Pharmacoepidemiology and drug safety: a current issue. With the prospect that even more innovative drug therapies will be introduced in the coming years, society is demanding new approaches to comparative risk/benefit evaluation, as well as new concepts. Such evaluations are usually carried out only once the relevant therapies have been used widely in daily practice.
During this 10 EC micromaster program, offered by the MSc Epidemiology program of Utrecht University and Utrecht University Medical Center, you will learn the principles and methods of clinical epidemiology and data analysis.
This course will enhance your understanding of public health epidemiology, the science upon which public health is based. Public health epidemiology involves describing and interpreting geographical and temporal patterns in population health, identifying prevention strategies, and estimating and evaluating the impact of risk factors and preventive measures on population health. It is therefore essential to have a thorough understanding of public health epidemiology if your work or study involves any public health-related issues. This course is therefore highly suited to epidemiologists in universities, hospitals, municipal health services and other governmental health organizations, and to PhD candidates working on an epidemiological or public health thesis.
Epidemiology is the basic science of public health. It is applied in describing and interpreting geographical and temporal patterns in population health, in identifying prevention strategies, and in estimating and evaluating the impact of risk factors and preventive measures on population health. The course will consist of 5 consecutive days of lecture and in-class exercises, and an exam.
We all eat, and most of us eat many different foods, yet we tend to forget rather quickly what we have eaten, and we often do not know the ingredients of the dishes we consume. These aspects make it hard to conduct nutritional epidemiological research, which is why it is important to learn how to overcome a number of specific challenges. This course will cover the most important ones, while helping you to design a well-constructed nutritional epidemiological study.
The course will address methodological aspects involved in the design, conduct, analysis and interpretation of nutritional epidemiological research. Topics include; intra-individual variation, measurement of error, misclassification, correlated variables, population homogeneity, dietary assessment, validity/reproducibility of dietary assessment, nutritional biomarkers, dietary guidelines and analysis issues as energy adjustment methods and dietary pattern analyses. These topics will be covered during lectures, practical assignments, computer practicals and seminars offered online using the Elevate Online Learning Platform.
The number of primary studies evaluating prognostic factors and models is rising per day. Alike for therapies and diagnostic tests, critically summarizing and analyzing the evidence form prognosis studies in a systematic review and meta analysis is beneficial for health care professionals seeking the best evidence.
The number of primary studies evaluating prognostic factors and models is rising. Critically summarizing and analyzing the evidence form prognostic studies in a systematic review and meta analysis, respectively, is beneficial for health care professionals seeking the best evidence. Reviews of prognostic studies are more challenging because of more variation in questions & designs, specific sources of bias & variation, and more complex statistical models, as compared to e.g. randomized therapeutic intervention trials . Several advances regarding the design, critical appraisal and statistical analysis in systematic reviews of prognostic studies, have recently been made. In this course we discuss and practice how to define your review questions, how to critically assess the methodological quality of primary prognostic studies, and which statistical methods to use for meta-analyses of the results of primary prognostic studies. The course consists of plenary presentations, small-group discussions, and computer exercises.
Systematic Reviews and meta-analyses are an important cornerstone of contemporary evidence-based medicine. The large majority summarize published aggregate data, but it is increasingly common that individual participant data (IPD) are obtained from primary studies. As a result, new opportunities arise and more advanced statistical methods are needed to properly analyze the available data. In this course, we discuss how a meta-analysis involving IPD may help to identify sources of heterogeneous treatment effects, to investigate the accuracy of diagnostic tests, to develop clinical prediction models and to externally validate such models. We place particular emphasis on statistical methods for dealing with between-study heterogeneity, and discuss how to interpret corresponding results. The course consists of plenary presentations, small-group discussions, reading assignments, and computer exercises.
Survival data, or more generally, time-to-event data (where the “event” can be death, disease, recovery, relapse or another outcome), is frequently encountered in epidemiologic studies. Censoring is a problem characteristic of most survival data and requires special data analytic techniques.
