The Research Studies Board offers three recurring elective courses: Writing, Reviewing and Publishing Scientific Papers, Systematic review and meta-analysis: Introduction to Cochrane methodology, and Laboratory animal science for researchers.
In Swedish: 10-12 October + 24 October
In English: 15-17 November + 1 December
Jan Lexell jan [dot] lexell [at] med [dot] lu [dot] se (jan[dot]lexell[at]med[dot]lu[dot]se),
Christina Brogårdh christina [dot] brogardh [at] med [dot] lu [dot] se (christina[dot]brogardh[at]med[dot]lu[dot]se)
PhD students at the Faculty of Medicine, with priority given to those who have passed their halfway review.
The aim of the course is for the doctoral student to deepen their knowledge and skills around the publication process and how to write and review a scientific manuscript.
The teaching takes place mainly through interactive educational activities. The course includes lectures, reviewing of scientific articles, group work, discussions, practical applications and independent study. The course is given during five days and starts with three course days, then one day of independent work, and ends 2 weeks later with one course day.
Autumn 2023 (this course is fully booked):
Week 47, 20-24 November. Mornings are in class and the afternoons consist of individual work.
Week 22, 27-31 May. Mornings are in class and the afternoons consist of individual work.
Matteo Bruschettini matteo [dot] bruschettini [at] med [dot] lu [dot] se (matteo[dot]bruschettini[at]med[dot]lu[dot]se)
Martin Ringsten martin [dot] ringsten [at] med [dot] lu [dot] se (martin[dot]ringsten[at]med[dot]lu[dot]se)
Stefan Hansson stefan [dot] hansson [at] med [dot] lu [dot] se (stefan[dot]hansson[at]med[dot]lu[dot]se)
The one week course is aimed towards PhD students and researchers at the Faculty of Medicine.
Participation is free for PhD students from European Economic Area (EEA) and Switzerland. Other external participants might require a fee for participation, see more on Cochrane Sweden’s website for this course.
The course is aimed at PhD students and researchers who wants to increase their knowledge about how to conduct a systematic review or evidence synthesis. The course is also relevant for people who will use systematic reviews, evidence synthesis or results from randomized trials to inform decisions in healthcare (clinicians, decision makers, guideline developers, or policy makers).
The course aims to introduce and increase participants knowledge about the Cochrane methodology to systematic reviews with a focus on systematic reviews of interventions. During the week we will go through the process from the initial idea and research question that can be explored in a systematic review, tools to support the systematic review process, risk of bias, meta-analysis, the GRADE-approach to judge uncertainty, best practice reporting of results in reviews, and the use of systematic reviews in guidelines and decision making.
The course will include lecturers and facilitators from several Cochrane Centers, each within their expert area. Lectures will be mixed with discussions and working in groups with exercises in the mornings, and after lunch participants will work individually within the Cochrane Interactive Learning-modules. There will be time to ask individual questions to our lecturers and facilitators about your own potential reviews or other evidence-related questions during the week.
The course will be aimed to be conducted on campus in Lund for all days.
To pass the course you will need to attend the days in class, have an active participation in discussions and teamwork during these days, and completion of the module 1-8 and quizzes in Cochrane Interactive Learning.
The course is rewarded with 1,5 ECTS credits (equal to one week full time studies) for enrolled PhD students. All participants will receive a certificate of attendance for the course.
Resources and literature
Cochrane Interactive Learning modules, available from https://training.cochrane.org/interactivelearning
Cochrane Handbook for Systematic Reviews of Interventions, available for free from https://training.cochrane.org/handbook
Additional articles, books and some pre-course work will be handed out before the course starts.
You can register through the link in the right hand margin. Chose the correct date of the course. If you are an external participant (outside of Lund University), please clearly state this and your affiliation and professional title in “Other comments”, and try to fill the other information in as good as possible (if not relevant leave blank)
Lena Uller, Docent, Respiratorisk Immunofarmakologi, Institutionen för experimentell medicinsk vetenskap, Lund
This is a compulsory course for doctoral students at Lunds University who aim to work with animals. You will register specifically for the species you aim to work with. No previous qualifications required. The course is equivalent to a FELASA B level but not yet formally certified by Felasa.
3 University credits for the full course, 2 credits when the practical part is not completed.
Time & Place
This is a web-based education using Canvas Catalog. You work on your own time at your own computer.
