Find your programme or course by following the steps

Step 1/4:

I am looking for a …

Programme

Choose

Course

Choose

Step 2/4:

My research background:

I have no research experience

Choose

I have research experience

Choose

Step 3/4:

I am interested in the following discipline:

Epidemiology

Choose

Clinical Epidemiology

Choose

Genetic Epidemiology

Choose

Public Health

Choose

Pharmaco Epidemiology

Choose

Health Economics

Choose

Clinical Research

Choose

No specific interest

Choose

Step 4/4:

I am interested in following a programme:

Full-time

Choose

Part-time

Choose

Step 2/4:

I am interested in the following discipline:

Advanced Statistics

Choose

Biostatistics

Choose

Clinical Epidemiology

Choose

Clinical Research

Choose

Epidemiology

Choose

Genetic Epidemiology

Choose

Health Economics

Choose

Methodology

Choose

Pharmaco Epidemiology

Choose

Public Health

Choose

Step 3/4:

I am looking for a course on this level:

Basic

Choose

Intermediate

Choose

Advanced

Choose

Step 4/4:

I want to follow a course in this time-frame:

Summer (July August September)

Choose

Autumn (October November December)

Choose

Winter (January February March)

Choose

Spring (April May June)

Choose

Your result based on your answers

Didn't find what you were looking for?

Try again!

Programme overview (based on your choices)

Programmes & specializations videos

Postgraduate Programme | 1 Year | FULL-TIME

For whom?

Consider one additional year of research training after your Master’s, if you’d like to acquire more research experience or increase your chances of qualifying for a PhD research project.

Read More
  • Epidemiology
  • Clinical Epidemiology
  • Public Health Epidemiology
  • Genomic & Molecular Epidemiology
  • 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.

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

    For whom?

    This Research Master programme provides a unique opportunity for ambitious students with a Bachelor degree in Medicine or Biomedical Sciences.There is a great need for clinicians who can combine patient care and research. This Research Master programme helps medical students to become clinical investigators and pursue an academic career simultaneously.

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

    Read More

    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 specialisations 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 More
  • Epidemiology
  • Clinical Epidemiology
  • Public Health Epidemiology
  • Health Economic Analysis
  • Medical Psychology
  • Biostatistics
  • Health Decision Sciences
  • Genomic & Molecular Epidemiology
  • Doctorate

    Postgraduate Programme | 1 Year | FULL-TIME

    For whom?

    Consider one additional year of research training after your Master’s, if you’d like to acquire more research experience or increase your chances of qualifying for a PhD research project.

    Read More
  • Epidemiology
  • Clinical Epidemiology
  • Public Health Epidemiology
  • Genomic & Molecular Epidemiology
  • Executive Education

    Executive Master of Science in Health Sciences | 2(+)years | Part-time | 70 EC points

    For whom?

    This Executive Master programme focuses on training individuals who have already  authored scientific publications, but wish to take a step further in developing a successful career in health science research. The programme is ideal for working professionals since you can fully customize it to fit your busy schedule.

    Read More
  • Epidemiology
  • Clinical Epidemiology
  • Health Decision Sciences
  • Genomic & Molecular Epidemiology
  • Courses

    28 Oct 2020 - 13 Nov 2020
    Clinical Epidemiology [CE02]

    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. Results from clinical epidemiological research are used in patient management decisions. Concepts from decision sciences are used 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 considered, using examples from the literature and from ongoing studies.

    We will be using blended learning: a combination of web-based materials, interactive lectures, workshops and practicums.

    The course is divided into 3 parts:

    1. Diagnosis
    2. Prognosis
    3. Interventions

    Assignments for each part need to be completed prior to the next part.


    The course dates are as follows:

    Oct 30 - Nov 1: online learning

    Nov 1, Nov 4: AM: online learning, PM: office hours (optional)

    Nov 5: AM: online learning, PM: in-class sessions

    Nov 6: self-study / finish online learning assignments

    Nov 7 - 8: in-class sessions

    Nov 11: in-class sessions

    Nov 12: AM: self-study, PM: exam

    Nov 13 - 15: group assignment and final course assignment

    Nov 15: deadline for assignments

    Read More

    26 Oct 2020 - 30 Oct 2020
    Public Health Research: Analysis of Determinants [HS02b]

    About this course

    Public Health Research: from Epidemiology to Health Promotion

    Module: Analysis of Determinants

    This module elaborates on research of the analysis of determinants of and inequalities in population health and risk factors of disease. Students will be introduced in:

    • current insights in the main determinants of population health and risk factors of disease;
    • determinants of inequalities in population health;
    • research methods for the analysis of these determinants and
    • challenges for future research in these issues.

