Real World Epidemiology: Oxford Summer School
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.
Taking into account so many factors related to the current situation still unknown, and considering health and safety of our attendees, our team has made the difficult decision to cancel the course in June 2022.
Online Registration for year 2022 is no longer available following the cancellation of the course.
Venue: LMH college
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 CHARACTERIZATION: Gain an understanding of the existing sources of routinely collected data for epidemiological research, and on how to characterize 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, and the use of such methods for treatment heterogeneity/personalised medicine research.
- HEALTH DATA SCIENCES and BIG DATA: 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
- “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