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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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  • Public Health Epidemiology
  • Epidemiology
  • Genetic & Molecular Epidemiology
  • Clinical 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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  • Public Health Epidemiology
  • Biostatistics
  • Medical Psychology
  • Epidemiology
  • Genetic & Molecular Epidemiology
  • Clinical Epidemiology
  • Health Decision Sciences
  • 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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  • Public Health Epidemiology
  • Health Economic Analysis
  • Biostatistics
  • Medical Psychology
  • Epidemiology
  • Genetic & Molecular Epidemiology
  • Clinical Epidemiology
  • Health Decision Sciences
  • 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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  • Public Health Epidemiology
  • Epidemiology
  • Genetic & Molecular Epidemiology
  • Clinical 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
  • Genetic & Molecular Epidemiology
  • Clinical Epidemiology
  • Health Decision Sciences
  • 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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    27 Nov 2019 - 05 Feb 2020
    Scientific Writing in English for Publication [SC07]

    About this course

    Course days in 2018-2019 will be: Wednesday November 28, Wednesday December 12, Monday January 16 and Wednesday February 6.The first 3 sessions will be on mornings or afternoons depending on your group.

    The last session on the 6th will be in the morning, afternoon or evening depending on your group and other course schedule.


    Writing to be read

    This course will focus on:

    • Communicating the point and importance of your research;
    • Writing a clear and readable scientific article.The course consists of 4 half-day sessions and 3 writing assignments that will receive individual feedback from the instructor as well as other course participants. Attending all 4 sessions and completing all writing assignments is compulsory.

    The course will be intensive—writing takes time—so we suggest that participants reserve considerable time for this course.

    Participants will be guided through the writing process in 3 assignments:

    1. Clarifying the point of the research;
    2. Completing the Hourglass Template with the main messages for the Introduction, Methods, Results, and Discussion of your research;
    3. Writing the Abstract and Title.

    Part of the work will be peer reviewing. Participants will critically discuss each of the three assignments with a peer-review partner (i.e. another course participant). The remaining members of the peer-review group will review and critique each assignment as well. This implies that participants must be willing to work closely with a peer-review partner during the course and meet deadlines for peer reviewing. After revising texts based on these reviews, participants then send them to the instructor, who will provide both substantive and language tips.

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    27 Jan 2020 - 31 Jan 2020
    Advanced Topics in Decision-making in Medicine [EWP02]

    About this course

    This course deals with intermediate- to advanced level topics in the field of medical decision making. Topics that will be addressed include building decision models, evaluation of diagnostic tests, utility assessment, multi-attribute utility theory, Markov cohort models, microsimulation state-transition models, calibration and validation of models, probabilistic sensitivity analysis, value of information analysis, and behavioral decision making. The course will focus on the practical application of techniques and will include published examples and a computer practicum. Students will learn to apply state-of-the-art modeling methods using freely available open source software to evaluate the comparative effectiveness and cost-effectiveness of health interventions. While the primary emphasis is on application, essential underlying theoretical concepts will also be discussed. During the course you will have the opportunity to work on a decision problem which you select yourself. Many students use the course as a way to start writing a paper on a decision model in the field of their interest.

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    25 Nov 2019 - 06 Dec 2019
    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.

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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 is needed after the results of a RCT or guidelines are published also known as implementation and dissemination. Because implementation is far from straightforward the and last decade the field of implementation science has emerged. This is a multidisciplinary field e.g. medicine, nursing, psychology, pharmacy, engineering, that aims to improve the relevance and uptake of research-based knowledge in real-world settings.

    The course is made up of lectures in the morning, with interactive workshops in the afternoon. During these workshops, participants work on a proposal for an implementation research project. The proposals will be presented on the last day.

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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 Dec 2019 - 30 Jun 2020
    Intervision [SC12]

    About this course

    Intervision is a method to collaboratively analyse the experiences you (have) come across during your research project and study programme in a practical and systematic way, together with your peers. This analyses leads to solutions, alternatives and advice, and will often also give you more insight into your own functioning.

    Examples of experiences you can discuss during the intervision are: experiences during your research phase (e.g. dealing with hierarchy or work load), personal experiences (e.g. giving presentations), succes experiences (e.g. something you were not looking forward to went very well), or other experiences.

