140.624.01
Statistical Methods in Public Health IV
 Location:
 East Baltimore
 Term:
 4th term
 Department:
 Biostatistics
 Credits:
 4 credits
 Academic Year:
 2021  2022
 Instruction Method:
 Synchronous Online with Some Asynchronous Online
 Class Times:

 Tu Th, 10:30  11:50am
 Lab Times:


Wednesday, 3:30  5:20pm

 Auditors Allowed:
 Yes, with instructor consent
 Grading Restriction:
 Letter Grade or Pass/Fail
 Course Instructors:

 James Tonascia
 Mark Van Natta
 Contact:
 James Tonascia
 Resources:
 Prerequisite:
140.621, 140.622 and 140.623 OR 140.611, 140.612, 140.613. 140.614, AND 140.620
 Description:

Expands studentsâ€™ abilities to conduct and report the results of a valid statistical analysis of quantitative public health information. Develops more advanced skills in multiple regression models, focusing on loglinear models and on techniques for the evaluation of survival and longitudinal data. Also presents methods for the measurement of agreement, validity, and reliability.
 Learning Objectives:

Upon successfully completing this course, students will be able to:
 Frame a scientific question about the dependence of a continuous, binary, count, or timetoevent response on explanatory variables in terms of linear, logistic, loglinear, or survival regression model whose parameters represent quantities of scientific
 Design a tabular or graphical display of a dataset that makes apparent the association between explanatory variables and the response
 Choose a specific linear, logistic, loglinear, or survival regression model appropriate to address a scientific question and correctly interpret the meaning of its parameters
 Appreciate that the interpretation of a particular multiple regression coefficient depends on which other explanatory variables are in the model
 Estimate the unknown coefficients and their standard errors using maximum (or partial) likelihood and perform tests of relevant null hypotheses about the association with the response of particular subsets of explanatory variables
 Check whether a model fits the data well; identify ways to improve a model when necessary
 Use several models for the analysis of a dataset to effectively answer the main scientific questions
 Describe how longitudinal data differ from crosssectional data and why special regression methods are sometimes needed for their analysis
 Summarize in a table, the results of linear, logistic, loglinear, and survival regressions and write a description of the statistical methods, results, and main findings for a scientific report
 Perform data management, including input, editing, and merging of datasets, necessary to analyze data in STATA
 Complete a data analysis project, including data analysis and a written summary in the form of a scientific paper
 Methods of Assessment:
This course is evaluated as follows:
 20% Quizzes
 40% Project(s)
 40% Exam(s)
 Instructor Consent:
No consent required
 Special Comments:
IT IS NOT NECESSARY TO REGISTER SEPARATELY FOR LABS. Instructional labs are Tuesday (3:305:20), Wednesday (3:305:20), or Thursday (1:303:20). Computing labs are Monday  Friday, 2:304:20. Students will use the Stata statistical analysis software for problem sets; Stata is installed for their use in the computer labs.