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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.

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  • 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.

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  • 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.

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    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.

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  • 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.

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  • 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.

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

    30 Mar 2020 - 03 Apr 2020
    Planning and Evaluation of Screening [HS05]

    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.

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    25 May 2020 - 29 May 2020
    Bayesian Statistics [CE09]

    About this course

    There is growing acknowledgement of the value of Bayesian methods for complex models in biostatistics and epidemiology, in dealing with issues such as multiplicity, measurement error, spatial associations and hierarchical structure. This course will introduce the essentials of Bayesian ideas, emphasizing practical application using exact and simulation-based software. Examples will include the use of Bayesian methods in clinical trials, institutional comparisons, smoothing of disease rates, and frailty models.

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    24 Feb 2020 - 28 Feb 2020
    Using R for Statistics in Medical Research [BST02]

    About this course

    R has recently become one of the most popular languages for data analysis and statistics. This course teaches students the basics syntax and data types of this statistical programming language. The aim of this course is to equip students with the R knowledge needed to explore their own data, make data visualizations and perform basic statistical analysis.


    The course covers practical issues in statistical computing which includes reading data into R, bringing them in the correct structure and writing R functions. This course further focuses on the concepts and tools behind reporting data analyses in a reproducible manner and building simple interactive web applications. In particular, useful features will be introduced such as debugging code, version control using git, markdown reports and shiny apps.


    Written exam: during the course students are asked to perform several programming/analysis tasks in R in the computer lab. The exam is open-book.

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    23 Mar 2020 - 27 Mar 2020
    Implementation Science [CE18]

    About this course

    Those of us who work in health care know that promising research results or evidence-based guidelines do not easily become standard of clinical care. Therefore, a next step, also known as implementation and dissemination, is needed after the results of a RCT or guidelines are published. The field of implementation science has emerged, and this multidisciplinary field with professionals from medicine, nursing, psychology, pharmacy, engineering, aims to enhance the uptake of research-based knowledge in real-world settings.

    The course includes lectures, guest speakers, and interactive workshops. During these workshops, participants work on a proposal for an implementation (research) project. The proposals will be presented on the last day.


    Topics for lectures/curriculum

    • Introduction to Implementation Science
    • Frameworks, and models in implementation science
    • Individual and contextual influences on implementation
    • Effective implementation & use of change strategies
    • Implementation research & outcomes
    • De-Implementation (de-adoption)
    • Publishing implementation research

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    23 Mar 2020 - 27 Mar 2020
    Biostatistical Methods I: Basic Principles Part A [CC02A]

    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.

    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).

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    23 Mar 2020 - 27 Mar 2020
    Pharmaco-epidemiology and Drug Safety [EWP03]

    About this course

    Pharmaco-epidemiology plays a role of increasing importance in the field of drug safety and regulatory decision making. On the one hand, the introduction of computers into clinical practice facilitates the performance of large-scale cohort studies and nested case-control studies. On the other hand, it creates some problems regarding the quality of outcome and exposure assessment. Because the commercial consequences of pharmaco-epidemiological studies may be enormous, discussions in this area may be heated.


    In this course, referring to established drug safety problems will highlight some of the complex aspects of outcome and exposure assessment in pharmaco-epidemiology.


    Teaching Methods

    Plenary teaching as well as computer exercises on the analysis of cohort- and case-control data.

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    20 Apr 2020 - 24 Apr 2020
    Advanced Decision Modeling [CE15]

    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.

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    18 May 2020 - 20 May 2020
    Cardiovascular Epidemiology [EP20]

    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.

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    18 May 2020 - 05 Jun 2020
    Public Health in Low and Middle Income Countries [PU06]

    About this course

    This module aims to teach methods to assess the health of populations in low and middel income countries and to quantitatively evaluate the effects of interventions on population health. Students are taught use modern techniques as health impact assessment to predict changes in population health due to particular programmes, for example control programmes for infectious diseases.

