# 140.624.01 STATISTICAL METHODS IN PUBLIC HEALTH IV

Department:
Term: 4th term
Credits: 4 credits
Contact: James Tonascia
Course Instructor:
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.

Old Learning Objective:

1) 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 interest 2) Design a tabular or graphical display of a dataset that makes apparent the association between explanatory variables and the response 3) 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. 4) Appreciate that the interpretation of a particular multiple regression coefficient depends on which other explanatory variables are in the model 5) 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 6) Check whether a model fits the data well; identify ways to improve a model when necessary 7) Use several models for the analysis of a dataset to effectively answer the main scientific questions 8) Understand how longitudinal data differ from cross-sectional data and why special regression methods are sometimes needed for their analysis 9) 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 10) Perform data management, including input, editing, and merging of datasets, necessary to analyze data in Stata 11) Complete a data analysis project, including data analysis and a written summary in the form of a scientific paper

New Learning Objective(s):
Upon successfully completing this course, students will be able to:
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 interest
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.
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 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

Methods of Assessment: Student evaluation based on problem sets, a data analysis project, and a final exam.
Location: East Baltimore
Class Times:
• Tuesday 10:30 - 11:50
• Thursday 10:30 - 11:50
Lab Times:
• Tuesday 3:30 - 5:20
• Wednesday 3:30 - 5:20
• Thursday 1:30 - 3:20
Enrollment Minimum: 10
Instructor Consent: No consent required
Prerequisite:

140.623

Auditors Allowed: Yes, with instructor consent