Summer Course 2018
31st Residential Summer Course in Epidemiology
2 July – 6 July 2018
|Week 3, parallel morning module 1
|Advanced statistical topics
|Per Kragh Andersen with Corrado Lagazio and Michaela Baccini
|Week 3, parallel morning module 2
|Genetic epidemiology: genome-wide association studies and whole genome approaches to the study of disease
|David Evans and Gibran Hemani|
|Genetic epidemiology is concerned with the genetic aetiology of medically relevant traits and diseases in human populations. In this course we will learn how to design, conduct and analyse human genome-wide association studies (GWAS). Over the last decade, GWAS have completely revolutionized the field of genetic epidemiology, providing seminal insights into the biological basis of health and disease, and simultaneously identifying a wealth of drug targets for existing and novel pharmacotherapies. Indeed many of the genetic variants identified by GWAS are now finding useful roles in Mendelian randomization studies which investigate possible causal relationships between risk factors and medically relevant outcomes (see the Aetiological Epidemiology course run by Lawlor and Tilling). Whilst a substantial portion of this module focuses on using GWAS to identify genetic variants involved in the aetiology of complex traits and diseases, we will also show how GWAS data can be leveraged to predict individuals’ trait values, estimate heritability, and discover genetic relationships between different diseases that may be difficult to measure in the same study|
| Week 3, parallel morning module 3
|Advanced topics in epidemiology|
|Jan Vandenbroucke with Irene Petersen
The origins and usefulness of several advance study desings and methods of analyses will be studied – each time ending with a current positioning: what is state-of-the art and what are the potential applications and pitfalls (with practical examples)
|Week 3, parallel morning module 4
|From epidemiology to the burden of disease: putting risks in perspective|
|Nino Künzli and Thomas Fürst
Epidemiology is a core science to investigate and quantify the association between risk factors and health outcomes. However, public health professionals and policy makers need to understand the public health relevance of risks to plan and prioritize prevention and policy making. The epidemiology-based assessment of the risk related burden of disease provides the bridge between public health science and policy. This course will familiarize students with the use of epidemiology in quantitative risk assessment and the comparison across risks. Based on a range of examples, students will i) learn how epidemiology contributes to quantitative risk assessment, ii) understand the tools to assess the health burden and iii) critically interpret the derived outputs. Exercises based on the burden of disease data will train critical thinking for the comparison of different public health risks.
Approach: Lectures on concepts, self-studies with on-line exercises and group discussions will foster the understanding of how epidemiology is used in risk assessment. Students are expected to bring their personal note books to independently work on line, on the web.
|Week 3, parallel afternoon module 1
|Olof Akre and Emanuele Pivetta
In this one-week course, we focus on three topics:
Diagnostic testing: Diagnostic accuracy, bias and study design in diagnostic testing.
Prognostic modeling: The concept of a prognostic model, building and testing the validity of a model, interpreting data from prognostic models.
Intervention study design: Introduction to RCTs and innovative design, comparison between RCT and observational studies, confounding by indication.
Approach: A combination of lectures and personal assignments, to be completed in pairs and discussed in the group.
|Week 3, parallel afternoon module 2
|Causal methods in epidemiology:
Mendelian randomization and triangulation
|Debbie Lawlor and Carolina Borges
One of the key uses of epidemiology is to identify causes of disease and health related behaviours in populations. This is necessary to provide the evidence base for prevention and treatment targets for which interventions can be developed and their effectiveness tested. Mendelian randomization (MR) is a relatively new method for exploring causality in observational studies. It refers to the use of genetic variants as instrumental variables to understand causal effects of non-genetic modifiable risk factors (including ‘physiological’ risk factors such as blood pressure, fasting glucose, CRP and lifestyle risk factors such as smoking, caffeine consumption and alcohol intake). It is a method that is increasingly used in epidemiology. Other approaches to causal inference, including cross-cohort comparisons, negative control studies, and matching designs such as within sibship analyses are also increasingly used. By the end of this module students should be able to:
The course will use a mix of lectures, computer exercises and small group practicals.
There is no requirement to understand genetics (we will provide a brief background about genes to the level needed for their use in MR), but students should have a good grounding in the principles of epidemiology, including a clear understanding of confounding and the assumptions of multivariable regression analyses.
|Week 3, parallel afternoon module 3
|Josep M. Antó and Jordi Sunyer
|Week 3, parallel afternoon module 4
|Concepts and methods in causal mediation analysis|
|Bianca De Stavola and Rhian Daniel
In diverse fields of empirical research attempts are made to decompose the effect of an exposure on an outcome into its effects via a number of different pathways.
Path analysis has a long tradition in dealing with this type of enquiry, but more recent contributions in the causal inference literature have led to greater understanding of relevant parameters and the assumptions under which they can be identified. For this reason the module first starts with an introduction to causal language and causal diagrams before embarking on a comparison of the two schools.
There will be four sessions:
Further issues and discussion (with computer practical in Stata)