# STATISTICAL METHODS IN PUBLIC HEALTH IV Syllabus

140.624.01 | AY 2013-2014 - 4th Term | 4 Credit(s)
TTh 10:30:00 AM
• Contact Information
Faculty
James Tonascia
• Course Learning Objectives

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

• 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, log-linear, or survival regression model appropriate to address a scientific question and correctly interpret the meaning of its parameters
• Explain that the interpretation of a particular multiple regression coefficient depends on which other explanatory variables are in the model
• Estimate the undiscussn 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 cross-sectional data and why special regression methods are sometimes needed for their analysis
• Summarize in a table, the results of linear, logistic, log-linear, 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
• Frame a scientific question about the dependence of a continuous, binary, count, or time-to-event response on explanatory variables in terms of linear, logistic, log-linear, or survival regression model whose parameters represent quantities of scientific
• Course 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 log-linear models and on techniques for the evaluation of survival and longitudinal data. Also presents methods for the measurement of agreement, validity, and reliability.
• Intended Audience
Master's or doctoral students who wish to gain skills in using statistical methods in data analysis
• Methods of Assessment
Student evaluation based on problem sets, a data analysis project, and a final exam.
• Prerequisites
140.623
• Required Text(s)

• Dupont, W. D. :  Statistical Modeling for Biomedical Researchers: A Simple Introduction to the Analysis of Complex Data, 2009,  Cambridge University Press, Cambridge, U.K

Also recommended (optional):

• Bernard Rosner. Fundamentals of Biostatistics 2000, Duxbury Press, Pacific Grove, CA
• Hamilton, L: Statistics with Stata
• Course Schedule

Please see the course Session for a full list of dates and items for this course.