Content of the course
The course is in English and contains 15 modules
- Module 1: Ethics and Animal Use
- Module 2: Swedish Legislation
- Module 3: Animal Records
- Module 4: Identification Methods
- Module 5: Humane Endpoints
- Self-assessments Legislation, Animal Records, ID & Humane Endpoints
- Module 6: Biology
- Module 7: Ethology
- Module 8: Husbandry
- Module 9: Animal Care and Supervision
- Self-assessments Husbandry, Animal Care and Supervision
- Module 10: Anaesthesia, Analgesia and Euthanasia
- Module 11: Diseases in Laboratory Animals
- Module 12: Animal Experimental Methodology
- Module 13: Genetically Modified Organisms
- Module 14: Alternative Methods
- Module 15: Safety in Biomedical Facilities
To complete the course
Estimated time to complete the course is 40 h. The different modules will be examined continuously with self-assessments. Upon completing the theoretical part, there is a practical part which extent depends on your planned upcoming practical activities. Upon this you will receive a certificate valid for operate with animals.
All literature is available on Canvas Catalog with additional links to Internet sites, which contain further information.
If you have questions about the course, please contact: djurutbildning [at] med [dot] lu [dot] se (djurutbildning[at]med[dot]lu[dot]se)
Other elective courses are offered as needed and are published on this website as they become available. If you have suggestions for an elective course that you would like to take and that you think we should offer, please contact PhDcourses [at] med [dot] lu [dot] se (PhDcourses[at]med[dot]lu[dot]se)
Points: 1.5 hp (fulltime)
Dates: 13th May – 17th May 2024
The course provides a background to the issue of missing data and to the consequences of simple ad hoc methods to address the issue. The advantages and shortcomings of different methods will be discussed. The method in focus on the course is multiplied imputation (MI), which participants will have the opportunity to test in the laboratory components.
Objective: The aim of the course is to make participants aware of the consequences of incorrect handling of missing data in medical research in general and to provide them with tools for correct handling of missing data in their own research.
The course content covers the following themes:
- Introduction to missing data
- Identifying missing data
- Potential consequences of missing data
- Mechanisms for the generation of missing data
- Brief overview of methods for handling missing data
- Multiple imputation
- Brief theoretical background to MI
- The chained equations method
- Constructing an imputation model
- Analysing imputed data
- Diagnosis of the MI model (model validation)
- Reporting MI results and the limitations of the method
- Guidelines for reporting analyses of MI-generated data
- Limitations of the MI method
Monday, Tuesday (morning), Wednesday and Friday (morning) – in-class activities, Tuesday (afternoon) and Thursday – own work, Friday (afternoon) - examination.
Aleksandra Turkiewicz, docent, CStat, Enheter för klinisk epidemiologi, Kliniska Vetenskaper, Lund
Pär-Ola Bendahl, docent, fil. dr., Institutionen för kliniska vetenskaper Lund, Lunds universitet (par-ola [dot] bendahl [at] med [dot] lu [dot] se (par-ola[dot]bendahl[at]med[dot]lu[dot]se))
Jonas Björk, professor, fil. dr., Institutionen för laboratoriemedicin, Lunds universitet (jonas [dot] bjork [at] med [dot] lu [dot] se (jonas[dot]bjork[at]med[dot]lu[dot]se))
Language: The course is given in English
Target group and requirements: To be admitted to the course, applicants must have prior knowledge equivalent to Applied Statistics I and II. In particular, they are to be familiar with the theory of linear and logistic regression models and be able to adapt these models and interpret corresponding output from their statistics software. The choice of software is optional, but the participants are required to be familiar with the program they choose and that it has the method multiple imputation via chained equations implemented (e.g. R, Stata or SPSS). The participants are responsible for ensuring that they have a suitable and working statistics program installed on their computer before the start of the course.
Number of participants: 20
Karin Engström (karin [dot] engstrom [at] med [dot] lu [dot] se (karin[dot]engstrom[at]med[dot]lu[dot]se))
PhD students at the Faculty of Medicine.
One week (1.5 ECTS credits). Five days are scheduled, including independent study.
To be announced.
Week 40 (2-6 October) 2023. Monday to Thursday 9:00 to 16:00. The final day, attendance is not mandatory.
Number of participants
The aim of the course is to provide basic knowledge of the R programming language to facilitate the independent use of tools written and/or implemented in this language, such as statistical analysis packages. The focus of the course is on data management (such as importing, summarizing information and changing columns in tables) and visualization (such as creating different types of graphs).
