STATISTICAL REASONING IN PUBLIC HEALTH II Syllabus
Course Learning Objectives
Upon successfully completing this course, students will be able to:
- Interpret the results from simple linear regression to assess the magnitude and significance of the relationship between a continuous outcome variable and a binary, categorical or continuous predictor variable.
- Assess the strength of a linear relationship between two continuous variables via the coefficient of determination (R squared) and/or it's counterpart, the correlation coefficient
- Interpret the results from simple logistic regression to assess the magnitude and significance of the relationship between a binary outcome variable and a binary, categorical or continuous predictor variable
- Interpret the results from simple Cox regression to assess the magnitude and significance of the relationship between a time to event variable and a binary, categorical or continuous predictor variable
- Explain the assumption of proportional hazards, and what this mean regarding the interpretation of hazard (incidence rate) ratios from Cox regression models
- Explain how most of the hypotheses tests covered in Statistical Reasoning 1 can be expressed as simple regression models
- Describe the conditions necessary for an exposure/outcome relationship to be confounded by one or more other variables
- Explain how to interpret an adjusted association
- Explain the concept of effect modification, and how it differs from confounding
- Describe the process for assessing whether an outcome/exposure association is modified by another factor.
- Discuss why multiple regression techniques allow for the analysis of the relationship between an outcome and a predictor in the presence of confounding variables
- Utilize the results from all regression types covered (linear, logistic and Cox) to assess confounding and effect modification.
- Use the results from linear regression model to predict the mean value of a continuous outcome variable for different subgroups of a population defined by different predictor set values.
- Use the results from logistic regression models to predict the probability of a binary condition for different subgroups of a population defined by different predictor set values.
- Explain what a propensity score is, and how it can be useful for estimated an adjusted outcome/exposure relationship ion the presence of potentially many confounders
Narrated and Annotated Lecture Slides
Narrated and annotated versions of all course lectures are available at this URL.
Assignment and Exam Dates
HW1 due 11/8 11:59 pm
Quiz 1 11/12 2:00 PM -11/14 11:59 PM
HW2 due 11/21 11:59 pm
Quiz 2 11/26-11/27
HW3 due 12/7 11:59 pm
Quiz 3 12/10-12/12
Quiz 4 (Final Quiz) 12/18-12/22
Grading Components and Policy
Three Homework Assignments (16% each, for a total of 48%)
Three Quizzes (11% each for a total of 33%)
Final Quiz (19%)
An “A” is guaranteed if the cumulative average >= 90%.
A “B” is guaranteed if the cumulative average >= 80% but < 90%.
A “C” is guaranteed if the cumulative average >= 65% but < 80%.
Steven Christiano, Carrie Epstein, Sarah Naeger, Prasad Patil and Shuo Xu
Office hours will commence on Tuesday, November 5, 2013.
The schedule for office hours is as follows:
Tuesday 12:15-1:15, Room W4007
Tuesday 5-6 pm, Room W2033
Wednesday 12:15-1:15, Room W4007
Thursday 12:15-1:15, Room W4007
Thursday 5-6 pm, Room W2033
Course DescriptionProvides a broad overview of biostatistical methods and concepts used in the public health sciences, emphasizing interpretation and concepts rather than calculations or mathematical details. Develops ability to read the scientific literature to critically evaluate study designs and methods of data analysis. Introduces basic concepts of statistical inference, including hypothesis testing, p-values, and confidence intervals. Topics include comparisons of means and proportions; the normal distribution; regression and correlation; confounding; concepts of study design, including randomization, sample size, and power considerations; logistic regression; and an overview of some methods in survival analysis. Draws examples of the use and abuse of statistical methods from the current biomedical literature.
There are no required texts for the course. For many of the course lectures, however, I will recommend some readings from some of the following electronic texts (available through Welch Library):
Ambrosius W: Topics in Biostatistics http://www.springerprotocols.com/BookToc/doi/10.1007/978-1-59745-530-5
Dawson B, Trap R: Basic & Clinical Biostatistics www.accessmedicine.com.ezproxy.welch.jhmi.edu/resourceTOC.aspx?resourceID=62
Gauch R. It's Great! Oops, No It Isn't: Why Clinical Research Can't Guarantee the Right Medical Answers link.springer.com.ezproxy.welch.jhmi.edu/book/10.1007/978-1-4020-8907-7/page/1
Kestenbaum B: Epidemiology and Biostatistics: An Introduction to Clinical Research http://link.springer.com.ezproxy.welch.jhmi.edu/book/10.1007/978-0-387-88433-2/page/1
Please see the course Session for a full list of dates and items for this course.
Academic Ethics Code
Students enrolled in the Bloomberg School of Public Health of The Johns Hopkins University assume an obligation to conduct themselves in a manner appropriate to the University's mission as an institution of higher education. A student is obligated to refrain from acts which he or she knows, or under the circumstances has reason to know, impair the academic integrity of the University. Violations of academic integrity include, but are not limited to: cheating; plagiarism; knowingly furnishing false information to any agent of the University for inclusion in the academic record; violation of the rights and welfare of animal or human subjects in research; and misconduct as a member of either School or University committees or recognized groups or organizations.
Disability Support ServicesIf you are a student with a documented disability who requires an academic accommodation, please contact the Office of Student Life Services at 410-955-3034 or via email at email@example.com.