Erasmus Summer Programme Courses


WEEK 1, AUGUST 09 – 13, 2021

    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.


    //Please note that the course information is subject to change and will be updated from time to time. We will do our utmost best to ensure the accuracy and reliability of the information on this website.//

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    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.


    //Please note that the course information is subject to change and will be updated from time to time. We will do our utmost best to ensure the accuracy and reliability of the information on this website.//

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    From 13:00 till 16:00 CEST
    Introduction to Global Public Health [ESP41]

    About this course

    Faculty: 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.


    //Please note that the course information is subject to change and will be updated from time to time. We will do our utmost best to ensure the accuracy and reliability of the information on this website.//

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    From 13:00 till 18:00 CEST
    Principles of Genetic Epidemiology [ESP43]

    About this course

    Faculty: Abbas Dehghan MD, PhD (Associate professor, Imperial College London) and Mohsen Ghanbari MD, PhD (Assistant professor, Erasmus MC)

    This course aims to give a basic introduction to various methods used in classical genetic epidemiology. In combination with the other course Genome-wide association studies present in the Erasmus Summer Program, 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. Participants are introduced to the basic principles of population genetics, segregation, linkage and association analyses. The relevant background of human genetics and statistics is presented. The course consists of two parts: theoretical lectures and practical assignments. The goal of the course is that participants are able to interpret the findings in modern genetic research.


    //Please note that the course information is subject to change and will be updated from time to time. We will do our utmost best to ensure the accuracy and reliability of the information on this website.//

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    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.


    //Please note that the course information is subject to change and will be updated from time to time. We will do our utmost best to ensure the accuracy and reliability of the information on this website.//

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    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.


    //Please note that the course information is subject to change and will be updated from time to time. We will do our utmost best to ensure the accuracy and reliability of the information on this website.//

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WEEK 2, AUGUST 16 – 20, 2021

    From 8:45 till 16:00 CEST
    Regression Analysis [ESP09]

    About this course

    Faculty: Prof. Brian Marx, PhD

    This intermediate level course aims at providing theoretical and practical training for epidemiologists, clinicians and other professionals of related health disciplines in statistical modeling with particular emphasis on straight line linear and multiple regression. Included topics are: review of straight line regression and correlation, ANOVA for straight line regression, appropriateness of straight line model, polynomial regression, multiple regression analysis, partial F-test, dummy/indicator variables, statistical interaction, comparing straight line regressions, analysis of covariance, estimation and interpretation, goodness-of-fit, model selection, collinearity and outlier diagnostics. Additionally, extensions to some generalized linear models, such as logistic (binomial) regression and Poisson regression, will be introduced and interpreted through examples-- thus helping to bridge the material presented in ESP66 (Logistic Regression).


    //Please note that the course information is subject to change and will be updated from time to time. We will do our utmost best to ensure the accuracy and reliability of the information on this website.//

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    From 8:45 till 11:45 CEST
    Methods of Public Health Research [ESP11]

    About this course

    Faculty: Prof. Lex Burdorf, Ir. PhD

    This course provides an introduction to essential study designs and analytic methods available to public health researchers to describe the influence of important determinants on public health and to evaluate effects of primary preventive intervention on public health. This course focuses on population health rather than individual health and explains why different designs and methods are required, such as ecological studies and multilevel analysis. The course targets three key issues: (1) summary measures of population health, such as life expectancy, (2) measures of association and relative importance of specific causes for population health, such as population attributable fraction, and (3) evaluation of population interventions through study designs based on natural experiments instead of RCT. Each morning will start with a lecture on new methods. Subsequently, an individual-based online exercise is offered to practice the new methods. At the end of the morning, the importance of the method is illustrated in a hot topic in research.


    //Please note that the course information is subject to change and will be updated from time to time. We will do our utmost best to ensure the accuracy and reliability of the information on this website.//

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    From 13:00 till 16:00 CEST
    History of Epidemiologic Ideas [ESP53]

    About this course

    Faculty: Prof. Alfredo Morabia, MD PhD

    Because of the key role it played in forecasting and modelling the evolution of the covid-19 pandemic, epidemiology is better known now by the public at large, but is it? What is epidemiology? The methods (group comparisons) and concepts (confounding, bias, interaction) of epidemiology have been developed through attempts to understand the causes of major epidemics of infectious diseases, as well as the epidemic rise of non-communicable diseases, from the 17th century through the 21st. Its unique combination of population thinking and group comparison has helped society to control and prevent these major scourges. The course reviews these epidemics and pandemics, and discusses their impact on the research methods and concepts taught today in epidemiology. The course is comprised of lecture by Alfredo Morabia, but students are requested to respond to short questions at the end of every lecture. The responses are discussed the next day in class.


    //Please note that the course information is subject to change and will be updated from time to time. We will do our utmost best to ensure the accuracy and reliability of the information on this website.//

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    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.


    //Please note that the course information is subject to change and will be updated from time to time. We will do our utmost best to ensure the accuracy and reliability of the information on this website.//

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    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.


