Design and Analysis of Randomized Clinical Trials
Praktische info:
Leertraject
Inschrijven?
- Voorwaarden: The basics of statistical concepts, techniques and inference, knowledge of linear regression and categorical data analysis are considered known.
- Prijs: €890
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In a rapidly changing (professional) context, lifelong learning has become a must. With microcredentials Ghent University offers a new type of course for a broad group of lifelong learners.
Microcredentials are short academic programmes that meet the high quality standards of Ghent University. Both in terms of content and practice, the development of a microcredential is primarily focused on the needs of professionals and lifelong learners. The focus of the programme is on a well-defined set of learning outcomes. They consist of a limited number of subjects to which credits are linked. For following a microcredential, your learning account will be used . If you successfully complete the programme, you will receive credit certificates for the individual subjects and a recognised certificate that offers clear added value for professionals and employers. Microcredentials are organised by the 'academies for lifelong learning' of Ghent University.
Content
This course builds on the course on Principles of statistical data analysis, Analysis of continuous data and Categorical Data Analysis to teach knowledge and ideas to draw evidence from randomized studies with a focus on applications in medicine and public health. It will provide the student with an understanding of statistical principles, methodology and concepts involved in clinical research across the various phases. The student should acquire the skills to design a clinical trial, be aware and understand the limitations related to clinical trials, be aware of formal guidelines and be able to analyze the data according to appropriate statistical techniques.
Detailed content
- Clinical Trials
- Protocol document and the role of the statistician therein
- Types of experimental designs (parallel, cross-over, factorial)
- Endpoints and estimands
- Sample Size/Power calculations
- Treatment allocation
- Data analysis and prognostic factors
- Concepts and methodology for (data-dependent) stopping and early termination of trials
- (interim analysis, adaptive designs and data monitoring committees)
- Meta-analysis
- Reporting of results, potential pitfalls and data quality
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