5 Facts About Real-World Data

Real-world data (RWD) are health-related information reported and collected in real-world medical settings. These data are collected, de-identified and stored in a variety of sources to be later analyzed alongside similar data. RWD may provide new insights about a medication beyond what is available from a clinical trial alone.¹

RWD analyses generate insights about a medicine’s effectiveness, safety and associated costs. These data may help explore additional research questions, complement clinical trial findings and fill knowledge gaps related to how a medicine is used in real-world medical settings. RWD may also be referenced in future investigational studies to increase efficiency and reduce costs.¹

Additionally, RWD analyses are explored by regulatory organizations to monitor for and act on any unforeseen risks with medicines following regulatory approval. These data are also used to evaluate potential un-studied outcomes that may lead to improved patient care.²

However, not all RWD are collected and maintained in a way that provides sufficient reliability. The source and type of RWD may limit how results and endpoints can be applied to the overall patient population. RWD should not be used as stand-alone evidence for healthcare decision making. Please see additional RWD limitations below.

Real-world data (RWD) are only as good as the data source and methodology used to analyze that data. When determining the right investigative approach for a RWD study, researchers start with the key questions they are looking to answer; typically, this is either tied to patient outcomes or healthcare costs. Then, based on the information necessary to find a credible answer, appropriate data sources with the correct details, and without important information gaps that may impact findings, are selected. As a standard practice, RWD should be completely free of patient-specific identifying information and balanced across treatment arms to allow for drawing reliable conclusions.

Well-conducted RWD studies should have the objectives, study design, methodology and various endpoints described in a formal protocol, with the data analysis plan described in advance, if possible. Robust RWD analyses typically use advanced statistical methods designed to address potential issues, such as confounding factors, which are circumstances that affect the conclusion but are not understood at the time of drawing the conclusion. Failure to use appropriate methods can lead to RWD studies that generate incorrect or unreliable conclusions. For example, data with very low event rates can allow for conclusions that are not statistically significant and clinically confounding to be drawn.