Survival data, or more generally, time-to-event data (where the “event” can be death, disease, recovery, relapse or another outcome), is frequently encountered in epidemiologic studies. Censoring is a problem characteristic to most survival data, and requires special data analytic techniques.
As one of the very few online medical courses in veterinary epidemiology that is available, this course is highly suitable for veterinary researchers with an interest in epidemiology, who want to understand the concepts of epidemiology and apply these concepts in data analysis. It is taught by epidemiologists from the Faculty of Veterinary Medicine, and forms part of the MSc Epidemiology program of the UMC Utrecht and Utrecht University.
In this course you will learn the "state-of-the-art" in veterinary epidemiology. the course will focus on those aspects that are specific for epidemiology in animal health, but these concepts are relevant also in many other situations.
In both research and clinical care, biological phenomena are continuously being measured and quantified at a massive scale. We often take the reproducibility of these measurement for granted, and assume that the same test under the same conditions will provide similar results. This is, however, rarely the case.
In this course, principles and methods of non-experimental prognosis research will be discussed.
Prognosis is a key concept in patient care. The methodology of prognostic research is however relatively underdeveloped. This is in contrast to its growing importance in clinical medicine. In the course, principles and methods of non-experimental prognostic research will be discussed. In lectures, practical exercises and discussion of examples, the practice of prognostic research in a clinical setting is addressed. Emphasis will be on design and statistical analysis of prognostic studies, construction and estimation of prediction rules and approaches to validation and generalization of research results. Problems with small datasets will be extensively discussed.
The rapid growth of advanced molecular methods has revolutionized our understanding of epidemiology of infectious diseases. This course will introduce you to basic molecular typing tools, but also to next-generation sequencing and will illustrate applications of these tools in epidemiological studies. In this course, you will work with whole genome sequences, learn about sequence alignment and construct phylogenies. You will get an introduction to coalescent theory and see examples of the use of molecular epidemiology in outbreak analyses. Examples of genome-wide association studies and microbiome studies will be presented. The application of mathematical modelling of pathogen evolution and epidemiology will be discussed.
In the biosciences, response variables are often observed more than once per individual. This enables the researcher to study the development of the variable of interest within individuals, thereby eliminating the variation among individuals, and thus increasing the power of the design. However, since observations on the same individual are almost always correlated, special methods are needed to deal with this dependence.
Even in well designed and conducted epidemiological studies, data will be missing. This may include missing observations of the exposure and under study, confounders, or the outcome.
The aim of this course is to introduce economic evaluation methodology and practice to persons unfamiliar with economic thinking in a healthcare and prevention context. Participants will be introduced to the main approaches to economic evaluation, the measurement of costs and effects, including quality of life measurements, economic modeling, and uncertainty analyses. Participants will also learn to assess economic evaluations as published in the literature.
This course is focused on the dynamic nature of infectious diseases, which clearly distinguishes infectious disease epidemiology from the epidemiology of other diseases, such as cardiovascular and oncology. In addition ot the dynamics of specific infections such as HIV, dynamics of genotypes and genes (such as those encoding antimicrobial resistance) and principles of population biology will be addressed.
Learn the basics of machine learning, with a special focus on sparse data as they occur in high dimensional ‘omics’ types of data.
Statistical inference is intended to aid in answering scientific questions about a population, based on a sample from this population, i.e. on data that are subject to variability. The data generating mechanism is described as a probability model that is completely specified except for a limited number of unknown parameters. The questions that can be answered are a) are the data consistent with the model? and b) assuming that a) is fulfilled, what can be concluded about values of the unknown parameters? In this course, the basic principles of statistical inference are presented, with an emphasis on likelihood methods. Methods are illustrated by the classical linear model.
In modern health research genetic association studies play a large role. Genome-wide association studies are being performed to discover genetic variations associated with both continuous traits and diseases. Results from these studies are subsequently being used to better understand the mechanisms that lead to diseases, and to understand whether associations found in observational studies between risk factors and diseases are indeed causal.
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