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This course is intended to build upon the foundational knowledge gained in CHEP 800 (Epidemiology) and CHEP 805 (Biostatistics) – building upon topics of bias, epidemiologic study design, inferential and descriptive statistics, and statistical model building. Course topics include: causation, bias, multivariable modelling, effect measure modification, mediation, study design, and “new” techniques in epidemiology (machine learning and predictive modelling). Methods of instruction will feature lectures and interactive lab sessions focused on the application of methods using the R statistical programming language. Focus will be on the understanding of modern epidemiological practice and analytical skills in order to conduct independent public health practice and research.
CHEP 800 (Epidemiology) or PUBH 800 (Epidemiology) AND CHEP 805 (Biostatistics I) or PUBH 805 (Biostatistics I); or permission of the instructor
By the completion of this course, students will be able to
- Apply concepts of causal reasoning in epidemiology research, including the use of Directed Acyclic Graphs and a counterfactual approach.
- Critically assess epidemiological study design, analytical methods and results.
- Construct a research question, devise an appropriate study design and conduct data analyses
- Conduct appropriate data management and visualization activities
- Effectively communicate research findings, both orally and in writing
- Critically assess and apply modern epidemiologic methods, such as causal inference and machine learning, appropriately
I acknowledge our shared connection to the land and recognize that First Nations and Métis peoples on Treaty 6 Territory and all Indigenous peoples have been and continue to be stewards for social justice, equity, and land-based education. In the spirit of reconciliation may we all strive to learn and support the work of Indigenous communities as allies and return their land.
The Department of Community Health and Epidemiology strives to be a leader in COVID-19 prevention. This is both an individual and collective challenge. If you feel sick or unwell, please do not come to class. Every accommodation will be made to support your learning if you are not able to come to class in person. If University of Saskatchewan policy regarding COVID-19 change during the course, I will update you as soon as possible and we will adapt the course as needed.
This course follows the general principles for GenAI from USask see here. They are summarised below
- Acknowledge AI tools: “All persons, sources, and tools that influence the ideas or generate the content should be properly acknowledged” (p. 3). Acknowledgement may be done in different ways, according to context and discipline, and should include the input to the tool.
- Do not list AI tools as authors: Authors must take responsibility and be accountable for content and an AI tool cannot do so.
- Recognize limits and biases of AI tools: Inaccuracies, errors, and bias are reproduced in AI tools in part because of the human produced materials used for training.
In general, my opinion is that you should exploring these tools, what they can do, and how you can integrate them into your work. We will be using co-pilot later in this course. These tools are great for editing, formatting, generating ideas, and writing very basic code. Not sure where to start, here is a list of tools https://www.futurepedia.io/. It's critical that when you use these tools you are very aware of bias and that you intervene to correct the text. Here are my general rules for AI in this course.
Unacceptable use
- It is academic misconduct on this course if the entire student assignment is generated by generative artificial intelligence (GenAI) technology or tools.
Acceptable use
- You can use GenAI technology or tools to edit content that they have initially drafted with the use to GenAI, including making changes that improve the clarity of writing and change wording or structure.
- You can use GenAI technology or tools to format, including generating a template or structure that match specifications.
- You can use GenAI technology or tools to get ideas, including suggesting topics, options, ways of doing things.
Requirements
- If you use GenAI you must include a 100 word reflective essay about the experience as part of your self-evaluation. You can see the guide on citing GenAI from the library here.
- Be very careful with reference. Many of these tools just make up random references.
- I will not use tools like GPTZero to detect whether you have used AI tools or not. We are making a agreement to be honest with each other here. This is small class. We have that luxury.
| Week | Date | Topic | Data Work |
|---|---|---|---|
| 1 | September 10 | Intro/Epi Review | Intro R |
| 2 | September 17 | Descriptive Epi and Study Design | Data Wrangling |
| 3 | September 24 | Bias | Data Visualization |
| 4 | October 1 | Confounding | Quantitative Bias Adjustment |
| 5 | October 8 | Direct Acyclic Graphs (DAGs) | Causal Quartet |
| 6 | October 15 | Causal Inference | Counterfactuals |
| 7 | October 22 | Missing Data | Missing Data |
| 8 | October 29 | Epi Communication | [No Data Work] |
| 9 | November 5 | Overview of Statistical Models | Logistic Regression |
| 10 | November 12 | Reading Week | |
| 11 | November 19 | Interaction/Effect Measure Modification | Effect Measure Modification |
| 12 | November 26 | Mediation | Mediation Analysis |
| 13 | December 3 | Special Topics | [Coming Soon] |
| 13 | December 10 | Student Presentations |
- Subject to change depending on speed
There is no one text for this course – lectures benefit from the following texts and articles. Students wishing to pursue employment as an epidemiologist and/or conduct independent epidemiological research may wish to purchase one or more of the resources; reading relevant sections of textbooks in the U of S library holdings is perfectly acceptable. Using the 3rd edition of the book is acceptable as well. However, versions older than the 3rd will not include many of the topics we cover in the class.
