Curriculum
Curricular Content
The doctoral program consists of original research as well as of curricular content, with mandatory and optional modules of at least 12 ECTS points, for which a minimum of three years in full-time employment is scheduled. However, in justified cases exceptions to this may be authorized by the doctoral program commission.
Programm
Compulsory Modules
Introduction to Epidemiology (Milo Puhan, Viktor von Wyl et al.)
The overall goal of this 4-week block course Introduction to Epidemiology is to introduce students to the major questions of clinical and epidemiologic research and to methods to address these questions. The course follows an overall framework (Figure) that describes the course of scientific discovery from the detection and burden of disease and its causes, to diagnosis and prognosis of disease up to the development and evaluation of preventive and treatment interventions and their consequences for population health. We will discuss study designs in the context of existing knowledge and the type of evidence needed to advance knowledge for specific questions. Thereby, students learn to combine subject knowledge and methods expertise to design, conduct and interpret substantive medical research. The course will provide a basis for further studies and research in the fields of Medicine and Public Health (on Master or PhD level), both of which are very dynamic and diverse fields.
Next conduct in Fall Semester 2025
Advanced Medical Research Methods (Milo Puhan, Henock Yebyo, Miquel Serra-Burriel)
Module Director: Prof. Milo Puhan, MD, PhD
Link: Epidemiology, Biostatistics and Prevention Institute
This course has been designed for Clinical Science PhD students to learn and experience the scientific and practical aspects of applied clinical research methods.
Course Description:
The aim of this course is to introduce students to advanced research methods and apply previous knowledge in epidemiology and biostatistics into real-life research. To achieve this, the lectures will cover novel study designs (special designs of RCTs and observational studies like factorial RCTs and nested case-control studies), advanced statistical methods (propensity scores, missing data). We will introduce special topics in epidemiology related to modifiable risk factors (nutrition and physical activity) which can be applied to a variety of outcomes, as well as current topics in research (molecular epidemiology, big data and translational research). The ‘lab’ sessions will provide practical techniques, (using R) that will further expand the set of tools that the future PhDs will be able to apply in their research. Furthermore, the practical experience will be complemented by the group exercise of writing protocol for a fictional RCT (designed and developed during the semester) under a guidance of an epidemiologist/researcher at the ZAM.
Gruppenprojekt:
Drei Gruppen zu je 3–4 Studierenden, vorzugsweise mit unterschiedlichen Interessen und Hintergründen, entwerfen ein durchführbares RCT zur Lösung eines realen Public-Health-Problems. Jede Gruppe wird von einer Lehrperson betreut. Weitere Informationen folgen in der ersten Sitzung.
Group Project:
Three groups of 3 to 4 students, preferentially with diverse interests and backgrounds, will design a feasible RCT to address a real public health problem. One lecturer will be assigned per group. Further administrative information will be provided at the beginning of the first lecture.
Prerequisites:
Intro to Epidemiology or RCT course (BME361) & Basic course in Biostatistic (ie Clinical Biostatistics or similar) & experiences in R.
Next conduct in Fall Semester 2025
Clinical Biostatistics (Leonhard Held, Stefanie von Felten)
The aim of the course "Clinical Biostatistics" is to give students an introduction to statistical methods in clinical research.
The following topics will be addressed: randomized controlled trials, bias, hypothesis tests and sample size calculation, randomization and blinding, confidence intervals and p-values, analysis of continuous and binary outcomes, multiplicity, subgroup analysis, protocol and protocol deviations, some special designs (crossover, equivalence, and clusters), analysis of diagnostic studies, analysis of agreement.
Please note that PhD students are asked to use the time between 12 and 15 h to prepare work for the lab.
Next conduct in Fall Semester 2025
Case Studies in Clinical Biostatistics (Ulrike Held) – 1 ECTS-Punkt
The aim of the course is to give students practice in different stages of clinical research projects: study design, primary outcome definition and sample size calculation, plausibility checks, data analysis and modelling, computation, interpretation, and communication of results, as well as dissemination according to EQUATOR guidelines. In 3 research projects, students will face real-world problems typically associated with study design, data analysis and reporting. A focus of the course will be on good research practice, application of statistics knowledge and reproducibility. We will use the statistical programming language R in combination with R Markdown for reproducibility and dynamic reporting.
Project 1: Comparison of the means of two populations, hypothesis testing with parametric and non-parametric tests, confidence intervals. Baseline adjustment with ANCOVA model.
Project 2: Research protocol for a clinical study, primary outcome, secondary outcomes, sample size determination.
Project 3: Estimation of the treatment effect in a randomized experiment with a time-to-event outcome, Kaplan-Meier curves, Cox proportional hazards model.
Students are encouraged to work in groups. At the end of each project, students will be asked to hand in individual reports and present their results in a 15 min talk. The talks and reports will be assessed. In order to enroll in this course it is mandatory without exception to have passed CS16_003 Clinical Biostatistics (Vorlesung und Übung).
