LONGITUDINAL
DATA ANALYSIS
FALL 2012
Instructor: 
GuanHua Huang, Ph.D. 

Office: 423 Joint Education Hall 

Phone: 035131334 

Email: ghuang@stat.nctu.edu.tw 
Class meetings: 
Wednesday 9:0012:00 at 407 Joint Education Hall 
Office hours: 
By
appointment 
Class website: 

Credit: 
Three (3) credits 
Longitudinal data consist of multiple measures over
time on an individual. This type of data occurs
extensively in both observational and experimental biomedical studies, as well
as in studies in sociology and applied economics. This course will provide an
introduction to the principals and methods for the analysis of longitudinal
data. While some theoretical statistical detail is given (at
the level of appropriate for a Master’s student in Statistics), the primary
focus will be on data analysis and interpretation.
The objects of his course are
Ÿ To identify features of longitudinal data and explain the roles of
longitudinal data in studying real data phenomenon.
Ÿ To use a generalized linear model to make inferences about the
relationship between responses and explanatory variables while accounting for
the correlation among repeated responses for an individual.
Ÿ To use marginal, random effects, or transition models for longitudinal
data when the repeated observations are binary, count, or Gaussian/nonGaussian
continuous.
Ÿ To familiarize the usage of statistical software implementing these longitudinal
data analytic methodologies.
Ÿ To provide references for your future research.
Handouts corresponding to each lecture
will be available on the class website before each class. Reading assignments are from the following two books:
Ÿ
Diggle PJ, Heagerty P, Liang KY and Zeger SL (2002). Analysis
of Longitudinal Data, 2^{nd} edition. Oxford University Press.
Ÿ
McCullagh P and Nelder JA (1989). Generalized
Linear Models, 2^{nd} edition. Chapman and Hall.
Students
are expected to have background on undergraduate probability, and mathematical
statistics. Some knowledge on (generalized) linear regression will be helpful.
The course grade will be based on four to five homework assignments (50%), one midterm
exam (20%),
and one final exam (30%).
COURSE OUTLINE
Diggle PJ,
Heagerty P, Liang KY and Zeger SL (2002). Analysis of Longitudinal Data, 2^{nd}
edition. (Diggle et al.).
McCullagh P
and Nelder JA (1989). Generalized Linear Models, 2^{nd} edition.
(McCullagh & Nelder)
Module 
Topic 

1 
Generalized
linear models (GLM) for independent data Ÿ
The origins of GLM Ÿ
Systematic and random components of GLM Ÿ
Some statistical properties of GLM Ÿ
Linear regression Ÿ
Logistic regression Ÿ
Poisson regression 
McCullagh
& Nelder Chapters 2, 3, 4, 5, and 6 
2 
Introduction
and examples 
Diggle et
al. Chapter 1 
3 
Exploring
longitudinal data 
Diggle et
al. Chapter 3 
4 
Linear
modes for longitudinal data 
Diggle et
al. Chapter 4 
5 
Parametric
models for covariance structure Ÿ
Parametric models for covariance structure Ÿ
Analysis of variance methods 
Diggle et
al. Chapters 5, and 6 
6 
GLM for
longitudinal data Ÿ
Marginal models Ÿ
Random effects models Ÿ
Transition models 
Diggle et
al. Chapters 7, 8, 9, and 10 