Real World Epidemiology: Oxford Summer School

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June 26, 2019
NDORMS
Oxford
United Kingdom

Introduction to Pharmaco-Epidemiology: Drug Utilisation, Drug Safety, and RMM Effectiveness

Susana Perez-Gutthann, RTI Health Solutions Vice President and Global Head of Epidemiology, will be teaching Introduction to Pharmaco-Epidemiology: Drug Utilisation, Drug Safety, and RMM Effectiveness at the 4th Oxford Summer School.
This course is now fully booked, but if you'd like to see this year's course program, click here

The course will 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. 

AUDIENCE
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. 

LEARNING GOALS

  1. 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
  2. EPIDEMIOLOGICAL STUDY DESIGN/S: Be able to discuss common and advanced study designs and their implementation using real-world data.
  3. 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.
  4. PREDICTION MODELLING: Learn basic concepts on the design and evaluation of prognostic/prediction models developed using real-world data.
  5. “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
  6. 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.

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