Fall 2020 - STAT 305 D100
Introduction to Biostatistical Methods for Health Sciences (3)
Class Number: 3788
Delivery Method: In Person
Course Times + Location:
Mo 12:30 PM – 2:20 PM
REMOTE LEARNING, Burnaby
We 12:30 PM – 1:20 PM
REMOTE LEARNING, Burnaby
Exam Times + Location:
Dec 11, 2020
12:00 PM – 3:00 PM
REMOTE LEARNING, Burnaby
Prerequisites:One of STAT 201, STAT 203, STAT 205, STAT 270, or BUEC 232.
Intermediate statistical techniques for the health sciences. Review of introductory concepts in statistics and probability including hypothesis testing, estimation and confidence intervals for means and proportions. Contingency tables and the analysis of multiple 2x2 tables. Correlation and regression. Multiple regression and model selection. Logistic regression and odds ratios. Basic concepts in survival analysis. This course may not be used to satisfy the upper division requirements of the Statistics major or honours program. Quantitative.
Course Details: Synchronous and online.
This upper-division course provides an opportunity for the further development of analytic skills acquired in basic courses in statistics and the health sciences. It concentrates on the relatively few techniques that are currently most used in health research, but it also seeks to provide a conceptual basis for understanding other techniques. The course focuses on unifying principles and widely applicable methods as opposed rote memorization of an array of unrelated ad-hoc procedures. The material is presented descriptively, from the point of view of understanding and practical use.
The emphasis of the course is on analysis (rather than design) of observational studies where there is one outcome variable of primary interest and where the data are made up of multiple independent observations. Important areas not covered are: classical multivariate analysis (e.g., factor analysis, discriminant analysis, etc.), longitudinal data analysis, time series, random effects models, and experimental design considerations (e.g., Latin squares, etc.).
By the end of the course the participant should:
1. understand the concept of a statistical model and how such models correspond to specific hypotheses or questions,
2. be able to interpret the results of an analysis in relation to the original questions or hypotheses that motivated the analysis,
3. be familiar with data analysis methods commonly used in health sciences and understand the basic limitations of competing methods,
4. understand and be able to critique the analysis methods described in published health research papers,
5. be able to communicate effectively with statistical consultants.
The scheduling of the following topics is approximate:
1. Review of introductory statistics from the pre-requisite course: Hypothesis testing, estimation and confidence intervals for means and proportions.
Review of basic concepts of probability with applications including diagnostic testing, sensitivity and specificity, the relative risk and the odds ratio.
2. Contingency Tables: The Chi-square test, r x c tables, multiple 2x2 tables, Simpson's paradox, Mantel- Haenszel method.
3. Correlation and simple linear regression: Regression concepts, estimation and testing for regression coefficients, evaluation of the model.
4. Multiple linear regression: Inference for regression coefficients, confounding and interaction, indicator variables, model selection, prediction, model assumptions and checking.
5. Logistic regression: Odds ratios, inference for regression coefficients, model assumptions, case-control studies.
6. Time permitting: Survival analysis including life tables, censoring, Kaplan-Meier method, log-rank test.
Mode of teaching:
- Lecture: Synchronous
- Midterm: Take home
- Final exam: Take home
- Assignments 30%
- Midterm 30%
- Final Exam 40%
Above grading is subject to change.
MATERIALS + SUPPLIES:
Access to high-speed internet.
Department Undergraduate Notes:
Students with Disabilites:
Students requiring accommodations as a result of disability must contact the Centre for Accessible Learning 778-782-3112 or email@example.com
Students looking for a Tutor should visit http://www.stat.sfu.ca/teaching/need-a-tutor-.html. We accept no responsibility for the consequences of any actions taken related to tutors.
ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS
SFU’s Academic Integrity web site 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
TEACHING AT SFU IN FALL 2020
Teaching at SFU in fall 2020 will be conducted primarily through remote methods. There will be in-person course components in a few exceptional cases where this is fundamental to the educational goals of the course. Such course components will be clearly identified at registration, as will course components that will be “live” (synchronous) vs. at your own pace (asynchronous). Enrollment acknowledges that remote study may entail different modes of learning, interaction with your instructor, and ways of getting feedback on your work than may be the case for in-person classes. To ensure you can access all course materials, we recommend you have access to a computer with a microphone and camera, and the internet. In some cases your instructor may use Zoom or other means requiring a camera and microphone to invigilate exams. If proctoring software will be used, this will be confirmed in the first week of class.Students with hidden or visible disabilities who believe they may need class or exam accommodations, including in the current context of remote learning, are encouraged to register with the SFU Centre for Accessible Learning (firstname.lastname@example.org or 778-782-3112).