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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.nlMaster
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.
Read MoreMaster 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.
Read MoreResearch 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.
Read MoreCourses
07 Apr 2026 - 09 Apr 2026
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.
Read More07 May 2026 - 13 May 2026
Bayesian Methods for Clinical Trials [EL037]
About this course
The course consists of two parts.
In Part I, the general principles of setting up and conducting clinical trials will be reviewed. A focus will be on randomized clinical trials. Both general interventions as well as drug developments will be treated. The Bayesian methodology will be introduced and the use of Bayesian software (JAGS) will be exemplified. Finally, the classical (frequentist) and the Bayesian approach will be contrasted.
In Part II, several topics in clinical trials will be discussed from a Bayesian perspective. The set up, conduct and analysis of (some of) the following types of clinical trials will be exemplified (in a Bayesian manner):
• Sample size & power calculations
• Monitoring a sequential trial and multiple testing issues
• Adaptive and platform trials
• Bridging studies
• Dose finding studies
• Studies in oncology
• Pharmacodynamic and pharmacokinetic studies
• The incorporation of historical information and real world data for the analysis of orphan studies
• Meta-analyses and network meta-analyses
In this part JAGS will be again used as Bayesian software, but in addition several dedicated R packages.
References (selection of)
1. Lesaffre, E. and Lawson, A., Bayesian Biostatistics, 2012, John Wiley & Sons, West Sussex, UK
2. Lesaffre, E,, Baio, G. and Boulanger, B. (eds), Bayesian Methods in Pharmaceutical Research, 2020, Chapman & Hall/CRC, Boca Raton, US
3. Yang, H. and Novick, S. J., Bayesian Analysis with R for Drug Development. Concepts, Algorithms and Case Studies, 2019, CRC Taylor & Francis Group, Boca Raton, US
4. Holmgren, E.B., Theory of Drug Development, 2014, CRC Taylor & Francis Group, Boca Raton, US
08 Apr 2026 - 10 Apr 2026
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.
Read More09 Mar 2026 - 13 Mar 2026
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.
13 Apr 2026 - 17 Apr 2026
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.
Read More13 Apr 2026 - 17 Apr 2026
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.
Read More13 Apr 2026 - 17 Apr 2026
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.
Read More16 Mar 2026 - 01 Apr 2026
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.
18 May 2026 - 28 May 2026
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.
Read More18 May 2026 - 22 May 2026
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.
20 Apr 2026 - 22 Apr 2026
Causal Thinking [EL036]
About this course
This course is intended to supplement and build on the training in causal inference received by NIHES students in CK010: Study Design and ESP48: Causal Inference. A student taking all 3 of these courses will have seen all important topics in causal inference. Additionally, we will take a closer look at the assumptions that underlie all the most commonly used ways to estimate causal effects (confounder control, instrumental variable analysis, regression discontinuity and differences in differences) emphasizing a deeper intuition for why they are needed. We will also go deeper into concepts such as bias analysis and triangulation. This course will use counterfactual notation and basic concepts in logic and probability. Students will be evaluated based on a group project where they use triangulation in a real, applied example.
Read More20 Apr 2026 - 24 Apr 2026
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.
Read More27 May 2026 - 29 May 2026
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.
See 'how to apply' for the course registration period.
Read More28 Apr 2026 - 15 May 2026
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.
Read More