Week 3: Special Modules

Summer Course 2020
33rd Residential Summer Course in Epidemiology

29 June – 3 July 2020

Week 3, parallel morning module 1
Advanced topics in statistics
Per Kragh Andersen, Corrado Lagazio and Michaela Baccini        

  • Competing risks
  • Recurrent events and longitudinal data
  • Cohort sampling
  • Propensity score
  • Causal modelling

 

Week 3, parallel morning module 2
Advanced topics in epidemiology:
Triangulation of genetic instrumental variables and other causal methods
[Mendelian Randomization, negative control analyses, family designs, cross-context comparisons,
and triangulation]
Debbie Lawlor and Carolina Borges 
A major aim of epidemiology is to identify causes of disease and health related outcomes in populations. This is necessary to provide the evidence base for identifying prevention and treatment targets for which interventions can be developed and their effectiveness tested. This has often been undertaken by applying multivariable regression analyses (or similar methods) to observational data (e.g. cohort or case control studies). Results from these studies may give biased causal estimates because of confounding, reverse causality, selection bias or other sources of bias.       

In recent years several novel (to epidemiology) methods have been developed to explore causality and the idea that ‘triangulating’ results from different methods can provide more robust causal understanding is gaining traction. The idea of triangulation is that if different methods that all have very different key sources of bias point to the same causal answer we have more confidence that is the correct answer than if we just had information from one of those methods.

In this course students will learn about cross-context comparisons, negative control studies, matched designs (specifically within sibship comparisons) and instrumental variable analyses (with a particular focus on Mendelian randomization in which genetic instruments are used). They will also learn about triangulating findings from these different methods and more conventional multivariable regression approaches to improve causal understanding.

By the end of this module students should be able to:

  1. Understand the principles and assumptions, strengths and limitations of each of the following methods:
    1. Cross-context comparisons;
    2. Negative control studies;
    3. Matched within sibship analyses;
    4. Mendelian randomization
    5. Other instrumental variable methods.
  2. understand the concepts behind sensitivity analyses to explore violation of key assumptions of each of these methods
  3. Complete a (straight-forward) one-sample and two-sample MR analysis
  4. Understand how different methods might be triangulated to improve causal inference

The course will be taught with lectures, paper & pen and computer (using Stata) 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.

More information

 

 Week 3, parallel morning module 3
Applied epidemiology:
Environmental epidemiology
Jordi Sunyer and Martine Vrijheid      

The course on applied epidemiology is based on short lectures, group work and group discussion of case studies. We aim to review the methodological issues related to the epidemiologic study of the health consequences of exposures that are involuntary and that occur in the general environments (from cities to global, from individuals to in/outdoors and from physical to social). We cover designs, exposure measurement, co-exposures, modelisation, air pollution, built environment, climate change, exposome, child development, and impact assessment.

  • Epidemiological designs for short temr exposures
  • Exposome
  • Child development
  • Built environment
  • Risk and impact assessment

 

Week 3, parallel morning module 4
Epidemiology and public health:
From epidemiology to the burden of disease
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 a 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 concepts and 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 and burden of disease studies. Students are expected to bring their personal note books to independently work on line, on the web.

More information

 

Week 3, parallel afternoon module 1
Applied Epidemiology:
The evaluation of medical tests
Patrick M Bossuyt        

It is increasingly recognized that medical tests should be properly evaluated in well-designed studies before they can be put on the market and recommended in clinical guidelines. This has generated interesting methodological developments and increasing awareness among epidemiologists.

In this module, we offer a general framework for the evaluation of medical tests, distinguishing between scientific validity, analytical/technical performance, clinical practice performance, and clinical effectiveness/utility. We then discuss studies for diagnostic tests, for prognostic tests, for predictive markers, for screening strategies, and RCT of tests. The emphasis is on study design and sources of bias.

Approach: A combination of lectures and small assignments.

 

Week 3, parallel afternoon module 2
Advanced topics in epidemiology:
Methods to deal with unobserved information in observational studies
[Quantitative bias analysis, instrumental variables, self-controlled study designs, multiple imputation of missing data]
Irene Petersen and Henrik Stovring     Observational studies in epidemiology are susceptible to an array of biases due to confounding, misclassification and missing data that may threaten their validity. Often such problems are qualitatively discussed in papers, but to a lesser extent quantified. In this course we will demonstrate modern analytic techniques and epidemiological study designs that will enable course participants to quantify and deal with unobserved information in observational studies.  

The course participants will be introduced to quantitative bias analysis, instrumental variable analysis, self-controlled study designs and multiple imputation.

  • Quantitative bias analysis
  • Instrumental variable analysis
  • Self-controlled study design
  • Missing Data and Multiple Imputation part I
  • Missing Data and Multiple Imputation part II 

 

Week 3, parallel afternoon module 3
Epidemiology and public health:
Principles of prevention in the precision medicine and Big Data era
Rodolfo Saracci        

This module presents to researchers, health professionals and clinicians particularly interested in prevention a perspective critically examining whether the population and the individualized approaches, as a classically outlined by Geoffrey Rose in the 1980s, still represent useful concepts and operational principles or whether the availability of massive health data on each person makes them obsolete leading to a unified ‘precision prevention’ approach. Relevant methodological aspects will be reviewed, involving an introductory presentation of causal versus predictive models and of machine learning instruments. Specific ethical issues that prevention research and measures raise will be sketched for discussion.

Approach: lectures, reading of papers with critical discussion and ‘pros and cons’ arguments.

 

Week 3, parallel afternoon module 4
Applied Epidemiology:
Infectious disease epidemiology
Tyra Grove Krause and Steen Ethelberg        

Vaccines, antibiotics and hygiene measures have played an important role in the fight against infectious diseases. However, worldwide inequalities in accessing health care including treatments and vaccines, re-emergence of vaccine preventable diseases, and the threat of antimicrobial resistance as well as the risk of emerging new pathogens underline the fact that infectious diseases remain a global public health challenge.

This course will introduce the epidemiological fields of transmissibility, vaccinology, disease surveillance and outbreak investigations. By the end of this module, the student should be able to understand:

  1. The terminology and definitions used in infectious disease epidemiology
  2. Principles of disease transmission including mathematical models for epidemics
  3. Basic concepts of vaccinology
  4. Principles of infectious disease surveillance and interpretation of surveillance data
  5. The 10 steps of an outbreak investigation
  6. The use and interaction of microbiological and epidemiological methods in outbreak detection and control.
  7. The use of epidemiological study designs in infectious disease epidemiology

The course will use a mix of lectures and small group case studies.

Prior knowledge of infectious diseases is not needed but students should have a good understanding of principles of epidemiology, including a basic knowledge of measures of frequency and associations and epidemiological study designs.