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    15 Apr 2020 - 17 Apr 2020
    Quality of Life Measurement [HS11]

    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.

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    11 May 2020 - 15 May 2020
    Preventing Failed Interventions in Behavorial Research [MP05]

    About this course

    'The success and common risk factors of trials'

    This course elaborates on successful ingredients for different phases of an intervention study: design, data collection and implementation. The focus is on learning from encountered difficulties, mistakes and failures from previous research and researchers. Questions that will be addressed are for instance: How do you choose the most suitable design? How many participants are necessary for a successful intervention? How to implement your intervention?

    The assessment of the course will exist of a short presentation (pitch) of a critical appraisal of an intervention study, preferably your own.

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    11 May 2020 - 15 May 2020
    Missing Values in Clinical Research [EP16]

    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.

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    11 Mar 2020 - 17 Mar 2020
    Advanced Analysis of Prognosis Studies [EWP13]

    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.

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    10 Jun 2020 - 10 Jun 2020
    Integration module [PU04]

    About this course

    Master students in Public Health will have to demonstrate their ability to integrate their knowledge and expertise into evidence-based advice for policy makers and practitioners. Based on the (draft) research paper the student will make a presentation of 10 minutes, addressing the following topics:

    • What is the problem addressed?- How does your study contribute to this problem?
    • How will your results impact population health?
    • What action should policy makers and professionals take?
    • These presentations will be followed by a discussion with faculty and fellow students. In addition, students are required to write a one page reflection on the courses in the programme.

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    08 Jun 2020 - 08 Jun 2020
    Site visit to the Municipal Health Service Rotterdam [PU03]

    About this course

    The site visit is a orientation on public health practice in the Netherlands. The visit will be to the Municipal Public Health Service of Rotterdam (GGD Rotterdam). The objective is to provide the participant with a brief insight on how the GGD is organized and which services are provided to the community. After the field visit the participant is able to describe the learning experience regarding the visit in a structured report and to compare the services provided by the GGD and the way that these services are implemented with public health services in the participant's country of origin or country of work.

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    06 May 2020 - 08 May 2020
    Mendelian Randomisation [GE10]

    About this course

    Starting this year, the GE10 course will be given on a yearly basis. The syllabus and programme description will be published in the course of week 8 (Feb 17-21). In the meantime, registration is open.


    Faculty: Jeremy Labrecque, Caroline Bonilla, Kate Trajanoska and Fernando Rivadeneira.

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    06 Apr 2020 - 23 Apr 2020
    Repeated Measurements [CE08]

    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 avaliable 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 on Friday 8 May 2020.


    Please note that this courses has a blended design, which means that it includes both online modules and in-class meetings. The first meeting lasts 1 hour, the following meetings last 2 hours. The meetings are planned on the following days:

    • Mon 6 April
    • Thu 9 April
    • Tue 14 April
    • Thu 16 April
    • Mon 20 April
    • Thu 23 April
    • Assignment deadline Fri 8 May

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    06 Apr 2020 - 09 Apr 2020
    Medical Demography [HS04]

    About this course

    The first day of the course starts with an overview of the different types of "what if" questions raised in public health, and an overview of the types of models that could be used to address such questions. Next we will briefly discuss population projection techniques. Further, the student will learn to apply the life table, which is a simple yet powerful technique to model the population dynamics underlying public health.


    The second day will focus on summary measures that combine mortality and morbidity estimates, such as healthy life expectancies and disability-adjusted life expectancy. It will discuss two types of life tables that are used to this end: the Sullivan life tables, and the multi-state life tables. Attention is also given to assessing the effect on population health of specific diseases, such as stroke or cancer.


    The third day will discuss the use of risk factor models, which are applied to assess the ways in which changes in risk factors such as smoking and overweight could affect population health. Examples will be based on the Dynamo model, which is a new, comprehensive model to be used in health impact assessments across Europe.


    The last day will illustrate the use of population health models in a series of studies that evaluated the population health impact of different types of interventions. The emphasis is given to the Prevent model, which will be applied to assess the impact of life style changes on future cancer incidence.


