Data Science with Real World Data in Pharma

Overview

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.

Syllabus

Real world evidence: what it is and how it fits into the drug development

In this module we briefly introduce the phases in drug development and the evidence generation process to bring treatments to patients. We then exemplify how real-world data/evidence fits into the drug development.

Understanding the Limitations of Real-World Data

In this module, we explore the limitations of real-world data. We discuss several sources of real-world data and explain their strengths and weaknesses. We then create clearer definitions of the types of bias that can be encountered when exploring real-world data.

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.

Designing and conducting your own Real World Data study

In this module we will design and conduct our own study using synthetic data to explore the concepts learned in modules 1-3.

Real World Evidence: what for? From two different stakeholder perspectives

In this module we consider the point of view of two critical stakeholders: regulators and payers. We see their position about real world data/evidence and its acceptance. We also explore specific use cases of how real world evidence has been used in practice.