Szklo M, Nieto FJ. Epidemiology: Beyond the Basics (4th edition). Burlington, Mass: Jones & Bartlett, 2018. Textbooks are available on reserve at the Health Sciences library or you may wish to purchase one or more resources from the University of Saskatchewan Bookstore for future reference: http://www.usask.ca/bookstore/
Hernán MA, Robins JM (2020). Causal Inference: What If. Boca Raton: Chapman & Hall/CRC. Free Online.
Use of a statistical software program R is required for this course. Course examples will be provided in R.
| Assignment | Grade |
|---|---|
| Data Wrangling and Descriptive Statistics | 10% |
| Bias | 15% |
| DAG (Directed Acyclic Graphs) | 15% |
| Logistic Regression – Focus on confounding | 15% |
| Linear Regression – Focus on effect modification | 15% |
| Epi Communication – Part 1 (One Pager) | 15% |
| Epi Communication – Part 2 (Oral Presentation) | 15% |
| Total | 100% |
Value: 10% of final grade
Due Date: September 26, 2025
Description: Students will analyze a dataset and provide descriptive statistics in a report format.
Value: 15% of final grade
Due Date: October 10, 2025
Description: Students will perform the detection and description of selection and information bias and evaluate the extent of bias.
Value: 15% of final grade
Due Date: October 31, 2025
Description: Students will structurally evaluate confounding and bias using Directed Acyclic Graph.
Value: 15% of final grade
Due Date: November 14, 2025
Description: Students will interpret and create a written communication document from the results of a regression analysis.
Value: 15% of final grade
Due Date: November 28, 2025
Description: Students will perform model fitting and diagnosis for logistic regression, followed by the investigation, interpretation, and visualization of confounding.
Value: 15% of final grade
Due Date: December 12, 2024
Description: Students will perform model fitting and diagnosis for linear regression, followed by the investigation, interpretation, and visualization of interaction and effect measure modification.
Value: 15% of final grade
Due Date: December 10, 2024
Description: Students will present on an epidemiological topic of interest to them and provide critical information for communicating to different interest groups in public health.
In this course we will use the CanPath Student Dataset that provides students the unique opportunity to gain hands-on experience working with CanPath data. The CanPath Student Dataset is a synthetic dataset that was manipulated to mimic CanPath’s nationally harmonized data but does not include or reveal actual data of any CanPath participants.
The CanPath Student Dataset is available to instructors at a Canadian university or college for use in an academic course, at no cost. CanPath will provide the Student Dataset and a supporting data dictionary.
- Large sample size (Over 40,000 participants)
- Real-world population-level Canadian data
- Variety of areas of information allowing for a wide range of research topics
- No cost to faculty
- Potential for students to apply for real CanPath data to publish their findings
Information on literal descriptors for grading at the University of Saskatchewan can be found at: https://students.usask.ca/academics/grading/grading-system.php#GradingSystem Please note: There are different literal descriptors for undergraduate and graduate students.
More information on the Academic Courses Policy on course delivery, examinations and assessment of student learning can be found at: http://policies.usask.ca/policies/academic-affairs/academic-courses.php The University of Saskatchewan Learning Charter is intended to define aspirations about the learning experience that the University aims to provide, and the roles to be played in realizing these aspirations by students, instructors and the institution. A copy of the Learning Charter can be found at: http://www.usask.ca/university_secretary/LearningCharter.pdf
The following describes the relationship between literal descriptors and percentage scores for courses in the College of Graduate Studies and Research:
A superior performance with consistent strong evidence of
- a comprehensive, incisive grasp of subject matter;
- an ability to make insightful, critical evaluation of information;
- an exceptional capacity for original, creative and/or logical thinking;
- an exceptional ability to organize, to analyze, to synthesize, to integrate ideas, and to express thoughts fluently;
- an exceptional ability to analyze and solve difficult problems related to subject matter.
A very good to excellent performance with strong evidence of
- a comprehensive grasp of subject matter;
- an ability to make sound critical evaluation of information;
- a very good to excellent capacity for original, creative and/or logical thinking;
- a very good to excellent ability to organize, to analyze, to synthesize, to integrate ideas, and to express thoughts fluently;
- a very good to excellent ability to analyze and solve difficult problems related to subject matter.