    The intervision sessions serve two purposes: they are meant to teach you reflective skills, and to help you work on solutions to problems you have faced or are facing during your research project and study programme.

    The intervision course consists of 4 meetings (1-2 hours each) spread out through the year, with an introductory meeting on 18 December 2019.

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    18 Nov 2019 - 22 Nov 2019
    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.

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    18 Nov 2019 - 22 Nov 2019
    SNPs and Human Diseases [GE08]

    About this course

    The analysis of DNAvariations, including Single Nucleotide Polymorphisms (SNPs), is a standardresearch approach to understand causes of disease, in particular the so-called"complex" diseases such as diabetes, osteoporosis, cancer, Alzheimerdisease, etc. The field is changing fast with large scale projects (Humangenome, dbSNP, HapMap, 1000genomes, ENCODE) and novel technology beingcontinuously introduced, including Next Generation Sequencing.

    The course will deal with five main topics, which are in logical order:

    • General Introduction and Study design,
    • Bio informatic tools for SNP finding and analysis,
    • Genotyping techniques and DNA management,
    • Data analysis, and
    • Examples of research in which SNPs are used.
    Every day will cover one topic. The programme for every day will consist of four to six presentations, including international speakers, and there are learning-by-doing sessions. The possibility exists for participants to discuss their own data and work. This course is organized by the Molecular Medicine postgraduate school (MolMed) in collaboration with NIHES.For more information and application check the MolMed website.

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    18 Nov 2019 - 22 Nov 2019
    International Comparison of Health Care Systems [HS03a]

    About this course

    Insight into the structure, process and outcome of health care systems is vital to be able to implement health care reforms that are effective in improving the health system performance. International comparisons of health care systems and the underlying political, organizational and financial arrangements are a multidisciplinary research field with a mixture of quantitative and qualitative methods. This course will present the various methodological approaches and will build on recent national and international experiences with comparative research.


    The course starts with a clear conceptualization and definition of a health care system, definitions of key system components such as the service delivery system (through professionals and institutions), financing, role of the government and role of patients. Health system performance will be discussed in terms of effectiveness, equity and efficiency. Analytical perspectives taken will come from public health as well as from political sciences and economics. The course will also deal with the recent work performed by international organisations such as the WHO and OECD with respect to health system performance measurement and management.

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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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    17 Feb 2020 - 21 Feb 2020
    Psychopharmacology [MP03]

    About this course

    Medical psychology is all about the interaction between mind and body: how do physical complaints affect our psychological functioning? But also: how does our psychological functioning affect us physically? When dealing with this interaction in a clinical setting, drug treatments often play an important role. Patients receiving drug treatment for psychiatric disorders frequently suffer physical side-effects, and drugs prescribed for somatic disorders can influence our mental state.

    Therefore, medical psychologists need to know which drugs are prescribed for common psychiatric and somatic disorders, and need to have a basic understanding of how these (psychoactive) drugs work, how and why they invariably lead to side-effects, and how these side-effect affect compliance. We will look at drug treatment for psychiatric disorders such as depression and schizophrenia, but also at drugs like corticosteroids – used in the treatment of somatic conditions such as inflammatory bowel disease – which have been found to increase the risk of suicidal behavior and neuropsychiatric disorders (i.e. depression, panic and manic episodes).

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    17 Feb 2020 - 21 Feb 2020
    Advanced Clinical Trials [EWP10]

    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.


    The final assignment of this course is due 1 March 2019.


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    16 Dec 2019 - 16 Jan 2020
    STeLA Leadership workshop [SC11]

    About this course

    As a researcher, knowing how to organize teamwork or even lead a research group, might be equally important as knowing how to do research. How do you make sure you as a team reach your goals in time? Can you improve your teamwork dynamics?

    This full-day workshop on teamwork and leadership is organized in collaboration with STeLA Europe (Science and Technology Leadership Association), an organization that brings science and engineering students together from prestigious universities worldwide to provide leadership education and facilitate multicultural dialogue on global issues. By means of material based on the distributed leadership theory, developed at MIT, STeLA will provide you with the skills to shape your teams to let teamwork flourish. STeLA will challenge you throughout the day in multiple intense teamwork sessions.


