Advanced Epidemiological and Statistical Methods

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Tutors: Prof David Gunnell and Dr Margaret May (course organisers), Prof Yoav Ben-Shlomo, Simon Cousens, Prof George Davey Smith, Prof Debbie Lawlor, Dr Richard Martin, Dr Chris Metcalfe, Dorothea Nitsch, Prof Jonathan Sterne.

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The contributors are epidemiologists and medical statisticians from the University of Bristol's Department of Social Medicine and the London School of Hygiene and Tropical Medicine.

Duration: Five days.

Dates: 28 June - 2 July 2010.

Course fee: £900.

Course aims and objectives: To provide a grounding in the concepts and analysis of life-course data, measurement error, clustered data, missing data and causal models. By the end of the course students should:

Understand the concepts of additive and multiplicative interactions and cumulative exposures, and use Stata to explore these concepts;
Understand the potential causes of measurement error, how to minimise and monitor these in study design, and how to incorporate information about measurement error into analyses;
Understand how clustered data arise, the impact this may have on usual methods of analysis, and be able to carry out simple appropriate analyses using Stata;
Understand the problems caused by missing data, and be able to carry out exploratory analyses, imputation and sensitivity analyses using Stata;
Use a causal diagram to assess possible causal pathways, and understand models for exploring causality using observational data.

Who the course is intended for: This course is intended for researchers, applied statisticians and epidemiologists who are familiar with basic epidemiology (to at least the level covered by the "Basic Epidemiology" course), and have experience in analysing epidemiological data. Participants should have a knowledge of basic regression models and their implementation in Stata of at least the level achieved in the "Introduction to Regression Models" course.

Course outline:

Design issues in epidemiology;
Life-course epidemiology;
Interactions: additive and multiplicative, cumulative exposures;
Measurement error: description, design issues, monitoring measurement error, methods of analysis (including ICC, SIMEX, errors in binary variables and methods for dealing with inter-related errors);
Clustered data: description, problems with simple analyses, methods of analysis including generalized estimating equations and multilevel models;
Missing data: loss to follow-up, missing-data mechanism, simple methods of analysis, likelihood-based methods, imputation and sensitivity analyses;
Causal models: causal diagrams, path analysis, structural equation models, propensity scores, time-dependent confounding and marginal structural models;
An opportunity for participants to present their own planned research or research in progress.