Spring 2020 - HSCI 804 G100

Biostatistics for Population Health Practice II (3)

Class Number: 7081

Delivery Method: In Person

Overview

  • Course Times + Location:

    Fr 2:30 PM – 5:20 PM
    BLU 11660, Burnaby

  • Prerequisites:

    HSCI 801.

Description

CALENDAR DESCRIPTION:

Statistical methods related to public health. Probability distributions, basic statistical inference on means and proportions and general concepts of hypothesis testing. Measures of association. Simple and multivariable linear regression models, dummy variables, and logistic regression models. Survival data analysis.

COURSE DETAILS:


•  Review of probability distributions, inference on means, medians and proportions, confidence intervals and hypothesis testing 

•  Association between variables. Correlation coefficient and coefficient of determination. Relative Risk, Odds and Odds Ratio, Hazard and Hazard Ratio. 

•  Simple and multivariable linear regression models. Confidence intervals and inference testing in linear regression. Dummy variables and interactions. Confounding and collinearity. Prediction with linear regression. Model selection and goodness of fit. 

•  Logistic regression models. Confidence intervals and inference with logistic regression. Prediction with logistic regression. 

•  Survival data analysis. Kaplan-Meier estimate and log-rank test. Cox regression and proportional hazard assumption.
• Data analysis using statistical computing packages: SAS.

COURSE-LEVEL EDUCATIONAL GOALS:

•  
Distinguish between data types: continuous, categorical, binary, time-to-event.
•  Understand and properly use the data underlying probability distributions for each type of data.
•  Recognize key assumptions underlying regression analyses and be able to properly relate regression types to appropriate data types.
•  Perform linear, logistic, and Cox regression analysis with multiple variables to draw valid health inferences from data. 

•  Conduct statistical computations; interpret results using graphical and tabular displays for regression and write about them.

•  Communicate effectively in written and presentation form with statistical practitioners using regression methods. 

•  Develop computational skills with statistical package SAS.

Grading

  • Assignments 40%
  • Midterm 30%
  • Group Project 20%
  • Class participation 10%

NOTES:

Class Pre-requisite: HSCI801 or consent of instructor

Materials

RECOMMENDED READING:

•  Primary Text: Applied Regression Analysis and Other Multivariable Methods. (4th edition)- Klenbaum, Kupper, Nizam, Muller (2007)

•  Supplemental Text: The Little SAS Book: A Primer. (2nd or 3rd Edition) Delwiche and Slaughter.  

• Computer Program: SAS (WINDOWS version) available at computer labs AQ3144 and WMC 2502. SFU also provides SAS license and download for students to download and install SAS in own computers.

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.

Registrar Notes:

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

ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS