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Programme overview (based on your choices)

Majors

Erasmus Summer Programme (ESP)

For more information about the Erasmus Summer Programme (ESP), please go to:

www.erasmussummerprogramme.nl

Master

Master of Science in Health Sciences | 1 Year | FULL-TIME | 70 EC points

For whom?

This MSc programme focuses on training students who are already educated in research methodology, but wish to take a step further in developing a successful career in health science research. This programme is also interesting if you want to enhance your chances of pursuing a PhD.

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  • Epidemiology
  • Clinical Epidemiology
  • Public Health Epidemiology
  • Biostatistics
  • Health Decision Sciences
  • Genomic & Molecular Epidemiology
  • Master of Science in Health Sciences | 2(+)years | Part-time | 70 EC points

    For whom?

    This world-class programme is ideal for the working health professional, who wishes to take a step further in developing a successful career in health science research. The programme can be fully customized to fulfill your professional and personal aspirations and fit your busy schedule.

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  • Epidemiology
  • Clinical Epidemiology
  • Public Health Epidemiology
  • Biostatistics
  • Health Decision Sciences
  • Genomic & Molecular Epidemiology
  • Research Master in Health Sciences | 2 Years | FULL-TIME | 120 EC points

    For whom?

    Just graduated with a Bachelor Degree in clinical, public health or biomedical sciences and want to start making substantial contributions to future developments in medicine as a researcher? Then this Research Master is for you! With a wide range of majors and guidance from some of the greatest minds in these fields, you will be well on your way to a very successful research career.


    If you are a medical student of Erasmus MC, we have accustomed the Research Master programme to your Bachelor and Master in Medicine.

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  • Epidemiology
  • Clinical Epidemiology
  • Public Health Epidemiology
  • Biostatistics
  • Health Decision Sciences
  • Genomic & Molecular Epidemiology
  • Courses

    02 Apr 2024 - 04 Apr 2024
    Mendelian Randomisation [EL018]

    About this course

    With the advent of a very large number of genetic databases and resources, opportunities to conduct Mendelian randomization(MR) studies are quickly increasing. The MR approach proposes using genetic variants as instrumental variables to test or estimate the potential causal effect of a (non-genetic) risk factor on a disease or health-related outcome. When the assumptions are met, the MR approach can overcome the limitations of associations drawn from observational epidemiology and help prioritizing potential targets for pharmaceutical and public health interventions. This 3-day course aims to provide all the tools necessary first to understand the basic principles of causal inference underlying MR and second to perform an MR study; covering both simple and complex statistical methods for causal inference within one- and two- sample Mendelian randomisation frameworks. During the first day, basic principles of causal inference and mediation analysis will be covered. On the second day, students will apply the concepts learned on day 1 within a Mendelian randomisation framework; including methods to assess instrumental variable assumptions and working on hands-on practical sessions employing online tools like MR-base, but also using specific R-libraries. During day 3, examples of published MR studies will be presented followed by discussion of the topics and a short Q&A session. In addition, students will be able to run specific hands-on analyses with diverse summary level datasets. 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 MR analyses." Starting this year, the EL018 course will be given on a yearly basis.


    See 'how to apply' for the course registration period.

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    08 Apr 2024 - 12 Apr 2024
    Operations Management [EL035]

    About this course

    Operations management is concerned with evaluating the performance of operating units, understanding why they perform as they do, designing new or improved operating procedures and systems for competitive advantage, making short-run and long-run decisions that affect operations, and managing the work force. To understand the role of operations in any organization, a manager must understand process analysis, capacity analysis, types of processes, productivity analysis, development and use of quality standards, and the role of operating strategy in corporate strategy. The course will also present the focused management approach which can help an organization achieve much more with existing resources. The course will demonstrate how operations management—in particular Lean and the Theory of Constraints (TOC)—can rapidly advance value and performance in any health care organization. Utilizing a systems approach that will be relevant for health care managers and executives, it unpacks and demystifies concepts such as performance measures, operations, quality, cost accounting, pricing, and value enhancement, all as they relate to eliminating waste and non-value-adding activities.

    See 'how to apply' for the course registration period.

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    10 Apr 2024 - 12 Apr 2024
    Advanced Analysis of Prognosis Studies [EL014]

    About this course

    Prognostic models are increasingly published in the medical literature each year. But are the results relevant for clinical practice? What are the critical elements of a well developed prognostic model? How can we assume that the model makes accurate predictions for our patients, and not only for the sample that was used to develop the model (generalizability, or external validity)?


    In the course we will address these and other questions from a methodological perspective, using examples from the clinical literature.The participants will be encouraged to participate in interactive discussions and in practical computer exercises.

    See 'how to apply' for the course registration period.

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    10 Jun 2024 - 12 Jun 2024
    Cardiovascular Epidemiology [EL010]

    About this course

    Cardiovascular disease remains the leading cause of morbidity and mortality worldwide. The overall objective of the cardiovascular epidemiology course is to produce epidemiologists and other health scientists with the essential knowledge to carry out high quality research in cardiovascular disease.

    See 'how to apply' for the course registration period.

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    10 Jun 2024 - 14 Jun 2024
    Psychopharmacology [EL029]

    About this course

    More than one billion people worldwide are living with a mental or addictive disorder, making them both leading causes of disability and a significant risk factor for premature mortality (Arias et al. (2022) eClinicalMedicine). Treatment of mental disorders usually involves drug therapy, psychotherapy, or a combination of both. Psychopharmacology, the topic of this course, is the scientific study of the effects drugs have on mood, sensation, thinking, and behavior. In this crash course on psychopharmacology, we will look at drug treatment for psychiatric disorders such as depression, anxiety and ADHD. How do these (psychoactive) drugs work? How and why do they invariably lead to side-effects? And how do these side-effect affect adherence?

    To answer these questions, we should strive to become a ‘neurobiologically empowered psychopharmacologist’, according to the renowned psychopharmacologist Dr. Stephen Stahl. In this course we therefore aim to give you at least a basic understanding of the underlying neurobiology of anxiety, depression, ADHD, addiction and cognition.

    As a final topic, to explain how the most effective drug dose for one person can be either ineffective or dangerous for somebody else, we will also cover both pharmacokinetics (how our bodies interact with the drugs we take) and pharmacogenetics, the study of the effect of genomic variations on drug response.

    See 'how to apply' for the course registration period.

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    11 Mar 2024 - 15 Mar 2024
    Advanced Clinical Trials [EL013]

    About this course

    The Randomized Controlled Clinical Trial (RCT) is the most reliable method of assessing the efficacy and effectiveness of interventions. In order to provide the best possible evidence-based health care, health professionals must be able to judge the scientific merits and clinical relevance of published RCTs. In addition, they may be involved in designing and performing a RCT and are frequently asked to recruit patients for RCTs.

    Reports published in major medical journals show a surprising variability in methods including choice of study design, blinding, avoidance of bias, outcome measures, effect parameters, sample size calculations, data analysis techniques, presentation of results in tables and figures, and inferences made from the results. Hence, appraising trial reports can be challenging. In designing RCTs many difficult decisions need to be made with respect to these same issues.

    In this course these topics and issues will be addressed and developed through lectures and group practical sessions. A laptop during classroom sessions is required in order to do the practical assignments.

    See 'how to apply' for the course registration period.

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    13 May 2024 - 17 May 2024
    Introduction to the Analysis of Population Proteomics & Metabolomics [EL020]

    About this course

    This course aims to give an introduction to the analysis of proteomics and metabolomics data, two emerging approaches that help better understanding of molecular pathways and promise identification of novel biomarkers for complex diseases. The course offers an excellent introduction to these ‘omic’ topics and gives the opportunity to analyse example datasets.

    The course targets a wide-range of participants, including students, epidemiologists, clinicians and molecular biologists with little background in genetic epidemiology. Participants are introduced to the basic principles of protein and metabolite profiling and association analyses at population level. The relevant background of genetic epidemiology 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 analyze and interpret the findings in modern population genetic and genomic research.

    See 'how to apply' for the course registration period.

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    13 May 2024 - 15 May 2024
    Quality of Life Measurement [EL023]

    About this course

    In recent years, the patient's assessment of quality of life has developed to an important outcome measure in epidemiology and health services research. Moreover, quality of life measures are increasingly used as criteria in reimbursement policy, most notably in QALY-analysis.

    The aim of the course is to provide the participants first, with a review of the instruments currently available; Second, participants are provided with the knowledge required to select measures of quality of life that are both valid and sensitive for the research objectives of the participants;

    Third, participants will acquire the knowledge and practical skills necessary to adjust standard measures of quality of life instruments for their specific disease area’s, with a special focus on reimbursement. The programme consists of presentations, exercises and demonstrations of practical issues. Participants are invited to email their specific interest at forehand, and these topic will be discussed during the course.

    Programme:

    • Background of ‘health status' and ‘quality of life’.
    • Main principles of construction of a quality of life questionnaire.
    • Available instruments.

    Application.

    • Adaptation instruments for specific research questions: increase sensitivity.
    • QALY-analysis.
    • Practical and ethical value of measuring quality of life in a reimbursement setting.

    A facultative pre-course virtual welcome reception will be hosted on the Friday before the official start of the course. We highly recommend you attend this event as well!

    See 'how to apply' for the course registration period.

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    13 May 2024 - 17 May 2024
    Introduction to the Analysis of Population Epigenomics & Transcriptomics [EL034]

    About this course

    This course aims to give an introduction into the analysis and interpretation of epigenomics and transcriptomics data in the setting of population-based studies. We will introduce both types of omics and discuss their technical background, quality control and normalization, analytical approaches, interpretation of results and follow-up analyses.

    The course will include short practical sessions during which course participants can learn to with epigenomic and transcriptomic data using R.

    See 'how to apply' for the course registration period.

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    15 Apr 2024 - 19 Apr 2024
    Psychiatric Epidemiology [EL008]

    About this course

    This four-day course focuses on the principles and practice of psychiatric epidemiology. Basic concepts and issues that are specific to psychiatric epidemiology are covered. We will cover a range of psychiatric disorders such as depression, anxiety and sleep disorders and research approaches such as genetics and imaging to illustrate concepts and practice of psychiatric epidemiology. These topics will be covered by speakers from across the field.


    See 'how to apply' for the course registration period.

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    15 Apr 2024 - 19 Apr 2024
    Planning and Evaluation of Screening [EL022]

    About this course

    This course focuses on the design and the evaluation of health care programmes for the early detection of disease or screening. Screening takes place in a population without symptoms of the disease. The screening test characteristics have consequences for the favourable (improvement of prognosis by early detection, life years saved and deaths prevented) and unfavourable (overdiagnosis, unnecessary treatments) effects of screening.


    There are a number of designs for the assessment of the effectiveness of screening, such as randomized-controlled trials, observational prospective studies and case control studies. The pros and cons of each of these designs will be discussed. Evaluation methodologies, such as cost-effectiveness, cost-utility and technology assessment will be explained, including the concepts of quality adjustment of life years and of time preference. Detailed case studies include cervical, breast and prostate cancer screening, genetic screening, youth health care and screening for tuberculosis, e.g. for high risk groups. Several computer aids for the evaluation of screening are presented.

    Only the afternoon sessions require physical presence.

    See 'how to apply' for the course registration period.

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    18 Mar 2024 - 28 Mar 2024
    Repeated Measurements [EL002]

    About this course

    This course covers statistical methods to be used when one or more variables are repeatedly measured in time on the same experimental unit. For instance, in a clinical trial, the outcome variable can be measured at baseline and at different times during the treatment period. In a meta-analysis, the study can be regarded as the experimental unit and the observations of patients within the same study as repeated measurements.

    In the last 10 or 15 years much progress has been made in the development of new methods of analysis. In recent years several of these new methods have been implemented in a wide variety of computer packages.

    The course starts with a short overview of simple methods for analyzing repeated measurements data, followed by a short recap of the most basic concepts of linear algebra needed for the presentation of the most advanced models.

    Then the main focus turns on more advanced methods. For approximately normally distributed repeated measurements outcomes marginal and linear mixed models are introduced. For non-normal responses, first the generalized estimating equations (GEE) approach for marginal inferences is presented, followed by extensions of random effects models to categorical outcomes. All these methods are exemplified using data from of clinical and epidemiological studies.

    Computer practicals in the statistical programming language R will be used to acquire hands on experience in applying these techniques to real data. All code used during the course will be live demonstrated using a web app, which will be made available to participants.


    Participants need to bring their own laptop with certain pre-installed software (instructions will be given before start of the course). The assignment for this course needs to be handed in two weeks after the ending of the course.

    Please note that this courses has a blended design, which means that it includes both online modules and live meetings. The first meeting lasts 1 hour, the following meetings last 2 hours. Class days are listed under 'Course days' above.

    See 'how to apply' for the course registration period.


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    18 Mar 2024 - 22 Mar 2024
    Advanced Decision Modeling [EL006]

    About this course

    This week-long, project-based course aims to provide students with an understanding of advanced methods used in decision-analytic modeling and cost-effectiveness analyses. These include topics like the latest methods for calibration and validation, quantifying uncertainty, and consideration of heterogeneity of patient benefits and equity issues. The course combines lectures and readings to give theoretical foundation and perspectives with in depth project work and presentations to give practical concrete understanding in a way that furthers students’ specific research goals.

    Course Structure:
    Each day will begin with a lecture by Professor Goldhaber-Fiebert on an advanced methods topic. After the lecture, lab sessions will commence with students working on their projects as Professor Goldhaber-Fiebert circulates through the room and students assist each other in a collaborative environment. Most days Professor Goldhaber-Fiebert will also give an afternoon lecture. In addition, at the end of days 2, 3, and 4, Professor Goldhaber-Fiebert will give an additional, shorter, informal lecture (i.e., "a chalk talk") on a methods topic tailored to specific issues that are arising within students’ projects. Additionally, throughout the week, Professor Goldhaber-Fiebert will have one-on-one meetings with students about their projects.

    See 'how to apply' for the course registration period.

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    24 Apr 2024 - 26 Apr 2024
    Competing Risks and Multi-State Models [EL001]

    About this course

    Competing risks and multi-state models play an increasingly important role in the analysis of time to event data. Regarding competing risks, there is a lot of confusion regarding the proper analysis. The most important reason for the confusion is conceptual: which quantities can be estimated and what do they represent. Once the concepts are understood and the proper type of analysis has been chosen, most analyses are straightforward and can be performed with standard software for survival analysis. For multi-state models with exactly observed transition times, estimation is reasonably straightforward and the real challenge is in (dynamic) prediction.

    The overarching goal of the course is to provide a solid introduction to these topics and thereby increase the analytical validity in this field.

    In the first part of the course we cover competing risks analysis: what are competing risks and when do we need to take them into account; the independence assumption; cause-specific cumulative incidence; cause-specific hazard and subdistribution hazard; competing risks as a multi-state model. We will also cover regression models on both cause-specific and subdistribution hazard (Fine-Gray model) and discuss the difference in interpretation. We show how analyses can be performed with standard software. In the second part of the course, the extension to multi-state models is discussed. The course will cover topics including transition intensities and transition probabilities, nonparametric estimation and regression models, as well as methods to obtain predictions of future events, given the event history and clinical characteristics of a patient. With right censored and/or left truncated data, we show that it is possible to perform many types of analyses using standard software, using the same techniques as in multi-state representation of the competing risks model.

    See 'how to apply' for the course registration period.

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    27 May 2024 - 07 Jun 2024
    Missing Values in Clinical Research [EL009]

    About this course

    Missing data frequently occur in clinical trials as well as observational studies. An important source for missing data are patients who leave the study prematurely, so-called dropouts. Alternatively, intermittent missing data might occur as well.


    When patients are evaluated only once under treatment, then the presence of dropouts makes it hard to comply with the intention-to-treat (ITT) principle. However, when repeated measurements are taken then one can make use of the observed portion of the data to retrieve information on dropouts. Generally, commonly used methods to analyse incomplete data include complete-case (CC) analysis and, in longitudinal studies, an analysis using the last observation carried forward (LOCF). However, these methods rest on strong and unverifiable assumptions about the missing mechanism. Over the last decades, a number of analysis methods have been suggested, providing a valid estimate for, e.g., the treatment effect under less restrictive assumptions.


    The assumptions regarding the dropout mechanism have been classified by Rubin and co-workers as: missing completely at random (MCAR), missing at random (MAR) and missing not at random (MNAR).


    In the first part of the course we will review various repeated measurements models and indicate under which missing data mechanism they will provide valid estimates of the treatment effect. Finally, since it is impossible to verify that the dropout mechanism is MAR we argue that, to evaluate the robustness of the conclusion, a sensitivity analysis thereby varying the assumption on the dropout mechanism should become a standard procedure when analyzing the results of a clinical trial.


    The second part of the course focuses on multiple imputation (MI), specifically the fully conditional specification (FCS, MICE), which is often considered the gold standard to handle missing data. We will discuss in detail what MI(CE) does, which assumptions need to be met in order for it to perform well, and alternative imputation approaches for settings where MICE is not optimal. The theoretic considerations will be accompanied by demonstrations and short practical sessions in R, and a workflow for doing MI using the R package mice will be proposed.


    Examination for this course consists of two assignments.

    See 'how to apply' for the course registration period.

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    29 May 2024 - 07 Jun 2024
    Sustainable Public Health [EL025]

    About this course

    In the ‘2030 Agenda for sustainable development’, the United Nations described 17 Sustainable Development Goals (SDGs), including interrelated goals on poverty reduction, population health, the living environment, and climate change. Achieving these goals requires multidisciplinary and international collaboration, in which public health experts also need to play an important role. This course is focussed on three important questions: What is the evidence for these connections, which public health interventions can synergistically work towards a sustainable future, and how to advise local or national governments best about this? Although priorities differ between countries, these questions are universal.

    The programme consists of ‘capita selected lectures’, lectures and training in valorisation, and a group exercise.

    See 'how to apply' for the course registration period.

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