Erasmus Summer Programme Courses
WEEK 1, AUGUST 07 – 11, 2023
From 8:45 till 11:45 CEST
Principles of Research in Medicine and Epidemiology [ESP01]
About this course
Faculty: Prof. Arfan Ikram, MD PhD
This course provides an overview of the fundamentals of quantitative medicine. The principles of biomedical research are discussed and how these together form the building blocks towards evidence-based medicine. There will be thorough discussion on how to quantify disease occurrence, how to compare disease occurrence across groups, how to design a study, and how biases can impact properinferences from a study. The lectures will be illustrated with examples taken from the contemporary scientific literature.
The format of the course includes lectures, interactive discussions in small groups of 2-3 students, quizzes, and daily break-out working sessions of 15-20 students.
This course is of interest to anyone working in quantitative research. It will be of particular interest to students starting their research career or those who have experience in research outside of epidemiology. After successful completion of this course, students will be ready to engage in further (intermediate) level training in epidemiology.
From 13:00 till 18:00 CEST
Introduction to Data-analysis [ESP03]
About this course
Faculty: Prof. Adelin Albert, PhD
Nobody today denies the importance of data analysis in medical practice. However, do we really understand how statistics operates and improves our scientific skills? This course is a general reminder of the basics we all should know in statistics. We review the notions of population, sample, variables and data. We show how data are summarized numerically or graphically. Based on randomness and probability, data become a powerful tool to make decisions. Emphasis will be placed on confidence intervals, hypothesis testing, and the renowned p-value. We revise the most commonly applied statistical tests including survival analysis, logistic regression and Cox models because of their wide use in the medical literature. During the course, we give a brief introduction to the "Point-and-Click" (Rcmdr) interface of the cost-free R software which is easy to use and can be of great help to the course participant.
From 13:00 till 16:00 CEST
Introduction to Global Public Health [ESP41]
About this course
Faculty: Prof. Rajiv Chowdhury, MD PhD
The key aim of this course is to learn about the principal issues surrounding global health and the main outcome of the course will be a better understanding of how epidemiology and public health can more effectively protect the health of disadvantaged populations in the changing global context.
Some of the specific health issues to be discussed include: threats to health from pre-existing and emerging communicable diseases; various aspects of global pandemic (including global health systems reform and vaccine equality), the global rise of the non-communicable diseases (NCD) such as cardiovascular disease and diabetes; maternal and child health issues, and the impact of global environmental change.
Additionally, other related issues such as concepts and realities of health systems around the world, impact of globalization on health, and how to measure global health will be discussed. For each health problem, where appropriate, there will be a discussion of: burden of disease, major determinants, intervention policies and programmes, and evaluation of the effectiveness of the interventions. A key focus of the course would be small group interactions.
From 13:00 till 16:00 CEST
Principles of Genetic Epidemiology [ESP43]
About this course
Faculty: Abbas Dehghan MD, PhD (Associate professor, Imperial College London), Mohsen Ghanbari MD, PhD (Associate professor, Erasmus MC) and others
This course aims to give a basic introduction to methods used in classical genetic epidemiology. In combination with the other course Advanced Analysis of Genome-wide Association Studies present in the Erasmus Summer Programme, the course offers an excellent introduction to genetic epidemiologic research. The course targets a wide-range of participants, including students, epidemiologists, clinicians and molecular biologists with no or little background in genetic epidemiology. The relevant background of human genetics and statistics is presented. Participants are introduced to the basic principles of population genetics, segregation, linkage and association analyses. The course consists of theoretical lectures and (optional) practical sessions after the class. The goal of the course is that participants are able to understand and interpret the findings in modern genetic research.
From 8:45 till 16:00 CEST
Logistic Regression [ESP66]
About this course
Faculty: Prof. Stanley Lemeshow, PhD
The aim of this course is to provide theoretical and practical training for biostatisticians, epidemiologists, medical researchers and professionals of related disciplines in statistical modeling with particular emphasis on logistic regression. The increasingly popular logistic regression model has become the standard method for regression analysis of binary, multinomial and ordinal response data in the health sciences. Students will become familiar with statistical software packages and the analysis of a real data sets.
From 8:45 till 11:45 CEST
The Public Health Approach: From the Black Death to COVID-19 [ESP84]
About this course
Faculty: Prof. Alfredo Morabia, MD PhD
The field of public health has developed a distinctive way of approaching human health by shifting its focus from the health of individuals to the health of populations. This course revisits the history of this population thinking and how it has helped address and combat a series of historic epidemics.
