Master students of Bio-Pharmaceutical Sciences, Biomedical Sciences, Life Science & Technology, Molecular Science & Technology, Mathematics/Statistics.
Late-stage clinical outcome of pharmaceutical interventions plays a key role in the drug life-cycle. Besides its pivotal role in regulatory milestones, it also bridges discovery and early (pre-) clinical stages to post-approval/market access and real-world stages of this life cycle. With several thousands of clinical trials conducted, and made publicly available each year, late-stage clinical outcome also poses an enormous data challenge, both in terms of data identification, -extraction, analysis. During this class the concepts of clinical trial design- and conduct, systematic literature review, statistical meta-analysis, as well as other data science aspects will be addressed. Furthermore, integration of late-stage clinical outcome into end-to-end (bench-to-bed) predictive modelling strategies will be addressed on the basis of specific case-studies and hands-on exercises.
This course aims to:
Raise students’ interest and enthusiasm for clinical trials, systematic literature review, meta-analysis and drug-life cycle management.
Give an introduction of the most important concepts, value proposition and analytical methods used and their application in pharmaceutical outcomes research.
Give the student sufficient background to conduct and interpret systematic literature review of pharmaceutical outcome research.
Introduce a number of data science elements on data identification, -curation, -analysis and -visualization that are relevant for pharmaceutical outcome research.
Provide an opportunity for students to get hands-on exposure to pharmaceutical outcomes research.
Note that these are expert lectures series and that hence the content might change every year.
At the end of this course the student is able to:
Understand the basic concepts of pharmaceutical outcomes research and its position in drug life-cycle management.
understand key features of clinical trials as the basis for pharmaceutical outcomes research such as design, conduct, analysis and reporting.
Appreciate key data science elements in pharmaceutical outcomes research such as data identification, curation, organization, visualization and analysis and value communication.
Understand essential data analytical features in meta-analysis, such as variability between/within trials, explanatory variables, publication bias, weighing, missing data, etc.
Conduct a basic systematic literature review, including meta-analysis and reporting/communication.
Effectively use data visualization software.
Present a scientific research paper to peers.
This course is scheduled for semester 2, period 4.
A detailed course schedule will be published on Brightspace.
Mode of instruction
The student’s capacity will be assessed in two ways:
An exam (open questions, 50%).
A scientific communication describing a systematic literature review (paper, or an interactive app, 50%).
The final grade should be 6.0 to successfully complete this course.
A reading list will be announced during the course.
Application via uSis for both the course and exam is mandatory. Registration for the course closes 14 days before the start of the course or earlier when the maximum number of students is reached. Registration for the exam closes 7 days before the exam date or earlier when the maximum number of students is reached.
Coordinator: Prof dr Elizabeth (Liesbeth) de lange (email@example.com, tel 071-5276330).
This information is without prejudice. Alterations can be made for next year.