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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
01 Sept 2025 - 26 Sept 2025
Review of Mathematics and Introduction to Statistics [CK001]
About this course
Several courses in the NIHES curriculum require a good working knowledge of basic concepts in mathematics and statistics. These courses include Biostatistics I (CK020), Biostatistics II (CK030), Repeated Measurements (EL002) and Bayesian Statistics (EL003). The course Review of Mathematics and Introduction to Statistics aims to prepare you for these statistical courses by helping you to obtain a sufficient working knowledge of mathematics and statistics.
This course is a self-study course based on online material (videos from external sources) and the material in an accompanying reader. A Q&A session will be organized near the final course deadline, and the organizers of the course are available for questions during the course. There will be no lectures or tutorials aside from the Q&A. A number of exercises and a practice test are included in the course materials.
The content of this course is divided into the following topics:
- Basic mathematical operations
- Functions
- Differentiation
- Optimization
- Integration
- Vectors and matrices
- Basic concepts in statistics
This course was previously registered under the course code BST01.
Read More02 Jun 2025 - 06 Jun 2025
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.
02 Sept 2025 - 15 Sept 2025
This thing called Science [REMA01]
About this course
This course explores the history, philosophy, and integrity of scientific practice. Students will examine what makes science a special activity and what ethical challenges researchers face. The course provides a common starting point for students across all research master programs at the Erasmus MC and encourages reflection on the role and responsibilities of science in society. Through a mix of lectures, discussions, and group work, participants will prepare themselves for doing science with high standards of integrity.
The course is structured around four perspectives:
• science as method
• science as practice
• science as community
• science as career.
08 Sept 2025 - 26 Sept 2025
Study Design [CK010]
About this course
In this course, the principles and practice of cohort and case-control studies will be taught as well as the important topics underlying epidemiologic studies such as validity and precision. The theory underlying the different design options and concepts will be discussed in depth using causal inference and counterfactuals. Also, a historical perspective on causal thinking will be presented. The course focuses on the classical approach but also addresses modern concepts. Lectures will be complemented by exercises using current examples of epidemiological studies.
Participants will be asked to work out a study design and prepare a formal presentation in the last week of the course.
For students in our master programmes, the core concepts presented in this course will be assessed in the core competences exam that bundles the fall semester courses. This is in addition to the assessment during the course in the form of a presentation. The core competences exam is only mandatory for students starting their programme in August 2021 or later, while the presentation during the course is mandatory for all participating students.
17 Nov 2025 - 28 Nov 2025
Clinical Epidemiology [CK040]
About this course
Research questions in clinical epidemiology originate from clinical practice. Caring for patients commonly triggers the research-minded clinician to question his/her knowledge and decisions. Questions may revolve around risk factors, prevention, diagnosis, prognosis and/or interventions leading to research studies. Results from clinical epidemiological research are used in patient management decisions. Understanding the research results, recognizing the limitations, and knowing how to apply them are essential to translate clinical research results to application in day-to-day clinical practice.
In this course, the principles and practice of clinical epidemiology and the application of the results to clinical decision making will be discussed, using examples from the literature and from ongoing studies. The course is divided into 3 parts:
1. Diagnosis
2. Prognosis
3. Interventions
We use blended learning: a combination of video’s, readings, assignments, question-and-answer sessions, interactive lectures, and workshops.
The assignments involve performing calculations. For the Diagnosis part we work with Excel. For the Prognosis part you will be using R code that has been written for you. To benefit maximally from this course we advise you to do the introduction to R - online course: install R and R Studio, learn basic R syntax, and learn basic R studio functionality. The DataCamp course Introduction to R is very helpful: https://www.datacamp.com/courses/free-introduction-to-r. The full DataCamp course will be available for course participants.
For students in our master programmes, the core concepts presented in this course will be assessed in the core competences exam that bundles the fall semester courses. This is in addition to the assessment during the course in the form of assignment(s). The core competences exam is only mandatory for students starting their programme in August 2021 or later, while the assignments during the course are mandatory for all participating students.
19 May 2025 - 28 May 2025
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 More19 May 2025 - 23 May 2025
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.
Read More27 Oct 2025 - 14 Oct 2025
Biostatistics II [CK030]
About this course
This course presents statistical regressions models for the analysis of dichotomous, count, and time-to-event data. In the first part, the course builds upon the introductory presentation of logistic regression from the Biostatistics I course and shows some of its extensions, including the conditional logistic regression model. The course then introduces regression models for the analysis of count data. The last part focuses on the statistical analysis of time-to-event data, starting from simple statistical tests and followed by the presentation of accelerated failure time and Cox proportional hazards models. For each modeling framework, a detailed discussion is given on how to build the model to answer the scientific questions of interest, estimate the model’s parameters, assess its assumptions, and finally, interpret the results of the analysis.
The course will be explanatory rather than mathematically rigorous, emphasizing application such that participants will obtain a clear view of the different modeling approaches and how they should be used in practice. To this end, the course includes several computer sessions, during which participants will learn to work with the R statistical language and implement the methods discussed in the theory sessions.
For students in our master programmes, the core concepts presented in this course will be assessed in the core competences exam that bundles the fall semester courses. This is in addition to the assessment during the course in the form of assignment(s). The core competences exam is only mandatory for students starting their programme in August 2021 or later, while the assignments during the course are mandatory for all participating students.
29 Sept 2025 - 17 Oct 2025
Biostatistics I [CK020]
About this course
This course provides an introduction to the basic concepts and techniques of statistical data analysis. The course starts with a presentation of fundamental notions of statistics and statistical inference under uncertainty. The course then continues with an in-depth presentation of classical regression models, namely, linear regression for continuous data, logistic regression for dichotomous data. Classical statistical parameter and non-parametric statistical tests are linked to these models. For each modeling framework, a detailed discussion is given on how to build the model to answer the scientific questions of interest, estimate the model’s parameters, assess its assumptions, and finally, interpret the results of the analysis.
The course will be explanatory rather than mathematically rigorous, emphasizing application such that participants will obtain a clear view of the different modeling approaches and how they should be used in practice. To this end, the course includes several computer sessions, during which participants will learn to work with the R statistical language and implement the methods discussed in the theory sessions.
For students in our master programmes, the core concepts presented in this course will be assessed in the core competences exam that bundles the fall semester courses. This is in addition to the assessment during the course in the form of assignment(s). The core competences exam is only mandatory for students starting their programme in August 2021 or later, while the assignments during the course are mandatory for all participating students.