    Note: HS02a, HS02b and HS02c will be tested after the HS02c course! If you are taking all three courses, you need to pass all three courses separately.

    Read More

    24 Aug 2020 - 11 Sept 2020
    Study Design [CC01]

    About this course

    In this course, the principles and practice of follow-up and case-control studies will be taught. The theory underlying the different design options will be discussed in depth. The course focuses on the classical approach but also addresses modern concepts. The practice of conducting follow-up and case-control studies with emphasis on issues of validity will be discussed. 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 on the last course day.

    Read More

    23 Nov 2020 - 11 Dec 2020
    Biostatistical Methods II: Classical Regression Models [EP03]

    About this course

    The aim of this course is to introduce several important statistical regression models for non-normal and censored outcomes that are widely applied in clinical and epidemiological research. The course starts with a brief presentation of the basic principles behind likelihood theory, followed by a detailed discussion of logistic regression for dichotomous data, Poisson regression for count data, and closes with an extended presentation of regression models for time-to-event data, including the Cox proportional hazards model and the accelerated failure time model.The course will be explanatory rather than mathematically rigorous, with emphasis given on application such that participants will obtain a clear view on 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 be asked to implement in practice the methods discussed in the theory sessions.

    Read More

    17 Aug 2020 - 21 Aug 2020
    Health Economics [ESP25]

    About this course

    Faculty: Ken Redekop, PhD


    Economic thinking is becoming increasingly important in health care.This course begins with a two day introduction of main concepts of health economics. The remaining three days are used to provide students with more in depth knowledge. The student will learn to analyze the cost-effectiveness of health care interventions (e.g., medicine, diagnostic test, health care programme).

    Both methodology and practical examples will be covered. Exercises are used to illustrate the various steps in economic thinking.


    //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.//

    Read More

    17 Aug 2020 - 21 Aug 2020
    Social Epidemiology [ESP61]

    About this course

    Faculty: Prof. Frank van Lenthe, PhD


    This course aims to 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 ethnicity 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.//

    Read More

    17 Aug 2020 - 21 Aug 2020
    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. Special attention will be paid on issues such as normalization, standardization, and categorization, combining multiple variables, combining multiple sources etc. The goal is to provide students with the understanding and tools to perform epidemiologic data analysis.


    //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.//

    Read More

    17 Aug 2020 - 21 Aug 2020
    Causal Mediation Analysis [ESP69]

    About this course

    Faculty: Linda Valeri, PhD


    The course will cover some of the 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 will cover 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, discussion will be given as to when the standard approaches to mediation analysis are valid. Using ideas from causal inference and natural direct and indirect effects, alternative mediation analysis techniques will be described when the standard approaches will not work. The no-confounding assumptions needed for these techniques will be described.


    SAS, SPSS and Stata macros to implement these techniques will be 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 will be 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 will be used to present material in lecture form. Extensive printed notes will be 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 will be 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.//

    Read More

    16 Nov 2020 - 20 Nov 2020
    Principles in Causal Inference [EP01]

    About this course

    Epidemiologic research often entails asking and trying to answer questions toward understanding the causes and consequences of health outcomes. Answers to such causal questions require us to combine data (e.g., from observational studies) with assumptions to estimate causal effects. This course will teach students to think critically and rigorously about the implications of study design and analysis toward addressing such causal questions. Students will learn formal causal inference "languages" – including the concept of a target trial, causal diagrams, and counterfactual theory – to articulate research questions, inform an analytic approach, and identify threats to validity such as confounding.