On completion of the course, the student shall be able to:
- perform basic data operations using R
- identify potential pitfalls when handling data with R
- create basic and visually appealing diagrams using R
- identify online resources to independently answer questions and troubleshoot when programming in R
The course provides an introduction to basic programming terms and concepts. Participants will learn, among other things, how to import data into RStudio/Posit, summarise data, create new data, cross-reference, merge data, select appropriate, graphically appealing charts in different formats, and export data and charts in different formats. We will focus on packages that are part of the tidyverse software package.
The pre-course assignment involves installing RStudio/Posit and conducting independent study in basic programming concepts and terminology according to the literature list. Access to a laptop is required. The course consists of four mandatory full days with a mix of different teaching methods such as lectures, programming demonstrations and individual exercises. On the last day of the course, students will work on the examination and teachers will be available during the day. On-site participation is not required for the last day of the course. Participants who for some reason cannot attend the mandatory lectures have the possibility to work on the given exercises on their own and contact teachers according to their scheduled availability.
The examination consists of solving assignments using programming in R. The programming scripts that are used to answer the questions are then sent to the teachers for evaluation.
The grades awarded are Pass and Fail.
Applicants who are admitted to postgraduate studies at Lund University are given priority. Other applicants affiliated with the Faculty of Medicine can be accepted if there are vacancies.
Literature on programming concepts is distributed before the start of the course.
6 November 6 – 1 December, 2023
Course content and aim: The course provides the participants with in-depth knowledge of different methods in regression analysis and how these methods can be applied in medical research.
The course consists of the following five themes:
- Introduction to theory and methods of regression analysis
- Linear regression for continuous outcomes: analysis, diagnostics, and robust methods. Analysis of variance (ANOVA).
- Logistic regression for binary outcomes: analysis, interpretation, and diagnostics. Prediction of outcome probabilities and transformation of parameter estimates into risk ratios and risk differences.
- Ordinal and multinomial logistic regression for categorical outcomes: analysis, interpretation, and diagnostics. Prediction av outcome probabilities.
- Poisson regression and other methods for count data: analysis, interpretation, and diagnostics. Prediction of outcome probabilities.
Monday November 6: Individual preparations, such as learning basic Stata commands. (Note: participants can choose between the statistical programs Stata and R. We will provide an introductory material to Stata for beginners.)
Tuesday November 7: Lectures and exercises 9-16
Thursday November 9: Lectures and exercises 9-16
Tuesday November 14: Lectures and exercises 9-16
Thursday November 16: Lectures and exercises 9-16
Tuesday November 21: Lectures and exercises 9-16
Wednesday November 22: Group projects 9-16
Thursday November 23: Group projects 9-16
Friday November 24: Project presentations 9-12
Monday November 27 – Friday December 1: Take-home exam (expected time to spend: 1-2 days)
Anton Nilsson, associate professor, PhD, Department of Laboratory Medicine, Lund University (anton [dot] nilsson [at] med [dot] lu [dot] se (anton[dot]nilsson[at]med[dot]lu[dot]se))
Pär-Ola Bendahl, associate professor, PhD, Department of Clinical Sciences Lund, Lund University (par-ola [dot] bendahl [at] med [dot] lu [dot] se (par-ola[dot]bendahl[at]med[dot]lu[dot]se))
Sara Ekberg, PhD, Karolinska Institutet (sara [dot] ekberg [at] ki [dot] se (sara[dot]ekberg[at]ki[dot]se))
Jonas Björk, professor, PhD, Department of Laboratory Medicine, Lund University (jonas [dot] bjork [at] med [dot] lu [dot] se (jonas[dot]bjork[at]med[dot]lu[dot]se))
PhD students in medicine. Participants should have passed Applied Statistics I and II, or equivalent.
Number of participants
Vach, W. Regression models as a tool in medical research. CRC Press, 1st ed., 2013. (Available as an e-book at the Lund University Library.)
Additional readings that will be made available to the participants.
Patrik Önnerfjord (patrik [dot] onnerfjord [at] med [dot] lu [dot] se (patrik[dot]onnerfjord[at]med[dot]lu[dot]se))
Lotta Happonen (lotta [dot] happonen [at] med [dot] lu [dot] se (lotta[dot]happonen[at]med[dot]lu[dot]se))
Prof. Johan Malmström (johan [dot] malmstrom [at] med [dot] lu [dot] se (johan[dot]malmstrom[at]med[dot]lu[dot]se))
PhD students at the Faculty of Medicine
The course equals one week (1.5 ECTS credits). Five days are scheduled as well as some some self-studies.
This course will be given physically and partly digitally.
Autumn 2023. Week 49 (Dec 4-8-). Self-studies the week before the start of the course are included (about 4 hours), consisting of reading course literature and part one of the assignment. Compulsory attendance all days from 8-16.
