**Generalized
linear latent and mixed modelling**

2 - 3 July 2004, Florence, Italy

**Please
note that no places are left at the short course**

Venue

Department of Statistics "G.
Parenti"

viale Giovanni Battista Morgagni 59

50134 Florence

**Registration
will start at 8.30 on Friday 2 July 9.30**

Brief Description

**Anders
Skrondal** and **Sophia Rabe-Hesketh** will
present a two-day course on Generalized linear latent and mixed
modelling on 2-3 July 2004 before the 19th International Workshop
on Statistical Modelling.

**Generalised
linear mixed models**, also known as hierarchical or multilevel
models are useful for clustered data where observations on the
same cluster cannot be assumed to be independent. Examples include
longitudinal data, custer-randomised trials, surveys with cluster-sampling,
genetic studies, meta-analysis and many other applications. The
models are generalized linear models in which the intercept and
some of the regression coefficients are allowed to vary randomly
between clusters to represent between-cluster heterogeneity and
induce within cluster-dependence. The random intercept and coefficients
can be viewed as latent variables. Latent variables are also often
used to represent the true value of a variable measured with error,
a typical example being dietary intake (continuous latent variable)
or diagnosis (categorical latent variable). **Measurement
models** relating the measured variables to the latent
variable can be used to investigate the characteristics of measurement
instruments or diagnostic tests. These models are called factor
or item response models if the latent variables are continuous
and latent class models if they are categorical. **Generalized
linear latent and mixed models** unify generalised linear
mixed models and measurement models in a single response model.
To allow for hierarchical dependence structures where clusters
are themselves nested in higher-level clusters, latent variables
can vary at different levels. The response model can be combined
with a structural model to regress latent variables on other latent
or observed variables varying at the same or higher level, giving
(multilevel) **structural equation models**. An important
application of structural equation models in biostatistics is
regression with covariate measurement error. Structural equation
models are also useful for modelling the dependence between different
processes, for instance the response of interest in a clinical
trial and the (non-ignorable) drop-out process. Maximum likelihood
estimation of Generalized linear latent and mixed models (GLLAMMs)
is implemented in a Stata
program gllamm
which also provides empirical Bayes prediction of the latent variables.

Provisional
Course Outline

There will
be lectures in the morning and computer labs in the afternoon.
Approximate course outline: