ANALYSIS OF LONGITUDINAL DATA Syllabus
Course Learning Objectives
Upon successfully completing this course, students will be able to:
- Prepare graphical or tabular displays of longitudinal data that effectively communicate the patterns of scientific interest
- Implement and interpret a general linear model to make scientific inferences about the relationship between response and explanatory variables while accounting for the correlation among repeated responses for an individual
- Implement and interpret marginal, random effects, or transitional generalized linear models to make scientific inferences when the repeated observations are binary, counts, or non-Gaussian continuous observations
- Implement analysis of longitudinal data within SAS or STATA
Course DescriptionExplores statistical models for drawing scientific inferences from longitudinal data. Topics include longitudinal study design; exploring longitudinal data; linear and generalized linear regression models for correlated data, including marginal, random effects, and transition models; and handling missing data. Intended for doctoral students in quantitative sciences.
Additional Faculty Notes:
This course has been and is developing from lecture notes, examples, data sets, and software from many sources including Biostatistics faculty, colleagues and students. In particular we would like to recognize Dr. Francesca Dominici, Dr. Scott L. Zeger, Dr. Marie Davidian, Dr. Paul Rathouz, Dr. Rafael Irizarry, Dr. Aidan McDermott, Dr. Michael Griswold, and many biostatistics students for their invaluable contributions to the materials used for this course.
Intended AudienceStudents interested in learning more about longitudinal data analysis
Methods of Assessment
Student evaluation based on submission of lab materials, two data analysis homework assignments and a data analysis final exam. Each homework and the final exam require the student to complete an analysis of a longitudinal data set, present the results, and write a brief scientific report of the analysis methods and results. Students will be peer review grading each other's assignments to further enhance the analysis and writing skills for everyone in the course.
Prerequisites140.621-624, former 140.601-604, or 140.651-654
Additional Faculty Notes:
Applied Longitudinal Analysis (Wiley Series in Probability and Statistics), Garrett Fitzmaurice, Nan Laird, James Ware, Wiley-Interscience (2011, Second Edition)
This text is available on-line through JHU electronic library: http://JHU.eblib.com/patron/
The authors have a site where SAS code and lecture notes are posted: http://www.hsph.harvard.edu/fitzmaur/ala/
Multilevel and Longitudinal Modeling using Stata (College Station, TX: Stata Press.). Sophia Rabe-Hesketh and Anders Skrondal (Third Edition).
NOTES: There will be readings assigned from the Fitzmaurice Laird and Ware text and they implement all examples using SAS. The second text is also very valuable especially for Stata users. The Rabe-Hesketh and Skrondal text will be the required text for multi-level models (140.656) for those of you taking both courses.
Highly recommended for Biostat ScM and PhD students:
Analysis of Longitudinal Data (Hardcover, 2002), Author: Kung-Yee Liang, Patrick Heagerty, Peter J. Diggle, Scott L. Zeger
Additional references that students may find useful:
- Applied Longitudinal Data Analysis for Epidemiology : A Practical Guide, Jos W. R. Twisk ,Cambridge University Press (2003)
- Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence, Judith D. Singer, John B. Willett, Oxford (2003)
- Modeling Longitudinal Data (Springer Texts in Statistics), Robert E. Weiss, Springer 1 edition (2005)
- Linear Mixed Models for Longitudinal Data, G. Verbeke, G. Molenberghs, Springer Series in Statistics (2000)
- Models for Discrete Longitudinal Data (Springer Series in Statistics), Geert Molenberghs, Geert Verbeke, Springer 1 edition (2005)
- Linear Mixed Models in Practice : An Sas-Oriented Approach , Geert Verbeke, Geert Molenberghs, Springer-Verlag (2000).
- A Handbook of Statistical Analyses using Stata , Sophia Rabe-Hesketh and Brian Everitt, Chapman & Hall/CRC (2004)
Please see the course Session for a full list of dates and items for this course.
Academic Ethics Code
Students enrolled in the Bloomberg School of Public Health of The Johns Hopkins University assume an obligation to conduct themselves in a manner appropriate to the University's mission as an institution of higher education. A student is obligated to refrain from acts which he or she knows, or under the circumstances has reason to know, impair the academic integrity of the University. Violations of academic integrity include, but are not limited to: cheating; plagiarism; knowingly furnishing false information to any agent of the University for inclusion in the academic record; violation of the rights and welfare of animal or human subjects in research; and misconduct as a member of either School or University committees or recognized groups or organizations.
Please refer to the syllabus posted withinn the "General Information" folder in the On-Line Library
The Monday Lab sessions are required for Biostat ScM and PhD students (and other interested persons may attend). These will review more of the techninical details of the statistical methods. Readings from the DHLZ text are assigned and lecture notes from prior years labs (written by Bruce Swihart) are provided. Students will be working in teams to understand one of the technical topics in the course, complete an exercise and then present the topic to the lab session. More details will be provided.
The Wednesday Lab sessions are designed for the entire course and provide an opportunity for the course instructors and students to review and discuss data analyses completed by the students in preparation for each homework assignment. For each lab session, the students will be submitting a small portion of results from their data analysis for each Homework assignment. This will be a time for discussion of the assignments and an opportunity to ask questions relating to the course material.
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