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Real-World Data
Last Updated: 2026-02-05 16:01:57
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
The course enables participants to... 1. describe the role and relevance of real-world data (RWD) in the context of healthcare and research; illustrate how RWD is used in regulatory decision-making and explain the difference between RWD and clinical trials data. 2. describe the different source systems of RWD; illustrate, with examples, the general and specific data interoperability challenges that arise in the use of RWD; illustrate how data presentation and quality affect the collection and use of RWD in research; characterise different data models; describe the governance, legal, and ethical challenges that arise in the use of RWD; and outline possible solutions. 3. describe different RWD data sources in Switzerland; explain common challenges to accessing RWD in Switzerland. 4. understand data content and target population coverage; apply acquired knowledge to an RWD data set. 5. demonstrate how RWD can be used for prediction (e.g., disease progression or relapse); describe how RWD can support differential diagnostics; describe how RWD can be used to design or applied in clinical trials. 6. describe how bias and confounding can be assessed in RWD; discuss methods for managing bias and confounding in RWD; discuss the impact of bias and confounding on the interpretability of results obtained from RWD. 7. understand the use of different data formats and queries; illustrate data structures and elements. 8. describe how data sources can be summarised or connected; discuss the benefits of summarising data; describe the challenges of summarising data; explain from a methodological perspective how to address these challenges. 9. be able to apply the knowledge acquired in the first part of the RWD workshop; be able to present the results: population structure, bias assessment, and confounding; to execute a prediction on the 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 |
|
27 h semesterly |