At the start of the course, students should have basic knowledge in Epidemiology and Statistics, such as basic summary statistics, hypothesis testing, confidence estimation and linear regression. Students are strongly advised to update their background knowledge in Epidemiology by studying the MOOC https://www.coursera.org/learn/population-health-study-design and for Statistics the MOOC: https://www.coursera.org/learn/responsible-data-analysis, prior to the start of this course. More information will be provided to participants via mail and via Brightspace.
Period: 26 September 2022 - 21 October 2022
In previous courses on epidemiology, data management, statistical software or (bio)statistics, you may have studied these subjects more or less as – standalone – disciplines. In practice however, research is only possible through strong interdisciplinary collaboration. This means that researchers must be able to combine and exploit knowledge across disciplines when formulating research questions based on a (clinical) problem, discussing and evaluating appropriate study designs, proposing the data collection and storage procedures and discussing the epidemiological and statistical analysis procedures, as well as software implementation which are appropriate. We will discuss the problem of scientific research design and methodology from a practical perspective. The course uses the open source statistical software R as basis for all computation, analysis, statistical method implementation, data manipulation, summarization and reporting. The programme RStudio is used as a general environment for R. The R component of the course covers calculations, reading data, manipulating data, making plots and reports, as well as basic statistical methods and programming skills. Note that the focus is on R as a language, so the course is not tailored to any specific application. We will start the course with the discussion of a real-life example in clinical scientific research. From this discussion, we will evaluate through a joint discussion which problems may be encountered when carrying out research and what the appropriate options are for study design and analysis. Extension of basic statistical and epidemiological methodology to modern molecular epidemiological application and research is discussed. The focus of the course is thus on the interdisciplinary aspects of applied research and how (molecular) epidemiology, (bio)statistics, software implementation and data management work together to help us solve practical research questions.
After the course the student:
Knows relevant research methodologies;
Understands the connection between epidemiological methodology, database design and management and concepts of statistical data analysis of clinical research questions;
Understands (dis)advantage of different study designs;
Applies concepts, based on examples of studies, and formulates the results as well as the assumptions upon which results and evaluations are based;
Is able to advise (in retrospective or pro perspective) a study design;
Applies relevant research methodologies and techniques in software and reporting;
Is able to use R to perform statistical and bioinformatics analyses in a reproducible way to substantiate conclusions and results;
Is able to summarize and visualize complex data in R based on the characteristics of the data and the research question in order communicate conclusions, motives and/or considerations;
Is able to align methodology to research question and context after having critically evaluated conducted studies;
Recognizes the limitations of using crude epidemiological concepts such as relative risk and odds ratio, and know how to adjust these using statistical methods such as logistic regression and survival analysis;
Is able to critically discuss and assess statistical methodology in clinical application.
All course and group schedules are published on our LUMC scheduling website or on the LUMC scheduling app.
Mode of instruction
Lectures, workgroups, (computer) practical sessions, self study exercises.
Written test (open book) with open questions on statistics and (molecular) epidemiology (80%, minimum grade to pass: 5.5)
Written examination assignment R (20 %, minimum grade to pass: 5.0)
The exam dates can be found on the schedule website.
An up-to-date working version of RStudio. The (R) course lecturers will provide you with instructions on how to install R and RStudio (see Brightspace).The following is a list of English text books which are suitable for study (not mandatory):
Epidemiology. An introduction. K.J. Rothman. 2012, New York, Oxford University Press, ISBN 0-19-513554-7;
Epidemiology, beyond the basics. M. Szklo, F.J. Nieto. 2004, Sudbury Massachusetts, Jones and Bartlett Publishers, ISBN 0-7637-4722-X (for optional further reading);
Medical Statistics at a Glance. Aviva Petrie and Caroline Sabin, Wiley, 2019, 4th Edition,
Registration for FOS courses, H2W, Scientific Conduct, Course on Lab Animal Sciences and CRiP takes place in lottery rounds in the beginning of July. After the lottery rounds: if you want to register for a course you are kindly asked to contact the student administration at email@example.com.