    To learn more about STeLA, please visit their website at https://www.stela-global.org/.

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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

    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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    10 Feb 2020 - 12 Feb 2020
    Competing Risks and Multi-State Models [BST03]

    About this course

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

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

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

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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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    07 Jan 2020 - 24 Jan 2020
    Introduction to Medical Writing [SC02]

    About this course

    During the second semester, full time Master of Science (70 ECTS) students will attend four workshops of three hours and one workshop of six hours on how to write correct and readable scientific articles in English. Each student will be able to bring their own work which will be commented on and corrected by participants and the teacher.

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

    About this course

    This 3-day course aims to give an overview of recent developments in drawing causal inferences from epidemiological data using Mendelian randomization and is aimed at individuals with a background in statistics or epidemiology with a strong statistics component. Theoretical discussion will be accompanied by introduction to available software and lab practicals.

    The first day will comprise a brief introduction to graphical models since these provide a natural framework for expressing and manipulating many of the concepts involved. We will then go on to causal modelling and the need for a formal causal framework before explicitly considering instrumental variable methods with Mendelian randomisation applications as the primary example.

    On the second day, a practical session in analysing Mendelian randomization studies will put into focus what can and cannot be done. We then continue with the discussion of various theoretical issues, including the introduction of Bayesian approaches to Mendelian randomization.

    The final day will start with a practical session in analysing Mendelian randomization studies using a Bayesian approach. We conclude by introducing current topics of interest including checking for violations of assumptions, the use of multiple instruments and implications for case-control data.

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

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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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    06 Jan 2020 - 24 Jan 2020
    Review of Mathematics and Introduction to Statistics [BST01]

    About this course

    Several courses in the NIHES curriculum require a good working knowledge of basic concepts in mathematics and statistics. These courses include Biostatistical Methods I: Basic Principles (CC02), Biostatistical Methods II: Classical Regression Models (EP03), Repeated Measurements (CE08) and Bayesian Statistics (CE09). The course "BST01: 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. There will be no lectures or tutorials, but the organizers of the course are available for questions during the course. 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
    • Integration
    • Vectors and matrices
    • Basic concepts in statistics

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    05 Dec 2019 - 15 Jan 2020
    Scientific Integrity [SC10]

    About this course

    These days doing research is a career. With this career comes an environment with incentives, pressures and temptations that may undermine the scientific quality of the work. How do you keep doing what is right when that may harm your career (at the very least at short notice)? We look at the history of science as a career choice. We will discuss what science is, and we will discuss some of the reasons researchers experience pressure to do less well than they intended. We will do this with the help of the movie 'On being a scientist'.

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    03 Feb 2020 - 07 Feb 2020
    Using R for Decision Modeling in Health Technology Assessment [CE16]

    About this course

    This course aims to teach how to build decision models in R to students who have a basic understanding of health decision science.

    • The course combines lectures with R coding exercise.
    • The course is project-based. You are encouraged to apply the theory and skills you learn during this course to a decision problem you select yourself.

    More detailed information about each session will be provided in the syllabus.

    Attendance of all lectures and practicums is highly recommended in order to be able to complete the assignments and case example successfully. Each day builds on knowledge and skills from the previous day. Clarification of the material taught is best done in the interactive teaching environment provided during classroom sessions.

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    03 Feb 2020 - 07 Feb 2020
    Advances in Genome-Wide Association Studies [GE03]

    About this course

    This 5-day advanced course aims to give an overview of new developments in the field of genome wide association studies for those with a background in genetics, epidemiology or statistics. In the first part of the course, issues concerning the design and analysis of genome-wide association (GWA) analysis will be covered using standard software such as Plink and genABEL. This part will include quality control, hands-on GWA analysis of quantitative and binary traits, methods to detect and correct for stratification, and to model epistastasis. In the second part we will extend to an integrated approach of data analysis including eSNPs and new developments in the analysis of whole sequence data.


    Finally, we will discuss the perspectives for genetic testing in clinical practice. A major part of the teaching programme consists of hands-on exercises.

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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 with computer practicals and/or pen-and-paper exercises.


    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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