals: The course aims to develop technical and cognitive skills at a foundational level that will be applicable to participation in collaborative epidemiological research teams as a research methodologist or content area expert.
Course Objectives: At the end of the course, students who participate will be able to:
1. Develop a hypothesis that is appropriate for secondary data analysis.
2. Draw a causal diagram (directed acyclic graph) and use it to determine which variables should be modeled.
3. Fit a model to secondary, cross-sectional data using SAS statistical software.
4. Interpret the parameters of their model and describe uncertainty in their results.
- Research hypothesis and causal diagram 30%
- Research plan 30%
- Final model and interpretation 40%
The emphasis of this class is skills development.
Foundational concepts required for actualizing practices in lab setting will be introduced through video and selected readings to be completed prior to class. Each student will work on an individual data analysis project throughout the semester. Class periods will be work labs where students can get support from each other and the instructor. Lectures will respond to the needs of students in the class to address challenges that are arising in their individual projects.
Students can preview video materials for class and hear an introduction to course design philosophy at http://bit.ly/2f88RR6.
Changes to the syllabus may be made, as necessary, within Faculty and University regulations.
If you have credit for HSCI 801 or equivalent, the instructor will waive the requirement for HSCI 802 on a case by case basis. Please e-mail to request.
MATERIALS + SUPPLIES:
We will be using SAS statistical software, which works better using a Windows operating system. However, students will be able to connect to the FHS lab remotely and run SAS on a machine there regardless of the operating system of your computer (i.e. with macOS).
Required books: All course materials will be provided by the instructor or available online.
REQUIRED READING NOTES:
Your personalized Course Material list, including digital and physical textbooks, are available through the SFU Bookstore website by simply entering your Computing ID at: shop.sfu.ca/course-materials/my-personalized-course-materials.
Graduate Studies Notes:
Important dates and deadlines for graduate students are found here: http://www.sfu.ca/dean-gradstudies/current/important_dates/guidelines.html. The deadline to drop a course with a 100% refund is the end of week 2. The deadline to drop with no notation on your transcript is the end of week 3.
ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS
SFU’s Academic Integrity website http://www.sfu.ca/students/academicintegrity.html is filled with information on what is meant by academic dishonesty, where you can find resources to help with your studies and the consequences of cheating. Check out the site for more information and videos that help explain the issues in plain English.
Each student is responsible for his or her conduct as it affects the university community. Academic dishonesty, in whatever form, is ultimately destructive of the values of the university. Furthermore, it is unfair and discouraging to the majority of students who pursue their studies honestly. Scholarly integrity is required of all members of the university. http://www.sfu.ca/policies/gazette/student/s10-01.html