    The course consists of lectures and many computer exercises.


    N.B.: This course is organised every other year.

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    02 Jun 2020 - 05 Jun 2020
    Psychiatric Epidemiology [EP12]

    About this course

    This four-day course focuses on the principles and practice of psychiatric epidemiology. Basic concepts and issues that are specific to both child and adult psychiatric epidemiology are covered. Psychiatric issues that will be used to illustrate concepts and practice of psychiatric epidemiology include: prevalence studies, longitudinal studies, the role of risk and resilience, and genetic epidemiology. Invited speakers will cover particular topics such as migration and psychiatirc disorder, the epidemiology of bipolar disorder, schizophrenia and addiction in more detail.Maximum 40 participants.


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    02 Mar 2020 - 06 Mar 2020
    An Introduction to the Analysis of the Next-generation Sequencing Data [GE13]

    About this course

    This course provides an introduction to working with Next-Generation Sequencing (NGS) data. It targets individuals who have access to NGS data and want to learn how to work with this data and what the possibilities and limitations of NGS are. Lectures will be complemented with practical sessions in which the student will gain hands-on experience with various tools and techniques.Subjects that will be covered include:

    • NGS: an introduction to methodology and techniques;
    • Basic statistics of NGS data, e.g. coverage;
    • Aligning the sequence reads;
    • Calling sequence and structural variants;
    • Dealing with various file formats (samtools, VCFtools, GATK);
    • Annotating sequence and structural variants;
    • Evaluating functional effects of the genetic variants on proteins;
    • Conversion to other formats;
    • Single variant and Collapsed genotype analyses with various tools (e.g. seqMeta, RAREMETAL and RVtest);
    • Finding variants with recessive effects and compound heterozygosity;
    • Search for rare variants in families and population based studies for complex phenotypes;
    • Search for rare variants in Mendelian disorders, and
    • Imputation of sequence variants.

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    02 Mar 2020 - 03 Apr 2020
    Survival Analysis for Clinicians [EWP24]

    About this course

    Survival analysis is the study of the distribution of life times, i.e. the times from an initiating event (birth, diagnosis, start of treatment) to some terminal event (relapse, death). Survival analysis is most prominently (but not only) used in the biomedical sciences. A special feature of survival data is that it takes time to observe the event of interest. A result of this seemingly innocent observation is that for a number of subjects the event is not observed, but instead it is known that it has not taken place yet. This phenomenon is called censoring and it requires special statistical methods. During the course different types of censored and truncated data will be introduced and techniques for estimating the survival function by employing both parametric and non-parametric methods will be illustrated. Also techniques for testing equality of survival functions (the log-rank test and alternatives) are discussed. Finally regression models for survival analysis, based on the hazard function (most notably the Cox proportional hazards model), will be studied in great detail. Special aspects such as time-dependent covariates and stratification will be introduced. Techniques to be used to assess the validity of the proportional hazards regression model will be discussed. The last part of the course touches on models for multivariate survival analysis, including competing risks and multi-state models and frailty models. Finally, aspects of the planning of clinical trials with lifetime data will be discussed.


    Teaching methods

    All aspects of the course will be illustrated with real data examples and will be practiced. In order to cater to a larger range of participants, this Survival Analysis course has had an update! Starting in 2020, the course has a blended design, which means it includes both online modules and in-class meetings. The first meeting lasts 1 hour, the following meetings last 2 hours. The meetings are planned in the mornings of the following days:

    • Monday 2 March
    • Thursday 5 March
    • Monday 9 March
    • Thursday 12 March
    • Monday 16 March
    • Friday 20 March

    The course is assessed using an assignment, for which the deadline is Friday 3 April.


    Survival Analysis (EWP24) is equivalent to the Survival Analysis course (ESP28) in the Erasmus Summer Programme and the part about survival analysis in Biostatistical Methods II: classical regression models (EP03).

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