Admission requirements
Description
This three-week course is about research methods for biomedical (pre-clinical and clinical) studies. The course aims at providing basic knowledge of frequently used study designs and methods for the analysis of data resulting from biomedical studies. Students actively work with these designs and methods, and learn to discuss strengths and weaknesses. Three themes will be central in this course:
Design Choices between different study designs in animals or in humans, such as case-control, follow-up, randomized controlled trials will be discussed, as well as aspects specific to each of the study designs, for instance the choice of controls, cross-over vs. parallel, randomisation, and blinding.
Analysis Statistical methods to analyze data from these studies will be taught and actively used. Methods include univariate and multivariate linear regression, ANOVA, logistic regression, survival analysis (Kaplan Meier survival curves and Cox regression), and linear mixed models.
Interpretation and validation Interpretation of the results involves the interpretation of relative risks, odds ratios and hazard ratios in univariate and multivariate analyses. Validation of the methods involves model checks, the reliability of measurements (inter- and intraobserver variation, kappa), the evaluation of diagnostic systems (ROC curves), and recognition of the principles of bias and confounding.
Course objectives
The student translates a clinical problem into a research question.
The student explains methods of study design used in a clinical or population setting.
The student designs a biomedical study in an efficient way.
The student chooses, applies and interprets the most appropriate statistical analyses.
The student evaluates the reliability and reproducibility of measurement systems.
The student recognizes and discusses the possibility of bias and confounding in a study.
The student discusses ethical and legal aspects of clinical studies with human subjects.
Timetable
All course and group schedules are published on MyTimeTable, as well as the exam dates and corresponding feedback session (exam feedback, exam inspection).
Mode of instruction
This course consists of a number of lectures, 4 working groups, 9 self-study assignments, and 6 computer practicals. During working groups and practicals, students will work together in pairs, or in small groups.
Assessment method
A written report on each of the computer practicals has to be submitted (pass expect for one question that is graded per practical; 10% of final grade).
Written examination (90% of final grade). The exam contains both multiple choice and “open questions”.
The final grade is published in uSis as soon as requirements have been met. There is a re-examination during spring.
Reading list
For the complete reading list for the bachelor of Biomedical Sciences, see the Study Materials List BW 2025-2026.
Registration
Registration must be done via MyStudyMap. Registration in MyStudyMap gives you automatic access to the course in Brightspace. For more information, please visit the Leiden University website for students.
Contact
Remarks
Information for exchange students
This module is part of an English taught third semester of the bachelor’s program in the study Biomedical Sciences at Leiden University Medical Center. EXCHANGE STUDENTS NEED TO PREPARE THE COURSE BY FOLLOWING AN OPEN COURSE published on brightspace and study additional preparatory material.
Combining biomedical modules with modules from other programs in Leiden is difficult or impossible due to different scheduling schemes. Exchange students with sufficient relevant background knowledge in biology/biomedicine are therefore encouraged and advised to follow the entire following module package:
Immunology (BW), 9 EC
Infectious Agents And Immunity, 6 EC
Introduction to the Neurosciences 6, EC
Design and analysis of Biomedical Studies, 6 EC
Transferable skills
This course is designed to help you develop several key transferable skills
Researching: You'll frequently analyze problems to select the most appropriate study design or method of analysis. This also includes identifying flaws in existing designs, honing your critical thinking, digital skills, and analytical abilities.
Collaborating: Through working groups and computer practicals, you'll team up with peers to find solutions. You'll also gain experience in presenting your findings and receiving constructive feedback from both peers and instructors.
Reflecting: We encourage you to deepen your understanding through optional course components. These non-obligatory activities are designed to spark your enthusiasm for the course content, improve independent learning, and prepare you for future engagement in the field of data science and epidemiology.