EPIB
612 Advanced
Generalized Linear Models: Correlated Data 4 credits Office
hours by appointment Erica E. M. Moodie Purvis Hall room 38B Phone: 3985520 (email preferable: first.last@mcgill.ca) 

Prerequisite: 

Objectives: 
This aim
of this course is to extend linear model methods for analysis of data
with meanvariance relationships and data with noniid errors. The class
will
highlight common features of tools for analysis of correlated data with
a focus on longitudinal data,
and to give a 
Content: 
This course will have theoretical content but will focus on applications. Students can use any statistical software they wish; the R software package will be used for inclass illustrations. R is available (free) at http://lib.stat.cmu.edu/R/CRAN. Topics
to be covered are: I. Generalized linear
models  Review regression and inference
Review of independent data
with nonconstant variance
Multinomial outcomes (POM, CRM) Overdispersion Approaches to inference  estimating functions (likelihood, quasilikelihood, empirical variance estimates) II. GLM for correlated continuous data Clustered data,
longitudinal data (repeated measures)
Weighted least squares: maximum likelihood, REML, robust variance Specification & estimation of the covariance matrix Linear mixed models Generalized Estimating Equations (GEE) III. GLM for correlated categorical data Longitudinal data
(repeated measures)
Clustered/multilevel data Multivariate responses Semiparametric methods: GEE, sandwich variance Likelihoodbased methods: GLMM ** CLASS NOTES are available on WebCT ** 
Texts:  Required
text: Diggle, P.J., Heagerty, P.J, Liang, K.Y. and Zeger, S.L. (2002). Analysis of Longitudinal Data, Second Edition. Oxford University Press. Additional resource: Collett, D. (2003) Modelling Binary Data, Second Edition. Chapman & Hall In particular: chapters 3, 68 and section 10.1 Fitzmaurice, G.M., Laird, N.M. and Ware, J.H. (2004). Applied Longitudinal Analysis, Wiley. Verbeke, G. and Molenberghs, G. (2000). Linear Mixed Models for Longitudinal Data. SpringerVerlag. 
Some tips on reportwriting, and some useful references on common errors in analysis and reporting: Altman (1998) Stats in Med 17: 26612674, Strasak et al (2007) Swiss Med Wkly 137:4449, Bacchetti notes. Some handy code and an example for creating LaTeX code for a descriptive table in R, courtesy of a previous student (Ben Rich). 
