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Viewing: ST 732 : Longitudinal Data Analysis

Last approved: Thu, 08 Mar 2018 09:02:46 GMT

Last edit: Wed, 07 Mar 2018 16:09:43 GMT

Catalog Pages referencing this course
Change Type
Major
ST (Statistics)
732
020402
Dual-Level Course
Cross-listed Course
No
Longitudinal Data Analysis
Longitudinal Data Analysis
College of Sciences
Statistics (17ST)
Term Offering
Spring Only
Offered Every Year
Spring 2018
Previously taught as Special Topics?
Yes
2
 
Course Prefix/NumberSemester/Term OfferedEnrollment
ST 790Spring 201620
ST 790Spring 201717
Course Delivery
Face-to-Face (On Campus)

Grading Method
Graded/Audit
3
15
Contact Hours
(Per Week)
Component TypeContact Hours
Lecture3.0
Course Attribute(s)


If your course includes any of the following competencies, check all that apply.
University Competencies

Course Is Repeatable for Credit
No
 
 
Marie Davidian
Professor
full

Open when course_delivery = campus OR course_delivery = blended OR course_delivery = flip
Enrollment ComponentPer SemesterPer SectionMultiple Sections?Comments
Lecture1515NoBased on previous classes.
Open when course_delivery = distance OR course_delivery = online OR course_delivery = remote


Is the course required or an elective for a Curriculum?
No
Introduction to modeling longitudinal data; Population-averaged vs. subject-specific modeling; Classical repeated measures analysis of variance methods and drawbacks; Review of estimating equations; Population-averaged linear models; Linear mixed effects models; Maximum likelihood, restricted maximum likelihood, and large sample theory; Review of nonlinear and generalized linear regression models; Population-averaged models and generalized estimating equations; Nonlinear and generalized linear mixed effects models; Implications of missing data; Advanced topics (including Bayesian framework, complex nonlinear models, multi-level hierarchical models, relaxing assumptions on random effects in mixed effects models, among others). Implementation in SAS and R.

The previous 732 course was an applied course, with both stat graduate students and also students from other fields.  We have combined that 732 with an applied multivariate course to become ST 537.  Here we are making ST 732 a course for Statistics PhD students.


No

Is this a GEP Course?
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Requisites and Scheduling
 
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Complete the following 3 questions or attach a syllabus that includes this information. If a 400-level or dual level course, a syllabus is required.
 
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This course will be taught as part of the instructor's regular course load.

Studies in which data are collected repeatedly on a sample of individuals over time (or some other condition) are ubiquitous in the health, social, and behavioral sciences; agricultural and biological sciences; education; economics; and business. Questions of interest in the context of such longitudinal data often focus on patterns of change of outcomes of interest over time and on elucidating factors that are associated with patterns of change in relevant populations of individuals. Because the study of change is so pervasive across almost all disciplines, statistical models and methods for the analysis of longitudinal data have become essential tools for practicing statisticians. Moreover, as studies and technologies giving rise to longitudinal data become increasingly complex, development of new methodology continues to be an active research area. This course will provide an overview of statistical models and methods for longitudinal data analysis. Fundamental modeling strategies and methodological developments will be presented in detail and their properties studied via theoretical arguments carried out at a heuristic level. Implementation in R and SAS will also be discussed.


The course will begin with a conceptual framework for thinking about longitudinal data, followed by a brief review of "classical" methods, whose limitations will be highlighted. The rest of the course will focus on more modern methods. Selected advanced topics will also be covered. This course is background for study of areas such as semiparametric theory, functional data analysis, and analysis in the presence of missing data.


Student Learning Outcomes


At the end of the course, students will be able to:


·       Translate a subject-matter problem involving the collection of longitudinal data into an appropriate statistical model framework and formalize the scientific questions of interest within that framework


·       Implement classical methods for the analysis of repeated measurement/longitudinal data and appreciate their drawbacks


·       Specify and implement population-averaged linear and nonlinear models for both continuous and discrete outcomes and identify when this type of model is an appropriate framework for a scientific question


·       Specify, and implement subject-specific linear and nonlinear models for both continous and discrete outcomes and identify when this type of model is an appropriate framework for a scientific question


·       Compare various methods of implementation of these models and their theoretical and practical properties


·       Discuss the implications of missing data for validity of longitudinal data analyses based on these models and methods


Evaluation MethodWeighting/Points for EachDetails
Written Assignment35Five homework assignments. Each will involve a combination
of analytical problems, data analyses, and simulation studies, where the latter two will involve programming on the part of the student.
Midterm30In class exam covering the first half of course material. The exam will be closed book, but students can prepare and use one 8 ½ x 11 page of notes (front and back).
Project25The final project will involve a substantial modeling and analysis task using methods covered in the course. Student will conceive, carry out, and interpret analyses and prepare a detailed report, suitable for presentation to a non-statistician scientist-collaborator, explaining the choices of analyses and their interpretation and summarizing the findings in the subject matter context.
Other10Instructor discretion: This portion is based on attendance (5%) and participation in class (5%).
TopicTime Devoted to Each TopicActivity
N/AN/AN/A
mlnosbis 11/6/2017:
1) Course length is 16 weeks; week 16 is final exam
Changed
2) Clarify whether the prerequisite is ST 702 AND ST 705 or ST 702 OR 705
Definitely "And." I don't know how to make it clearer.
3) Suggest that the Instructor's Discretion grading is divided into the various elements. Students will not like such ambiguity on the syllabus for grade determination.
We disagree. This is only 10% and the instructor has said the basis for it.
4) Weekly office hours should be defined on syllabus, even if it is "Mondays"
Changed.

cohen 1/18/2018:
In the section on the syllabus on Course Policies, it might be better to say something along the lines of "Missing more than ... classes" rather than "Chronic absenteeism." I would also suggest including the policy under the section on Attendance.
Changed

ABGS Reviewer Comments 2/5/2018:
- typo in the syllabus: Learning Outcomes > 4th bullet, 2nd line should be "continuous"
- Is ST 732 a required course for current Masters or PhD students? Since they are using the same course number but changing the material it seems like it can be confusing. It sounds like students on the "old program" would take ST 537. Response. It is not a required course, but it is now a core PhD elective. The prerequisites keep it from confusing anybody. Only PhD students in statistics will be eligible. ST 537 is for masters students and non-statistics majors.
- I am concerned with the grading category "Other 10 Instructor discretion: The instructor’s discretion portion will be based on attendance, participation in class, and instructor’s assessment of mastery of the material." It is fine for the instructor to grade participation. However I am concerned about attendance and how this grading component aligns with the attendance policy. The policy states "Attendance Policy Students are expected to attend each class period. Missing more than 4 classes will result in at least a 5 point reduction in the final score, as determined by the instructor." Could students lose 5 points from the policy and then an additional 10 from the discretionary category? Are students penalized twice for not attending? Similarly for mastery. Are students penalized twice, once for not doing well on assignments and then a second time within the instructor's discretion category. Response: we have clarified the point distribution.
Key: 5152