The public health approach has historically evolved in response to major crises like the plague, smallpox, cholera, tuberculosis, influenza, HIV/AIDS, and the COVID-19 pandemic. Through these examples, we will see that individual health outcomes are intimately tied to the health of the population. This public health approach also helps reveal and address behavioral and social determinants of health through the comparison of population health outcomes.
The scientific methods used to study populations help uncover patterns that would otherwise remain hidden when focused on individual health or anecdotal data. Public health professionals think and respond to crises by researching and comparing population data. The course will also discuss how the COVID-19 pandemic—and the successes and failures surrounding our response to it—reinforces the urgent need for the public at large to understand the rational of the public health approach.
Please note: this course incorporates the previous course on the history of epidemiologic methods (ESP53 History of Epidemiologic Ideas) into a larger frame of the history of public health methods.
WEEK 2, AUGUST 14 – 18, 2023
- Data Quality;
- Data Presentation;
- Data Documentation.
From 8:45 till 16:00 CEST
Causal Inference [ESP48]
About this course
Facultry: Dr. Sonja Swanson & Dr. Jeremy Labrecque
The goal of many epidemiologic studies is to quantify the causal effect of a treatment (or exposure) on an outcome. In contrast, commonly used statistical methods provide measures of association that may lack a causal interpretation even when the investigator adjusts for all potential confounders in the analysis of a properly designed study.
To eliminate the discordance between the causal goals and the associational methods in epidemiology, it is necessary to a) formally define causal concepts such as causal effect and confounding, b) identify the conditions required to estimate causal effects, and c) use analytical methods that, under those conditions, provide estimates that can be endowed with a causal interpretation. This course combines counterfactual theory and graph theory to present an integrated framework for causal inference from observational data, with a special emphasis on complex longitudinal data. Specifically, the course will introduce g-methods (inverse probability weighting of marginal structural models; parametric g-formula; and g-estimation of structural nested models) in the setting of time-fixed treatments and demonstrate inverse probability weighting for addressing causal questions regarding sustained treatment strategies. On the final day, alternative or complementary approaches will be discussed (e.g., instrumental variable approaches; quantitative bias analysis).
From 8:45 till 11:45 CEST
Practice of Epidemiologic Analysis [ESP65]
About this course
Faculty: Prof. Kamran Ikram, MD PhD and F.J.A van Rooij
Formulating a research question, considering the design of a study, collecting data, analyzing data and reporting of findings are crucial steps in any scientific research. However, other aspects that are equally important often receive less attention and in some cases may even be neglected. These topics include:
Specific topics covered include Data preparation, Missingness, Data restructuring (e.g. categorization, normalization, standardization), Visualisation. Most topics discussed in this course lie at the cross-roads of Epidemiology and Biostatistics and cover issues that are typically faced by starting researchers. For starting scientists it is important to realize that these aspects are an integral part of scientific research and form the basis of the scientific integrity of any research project. In this course, the first three days will be devoted to each of these three aspects: Quality, Presentation and Documentation. These will include lectures covering both theoretical aspects and live demonstrations with R and R Markdown. Furthermore, on the fourth day, the students will get an opportunity to gain some initial experience with hands-on exercises in the practical. Finally, on the last day, there will be lectures on selected topics, that are increasingly dominating the scientific landscape, but are usually covered from a statistical, mathematical or engineering perspective. Here, these topics will be discussed from the point of view of an epidemiologist, as there is much overlap with the practice of epidemiologic analysis!
From 8:45 till 16:00 CEST
Introduction to Bayesian Methods in Clinical Research [ESP68]
About this course
Faculty: Prof. Emmanuel Lesaffre, PhD
The Bayesian approach is an important alternative to the classical (called frequentist) approach to statistics. Indeed, the Bayesian approach has become increasingly important over the last three decades and is invading in all application areas. Especially with complex data the Bayesian approach has proven to be a very useful analysis tool, but also conceptually this approach is attracting recently many researchers.
The course introduces the participant to Bayesian methods for the analysis of clinical and epidemiological studies. While some math cannot be avoided, the emphasis in the course is on bringing over the intuitive ideas and the analysis of clinical and epidemiological data sets using Bayesian software. The course treats the basic concepts of the Bayesian approach, such as the prior and posterior distribution and their summary measures, the posterior predictive distribution. In addition, Bayesian methods for model selection and model evaluation will be treated. Markov Chain Monte Carlo techniques are introduced and exemplified. A great variety of clinical and epidemiological examples illustrates the techniques.
Medical publications are explored in discussion groups, as well as Bayesian analyses of real data sets will be exercised on an individual and on a group basis.
