We explore the existing sources of real world data, discuss common types of study and designs for its use, and look in-depth into the issues and solutions linked to big health data usage.
Course now full
Pharmacists, clinicians, academics (including statisticians, epidemiologists, and related MSc/PhD students); Industry (pharmacy or device) or Regulatory staff with an interest in the use of routinely collected data for research.
- DATA DISCOVERY AND VALIDATION STUDIES: Gain an understanding of the existing sources of routinely collected data for epidemiological research, and on how to perform validation studies to assess their quality
- EPIDEMIOLOGICAL STUDY DESIGN/S: Be able to discuss common types of study for the use of such data, including cohort, case-control, and case only studies.
- PHARMACO- AND DEVICE EPIDEMIOLOGY: Be aware of the applications of real world data in both pharmaco and device epidemiological studies, including drug/device utilisation and safety research.
- PREDICTION MODELLING: Learn basic concepts on the design and evaluation of prognostic/prediction models developed using real world data.
- BIG DATA METHODS: Be familiar with the basics of big data methods (including machine learning), and examples of their potential applications in ‘real world’ epidemiology.
- "REAL WORLD” SOLUTIONS: Understand relevant issues and learn potential solutions applied to the use of ‘real world’ data: a) data management and information governance, b) interaction with industry and regulators, c) stats/methods: missing information, bias, confounding, misclassification.