Spring 2020 - HSCI 804 G100
Biostatistics for Population Health Practice II (3)
Class Number: 7081
Delivery Method: In Person
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.
• 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.
- Assignments 40%
- Midterm 30%
- Group Project 20%
- Class participation 10%
Class Pre-requisite: HSCI801 or consent of instructor
• 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:
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