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Causal inference in epidemiology: recent methodological developments
10-14 November 2008
London School of Hygiene & Tropical Medicine
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COURSE ORGANISERS
Bianca De Stavola, Simon Cousens, Mike Kenward, and John Whittaker
Guest lecturer: Stijn Vansteelandt (Ghent University, Belgium)
THE COURSE
Most courses on statistical modelling in epidemiology concentrate on the use of regression models
to estimate causal effects while adjusting for measured confounders. These address uncertainty
due to sampling error but not, in general, other sources of error and uncertainty which may arise
from missing data, measurement error, uncontrolled confounding, and/or selection bias.
Recent methodological advances make it feasible to incorporate at least some of these sources of
error into statistical models so that quantitative assessments can be made of their impact on
estimates of causal effect and the uncertainty around those estimates. The proposed course will
discuss the current state of the art with respect to these issues, while retaining a practical
focus. Participants will acquire awareness of the common threads across these new methods and
competence in applying them in simple settings.
WHO SHOULD APPLY?
Participants will be expected to be numerate epidemiologists, or applied statisticians with
an interest in epidemiology and clinical trials.
COURSE CONTENT
The topics covered will be:
* Causal diagrams and the backdoor criterion for the identification of confounders
* Missing data and measurement error mechanisms as components of causal diagrams
* Methods to deal with the bias introduced by unmeasured confounders, measurement error and
missing data.
* Frequentist and a Bayesian overview of these methods.
* Practical experience of the above methods in Stata.
COURSE FEE: £ 800
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(apologies for multiple posting)
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