A satisfactory to good performance with evidence of
- a substantial knowledge of subject matter;
- a satisfactory to good understanding of the relevant issues and satisfactory to good familiarity with the relevant literature and technology;
- a satisfactory to good capacity for logical thinking;
- some capacity for original and creative thinking;
- a satisfactory to good ability to organize, to analyze, and to examine the subject matter in a critical and constructive manner;
- a satisfactory to good ability to analyze and solve moderately difficult problems.
A generally weak performance, but with some evidence of
- a basic grasp of the subject matter;
- some understanding of the basic issues;
- some familiarity with the relevant literature and techniques;
- some ability to develop solutions to moderately difficult problems related to the subject matter;
- some ability to examine the material in a critical and analytical manner.
An unacceptable performance.
- Percentage scores of at least 70% are required for a minimal pass performance in undergraduate courses taken by graduate students;
- Percentage scores of at least 70% are required for a minimal pass performance for each course which is included in a Ph.D. program;
- Percentage scores of at least 70% are required for a minimal pass performance in all courses used toward JSGS Public Policy and Public Administration programs and all core courses for Master of Public Health students, whether included in a Ph.D. program or a Master's program;
- For all other graduate courses, percentage scores of at least 60-69% are required for a minimal pass performance for each course which is included in a Master's program, provided that the student's Cumulative Weighted Average is at least 70%;
- Graduate courses for which students receive grades of 60-69% are minimally acceptable in a Postgraduate Diploma program, provided that the Cumulative Weighted Average is at least 65%;
- Students should seek information on other program requirements in the Course & Program Catalogue and in academic unit publications.
Submit via Canvas.
Will occur throughout the course via anonymous “start/stop/continue” assessments following lectures/lab. Also welcome via email or in-person at any time.
The University of Saskatchewan is committed to the highest standards of academic integrity and honesty. Students are expected to be familiar with these standards regarding academic honesty and to uphold the policies of the University in this respect. Students are particularly urged to familiarize themselves with the provisions of the Student Conduct & Appeals section of the University Secretary Website and avoid any behavior that could potentially result in suspicions of cheating, plagiarism, misrepresentation of facts and/or participation in an offence. Academic dishonesty is a serious offence and can result in suspension or expulsion from the University.
All students should read and be familiar with the Regulations on Academic Student Misconduct (https://secretariat.usask.ca/documents/student-conduct-appeals/StudentAcademicMisconduct.pdf) as well as the Standard of Student Conduct in Non-Academic Matters and Procedures for Resolution of Complaints and Appeals (http://www.usask.ca/secretariat/student-conduct-appeals/StudentNon-AcademicMisconduct.pdf)
For more information on what academic integrity means for students see the Student Conduct & Appeals section of the University Secretary Website at: http://www.usask.ca/secretariat/student-conduct-appeals/index.php
Students who have disabilities (learning, medical, physical, or mental health) are strongly encouraged to register with Access and Equity Services (AES) if they have not already done so. Students who suspect they may have disabilities should contact AES for advice and referrals. In order to access AES programs and supports, students must follow AES policy and procedures. For more information, check www.students.usask.ca/aes, or contact AES at 306-966-7273 or [email protected].
Students registered with AES may request alternative arrangements for mid-term and final examinations. Students must arrange such accommodations through AES by the stated deadlines. Instructors shall provide the examinations for students who are being accommodated by the deadlines established by AES.
Student Learning Services (SLS) offers assistance to U of S undergrad and graduate students. For information on specific services, please see the SLS web site http://library.usask.ca/studentlearning/.
The Student and Enrolment Services Division (SESD) focuses on providing developmental and support services and programs to students and the university community. For more information, see the students’ web site http://students.usask.ca.
Any student who faces challenges securing their food or housing and believes this may affect their performance in the course is urged to contact Student Central (https://students.usask.ca/student-central.php).
The Aboriginal Students Centre (ASC) is dedicated to supporting Aboriginal student academic and personal success. The centre offers personal, social, cultural and some academic supports to Métis, First Nations, and Inuit students. The centre is also dedicated to intercultural education, brining Aboriginal and non-Aboriginal students together to learn from, with and about one another in a respectful, inclusive and safe environment. Students are encouraged to visit the ASC’s Facebook page (https://www.facebook.com/aboriginalstudentscentre/) to learn more.
The International Student and Study Abroad Centre (ISSAC) supports student success in their international education experiences at the U of S and abroad. ISSAC is here to assist all international undergraduate, graduate, exchange and English as a Second Language students and their families in their transition to the U of S and Saskatoon. ISSAC offers advising and support on all matters that affect international students and their families and on all matters related to studying abroad. Please visit students.usask.ca for more information.