Elective courses

Course dates

Spring 2026

In Swedish: 30/3, 31/3, 1/4 (kl 9-15) and examination 10/4 (kl 9-12)
In English: 25/5, 26/5, 27/5 (kl 9-15) and examination 5/6 (kl 9-12)

Course Leaders

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)

Target group

PhD students at the Faculty of Medicine, with priority given to those who have passed their halfway review.

Purpose

The aim of the course is for the PhD student to deepen their knowledge and skills around the publication process and how to write and review a scientific manuscript.

Outline

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.

Course dates 

Spring 2026:

18-22 May. Mornings are in class and the afternoons consist of individual work.

Course organizers 

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)

Examinator

Stefan Hansson stefan [dot] hansson [at] med [dot] lu [dot] se (stefan[dot]hansson[at]med[dot]lu[dot]se)

Target group 

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.

Description

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.

Location

The course will be aimed to be conducted on campus in Lund for all days.

Examination
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.

Credits

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.

Registration

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)

Course leader

Lena Uller, Docent, Respiratorisk Immunofarmakologi, Institutionen för experimentell medicinsk vetenskap, Lund

Examiner

Lena Uller

Target Group

This is a compulsory course for PhD 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.

Credits 

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.

Course literature

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) 

Training in Laboratory Animal Science - to apply (Lund University Staff Pages)

The Research Studies Board offers four elective courses in Applied Statistics on a regular basis. The main target group is PhD students at the Faculty of Medicine at Lund University, but postdocs/senior researchers as well PhD students from other faculties or higher education institutions may also be admitted, depending on space availability. To be admitted, you must have passed Applied Statistics I and II, or equivalent. 

Courses will be offered according to the following schedule:

The schedule for the next course round can be found in the expandable section for this specific course further down on the homepage.

More information about the courses is provided below. As in Applied Statistics I and II, you will work with syntax-based statistical software on all these courses. We provide support for Stata and R, and we recommend that you choose one of these two. 

Applied Statistics III: Regression Analysis

This course provides participants with in-depth knowledge of different methods in regression analysis and how these methods can be applied in medical research. 

The course covers the following topics:

• 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
• Poisson regression and other methods for count data: analysis, interpretation, and diagnostics

Teachers: Anton Nilsson (anton [dot] nilsson [at] med [dot] lu [dot] se (anton[dot]nilsson[at]med[dot]lu[dot]se)), Pär-Ola Bendahl (par-ola [dot] bendahl [at] med [dot] lu [dot] se (par-ola[dot]bendahl[at]med[dot]lu[dot]se)), and Sara Ekberg (sara [dot] ekberg [at] reddooranalytics [dot] se (sara[dot]ekberg[at]reddooranalytics[dot]se)) 

Applied statistics III: Approaches to Handling of Missing Data

This course introduces the issue of missing data, describes the consequences of simple ad hoc methods to address the issue, and provides in-depth knowledge of the method of multiple imputation (MI).

The course covers the following topics:

• 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

Teachers: Aleksandra Turkiewicz (aleksandra [dot] turkiewicz [at] med [dot] lu [dot] se (aleksandra[dot]turkiewicz[at]med[dot]lu[dot]se)) and Pär-Ola Bendahl (par-ola [dot] bendahl [at] med [dot] lu [dot] se (par-ola[dot]bendahl[at]med[dot]lu[dot]se))

Applied Statistics III: Survival Analysis

This course provides participants with in-depth knowledge of methods in survival analysis and how these methods can be applied in medical research. 

The course covers the following topics:

• Introduction to theory and methods for survival analysis: Kaplan Meier survival curves, the logrank test, and Cox regression
• Parametric survival models: Models assuming proportional hazards and models not assuming proportional hazards
• Advanced and specialized analyses: Models for recurrent events, models with time-varying covariates, and models for competing risks

Teachers: Anton Nilsson (anton [dot] nilsson [at] med [dot] lu [dot] se (anton[dot]nilsson[at]med[dot]lu[dot]se)), Pär-Ola Bendahl (par-ola [dot] bendahl [at] med [dot] lu [dot] se (par-ola[dot]bendahl[at]med[dot]lu[dot]se)), and Rebecca Rylance (rebecca [dot] rylance [at] med [dot] lu [dot] se (rebecca[dot]rylance[at]med[dot]lu[dot]se))

Applied statistics III: Mixed Models for Analysis of Longitudinal and Clustered Data

This course provides participants with in-depth knowledge of how mixed models can be used for the analysis of data with repeated measurements or clustering, such as 

1. Repeated measurements of patients, animals, or other biological samples
2. Data clustered within individuals (two eyes, two cerebral hemispheres, several tissue or cell samples from the same individual or similar)

More information about this course will be posted during 2026.

