Instructor: Dr. Wenbin Lu (email:
lu@stat.ncsu.edu)
Lectures: T, TH 8:309:45pm, 1216 SAS Hall. Syllabus
Office Hour: T 4:005:00pm, TH,
3:004:00pm, 5212 SAS Hall (or by appointment)
TA: Ahn, Mihye (email: mahn@ncsu.edu)
Office Hour: 1101
SAS Hall, W 24pm.
Textbooks: Lecture notes prepared by Dr. Davidian will be used.
These may be purchased at the Sir Speedy across the street from Patterson on
Hillsborough. You should obtain a copy.
Useful Links:
Data Links:
Data Analysis Project:
Course
Schedule 

Week 1 
Lecture 1: Introduction for longitudinal data 

Lecture 2: Matrix review, multivariate normal review 


Week 2 
Lecture 3: Models for longitudinal data (Ch. 4) 

Lecture 4: Split plot model in SAS 


Week 3 
Lecture 5: SAS glm with the repeated statement. 

Lecture 6: Multivariate statistics via glm and the repeated statement and manova options (Ch. 6), restrictions of glm (Ch. 7) 

Week 4 
Lecture 7: Introduction to modeling longitudinal data in proc mixed, covariance structure selection 

Lecture 8: "Noint" parameterization and "Difference" parameterization for dental data. Analysis for the dialyzer data, where pressure is measured at different values 

Week 5 
Lecture 9: Analysis of the hip replacement data, with missing response values, proc mixed parameterscontrast and estimate statements 

Lecture 10: Units within blocks over time and comparisons with standard ANOVA 


Week 6 
Lecture 11: Random coefficient models (Ch. 9) 

Lecture 12: Questions
about Midterm. Random cofficient models, dental data and dializer data 


Week 7 
Lecture 13: The general linear mixed model: linear terms that are either fixed or random, Best Linear Unbiased Prediction (BLUP) of random quantities 

Lecture 14: More on BLUPS and EBLUPS. Weight lifting example 

Week 8 
Lecture 15: Testing of variance components 

Lecture 16: Generalized linear models, independent Y's. Poisson
example.



Week 9 
Lecture 17: GLM continued. Binary and gamma examples. 

Midterm Exam, March 11, Thursday, 8:309:45am 


Week 10 
Spring Break, no class 

Spring Break, no class 


Week 11 
Lecture 18: GLM continued. Binary and gamma examples. 


Lecture 19: Iterative
reweighted Least Squared (IRLS) estimation and algorithm.


Week 12 
Lecture 20: Score equations and asymptotic variance 


Spring Holiday, no class 

Week 13 
Lecture 21: Begin
GEE, extension of GLM's to longitudinal data (Ch. 12) 


Lecture 22: More GEE 

Week 14 
Lecture 23: GEE, sandwich matrix details 


Lecture 24: Random coefficient models in ordinal logistic regression, schizophrenia example 

Week 15 
Lecture 25: Random coefficient models in ordinal logistic regression, schizophrenia example 


Lecture 26: Choosing longitudinal modelsreview 

Week 16 
Lecture 27: Project
discussion 

Lecture 28: Project discussion 


Final Exam 
May 13th, Thursday, 8:0011:00am 
