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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
  • Genetic & Molecular Epidemiology
  • Clinical Epidemiology
  • 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
  • Genetic & Molecular Epidemiology
  • Medical Psychology
  • Clinical Epidemiology
  • 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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  • Public Health Epidemiology
  • Health Economic Analysis
  • Biostatistics
  • Genetic & Molecular Epidemiology
  • Medical Psychology
  • Clinical Epidemiology
  • 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
  • Public Health Epidemiology
  • Genetic & Molecular Epidemiology
  • Clinical Epidemiology
  • 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
  • Public Health Epidemiology
  • Biostatistics
  • Genetic & Molecular Epidemiology
  • Medical Psychology
  • Clinical Epidemiology
  • Epidemiology
  • Courses

    30 Oct 2019 - 15 Nov 2019
    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.

    Read More

    28 Oct 2019 - 1 Nov 2019
    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.

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    26 Aug 2019 - 06 Sept 2019
    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.

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    25 Mar 2019 - 29 Mar 2019
    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 Feb 2019 - 01 Mar 2019
    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.

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    23 Apr 2019 - 26 Apr 2019
    From Problem to Solution in Public Health [HS18]

    About this course

    The current challenges in public health require a strong link between science, policy and practice. This link is bi-directional. Professionals in practice can use their vast experience to guide better and more targeted research. Policy makers can identify which solutions may work or not and researchers can provide better evidence for public health programmes.


    In two Master Classes experienced researchers and policy makers will work together with participants on major public health problems. The public health problems selected are addiction & substance use, and injuries. Through intensive interaction participants will learn (1) how to make a comprehensive analysis of the problem, (2) how this analysis will guide the required evidence-base for tackling the problems, and (3) how to plan and evaluate appropriate preventive interventions.


    The Master Classes are restricted to 25 participants and will require an intense, active participation.

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    21 May 2019 - 07 Jun 2019
    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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    19 Feb 2019 - 21 Feb 2019
    Maternal and Child Health [HS09]

    About this course

    The health of women of child bearing age and of children have an important impetus on public health. The aim of the course is to provide an insight into child health from conception onwards.


    Determinants of fecundity, pregnancy and pregnancy outcome are discussed as a prerequisite for child health. Perinatal and infant mortality in an international perspective, growth and development are discussed as important health indicators. Preventive interventions such as vaccinations, screening programmes and health promotion are discussed. Special attention is given to the health of groups at risk for health problems such as children of low socio-economic classes and children of ethnic minorities. Psychosocial health problems are said to be on the increase. Facts and figures in an international perspective will be presented. In adolescence, life style habits are developed and appropriate health promotion is important. Examples of health promotion programmes are discussed. The programme consists of presentations, exercises and group discussions.


    Topics covered:

    • Determinants of fecundity, pregnancy and pregnancy outcome.
    • Perinatal and infant mortality.
    • Growth and development preventive interventions.
    • Psycho-social health problems.
    • The health of groups at risk.
    • Adolescence and health promotion.


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

    Read More

    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
    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 Mar 2019 - 22 Mar 2019
    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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    18 Feb 2019 - 22 Feb 2019
    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 Sept 2019 - 11 Oct 2019
    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).

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    15 Apr 2019 - 18 Apr 2019
    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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    14 Oct 2019 - 22 Oct 2019
    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.

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    14 Oct 2019 - 18 Oct 2019
    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!

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    14 Oct 2019 - 8 Nov 2019
    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.

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    13 May 2019 - 17 May 2019
    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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    13 Mar 2019 - 15 Mar 2019
    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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    12 Jun 2019 - 12 Jun 2019
    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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    11 Jun 2019 - 11 Jun 2019
    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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    09 Apr 2019 - 11 Apr 2019
    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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    08 Apr 2019 - 12 Apr 2019
    Epidemiology of Infectious Diseases [CE05]

    About this course

    Quantitative approaches play an important role in the understanding of the spread of infectious diseases and provide important tools for prevention. During the course, the basic notions that describe the mechanisms behind the spread of a disease will be introduced. Several examples of what can be learned from the use of quantitative models will be given.


