This course will provide an overview of the structures of commonly encountered retrospective data sources with a focus on large administrative data, as well as highlight design and measurement issues investigators face when developing a protocol using retrospective observational data. Retrospective studies require strong principles of epidemiologic study design and complex analytical methods to adjust for bias and confounding. PREREQUISITE: Students are expected to be familiar with relevant concepts and methodologies for analyzing real-world data, but this course does not require specific programming skills. Participants should come with a laptop with Google ChromeTM installed. After the study has been executed, we will discuss how these results could be communicated to decision makers. Participants will be guided step-by-step in using a software platform that will enable them to work within a longitudinal US insurance claims database with anonymized patients. The second half of the course is an active workshop where participants will use principles from the first half of the course to execute a decision-grade RWE study. The first section of the course focuses on what makes RWE “decision-grade.” We will review the most recent RWE frameworks and guidelines set by regulatory agencies and professional organizations, and we will examine case studies in which these guidelines were used in regulatory and HTA approval. In this course, participants will be guided through a hands-on analysis of real-world data to develop decision-grade real-world evidence (RWE) that could be used to support an indication expansion.
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