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 registration is now open. Book your place here.
Pharmacists, clinicians, academics (including statisticians, epidemiologists, and related MSc/PhD students), and industry (pharmacy or device) and regulatory staff with an interest in the use of routinely collected data for research.
- DATA DISCOVERY AND CHARACTERIZATION: Gain an understanding of the existing sources of routinely collected data for epidemiological research, and how to characterise whether they are fit for purpose to answer your research question/s
- EPIDEMIOLOGICAL STUDY DESIGN/S: Be able to discuss common and advanced study designs and their implementation using real world data.
- PHARMACO- AND DEVICE EPIDEMIOLOGY: Be aware of the applications of real world data in both pharmaco and device epidemiology, including drug/device utilisation, comparative effectiveness, and post-marketing safety research.
- PREDICTION MODELLING: Learn basic concepts on the design and evaluation of prognostic/prediction models developed using real world data.
- “REAL WORLD” SOLUTIONS: Understand relevant issues and learn potential solutions applied to the use of ‘real world’ epidemiology: a) data management, information governance, b) missing information and multiple imputation, and c) interaction with industry and regulators
- BIG DATA METHODS: Be familiar with the basics of big data methods, including a) machine learning, b) principles of common data models for multi-database studies, and c) digital epidemiology/patient data collection.
Monday 25 June to Friday 29 June 2018
The course will be held at Lady Margaret Hall, a University of Oxford college.
You can view a draft copy of the programme here.