    The relation between the specific characteristics of some infectious diseases and their spread will be exemplified, e.g. for tuberculosis, hepatitis, measles, influenza, AIDS. Only a basic level of mathematics is needed, and exercises will be given to practice the theoretical concepts.

    Topics covered are:

    • Important concepts and tools in modelling the spread of infectious disease.
    • Some important quantities related to the spread of infectious diseases (incidence, prevalence, cumulative incidence, incubation time, latent time, period of infectiousness); estimation of these quantities, with an example from the HIV epidemic.
    • Simulating large scale and small scale epidemics using the computer.
    • Basic reproductive number (R0) as central determinant of whether an epidemic develops.
    • Mode of transmission (e.g. airborne, sexual) in relation to the characteristics of spread.
    • Heterogeneous spread; the role of mixing between subgroups; contact patterns.
    • Molecular epidemiology as a new tool to monitor and model the spread of an infectious disease.


    Prevention programmes.

    • Vaccination strategies in relation to characteristics of the infectious disease.
    • Cost-effectiveness analyses of prevention and treatment.
    • Screening of pooled blood samples.

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    08 Apr 2019 - 12 Apr 2019
    Cancer Epidemiology [EP13]

    About this course

    Cancer is a major cause of morbidity and mortality in the developed world. The aim of this 5-day course is to provide an overview of the contributions of exogenous and endogenous factors to the risk of various cancers.The course starts with descriptive cancer epidemiology and an overview of current concepts of cancer development at the molecular and cell level. Subsequently, genetic and non-genetic risk factors for the most important cancers will be extensively discussed, as well as gene-environment interactions. Special attention will be given to risk factors for multiple primary cancers and recent results of chemo prevention studies. Although the emphasis of the course will be on etiologic factors, one session will specifically address time trends in cancer incidence, mortality and survival rates, followed by a discussion on whether or not we are winning the battle against cancer.

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    06 May 2019 - 10 May 2019
    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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    06 May 2019 - 10 May 2019
    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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    06 Mar 2019 - 08 Mar 2019
    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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    05 Aug 2019 - 23 Aug 2019
    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 Mar 2019 - 08 Mar 2019
    Principles of Epidemiologic Data-analysis [EWP25]

    About this course

    The course will present the basic precepts and the principles underlying the primary methods of epidemiologic data analysis. The aim of the course is for the participant to arrive at a coherent conceptualization of the core principles of epidemiologic data analysis. This is not a statistics course; although examples of analytic calculations are given and there are lab exercises assigned for homework, there is no emphasis on proficiency in the execution and calculation of results nor how to build mathematical models.The course begins with a discussion of the principles of epidemiologic data analysis, and then progresses to a discussion of precision and validity, placing a strong emphasis on a quantitative approach to analysis, using estimation, rather than a qualitative approach based on statistical significance testing. After covering the analysis of crude data, the focus shifts to the control of confounding using stratified analysis and multivariate models. Other topics that are covered include the analysis of matched data, the evaluation of interaction, the use of multivariate summary confounder scores (including propensity scores), marginal structural models, imputation of missing data, sensitivity analysis, and the estimation of trends in effect.The class presentations will be supplemented with discussion of selected published papers and computer assignments using the Episheet spreadsheet to illustrate key analytic concepts.


    Teaching Methods

    The course consists of lectures, case studies involving reading and classroom discussion, and assignments using a spreadsheet for epidemiologic data analysis, which is supplied.

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    04 Nov 2019 - 08 Nov 2019
    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.

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    04 Mar 2019 - 08 Mar 2019
    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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    03 Jun 2019 - 05 Jun 2019
    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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    01 Apr 2019 - 05 Apr 2019
    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 April 27 2018.

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