Number of participants
The target group is PhD students at the Faculty of Medicine, who learn the basics of biological mass spectrometry and clinical proteomics. The course is given full-time for PhD students (as an elective course) at the Faculty of Medicine in Lund. Subject to availability, it is also open to other PhD students, postdocs and other interested participants at the Faculty of Medicine (such as clinical researchers) or other faculties within Lund University. The course corresponds to one week of full-time studies.
Mass spectrometry (MS) is a technique for measuring the molecular weight (m/z) and quantity of biomolecules such as proteins and peptides. Clinical proteomics describes large-scale analysis of proteins in clinical samples (tissue/cells/liquid) and MS-based proteomics is widely used in life science, with applications ranging from basic research questions to precision medicine, e.g. the identification of biomarkers, or for bacterial identification in acute sepsis in order to select effective drug treatments and save lives.
Language of instruction
The course is given in English.
The aim of the course is to provide a basic understanding of biological mass spectrometry and clinical proteomics, and insight into current methods and applications within life science. The acquired knowledge will then be applied in the students' own projects.
Learning objectives: On completion of the course, the student will be able to:
- explain the basic principles of mass spectrometry and proteomics
- understand how biological MS can be used in a wider perspective as for example to obtain critical sequence information from unknown proteins, and how MS can be used to investigate protein structure and protein-protein interactions
- perform basic MS data analysis including identification and quantification of proteins
- describe and suggest analytical approaches to biological questions – discuss advantages and limitations
- participate in scientific discussions regarding proteomics technologies and critically evaluate scientific results
- plan how to incorporate mass spectrometry/proteomics into your own PhD work and enable this versatile high-performance technology to potentially improve your individual research project
Lectures include the basics of biological mass spectrometry and clinical proteomics, such as principles for identification and quantification of proteins, as well as examples of various applications presented by invited lecturers (senior researchers in biological MS and clinical proteomics).
The following components will be covered in the course:
- introduction to mass spectrometry and clinical proteomics
- separation techniques
- protein identification
- characterization of post-translational modifications (PTMs)
- quantitative proteomics
- experiment design
- interpretation of MS data, proteomics tools.
Lectures, group exercises (in pairs), PBL, instrument demonstration, tour of D13, computer exercises.
To pass the course, approved individual assignment is needed in addition to active participation in all course events.
The grades awarded are Pass and Fail.
No prerequisite. Applicants admitted to the doctoral programme at the Faculty of Medicine in Lund are prioritised.
Selected research articles and other study materials will be made available before and during the course.
Course given autumn 2023
Week 39-41 (27 September 27 – 13 October 13)
Fredrik Ek (Fredrik [dot] Ek [at] med [dot] lu [dot] se (Fredrik[dot]Ek[at]med[dot]lu[dot]se))
Ana Carneiro (Ana [dot] Carneiro [at] med [dot] lu [dot] se (Ana[dot]Carneiro[at]med[dot]lu[dot]se))
Marcus Järås (Marcus [dot] Jaras [at] med [dot] lu [dot] se (Marcus[dot]Jaras[at]med[dot]lu[dot]se))
Ph.D. students at a Faculty of Medicine
The course equals two weeks (3 hp/credits). 10 days are scheduled, as well as self-studies.including [?] independent study
Number of participants
The course covers the general process for development of a new drug from preclinical discovery via pre-clinical development and clinical trials. The course will addresses scientific, strategic and regulatory challenges from discovery to approval of a new drug and also includes key methods and terminology. It also covers the importance of the professional groups that are involved in the different phases of the development of a new drug. This course will prepares you for future work in the pharmaceutical industry as well as work in academia regarding innovations, early drug development and entrepreneurship.
On completion of the course, the student is able to:
- give an account of drug development from discovery to registration of a new
- drug and describe commonly occurring methods associated with drug development.
- give an account of important breakpoints in the drug development process
- give an account of different types of patents that occur in drug development
- describe the laws and rules that control the development of a new drug in the pre-clinical and clinical phase
- describe the different phases of the clinical development
- give an account of different possibilities for funding drug development in the preclinical and clinical phase
- participate in teamwork to provide theoretical and practical solutions for assignments related to drug development and clinical trials
- behave with a professional approach, respect others' opinions in discussions of drug development and clinical trials and meet set deadlines
- reflect on ethical considerations during the drug development process
- reflect on different strategies to achieve scientific, market-related and regulatory goals during drug development
The fundament in the course is comprised mainly of seminars by experts mainly from the Life science industry, but also from Region Skåne and Lund University. In addition to seminars, the students will carry out team-based activities. The working methods in the course mostly involve active learning, requiring the students to prepare before each teaching component. The students are expected to behave professionally and, just as in a future work situation, participate constructively in team-based activities.
