Graduate School of health and Medical Sciences

Epidemiological methods in medical research
Provider: Faculty of Health and Medical Sciences

Activity no.: 3602-25-00-00	There are 21 available seats
	Enrollment deadline: 09/12/2024

Date and time	09.01.2025, at: 10:00 - 20.03.2025, at: 17:00
Regular seats	35
Course fee	7,320.00 kr.
Lecturers	Brice Ozenne
ECTS credits	7.00

Contact person	Susanne Kragskov Laupstad E-mail address: skl@sund.ku.dk

Enrolment Handling/Course Organiser	PhD administration E-mail address: phdkursus@sund.ku.dk

Aim and content This course is free of charge for PhD students at Danish universities (except Copenhagen Business School), and for PhD Students from NorDoc member faculties. All other participants must pay the course fee. Anyone can apply for the course, but if you are not a PhD student at a Danish university, you will be placed on the waiting list until enrollment deadline. This also applies to PhD students from NorDoc member faculties. After the enrollment deadline, available seats will be allocated to applicants on the waiting list. Content Epidemiological investigations have made critical contributions to public health. Historical examples include establishing adverse effects of tobacco use on health, describing the spread of diseases and infectious etiology of HIV, or assessing the safety of vaccines in large populations. They have also addressed medical controversies using strict design of studies and careful methodological considerations. However, epidemiologic studies have often showed conflicting results, which has given space for criticism of epidemiology. This course aims at providing the methodological foundations of epidemiology and thereby rationalize decisions about the formulation of research question, study design, statistical methods, and communication of the results. This should promote scientifically sound epidemiological studies and critical assessment of epidemiological evidence. This course is spread over 10 full-days where you will be introduced to key concepts in epidemiology and statistical methods in epidemiology research. You will apply them to analyse historical datasets and reflect upon their usefulness and limitations. Toward the end of the course, you will be asked to make a short presentation either illustrating the use of concepts/methods seen during the course (e.g. on data from your Ph.D.) or discuss limitations & extensions of these concepts/methods. The course cover the following topics: - Purpose and role of epidemiology - Quantification of disease frequency and its association with an exposure - Introduction to various study designs: cohort, case-control, nested case-control, case-cohort - Introduction to causal inference: causality, confounding, collider, mediator, DAGs (directed acyclic graphs). Reasoning, illustrated using common fallacies in epidemiology: Simpson paradox, Berkson’s paradox, ecology fallacy, selection bias, immortal time bias. - Statistical methods for handling confounding (stratification, adjustment, standardisation, matching) - Statistical models for binary and time to event outcome (logistic regression, Cox regression, Poisson regression). Handling interactions and performing hypothesis testing. - Introduction to registry data analysis & common challenges. - Communication of epidemiologic results Learning objectives On conclusion of the course, participants should be able to conduct a ‘standard’ epidemiology study: - reformulate a ‘typical’ epidemiology research question in term of prevalence, rate, or risk. - define a parameter of interest answering the research question. - propose a study design relevant for the estimation of the parameter of interest. - argument about the strength and weaknesses of a study design. - argument about the variables to consider in the subsequent statistical analysis. - propose a statistical method relevant for the estimation of the parameter of interest. - interpret the results: their plausibility and how they answer the research question - communicate the methods used and the results obtained They should also be able to critically assess epidemiology articles: - describe the methodology used by a study based on the ‘materials and method’ section of an article and explicit its implications/assumptions. - summarize the results of a study based on the ‘result’ section of an article and discuss to which extend they provide evidence to answer the research question. Data management is not part of the learning objectives for this course. Acquisition of programming skills is not the focus of the course and will mostly be left to self-study. Form The course contains a mixture of lectures, exercise classes (including computer practicals), reading and discussion of scientific articles. Lecture slides, exercises & solution to the exercises (including R code) will be available from the course webpage. The course webpage will also contain optional reading under the form of scientific articles and technical notes. For the exercise classes, you are supposed to bring your own laptop with the statistical software you wish to use installed ( is the recommended software). The aim of these classes is to illustrate the use of the concepts and statistical methods seen during the lectures, display & interpret the