Methods for Causal Analysis and their application within Stata
Graham Dunn, Andrew Pickles and Milena Falcaro
Biostatistics Group, University of Manchester
25 and 26 September 2006
This 2-day course provides an overview of ideas drawn from statistics, epidemiology, economics and psychometrics that attempt to tackle the problem of causal inference. The theoretical bases and assumptions of methods are compared and contrasted, their implementations explained, and each will be illustrated using clinical trials or observational epidemiological studies. Practical exercises in will be undertaken in Stata.
Generalized Linear Latent and Mixed Models (GLLAMM): Random Effects and Latent Variable Models for Complex Data
Andrew Pickles and Milena Falcaro, University of Manchester
27-29 September 2006
The course shows how the concepts of random effects, latent variables and latent classes are related. These concepts are applied to continuous and binary response measures but also to common but less familiar ordinal scores. Factor, growth curve and trajectory models are all covered together with methods for complex missing data and sample designs. Examples are taken from the field of behavioural and social development. The course makes extensive use of the gllamm procedure implemented within the Stata program. A good understanding of regression and logistic regression will be assumed. Participants should also either already be familiar with random effects or latent variable modelling, or with routine data analysis within Stata.
Course details: http://www.ccsr.ac.uk/courses/external/2006-2007/details.html
Booking form: http://www.ccsr.ac.uk/courses/external/2006-2007/booking.html
Course enquiries: Kathryn Clements (tel.: +44 (0)161 275 4736)
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* http://www.ats.ucla.edu/stat/stata/
