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140.622.01
Statistical Methods in Public Health II

Location

East Baltimore

Term

2nd Term

Department

Biostatistics

Credit(s)

4

Academic Year

2023 - 2024

Instruction Method

In-person

Tu, Th, 10:30 - 11:50am

Lab Note

After the course opens, students will sign up in the CoursePlus Sign-up Sheets for a weekly Lab Session with review of a structured Lab Exercise. - The format is either onsite or online (synchronous virtual via Zoom). - The data analysis tool is either Stata or R. Note: Students choosing R should have prior experience using a computer programming language (Python, C, R, MATLAB, etc.)

Auditors Allowed

Yes, with instructor consent

Available to Undergraduate

No

Grading Restriction

Letter Grade or Pass/Fail

Course Instructor(s)

Contact Name

Frequency Schedule

Every Year

Resources

Prerequisite

140.621

Presents use of confidence intervals and and hypothesis tests to draw scientific statistical inferences from public health data. Introduces generalized linear models, including linear regression and logististic regression models. Develops unadjusted analyses and analyses adjusted for possible confounders. Outlines methods for model building, fitting and checking assumptions. Focuses on the accurate statement of the scientific question, appropriate choice of generalized linear model, and correct interpretation of the estimated regression coefficients and confidence intervals to address the question.

Learning Objectives

Upon successfully completing this course, students will be able to:
- Use statistical reasoning to formulate public health questions in quantitative terms
- Distinguish between the appropriate generalized linear regression models for expressing the relationship between a response (dependent variable or outcome) and one or more independent variables
- Recognize the assumptions required in using regression models and performing statistical tests to assess relationships between an outcome and a risk factor
- Use statistical methods for inference, including confidence intervals and tests, to draw valid public health inferences from study data
- Formulate and correctly interpret relationships in a linear regression model.
- Interpret the correlation coefficient as a measure of the strength of a linear association between a continuous response variable and a continuous predictor variable
- Interpret the coefficients, including interaction coefficients, obtained from a multiple linear regression analysis
- Estimate a confidence interval for a linear regression coefficient; interpret the interval estimates within a scientific context
- Distinguish the summary measures of association applicable to retrospective and prospective study designs
- Estimate two proportions and their difference, and confidence intervals for each; interpret the interval estimates within a scientific context
- Estimate an odds ratio, or relative risk, and its associated confidence interval; explain the difference between the two and when each is appropriate
- Interpret the coefficients, including interaction coefficients, obtained from a multiple logistic regression analysis
- Assess whether the relationship between a response (dependent) variable and an independent variable varies by the level of a second independent variable (effect modification)
- Recognize the influence of sample size on statistical inferences
- Use the Stata statistical analysis or R packages to perform regression analyses

Methods of Assessment

This course is evaluated as follows:

- 20% Assessments
- 10% Quizzes
- 70% Exam(s)

Enrollment Restriction

For onsite BSPH students or SON PhD students only. All other students may enroll in section 140.622.81 of this course.

Restricted to 350 students. Lectures are synchronous onsite. After the term starts and the course opens, each student signs up in CoursePlus for one 80-minute onsite Lab Session.