From 13:00 till 16:00 CEST
Fundamentals of Medical Decision Making [ESP70]
About this course
Faculty: Prof. John Wong, MD
This course will provide an introduction to health care decision making. Given uncertainty, trade-offs and values, how should patients, policymakers and clinicians decide among diagnostic and therapeutic choices to make optimal decisions? Medical interventions may have benefits but also adverse effects, e.g., undesirable complications or false or inconclusive results.
Clinical and health policy decisions necessitate weighing benefits and harms and trading off competing objectives (life expectancy, quality of life, costs). We will discuss a proactive approach involving decision analysis to integrate evidence and values for optimal and efficient care choices in the face of uncertainty.
Course content includes interpretation of clinical data and test results, testing and treatment thresholds, estimating prognosis, decision tree construction (e.g., Markov models and Monte Carlo simulations), life expectancy, quality of life assessment, cost-effectiveness analysis, health technology assessment, diagnostic reasoning, and shared decision making.
Teaching methods: Interactive lectures, breakout group discussions and exercises.
From 8:45 till 16:00 CEST
Advanced Analysis of Genome-wide Association Studies [ESP74]
About this course
Faculty: Prof. Fernando Rivadeneira, MD PhD, Carolina Medina Gomez, PhD and others
Genome-wide association studies (GWAS) constitute a powerful approach to investigate the genetic basis of complex traits and disorders. The course consists of lectures providing a conceptual framework on crucial aspects of quality control, genotype imputation, methods to detect and correct for stratification, meta-analysis and genomic annotation of GWAS signals. An overview of the most frequently used statistical tools will be accompanied by instructive hands-on computer exercises on the principles of analysis of quantitative traits and disease outcomes. State of the art procedures for running GWAS will be taught, including: Quality Control / QC (PLINK2); GWAS analysis including mixed models (rvtests and SAGE); Meta-analysis QC (EasyQC); and GWAS Meta-analysis (METAL). Post-GWAS functional follow-up procedures will include downstream analysis (FINEMAP and FUMA). While theoretical background is provided on all topics, this is by definition a "hands-on" practical course, meaning you will spend most of the day performing genetic analyses. The course format will allow interactive break-out discussion sessions on theoretical and practical aspects of running GWAS, together with expert-advice procurement on diverse components of collaborative research within networks and consortia.
Participants of this course should be familiar running Linux commands and with running scripts and packages in R-programming language. These skills are taught in the NIHES courses: EL016 (GE14) "Linux for Scientists" and EL019 (GE03) "Genome-wide Association Studies"; and participants are encouraged to follow these courses in advance.
Note for participants of the 2021 NIHES GE03 edition! This edition of GE03 was an advanced version, so content overlap with this ESP74 will be present. Please inquire before registering. There is no overlap for participants of the 2022 edition of the course (now EL019).
From 8:45 till 11:45 CEST
Gender and Health [ESP78]
About this course
Faculty: Dr. Maryam Kavousi MD PhD, Dr. Jeanine Roeters van Lennep MD PhD
And invited speakers
To realize gender equality in health care, sex and gender dimension needs to be integrated in all aspects of research and clinical practice. This course brings together experts from a multitude of disciplines including clinical, basic science, public health and policy and provides participants with resources that will assist them in developing and strengthening gender-equal clinical care and research programs. The course will focus on the critical health issues for women and men through the life cycle, challenges of integrating sex and gender from the health research, practice, and policy perspectives, as well as strategies to address these challenges.
This course is formerly known as Women’s Health (NIHES EP19).
Teaching methods: Interactive lectures
From 13:00 till 16:00 CEST
Gender in Research: Workshops [ESP79]
About this course
Faculty: Maryam Kavousi, MD PhD, Coordinator: Miranda van Duijn PhD
Invited speakers
During this workshop, we will explain how sex and gender can be included in the various phases of the research process. The workshop will focus on choices in design, methods and analysis. In addition, attention is paid to research team composition, publications and gender-sensitive implementation.
Our main purpose is to provide participants with insight into methods for incorporating sex and gender in research. Participants will also be divided into small groups to work on several projects. As such, this workshop will serve to form new communities of young, talented researchers who can join forces on addressing different gender-sensitive topics in future.
Teaching methods: Interactive lectures, exercises, practicals
From 13:00 till 16:00 CEST
Clinical Translation of Epidemiological Concepts [ESP83]
About this course
Prof. Arfan Ikram, Dr. Daniel Bos, Dr. Layal Chaker
Please note: the content of this course is similar to the course Clinical Translation of Epidemiology EL031.
