Fall 2019 - HSCI 801 G100
Biostatistics for Population Health Practice I (4)
Class Number: 8026
Delivery Method: In Person
Basic statistical concepts as applied to diverse problems in epidemiologic and public health research. Emphasizes interpretation and concepts rather than calculations. Basic study designs' statistics. Descriptive and graphical methods, fundamentals of probability distribution, rates and standardization, contingency tables, odds ratios, confidence intervals, hypothesis testing, life tables, Linear regression.
Introduction to statistical techniques required in epidemiologic and health care research. Review of descriptive and graphical methods, probability distributions. Rates and standardization. Diagnostic tests and ROC curves. Study designs in health research. General concepts in estimation and hypothesis testing. Inference for proportions, contingency tables and odds ratios.
COURSE-LEVEL EDUCATIONAL GOALS:
At the end of this course, students should be able to
-Design and interpret graphical and tabular displays for statistical information.
-Describe basic concepts of probability, random variation and commonly used statistical distributions.
-Distinguish between different measurement scales and the implications for data analysis techniques.
-Distinguish between observational and experimental study designs.
-Apply common statistical methods for inference, including hypothesis testing and estimation.
-Interpret results of statistical analyses found in health research.
-Apply computer software packages to perform common statistical analyses.
- Assignments 40%
- Labs 5%
- Midterms 25%
- Final exam 30%
There will be four assignments, two midterms and a final exam ·
-Assignments (40%): Assignments will help students to master the concepts presented in the class and to achieve the learning objectives. Each assignment will be linked to the topics covered in the lectures. They will often involve reading journal articles, doing data analysis with the computer, and problems taken from the textbook.
-Labs (5%): Labs will help students to learn how to use the Logiciel R to perform common statistical analyses. The evaluation is based on participation and hand-in lab reports. Logiciel R is provided in the computer lab BLU 11660. Alternatively, students may use other software on their personal computer after consulting with the instructor.
-Midterms (25%): There are two midterm exams, each count for 12.5%.
-Final Exam (30%)
Assignments must be handed in on time, late assignments will not be marked. You may discuss ways to approach homework with other students or TA’s. However, each homework project must be your own independent work. Missing exams due to illness: you are required to contact the instructor prior to the exam by e-mail or in person. A medical doctors note specifying the date of your absence is required.
Prerequisite: An undergraduate course in statistics.
MATERIALS + SUPPLIES:
Required: Marcello Pagano and Kimberlee Gauvreau (2000) Principles of Biostatistics, 2nd Ed. A copy of the textbook is on reserve that the SFU library.
Week 1 - Course outline and administrative details. Descriptive statistics. Numerical summary measures including median, quantiles, mean, standard deviation standard errors. Readings: Chapter 2, Chaper 3: Section 3.1, 3.2. Skip 3.2.4, 3.3, 3.4
Week 2 - Study designs in epidemiology, Readings posted on Canvas along with notes for Lecture #3 Greenhalgh, T. (1997) "How to read a paper: getting your bearings (deciding what the paper is about)" British Medical Journal, 315(7102), 243--246. Pocock et al. (2004) "Issues in reporting epidemiological studies: a survey of recent practice" British Medical Journal 329:883 Montori et al. (2004) "Users' guide to detecting misleading claims in clinical research reports" British Medical Journal 329:1093
Week 3 - The normal distribution, sampling distribution of the sample mean. Readings: Chapter 6.1 Chapter 7.1, 7.4. Skip 7.3 (We will do 7.2 later, but I recommend that you read it now.) Chapter 8 including Section 8.4
Week 4 - Confidence intervals for a single mean, t-distributions. Introduction to hypothesis testing and p-values Readings: Chapter 8 including Section 8.4 Chapter 9.1, 9.3., 9.4 Skip 9.2 Chapter 10, (all sections including 10.7 but Skip 10.6)
Week 5 - Hypothesis testing for a single mean. Type I errors, power and p-values, comparison of two means. Chapter 10, (all sections including 10.7 but Skip 10.6) Chapter 11, including 11.3 Skip 11.1, 11.2.2
Week 6+7 - Simple linear regression and correlation, goodness of fit. Readings: Chapter 17, including 17.4 Skip 17.3 Chapter 18, including 18.4 Skip 18.2.4 AND Skip 18.3.3
Week 8- Multiple Regression, adjusting for several variables Readings: Chapter 19, including 19.3. Skip 19.1.5 Interaction terms
Week 9 – Risk, odds and proportions in health research. Diagnostic testing. Readings: Chapter 6, Section 6.1, 6.2 and 6.5. Skip 6.3, and 6.4. Chapter 7.2
Week 11 – 2 by 2 tables, comparing two proportions. Measures of effect in epidemiology. The odds ratios (OR), relative risk (RR) and risk difference. Chi-squared tests. Confidence intervals and hypothesis tests for ORs and RRs in 2 by 2 tables. Readings: Chapter 14 including 14.7 skip 14.5 Section 15.3, skip 15.1, 15.2, 15.4 Chapter 16.1, skip 16.2, 16.3
Week 12 - Logistic regression, regression modelling and variable selection Readings: Chapter 20 including 20.4
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.
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