Mixed-Effects Models
			  Jose' C. Pinheiro
		    Biostatistics, Novartis Pharmaceuticals

			  COURSE DESCRIPTION

Mixed-effects models provide a powerful tool for analyzing grouped
data, because they model flexibly the within-group correlation often
present in this type of data. Examples of grouped data include
repeated measures, longitudinal data, multilevel data, and some
split-plot experiments, being collected in a wide variety of areas.
This course describes how to fit and analyze linear and nonlinear
mixed-effects models in S, using the NLME library. This is an applied
course, with emphasis on real life examples to motivate and illustrate
the use of mixed-effects models.


			       OUTLINE

1) Introduction
  - motivating examples of linear and non-linear mixed-effects models 
  - a sample of S functions and methods for fitting and
    analyzing mixed-effects models. 

2) Grouped data 
  - repeated measures data, longitudinal data, growth curve data
  - multilevel data
  - Data structures and Trellis displays for grouped data in S

3) Fitting linear mixed-effects models
  - the linear mixed-effects (LME) model
  - using the lme function in S
  - assessing the adequacy of an LME fit
  - confidence intervals, hypothesis tests, and predictions
  - fitting LME models to multilevel data

4) Extending the basic LME model
  - modeling the random effects covariance structure
  - variance functions to model unequal within-group variances
  - serial and spatial within-group correlation structures
  - an extended linear model with no random effects: the gls function

5) Fitting nonlinear mixed-effects models
  - brief review of nonlinear regression for independent observations
  - nonlinear mixed-effects (NLME) models for grouped data
  - the nlme function in S
  - assessing the adequacy of an NLME fit
  - confidence intervals, hypothesis tests, and predictions