140.622.02
Statistical Methods in Public Health II
Cancelled
 Location:
 East Baltimore
 Term:
 2nd term
 Department:
 Biostatistics
 Credits:
 4 credits
 Academic Year:
 2022  2023
 Instruction Method:
 TBD
 Class Times:

 Tu Th, 10:30  11:50am
 Auditors Allowed:
 Yes, with instructor consent
 Undergrads Allowed:
 Yes
 Grading Restriction:
 Letter Grade or Pass/Fail
 Course Instructors:
 Contact:
 Karen BandeenRoche
 Resources:
 Prerequisite:
 Description:

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 PhD, ScM and MHS degree candidates in departments to be determined
 Instructor Consent:
Consent required for some students
 Consent Note:
Consent required for nonPH students
 For consent, contact:
 Special Comments:
Registration is expected to open for this section on or about October 5, 2020.