NATIONAL CHIAO TUNG UNIVERSITY

INSTITUTE OF STATISTICS

 

LONGITUDINAL DATA ANALYSIS

FALL 2012

 

 

 


Instructor:

Guan-Hua Huang, Ph.D.

 

Office: 423 Joint Education Hall

 

Phone: 03-513-1334

 

Email: ghuang@stat.nctu.edu.tw

Class meetings:

Wednesday 9:00-12:00 at 407 Joint Education Hall

Office hours:

By appointment

Class website:

http://ghuang.stat.nctu.edu.tw/course/lda12/

Credit:

Three (3) credits

 

COURSE SUMMARY

 

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/non-Gaussian continuous.

Ÿ   To familiarize the usage of statistical software implementing these longitudinal data analytic methodologies.

Ÿ   To provide references for your future research.

 

HANDOUTS AND TEXTBOOKS

 

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, 2nd edition. Oxford University Press.

Ÿ   McCullagh P and Nelder JA (1989). Generalized Linear Models, 2nd edition. Chapman and Hall.

 

PREREQUISITES

 

Students are expected to have background on undergraduate probability, and mathematical statistics. Some knowledge on (generalized) linear regression will be helpful.

 

METHOD OF STUDENT EVALUATION

 

The course grade will be based on four to five homework assignments (50%), one midterm exam (20%), and one final exam (30%).

 

COURSE OUTLINE

 

Readings refer to:

Diggle PJ, Heagerty P, Liang KY and Zeger SL (2002). Analysis of Longitudinal Data, 2nd edition. (Diggle et al.).

McCullagh P and Nelder JA (1989). Generalized Linear Models, 2nd edition. (McCullagh & Nelder)

 

Module

Topic

Reading

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