results, and discuss assumptions. Practical are structured with first a ‘pen & paper’ exercise: in small groups you will discuss or exemplify concepts seen during the lectures (~30 min). It will be followed by a ‘software’ exercise (~2 hours) where you will be asked to perform some statistical analysis in relation to the statistical methods seen during the lectures. Except for the first practical, a substantial part of the code will be provided in the exercise statement so focus will be on understanding, interpreting, and communicating the results rather than programming (programming questions are welcome but mostly left to the initiative of the students). The last day of the course will be dedicated to communicating results of epidemiologic studies and good practices. You will be split in small groups (max. 15 students) and have the opportunity to present your own analysis or own reflexion about concepts /methods (max. 10 minutes presentation) and get feedback from the teachers and the students (about 8 minutes discussion). Participants The course is tailored for Ph.D.-students in health sciences with interest in epidemiologic research. Students are expected to have a basic knowledge in epidemiology, statistics and programming. Having completed the course in Basic Statistics and introduction to is advantageous but not mandatory. Max. 35 participants. Relevance to graduate programmes The course is relevant to PhD students from the following graduate programs at the Graduate School of Health and Medical Sciences, UCPH: - Biostatistics and Bioinformatics - Cardiovascular Research - Clinical Research - Public Health and Epidemiology - Psychiatry - And others programmes Course director Brice Ozenne, Associate Professor at the Section of Biostatistics; Contact: broz@sund.ku.dk Teachers The course is taught by experienced epidemiologists and biostatisticians: - Anne-Marie Nybo Andersen (AMNA), Professor at the Section of Epidemiology, University of Copenhagen. - Bendix Carstensen (BC), Senior Statistician at the Steno Diabetes Center. - Brice Ozenne (BO), Associate Professor at the Section of Biostatistics, University of Copenhagen. - Katrine Strandberg-Larsen (KSL), Associate Professor at the Section of Epidemiology, University of Copenhagen. - Per Kragh Andersen (PKA), Professor at the Section of Biostatistics, University of Copenhagen. Dates Thursdays 9, 16, 23, 30 January, 6, 20, 27 February, 6, 13, 20 March 2025 (all days 10.00-17.00). Statistical Software The recommended software for the course is the software (version 4.0.0 and above) which is freely available at https://www.r-project.org/. We also recommend the use of an IDE such as R-studio, freely available at https://www.rstudio.com/, to have a nice user-interface for working with . Participants who wish to use other software are free to do so but are expected to be autonomous, as the teachers may not be able to answer software-related questions. Some support will be provided for SAS users. The focus of the course is not on programming and students are supposed to have a basic knowledge about data management, data visualization, and how to compute basic summary statistics before the start of the course. Pre-course self-study material will be provided to remind basic commands but students not familiar with are encourage to follow a programming course (e.g. http://r.sund.ku.dk/ free online introduction to ). Textbook The course is based on two textbooks: - D. Clayton & C. Hills. Statistical Models in Epidemiology. 1993. Oxford University Press. It contains a concise and rigorous description of the statistical concepts and models used in epidemiology, as well as how to handle standard study designs. Each chapter contains short exercises with solutions at the chapter. This is the perfect book to get a precise understanding of the statistical methods seen during the course but it involves following some mathematical derivations. Most of the book is relevant for this course (except chapter 7, 12, 20, 21, 28, 34). - M. Szklo & F.J. Nieto. Epidemiology: Beyond the Basics. 2019. Jones & Bartlett (4th. ed.) Learning. This is a very accessible book about epidemiology with many concrete examples and less “mathematical” (thus also less concise). Each chapter contains exercises whose solutions can be found in appendix F. Most of the book is relevant for this course (except part of chapter 6, chapter 8 and 10). Language English Course location CSS Registration Please register latest 09 December 2024 Seats to PhD students from other Danish universities will be allocated on a first-come, first-served basis and according to the applicable rules. Applications from other participants will be considered after the last day of enrolment. Note: All applicants are asked to submit invoice details in case of no-show, late cancellation or obligation to pay the course fee (typically non-PhD students). If you are a PhD student, your participation in the course must be in agreement with your principal supervisor.

Click the search button to search Courses.

See which courses you can attend and when

Jan	Feb	Mar	Apr
May	Jun	Jul	Aug
Sep	Oct	Nov	Dec

New courses
Courses are published regularly. High demand courses are announced in spring and autumn.

Learn which courses are announced on fixed dates