This course introduces you to how Real World Data/Evidence can be used for pharmaceutical research and development and how it complements the evidence package for healthcare decision-making. If you are interested in applying data science to pharmaceutical research using data collected as part of routine clinical practice, this course is for you. The course will help you describe what it means to be a Real World Data Scientist in the pharmaceutical industry. You will discover the particularities of the data sources and learn how to generate high quality evidence and how that evidence is used by the stakeholders for decision making purposes. To be successful in this course, you should have a background in data analytics, statistics, or other technical fields. No experience in the pharmaceutical industry is expected. We thank Hannah Furby and Matt Secrest for her inspirational material.
Study designs
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Skills You'll Learn
Stakeholder Identification, Epidemiology, Data Science
From the lesson
Study designs and methods to control for bias
In this module we explore study designs for observational data and methods to control for bias (systematic errors). We also mention concrete examples used in pharmaceutical research.
Taught By
Adriana Reyes
Otto Fajardo