    Read More

    14 Sept 2020 - 09 Oct 2020
    Biostatistical Methods I: Basic Principles [CC02]

    About this course

    The analysis of collected data is an inevitable part of almost any medical research project. Consequently, knowledge of and insight in the basic principles of data-analysis are essential for medical researchers. The course CC02 - Biostatistical Methods I: basic principles is designed to teach classical and basic statistical techniques for the analysis of medical research data. The course comprises lectures as well as computer practicals, in which students will apply the widely used statistical software packages SPSS and/or R to work through exercises.

    CC02 consists of two parts. In part A, which lasts one week, basic applications of biostatistics will be introduced, including descriptive statistics, general principles of statistical hypothesis testing, statistical inferences on means and proportions, and interval estimates for association measures. In part B, which last two weeks, more advanced methods will be discussed, including linear correlation and regression, multiple linear regression, analysis of covariance, regression diagnostics, stratified analysis and time-to-event analysis. The logistic regression model and the Cox proportional hazard regression model will be introduced briefly.

    During the lectures, time will be spent on practical examples and exercises. Throughout the course, examples of SPSS- and R-programmes and -output will be demonstrated in relation to the topics that will be discussed.

    Biostatistical Methods I: Basic Principles, part A (CC02A) is equivalent to Introduction to Data-analysis (ESP03) and Biostatistics for Clinicians (EWP22).

    Read More

    12 Oct 2020 - 20 Oct 2020
    Clinical Translation of Epidemiology [CE01]

    About this course

    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.

    Read More

    12 Oct 2020 - 16 Oct 2020
    Public Health Research: Analysis of Population Health [HS02a]

    About this course

    Public Health Research: From Epidemiology to Health Promotion

    Module: Analysis of Population Health

    This module aims to teach methods to assess the health of populations at national and local levels. Students are taught to calculate, apply and interpret population-based measures of mortality, quality of life and disease occurrence. In addition, students learn methods to assess time trends in population health (e.g. APC methods) and to analyse inequalities in health between social groups.


    Note: HS02a, HS02b and HS02c will be tested after the HS02c course! If you are taking all three courses, you need to pass all three courses separately.

    Read More

    12 Oct 2020 - 06 Nov 2020
    Psychology in Medicine [MP01]

    About this course

    Medical psychologists study the way somatically ill people think, act and feel. The aim of this course is to teach students about psychological determinants of illness and illness behavior, the psychological consequences of somatic illness and psychological care for somatic patients. First, you’ll learn about ‘normal’ reactions to disease. We’ll then focus on abnormal and pathological reactions to somatic illness and on problems that patients might have in adjusting to their disease.


    We will discuss models that explain why some people find it difficult to adjust to their disease, such as the stress coping model and the stress vulnerability model. Other models that will be discussed in this course include the Health Belief Model, the Theory of Planned Behavior and the Stages of Change Model. These models are widely applied by medical psychologists in interventions for somatic patients. Modern neuroscientific models for understanding behaviour and behavioural disorders will be addressed as well.


    In this course, we will focus on various somatic problems, such as diabetes, inflammatory bowel disease, infertility, organ transplantation and chronic pain.


    Finally, basic theories of doctor-patient communication will be discussed, as communication between doctors and patients has become more and more important.


    The learning method in this course is problem-based learning. Furthermore, you will build a new model for understanding a complex and realistic problem in medical psychology.

    Read More

    10 Aug 2020 - 14 Aug 2020
    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 community trials and study designs based on natural experiments instead of RCT. Designs and methods will be illustrated in lectures and exercises and application will demonstrate their usefulness in current hot topics, such as health inequalities; causes and consequences of ageing; avoidable diseases such as cancer; and evaluation of complex societal interventions.

    The course will be relevant to those who have a basic knowledge of epidemiology, and who wish to start a career in public health research.

    Teaching methods:
    This course will use lectures, exercises, and group discussion as teaching tools.


    //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.//

    Read More

    10 Aug 2020 - 14 Aug 2020
    Methods of Health Services Research [ESP42]

    About this course

    Faculty: Prof. Niek Klazinga, MD PhD


    Health Services Research addresses issues such as access and quality of health care delivery, financing and use of health care services, workforce planning, implementation of change and the overall functioning and performance of health care systems.

    This introductory course provides insight in the various research questions, research designs, data-collection methods and analysis methods used in health services research. It puts emphasis on the links between research, policy and practice. The course is organized around lectures and group 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.//

    Read More

    10 Aug 2020 - 14 Aug 2020
    Introduction to Bayesian Methods in Clinical Research [ESP68]

    About this course

    Faculty: Prof. Emmanuel Lesaffre, PhD


    This course provides an introduction to Bayesian methods with an emphasis on the intuitive ideas and applications. The course treats the basic concepts of the Bayesian approach, such as the prior and posterior distribution and their summary measures (mean, median, credible interval, etc), the posterior predictive distribution. In addition, Bayesian methods for model selection and model evaluation will be treated.


    The Bayesian approach will also be compared, both conceptually as well as practically, with the classical frequentist approach. Markov Chain Monte Carlo techniques are introduced and exemplified in a variety of applications. The Bayesian approach will be illustrated in clinical trials, epidemiological studies, meta-analyses, diagnostic testing, agreement studies, etc. WinBUGS and OpenBUGS will be used as software. But also the use of their interfaces with R, i.e. R2WinBUGS and R2OpenBUGS will be illustrated.


    Course format:

    In the first three days of the course the Bayesian concepts will be explained. Theory and exercises will then be mixed depending on the topic. The final two days will be devoted to particular application areas and have largely a practical flavor. In addition the application of the Bayesian methodology in the medical literature will be highlighted.


    Teaching methods:

    Interactive lectures, exercises, practicals


    //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.//

    Read More

    10 Aug 2020 - 14 Aug 2020
    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 the uncertainty, trade-offs and values that are involved, how should patients, policymakers and clinicians navigate through a complex and tangled web of diagnostic and therapeutic choices, patient preferences, and resource constraints to make optimal decisions? Medical interventions may have benefits but also adverse effects, e.g., surgery may lead to undesirable complications, and diagnostic technologies may produce false or inconclusive results.


    In many clinical and health policy decisions it is necessary to counterbalance benefits and harms and to trade off competing objectives such as maximizing life expectancy vs. optimizing quality of life vs. minimizing the resources required. In this course we will discuss a proactive approach to such decisions and discuss the basic concepts underlying decision analysis in order to integrate evidence and values for optimal and efficient care choices in the face of uncertainty. Topics include diagnostic reasoning, test interpretation, treatment thresholds, test-treat thresholds, estimating life expectancy, quality of life assessment, health technology, decision models and cost-effectiveness analysis.


    Teaching methods: Interactive lectures, exercises and practicums.


    //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.//

    Read More

    10 Aug 2020 - 14 Aug 2020
    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.//

    Read More

    09 Nov 2020 - 13 Nov 2020
    The Placebo Effect [MP02]

    About this course

    The placebo effect has been studied since the 1950’s, starting with the original 1955 study of Beecher. In this course we will discuss several postulated underlying mechanisms of the placebo effect (e.g. expectancy, conditioning, affect-modulation, and doctor-patient communication). Furthermore we will debate the existence of the placebo effect and discuss the challenges in measuring the effect. Questions that will be addressed are for instance: can you deliver an open label placebo? Is it ethical to prescribe a placebo when a patient doesn’t know he is getting a sugar pill? Does the placebo effect exist outside of pain medication research? You will experience the strength of the placebo effect first hand in an experiment during the course.

    The assessment of the course will exist of the presentation of a research proposal for studying the placebo effect.

    Read More

    02 Nov 2020 - 06 Nov 2020
    Public Health Research: Intervention Development and Evaluation [HS02c]

    About this course

    Public Health Research: from Epidemiology to Health Promotion

    Module: Intervention Development and Evaluation.

    This module elaborates on the intervention development, implementation and evaluation phases in the model of planned promotion of public health.

    Students will:

    • be introduced to strategies and opportunities of primary and secondary prevention;
    • learn how to work from determinants to interventions, i.e. how to translate determinants into intervention goals and intervention components and;
    • learn about the opportunities and challenges of evaluation of primary and secondary prevention interventions and;
    • be introduced to theory and challenges in dissemination of prevention interventions.

    The course uses examples from health behaviour change, cancer screening, and vaccination.


    Note: HS02a, HS02b and HS02c will be tested after the HS02c course! If you are taking all three courses, you need to pass all three courses separately.

    Read More