Welcome to the CoursePlus Web site for STATISTICAL METHODS IN PUBLIC HEALTH IV (140.624.01), a course offered by the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health.
Course website: http://www.biostat.jhsph.edu/courses/bio624/
Expands students’ 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.
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
Additional Course Objective(s):
- To expand students' abilities to conduct and report the results of valid statistical analysis of quantitative public health information. To develop more advanced skills in multiple regression models, focusing on linear, logistic, and log-linear models and on techniques for evaluation of survival and longitudinal data.
Tue Thu 10:30 AM to 11:50 AM
Additional Faculty Notes:
Mark Van Natta, MHS
Master's or doctoral students who wish to gain skills in using statistical methods in data analysis
Grading Policy: Student evaluation based on problem sets, a data analysis project, and a final exam.
Grading Restrictions: Letter grade
Also recommended (optional):
- Bernard Rosner. Fundamentals of Biostatistics 2000, Duxbury Press, Pacific Grove, CA
- Hamilton, L: Statistics with Stata
- Stata User's Guide and Reference Manuals
- Rabe-Hesketh S and Everitt B: A Handbook of Statistical Analyses Using Stata, Third Edition