LAUSR

With the growing use of large real-world clinical datasets comes increased scrutiny about the quality of real-world data (RWD). The question of whether the data are good enough for decision-making is often raised. It is important to understand that quality cannot be assessed by looking at data in isolation: Any evaluation of RWD quality underpinning real-world evidence (RWE) must consider whether that data source has the information to answer a given research question. A way forward in the quality conundrum suggested by Girman et al is to establish a framework for evaluating data appropriateness, also known as fitness for purpose, meaning the degree to which the chosen data source aligns with the ability to accurately and reliably address the research question being posed. Here, we offer a simple framework for characterizing the attributes of any RWD source and key aspects of research questions to facilitate the optimal matching of research needs and data sources to achieve meaningful and reliable results.