The examination consists of mMultiple-choice questions to test learning outcomes for knowledge and understanding. If there are special reasons, other forms of assessment may apply. The examiner, in consultation with Disability Support Services, may deviate from the regular form of examination in order to provide a permanently disabled student with a form of examination equivalent to that of a student without a disability.
Marking scale: Fail or Pass.
To achieve the grade of Pass as a final grade, the grade of Pass is required on the multiple-choice question test.
PhD students at Medical faculty.
Relevant articles will arebe distributed to the students during the course. The students will also search for relevant information themselves.
kristoffer [dot] mattisson [at] med [dot] lu [dot] se (kristoffer[dot]mattisson[at]med[dot]lu[dot]se) (contact person)
Professor Karin Broberg
Preliminarily doctoral students and postdocs with an interest in understanding associations between the environment and health and how these association change with a changing climate. Doctoral students at Lund University Agenda 2030 Research School will be given first priority. If places are available, master’s students are also welcome.
Autumn 2023: full-time week 35-39 (28 August to 26 September). In order to facilitate participants with other commitments, joint course components such as lectures and group exercises will primarily take place three days a week.
The application period opens on 15 March and closes on 17 April. Open for late applications.
The course is given digitally in Zoom and on the learning platform Canvas.
7,5 hp (full-time)
Number of participants
The course aims at a broad target group and does not require any specific prerequisites, other than being accepted as a doctoral or master’s student.
This course, utilizing a multidisciplinary approach, aims to deliver knowledge and enhance understanding within the field of environmental epidemiology, with a focus on environmental health and relevant Sustainable Development Goals (SDGs).
The course contains the following elements:
- Introduction to:
- environmental medicine and environmental epidemiology
- climatology, climate modeling and climate change
- Focus on themes concerning different health aspects, environment and climate
- heat and cold
- the built environment
- lifestyle and diet
In addition to an in-depth theme, lectures connect with the overall purpose of the course.
The course starts with introductory lectures to provide the opportunity for participants to create the conditions needed to immerse themselves in health aspects and the environment. The course is comprised of lectures as well as individual assignments and group exercises within different themes. The in-depth work is introduced early, and runs throughout the course. The in-depth work is carried out independently, but time is allocated for individual feedback on project idea and projects from other course participants and teachers. During the final week, the in-depth work is presented. Prior to this, an opponent is appointed who leads a discussion in connection with the presentation of the work.
This is a full-time (100%) course taking place between weeks 35 and 39 (28 August 28 to 26 September, 2023). The course structure consists of group exercises, individual assignments and lectures. To aid those students who have additional commitments, joint elements of the course, such as group exercises and lectures, will be limited to three days a week whenever possible. Parallel to these scheduled components, individual project work will be ongoing throughout the course to facilitate more in-depth learning within a specific environmental health topic chosen by the student.
In-depth project. In order to pass the course, in addition to passing the in-depth work, active participation in all of the course's exercises and group work is required.
Articles as well as digital teaching material will be distributed before the start of the course and continuously during the course.
- Activity balance during health, ill health and sickness
- Collecting and using biobank samples in research
- Applied Epidemiology and statistics III: Causal inference with non-randomized data
- Approaches to handling of missing data (samarbete med GU - online course)
- Basic Data Handling and Visualization with R
- Clinical proteomics and biological mass spectrometry
- Complex interventions in health care with a special focus on the care of adults and older persons
- Diabetes research
- Drug development and clinical trials
- Epidemiology I - Introduction to Epidemiology
- Flow cytometry, introductory course
- Flow cytometry, continuation course
- Health and Environment with special focus on climate change and sustainability
- Introduction to programming
- MAX IV/ESS-based imaging for medical and biomedical research, experimental setup
- Medical Bioinformatics, Introduction
- Neutron scattering for medical and biomedical research, experimental part.
- Perspectives on gender and intersectionality in medical and health research
- Preclinical imaging
- Applied Epidemiology and Statistics III – Causal inference with non-randomised data
- Applied Qualitative Methodology II
- Applied Statistics III – Statistical methods for repeated measurements
- Applied Statistics III – Time Series Analysis in Clinical and Environmental Epidemiology
- Applied Statistics III – Survival Analysis
- X-ray micro- and nanoimaging for medical and biomedical research, experimental part
phdcourses [at] med [dot] lu [dot] se