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Real-World Data
Last Updated: 2026-06-01 11:33:06
Abstract
The course provides an overview of the importance of Real-World Data (RWD), different RWD sources, and how RWD can be exploited in healthcare, clinical and personalised health research, as well as in regulatory decision making. It highlights current trends and existing methods for using and analysing RWD.
Objective
After the course, participants will be able to 1. describe the role and relevance of RWD in the context of healthcare and research; illustrate how RWD are used in regulatory decision making; explain the difference between RWD and clinical trials data. 2. explain the challenges of RWD providing specific examples; illustrate how data presentation and quality affects the gathering and usage of RWD in research; characterize different data models; describe the governance challenges associated with RWD and outline possible solutions. 3. describe different sources of RWD in Switzerland; explain common challenges when accessing and using RWD; illustrate how other countries managed and mitigated these challenges. 4. understand data contents and target population coverage; apply the gained knowledge to a RWD dataset. 5. demonstrate how one can use RWD for a prediction task; describe how RWD can aid diagnosis; describe how RWD can be used to inform the design and implementation of clinical trials. 6. explain how bias and confounding in RWD can be assessed; discuss methods to deal with bias and confounding in RWD; discuss the impact bias and confounding can have on the interpretability of the results derived from RWD. 7. practice the use of CSV, RDF, and SPARQL; illustrate the data structures and elements. 8. describe how data sources can be pooled or merged; discuss the advantages of pooling data; explain the challenges and pitfalls of pooling data; explain how to adress these challenges and pitfalls from a methodological point of view. 9. apply knowledge gained in the first part of the course to real-world data; present the results: population structure, assessment of bias and confounding; conduct a prediction task on a data set.
General Information
- Language
- English
- Levels
- NDS , WBZ
- Frequency
- Yearly recurring
Examination
- Type
- ungraded semester performance
Registration & Places
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture with exercise |
Real-World Data
Block course
|
|
27 h semesterly |