Year: 2024
Type: preprint
Abstract: Real-world evidence (RWE), derived from analysis of RWD, is increasingly used to guide decisions in drug development, regulatory oversight, and clinical decision-making. Evaluating the fitness-for-purpose of RWD sources is one key component to generating transparent RWE. Here, we demonstrate tools that fill two gaps in the data grading literature. These are the need for quantitative data grading scores, and the need for scoring mechanisms that can be run in automated fashion and at scale. The Real World Data Score (RWDS) rates the overall quality and completeness of a RWD source across a range of customizable metrics. The Fitness Quotient (FitQ) grades how well a specific data source fits a specific RWE query. In concert, these tools give producers and consumers of RWE evidence to assess the quality of the underlying RWD.
Source: medRxiv
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W4391529239