ST 732 - 2011

Applied Longitudinal Data Analysis

Instructor

Meeting Times

Grader

Required Text

Lecture notes prepared by Marie Davidian will be used. These may be purchased at the Sir Speedy across the street from Patterson on Hillsborough. You should obtain a copy.

Prerequisite

Grading

Homework	10%
Data Analysis Project	20%
Midterm Exam (covers 1st half)	35%	Wednesday, March 2
Final Exam (covers 2nd half)	35%	Monday, May 9 (8-11 am)

There are no quotas or curves for A's and B's. I would be happy to give all A's. I value class participation: attendance and willingness to ask and answer questions in class. It will be used in deciding borderline grades.

Miscellaneous

1. Homework will be assigned once every two weeks. It will be due at the beginning of class. As a rule, I do not accept late homework. I allow students to work in groups on homework if they like, but no one should copy directly from someone else's paper (either present or past students). I stongly urge everyone to work on their own as much as possible.

   As the focus of the class is on the practical application of methods for longitudinal data analysis, many of the problems will involve using statistical software to carry out analyses on real data sets. To implement the analyses, we will use SAS; examples of the use of this software are included in the lecture notes. The principles underlying the methods we will learn involve much philosophy and subjectivity; thus, each homework will also include "essay"-type questions to help you to clarify your thinking on these matters.

   You should not obtain copies of previous student hw's or use them for helping to complete assignments. (Part of the reason for making the hw grade count only 10% is due to abuse of these rules. In particular, it is counter-productive to get much help on the hw because you will not learn the material using that approach.)

2. Data Analysis Project: Students will carry out an analysis of data collected in a study that will be described in detail in the assignment using methods covered in the class (which methods are relevant is to be determined by the student). Students will need to formalize the scientific questions posed by the investigator, carry out the appropriate analyses, interpret the results, and write a comprehensive report for the investigator reporting on all of these activities. The assignment will be handed out in late march and will be due on the last day of class.

3. The midterm and final exams will be in-class, closed book exams (a legal handwritten note sheet is allowed). The final will not be comprehensive except in the sense that the course builds on previous knowledge. It will cover the second half of the course. No cell phones or other electronic devices should be in sight or used in any way.

4. If you would like to audit the course, I require at least a 50% grade on homework to get an official audit. (After many years of teaching and sitting in on courses myself, I believe it is usually a waste of time to sit in a course without doing the homework.)

5. I will regularly ask for feedback on how the class is going. Please help me with your suggestions.

6. Academic Integrity: It is the understanding and expectation that a student's signature on any test or assignment means that the student neither gave nor received unauthorized aid. Consult the following website for further details on the code of student conduct: http://www2.ncsu.edu/prr/student_services/student_conduct/POL445.00.1.htm

Outline of Course Topics

1. Preliminaries: Introduction
   Review of matrix algebra
   Random vectors, multivariate normal distribution, review of linear regression
   Introduction to modeling longitudinal data, exploring covariance structure

2. Classical methods for normally distributed, balanced repeated measurements:
   Univariate repeated measures analysis of variance
   Multivariate repeated measures analysis of variance
   Drawbacks and limitations of classical methods

3. Methods for normally distributed, unbalanced repeated measurements:
   General linear models and models for correlation
   Random coefficient models
   Linear mixed effects models

4. Methods for non-normally distributed, unbalanced data:
   Probability models for discrete and continuous nonnormal data and generalized linear models
   Generalized estimating equations for population-averaged models

5. Advanced topics (brief overview):
   Generalized linear mixed effects models
   Nonlinear mixed effects models

For students with disabilities:

Reasonable accommodations will be made for students with verifiable disabilities. In order to take advantage of available accommodations, students must register with Disability Services for Students at 1900 Student Health Center, Campus Box 7509, 515-7653.

http://www.ncsu.edu/provost/offices/affirm_action/dss/

For more information on NC State's policy on working with students with disabilities, please see the Academic Accommodations for Students with Disabilities Regulation

http://www.ncsu.edu/policies/academic_affairs/courses_undergrad/REG02.20.1.php

Evaluations of course and instructor:

Online class evaluations will be available for students to complete during the last two weeks of class. Students will receive an email message directing them to a website where they can login using their Unity ID and complete evaluations. All evaluations are confidential; instructors will never know how any one student responded to any question, and students will never know the ratings for any particular instructors.

Evaluation website
Student help desk: classeval@ncsu.edu
More information about ClassEval