PhD Seminar (Christian Britschgi, Alexis Puhan)
The objective of this course is to have a more detailed look into diverse research topics, methods and problems. Sessions are either based on a talk by an experienced researcher followed by a student lead discussion or on a general research topic which is being prepared by a group of PhD students for discussion with peers. Examples for discussed topics include personalized medicine, biomarkers, evidence based medicine, graphs in publications a.o.
Optional Courses
Winning the Publication Game (Jürgen Barth)
Jürgen Barth teaches in this module the relevant steps to publish a manuscript. Participants will have the chance to exercise the process. This process includes the preparation work, writing the paper and submitting it. There are 10 major topic involved in the successful publication of a paper. Publication starts with the identification of the target group. Further, the main message has to be shaped. The lecturer gives advice on the covering letter for the editor. He instructs how to handle the comments of the reviewers.
Prerequisite for the participants is to have specific plans for a manuscript, that will be submitted within 6 months. In the course, the relevant steps for the submission of the manuscript are conferred. The lecturer will deal with all individual manuscripts.
All PhD students in their 2nd or 3rd year are welcome to register. By actively participating and doing the exercises, the participants will be able to develop the skills to win the publication game.
Next conduct in Fall Semester 2025
Get R_eady: Introduction to Data Analysis for Empirical Research (Ulrike Held, Monika Karin Hebeisen, Stefania Iaquinto)
The course offers an introduction to data analysis in the transdisciplinary field of empirical research in the programming language R. The R system of statistical computing is openly available from https://www.r-project.org and provides a simple and flexible software environment for statistical analyses and graphics. Tailored to the application of empirical research the course covers basics of functions and data formats in R, as well as the essential steps of a data analysis including data manipulation, descriptive statistics, statistical tests and graphical representations. Reflections on research methodology and transdisciplinarity will take place and critical thinking will be enhanced.
Implementation Science in Health Care (Lauren Clack, Rahel Naef et al.)
Implementation science is the scientific study of methods to promote the systematic integration of research findings and evidence-based practices into care delivery and the de-integration of low value care. Implementation science is a newer field of study that addresses the know-do gap in health care and builds on the insight that proving effectiveness of an innovation (practice, model of care, intervention, treatment modality etc.) does not automatically translate into effective adoption in clinical practice.
Implementation science therefore aims to:
- increase and accelerate the adoption of research findings and evidence-based practices;
- scale-up effective interventions to different contexts;
- develop knowledge on implementation strategies that are tailored to contextual barriers and enablers to adoption and research use;
- increase the involvement of clinicians, patients, families, and the public in research;
- achieve knowledge circulation i.e., to enable the transfer of knowledge from practice to research.
In this course, students will gain an understanding of the role of implementation science in clinical health research, familiarize themselves with implementation science methods, and develop skills by applying implementation science methods in their field of research.
Advanced Implementation Science in Health Care (Lauren Clack, Rahel Naef)
This module will help students to gain a deeper and applied knowledge of Implementation Science. Students will have the opportunity to tailor course content to their current projects and interests by selecting from a pre-defined list of implementation topics to be covered during the semester. Working in small groups, students will prepare their chosen topics and present them to the class. Every session will furthermore provide the opportunity to transfer learnings from the presented topic (s) to one’s own project. Topics to choose from (finalization in the first session):
- Human-centered design (co-design) and implementation science
- Tailoring implementation strategies
- Evaluating context
- Research logic models
- Theories, Models, and Frameworks
- De-implementation
- Quantitative & qualitative measures
- Health economic evaluation and implementation science
Next conduct in Fall Semester 2025
Statistische Modelle mit R (Christina Ramsenthaler)
The course focuses on key statistical analysis methods for a wide range of study types, including experimental studies, observational studies (cohort, case-control, descriptive cross-sectional and longitudinal studies), and secondary analyses (e.g., meta-analyses).
It covers essential techniques for analyzing quantitative data and introduces the most important statistical models used in health sciences, using the open-source software R:
Linear models and their special cases (LM: t-test, ANOVA, regression)
Regression models for specific data types such as count data and binary data (GLM: Poisson, logistic regression)
Multivariate methods (principal component analysis and factor analysis)
Introduction to survival analysis
Hierarchical models (LMM: linear mixed models)
Selected methods for meta-analysis
Working with the ggplot2 graphics package, using Tidyverse packages, and an introduction to RMarkdown for report generation
Transversal Skills
Here you can find the Graduate Campus courses for doctoral candidates offered in the 2025 spring semester.
Recommended MOOCs
The doctoral program committee recommends the following high-quality massive open online courses (MOOCs) on the topic of systematic review and meta-analysis:
Introduction to Systematic Review and Meta-Analysis
Understanding Systematic Reviews – an Introduction for Health Professionals
Retreat
The retreat of the Clinical Science PhD program takes place once a year in September. It offers doctoral candidates a platform to present their projects, network, and gain new insights – for example, through guest lectures, group work, or thematic workshops.
Participation is mandatory for all doctoral candidates enrolled in the program.