This course aims to bridge the gap between theoretical epidemiological concepts and application in clinical research and medicine. Understanding of basic epidemiological principles is therefore a prerequisite.
Students will learn how abstract concepts from epidemiological theory can be translated to clinically observable phenomena. Tools and skills taught in this course will be readily applicable in clinical research on etiology, efficacy, diagnosis and prognosis. Successful completion of this course will enable students to continue with more formal training in theoretical causal inference as well as advanced courses in clinical effectiveness and clinical epidemiology.
The course will consist of interactive lectures, working groups, group presentations and an individual assignment. Through in-class exercises the student will be provided with the opportunity to utilize the knowledge covered in the lectures on a study from the recent literature.
WEEK 3, AUGUST 21 – 25, 2023
- To apply a systematic framework for public health interventions to contemporary public health challenges
- To use observational data for the simulation of public health interventions
- To evaluate ‘natural experiments’ in the population
- To broaden the scope of the evaluation of public health interventions using a systems approach.
From 8:45 till 11:45 CEST
Topics in Meta-analysis [ESP15]
About this course
Faculty: Prof. Matthias Egger, MD MSc, Prof. Georgia Salanti, PhD
This course introduces the principles of systematic reviews and meta-analysis, with a focus on the potentials and limitations of this study design, including the problematic, massive rise of such analyses in the literature. The identification and exploration of between-study heterogeneity and of sources of bias, and the issues arising in meta-analysis of observational studies and in network meta-analysis will be covered.
The format of the course includes lectures and the critical appraisal of published meta-analyses in groups. Hands-on practicals in R cover basic meta-analysis, meta-regression and network meta-analysis, The course is based on the third edition of the book "Systematic Reviews in Health Research: Meta-Analysis in Context (John Wiley & Sons, 2022), but students do not need to purchase the book.
This course is primarily of interest to health researchers but also a resource for practitioners, students and instructors in the health sciences needing to understand research synthesis. After successful completion of this course, students will have gained an understanding of systematic reviews and meta-analysis and be able to conduct their own, state-of-the-art analyses.
From 8:45 till 16:00 CEST
Causal Mediation Analysis [ESP69]
About this course
NOTE: COURSE TIMETABLE DEVIATES FROM STANDARD COURSE TIMES
Faculty: Linda Valeri, PhD
The course covers recent developments in causal mediation analysis and provides practical tools to implement these techniques. Mediation analysis concerns assessing the mechanisms and pathways by which causal effects operate. The course covers the relationship between traditional methods for mediation in epidemiology and the social sciences and new methods in causal inference. For dichotomous and continuous outcomes, we discuss when the standard approaches to mediation analysis are valid. Using ideas from causal inference and natural direct and indirect effects, alternative mediation analysis techniques are described when the standard approaches do not work. The no-confounding assumptions needed for these techniques are described.
R packages to implement these techniques are covered and distributed to course participants and will be used in hands on exercises. SAS and Stata macros will be distributed and described as well. The use and implementation of sensitivity analysis techniques to assess the how sensitive conclusions are to violations of assumptions are covered. Discussion will be given to how such mediation analysis approaches can be extended to settings in which data come from a case-control study design. The methods will be illustrated by various applications.
The course will employ a combination of asynchronous learning, in class lecture and discussion, laboratory sessions and software demonstration. In a typical course day, mornings are devoted to in class discussion and laboratory sessions, afternoons are devoted to asynchronous learning. Slides are used to present material in lecture form. Extensive notes are available for students. A wide variety of examples from epidemiology and the social sciences will be used to illustrate the techniques and approaches. Ample time is given for discussion and questions. A variety of software packages will be discussed. Students will have worked exercises that they can complete on their own.
Timetable
08:45 - 11:45 live class
13:00 - 16:00 asynchronous components and homework
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From 8:45 till 11:45 CEST
Joint Models for Longitudinal and Survival Data [ESP72]
About this course
Faculty: Prof. Dimitris Rizopoulos, PhD
In follow-up studies, different types of outcomes are collected for each subject. These include longitudinally measured responses (e.g., biomarkers) and the time until an event of interest occurs (e.g., disease onset or death). These outcomes are often separately analyzed, but on many occasions, it is of scientific interest to study their association. This research question has given rise to the class of joint models for longitudinal and time-to-event data. These models constitute an attractive paradigm for the analysis of follow-up data that is mainly applicable in two settings: First when the focus is on a survival outcome, and we wish to account for the effect of endogenous time-dependent covariates measured with error, and second when the focus is on the longitudinal outcome, and we wish to correct for non-random dropout.
