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Course Catalog

140.612.11 STATISTICAL REASONING IN PUBLIC HEALTH II

Department:
Biostatistics
Term:
Summer Inst. term
Credits:
3 credits
Academic Year:
2013 - 2014
Location:
East Baltimore
Dates:
Wed 06/19/2013 - Fri 06/28/2013
Class Times:
  • M Tu W Th F,  1:30 - 5:00pm
Auditors Allowed:
No
Grading Restriction:
Letter Grade or Pass/Fail
Contact:
Ayesha Khan
Course Instructor:

Course Evaluation

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Prerequisite:

140.611

Description:

Provides 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.

Learning Objectives:

Upon successfully completing this course, students will be able to:

  1. Recognize different study designs and Discuss the pros and cons of each
  2. Learn methods for randomly assigning subjects to two groups
  3. Describe the concepts of confounding and statistical interaction; discuss how to recognize each
  4. Explain the relationship between power and sample size
  5. Create a scatterplot to visually assess the nature of an association between two continuous variables
  6. Interpret the calculated values of the correlation coefficient and the coefficient of determination, and Discuss the relationship between these two measures of association
  7. Perform a simple linear regression using Stata and use the results to assess the magnitude and significance of the relationship between a continuous outcome variable and a continuous predictor variable and for predicting values of the outcome variable
  8. Discuss why multiple regression techniques allow for the analysis of the relationship between an outcome and a predictor in the presence of confounding variables
  9. Perform a multiple linear regression using Stata and use the results to assess the magnitude and significance of the relationship between a continuous outcome variable and multiple continuous and categorical predictor variables and for predicting values of the outcome variable
  10. Perform a multiple logistic regression using Stata and use the results to assess the magnitude and significance of the relationship between a dichotomous outcome variable and multiple continuous and categorical predictor variables
  11. Perform a multiple logistic regression using Stata and use the results to assess the magnitude and significance of the relationship between a dichotomous outcome variable and multiple continuous and categorical predictor variables
  12. Interpret the results from a proportional hazards regression model
Methods of Assessment:

Exams

Instructor Consent:

No consent required