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)

Autumn 2026

In Swedish: 28/9-30/9 (kl 9-15) and examination 20/10  (kl 9-12)
In English: 16/11-18/11 (kl 9-15) and examination 27/11 (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 

Autumn 2026

23-27 November. 

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)) 

3 credits

Course dates

Fall 2026 Sept 30- Oct 16

Number of participants: 15

Course Leaders

Fredrik Ek fredrik [dot] ek [at] med [dot] lu [dot] se (fredrik[dot]ek[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)

Target group

The target group is doctoral students at the Faculty of Medicine, Lund University. Researchers who hold a doctoral degree and doctoral students from other faculties or higher education institutions may be admitted to the course subject to the availability of places.

Language of instruction 

English

Purpose

The main objective of the course is to help students understand how a new drug is developed—from the early stages in the lab (preclinical discovery), through preclinical testing, all the way to clinical trials in humans and eventual regulatory approval.

Course design 

The course is built around seminars led by experts from the life science industry, Region Skåne, and Lund University. Students will also engage in team-based activities, with a strong focus on active learning. 

Course content 

The course explores the full process of drug development—from preclinical discovery through clinical trials to regulatory approval. It addresses key scientific, strategic, and regulatory challenges, introduces essential methods and terminology, and highlights the roles of various professional groups involved. Students will gain a solid foundation for careers in the pharmaceutical industry or in academia focused on innovation, early drug development, and entrepreneurship.

Examination

Multiple-choice questions to test learning outcomes for knowledge and understanding.

Credits: 1.5 credits (full-time)

Dates: 30 November – 4 December 2026

Course content and aim: The course provides the 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 survival analysis 
  • What is survival data?
  • Kaplan-Meier survival curves and the logrank test
  • Cox regression and the proportional hazards assumption
• Parametric survival analysis
  • Parametric models assuming proportional hazards
  • Parametric models not assuming proportional hazards
• Advanced and specialized methods
  • Time-varying covariates and time-varying effects
  • Competing risks
  • Recurrent events

Software: Support will be given for Stata and R

Schedule:

Monday 30/11: 9:00-15:30

Tuesday 1/12: 9:00-15:30

Wednesday 2/12: Own work the whole day

Thursday 3/12: 9:00-15:30

Friday 4/12: Take-home exam

Location: BMC, Lund

Teachers:

Anton Nilsson, associate professor, Department of Translational 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, 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)) 

Rebecca Rylance, statistician, Department of Clinical Sciences Lund, Lund University (rebecca [dot] rylance [at] med [dot] lu [dot] se (rebecca[dot]rylance[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. 

Number of participants: 20

Literature: 

• Kleinbaum, D.H and Klein, M. Survival analysis – a self-learning text. Springer, 3rd ed., 2012. (Available as an e-book through Lund University Library.)

Additional materials that will be made available to the 

Course leader 
Karin Engström (karin [dot] engstrom [at] med [dot] lu [dot] se)

Examiner
Helena Persson

Target group: 
Ph.D. students at the Faculty of Medicine

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

Place
BMC E11073 Rådslaget

Time
Autumn 2026 - Week 42 (October 12-16)
Monday-Thursday 9-16. For the last day, attendance on-site is not compulsory.

Number of participants 
24

Language
English

Objectives
The purpose of the course is to provide basic knowledge of the programming language R to facilitate independent future use of applications written and / or implemented in this language, such as statistical analysis programs. 

Learning outcomes
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

Content 
This course introduces students to the basic terms and concepts used within programming. The focus is on the handling of data with R, e.g., importing it into RStudio/Posit, summarising, cross-referencing, merging, creating new data. Different forms of relevant and graphically appealing visualisations will also be covered, as well as exporting the data and diagrams created in different formats. Packages in the Tidyverse software collection are used.

Design
A pre-course assignment involves installing RStudio/Posit and studying basic programming concepts and terminology with the help of provided course literature. Access to a laptop computer is required. The course consists of four compulsory full days. Teaching methods include lectures, programming demonstrations, and individual exercises. On the final day of the course, students work independently on the examination, with teachers available online. On-site participation is not compulsory during the final day of the course. Students who are unable to participate in the compulsory classes have the opportunity to work on the specified exercises on their own and to contact the teachers according to their scheduled availability.

Assessment
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.

Grades
The grades awarded are Pass and Fail.

Admission requirements
Applicants who are admitted to postgraduate studies at Lund University are given priority. Other applicants affiliated with the Faculty of Medicine may be accepted if vacancies exist.

Required reading 
Literature on programming concepts is distributed before the start of the course. 

Course dates

Fall 2026

November 23–27

Course Leader

Märta Wallinius marta [dot] wallinius [at] med [dot] lu [dot] se (marta[dot]wallinius[at]med[dot]lu[dot]se)

Target Group

PhD students or postdoctoral researchers active within compulsory psychiatric or forensic psychiatric fields, or thereto related research areas. The course is part of the national research school COMPFOR, a collaboration between Lund University and the University of Gothenburg, but is offered as an elective course through the Faculty of Medicine, Lund University. Priority will be given to PhD students within COMPFOR.

Credits

3 ECTS credits

Purpose

The course will expand the PhD students’/researchers’ knowledge about prerequisites and implications for clinically applied, co-produced research within the context of (forensic) psychiatric care. 

Outline

The teaching is primarily conducted through interactive learning activities. The course includes lectures, seminars, workshops, group work, individual work, practical applications, and self-studies. The course is conducted over ten days, starting with five days on-site in Lund, followed by five days of independent work.

• 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