The course is explanatory rather than mathematically rigorous. Therefore emphasis is given in sufficient detail for participants to obtain a clear view of the different joint modeling approaches and how they should be used in practice. To this end, the course features a number of computer practicals in R showcasing the use of these models.
From 8:45 till 11:45 CEST
Advances in Clinical Epidemiology [ESP77]
About this course
Faculty: Prof. Albert Hofman, MD PhD
This course will discuss recent developments in epidemiologic methods for clinical research. It will review the various study designs and major issues in the validity of clinical epidemiologic studies. Advances in the design of clinical trials will be discussed. The application of novel causal inference methods and the use of instrumental variables will be addressed.
The course includes both didactic interactive lectures as well as discussions and workshops. The workshops will provide the opportunity to discuss, in greater depth, the principles covered in the lectures.
From 13:00 till 16:00 CEST
Data Science in Epidemiology [ESP80]
About this course
Faculty: Dr. Gennady Roshchupkin, PhD, Dr. Daniel Bos, MD, PhD and Prof. Kamran Ikram, MD PhD
Data science is a multi-disciplinary field where – besides domain-specific expertise – several fundamental scientific disciplines converge including mathematics, statistics, computer science, engineering and epidemiology. The aim of this new field is to combine scientific methods and algorithms to extract knowledge and insights from structured and unstructured data. Recent advances in technology allow for the collection of enormous amounts of health-related data. Consequently, skills pertaining to handle and manipulate these data and to extract relevant information have become crucial to perform high quality research. Unfortunately, many (clinical) researchers without a technical background frequently experience troubles obtaining or developing these skills. The aim of this course is to bridge this gap in knowledge by providing an interactive and hands-on programme about data science and how it can be applied in epidemiological research.
From 8:45 till 16:00 CEST
Microbiome Data Analysis in Population-based Studies [ESP81]
About this course
Faculty: Carolina Medina Gomez, PhD, Robert Kraaij, PhD & others
Our understanding of the link between the human gut microbiome and health outcomes is rapidly expanding. The different microbial communities within the human body affect cellular responses and shape many aspects of our physiology, starting to reveal critical roles in health and disease processes. From an epidemiological perspective, microbiome data analysis aims at elucidating the composition of microbial communities, their variation in response to environmental triggers and ultimately, their involvement in human health outcomes in the population. Thanks to the advances in next generation sequencing technology, microbiome profiling is now within reach of large epidemiological studies, bringing up new challenges for the analysis of microbiome data. This course seeks providing students with their first encounters with real microbiome datasets, learn about quality control requirements, preparing datasets for analysis, and finally, teach how to carry out typical analysis tasks, such as comparing microbial diversity and composition, clustering samples and computing associations. Uniquely, this course will also cover aspects of study design providing examples for population-based studies on gut, oral and skin microbiome studies. The course will comprise theoretical lectures (by local experts and guest speakers) together with hands-on interactive practical sessions.
Topics covered:
• Research question and study design of a microbiome study
• Technical and analytical applications for the analysis of gut, oral and skin microbiome.
• Sequencing techniques, data types (16S, metagenomics)
• Sample size and power in context of multidimensional and dynamic microbiome data
• Creation and pre-processing of microbiome data
• Quality control of reads, taxonomic composition and functional assignment
• Selection of microbiome analytical methods
• Estimating microbial evenness and diversity
• Testing differential "relative" and "absolute" abundance, including compositional approaches
• Determining taxonomic and functional richness
• Association analysis of microbiome data in relation to health outcomes in human populations
• Critical review of literature and real-life examples of disease (Acne, caries/periodontitis, diabetes, atopy, autism)
From 13:00 till 16:00 CEST
Methods to Evaluate Public Health Interventions [ESP82]
About this course
Faculty: Prof. F.J. van Lenthe, Prof. F. de Vocht
Public health in the next decades faces several challenges, including widening social inequalities, climate change, rapid urbanization, ageing, and perhaps new pandemics. There is an urgent need for knowledge which and how public health interventions can contribute to promote public health in societies.
Single (randomised) interventions are insufficiently capable of solving all of the above-mentioned challenges. Moreover, an explicit focus on the effectiveness of such interventions leaves many policy-relevant questions unanswered.
This course aims to equip students with state-of-the-art knowledge how to evaluate public health interventions that contribute to population health. Specific aims of the course are:
Lectures will be combined with exercises and group discussions. Examples will come from recent evaluations of large contemporary public health interventions.
Please note: this is a new course, there is no content overlap with (previous) ESP courses ESP11 Methods of Public Health Research and ESP61 Social Epidemiology.