Teachers: Aleksandra Turkiewicz (aleksandra [dot] turkiewicz [at] med [dot] lu [dot] se (aleksandra[dot]turkiewicz[at]med[dot]lu[dot]se)) and Rebecca Rylance (rebecca [dot] rylance [at] med [dot] lu [dot] se (rebecca[dot]rylance[at]med[dot]lu[dot]se)) 

Course leaders

Patrik Önnerfjord (patrik [dot] onnerfjord [at] med [dot] lu [dot] se)

Lotta Happonen (lotta [dot] happonen [at] med [dot] lu [dot] se)

Examiner

Prof. Johan Malmström (johan [dot] malmstrom [at] med [dot] lu [dot] se)

Target group

PhD students at the Faculty of Medicine

Scope

The course equals one week (1.5 ECTS credits). Five days are scheduled as well as some some self-studies.

Place

This course will be given physically and partly digitally.

Time

Spring 2026. Week 23 (Jun 1-Jun 5). 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 9-15.

Number of participants

Max 12

Language

English

General information

This is an electable course for PhD students who are interested in learning more about biological mass spectrometry and clinical proteomics. Mass spectrometry (MS) is a technique to measure the molecular weight (m/z) of biomolecules such as proteins or peptides. Proteomics describes the large-scale analysis of proteins in a biological sample and MS based proteomics is used extensively in the life science area with numerous applications spanning from basic research questions to precision medicine e.g. in the hospital where MS is used for bacterial phenotyping in acute sepsis to select the effective drug treatment and thereby save lives. Clinical proteomics focus on clinical samples such as tissues, cells and various biological fluids, that need special considerations to be successful. There are local infrastructures for biological mass spectrometry available at the Medical Faculty: translational proteomics (CTP), structural proteomics (SciLifelab) as well as the national resource for biological MS (BioMS).

The aim of the course is to provide the students with an introduction to current methodologies in the field of MS-based proteomics. The students should obtain an overview of typical proteomics applications and be introduced to proteomics experimental workflows to enable the technology to be included in their own research project.

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

Content and design

The course will cover basic principles of mass spectrometry, separation of proteins and peptides, sample preparation techniques, data acquisition methods, data analysis, analysis of post-translational modifications (PTMs) and bioinformatics analysis. There will also be invited lectures (senior researchers within biological MS and clinical proteomics) that will include specific applications in various research areas. 

Learning activities include lectures, group exercises, instrument demos (to generate MS data) and a round tour at BMC D13. Participants are expected to have access to a laptop. 

Furthermore, the course includes one compulsory assignment, in which the doctoral student is to reflect on a research situation (preferably from their own PhD-project) where biological mass spectrometry might be used, present it for the course participants and finally propose in writing how this technology can be used to enrich the previously described research situation.

Assessment

To pass the course, approved individual assignment is needed in addition to active participation in all course events. 

Grades

The grades awarded are Pass and Fail.

Admission requirements

Applicants admitted to research studies at the Faculty of Medicine in Lund are prioritized.

Literature

Selected research articles and other study materials will be made available before and during the c

1.5 credits (full time)

Dates: 18^th to 22^nd May 2026

General information

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 during the course is multiple imputation (MI), which participants will have the opportunity to test during the computer exercises.

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
  • Analysis of 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

Schedule:

Monday 08:30 to 16:30
Tuesday 08:30 to 13:00 (own work during afternoon)
Wednesday 08:30 to 16:30
Thursday (own work the whole day)
Friday 08:30 to 13:00 (take home exam during the afternoon)

Location: Lund, BMC

Teachers:

Aleksandra Turkiewicz, Associate Professor, CStat, Clinical Epidemiology Unit, Clinical Sciences Lund, Lund University (aleksandra [dot] turkiewicz [at] med [dot] lu [dot] se (aleksandra[dot]turkiewicz[at]med[dot]lu[dot]se)) 

Pär-Ola Bendahl, Associate Professor, PhD, Department of Oncology, 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))

Language: English

Target group: PhD students in medicine. Participants should have passed Applied Statistics I and II, or equivalent.

Course examiner:

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))

Number of participants: 20

• 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
• Glycobiology
• 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