    //Please note that the course information is subject to change and will be updated from time to time. We will do our utmost best to ensure the accuracy and reliability of the information on this website.//

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    From 8:45 till 16:00 CEST
    Causal Inference [ESP48]

    About this course

    Faculty: Prof. Miguel Hernán, MD & Dr. Sonja Swanson


    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. These so-called g-methods can be used under less restrictive conditions than traditional statistical methods. For example, g-methods allow one to estimate the causal effect of a time-varying treatment in the presence of time-varying confounders that are affected by the treatment.


    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. The course presents the latest methodologic developments for the design and analysis of longitudinal studies. Specifically, the course will introduce the three 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 static and dynamic treatment strategies.


    //Please note that the course information is subject to change and will be updated from time to time. We will do our utmost best to ensure the accuracy and reliability of the information on this website.//

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WEEK 3, AUGUST 23 – 27, 2021

    From 13:00 till 16:00 CEST
    Social Epidemiology [ESP61]

    About this course

    Faculty: Prof. Frank van Lenthe, PhD

    The unequal health consequences of the COVID-19 pandemic show the stubbornness of health inequalities, and the need for epidemiologists and public health experts with the skills to apply an equity perspective to public health challenges. In this course, I introduce and illustrate modern research methods in social epidemiology, i.e. the study of the social determinants and social outcomes of health. The three main areas to be covered are: the measurement of health inequalities, the explanation of health inequalities, and the evaluation of interventions and policies to reduce health inequalities. Application of the research methods will be illustrated with historical landmark studies as well as recent examples from the international literature.

    The programme consists of lectures, hands-on exercises, and group discussions. The focus will be on socioeconomic inequalities in health, but the role of other social factors (such as migration background and marital status) will also be discussed.


    //Please note that the course information is subject to change and will be updated from time to time. We will do our utmost best to ensure the accuracy and reliability of the information on this website.//

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    From 8:45 till 11:45 CEST
    Practice of Epidemiologic Analysis [ESP65]

    About this course

    Faculty: Prof. Kamran Ikram, MD PhD

    This is a course in which the theoretical background and practical application of basic epidemiologic analytic tools is discussed. Most topic discussed in this course lie at the cross-roads of Epidemiology and Biostatistics and covers issues that are typically faced by starting researchers. Topics covered are: Data preparation, Missingness, Categorization, Normalization, Standardization, Stratification, Multiple testing, Sample size calculation.

    The goal is to provide students with the understanding and tools to perform their initial epidemiologic data analyses.


    //Please note that the course information is subject to change and will be updated from time to time. We will do our utmost best to ensure the accuracy and reliability of the information on this website.//

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    From 8:45 till 16:00 CEST
    Causal Mediation Analysis [ESP69]

    About this course

    Faculty: Linda Valeri, PhD

    The course covers recent developments in causal mediation analysis and provide 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, continuous, and time-to-event 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.

    SAS, R and Stata macros to implement these techniques are covered and distributed to course participants. 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 lecture, discussion, and software demonstration. Powerpoint slides are used to present material in lecture form. Extensive printed 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.

    Timeline:

    14.30 - 16.30 hrs Live Lectures (attendance mandatory)

    17.00 - 20.00 hrs Live Lectures (will be recorded*)

    *will be published on Canvas after 20.00 hrs, and need to be viewed before the next Live Lecture.


    //Please note that the course information is subject to change and will be updated from time to time. We will do our utmost best to ensure the accuracy and reliability of the information on this website.//

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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.


    //Please note that the course information is subject to change and will be updated from time to time. We will do our utmost best to ensure the accuracy and reliability of the information on this website.//

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    From 8:45 till 16:00 CEST
    Genome-wide association studies [ESP74]

    About this course

    Faculty: Prof. Fernando Rivadeneira, MD PhD

    Genome-wide association studies (GWAS) constitute a powerful approach to investigate the genetic basis of complex traits and disorders. The course consists of virtual 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: GE14 "Linux for Scientists" and 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 year’s version of GE03 was still an advanced version, so content overlap with this ESP74 will be present. Please inquire before registering.


    //Please note that the course information is subject to change and will be updated from time to time. We will do our utmost best to ensure the accuracy and reliability of the information on this website.//

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    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

    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


    //Please note that the course information is subject to change and will be updated from time to time. We will do our utmost best to ensure the accuracy and reliability of the information on this website.//

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    From 13:00 till 16:00 CEST
    Data Science in Epidemiology [ESP80]

    About this course

    Faculty: Dr. Gennady Roshchupkin, PhD and Dr. Daniel Bos, MD, PhD

    Data science is a multi-disciplinary field that uses scientific methods and algorithms to extract knowledge and insights from structured and unstructured data. Recent advances in technology allows 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 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.


    //Please note that the course information is subject to change and will be updated from time to time. We will do our utmost best to ensure the accuracy and reliability of the information on this website.//

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