What is Real-World Evidence?

What is Real-World Evidence?

November 24, 2022 By

Real-world data (RWD) and real-world evidence (RWE) are becoming an integral part of health care decisions such as in the approving process, in clinical practice, in generating innovative, and new treatment approaches, etc. The Food and Drug Administration (FDA) uses RWD and RWE to make regulatory decisions and to monitor post-market safety and adverse events. The health care community uses these data to support coverage decisions and to develop guidelines and decision support tools. Medical product developers also use it to support clinical trial designs and observational studies.

The pass of the 21st Century Cures Act in 2016 has placed additional focus on the use of these types of data in supporting regulatory decision-making. RWE is defined by Congress as the data regarding the usage, or the potential benefits or risks of a drug derived from sources other than traditional clinical trials. Now, with the use of computers, mobile devices, wearables, and other biosensors, huge amounts of health-related data can be gathered and stored in real-time. This data holds tremendous potential, such as:

  • permitting a better design and conduct of clinical trials and studies;
  • helping to answer questions that were previously infeasible;
  • contributing to developing more sophisticated yet new analytical capabilities, and;
  • enabling the utilization and application of the new analytical results to medical product development and approval.

So, what is RWD and where does it come from? Real-world data is essentially the data relating to a patient’s health status and/or the delivery of health care routinely collected from a variety of sources. It can come from:

  • electronic health records;
  • claims and billing activities;
  • product and disease registries;
  • patient-generated data including in home-use settings, and;
  • data gathered from other mobile devices, wearables, or other sources.

Real-world evidence, on the other hand, is tailored to targeting clinical evidence for the usage and potential benefits or risks of a medical product derived from the analysis of RWD. Therefore, RWE usually comes from different study designs and/or analyses. As RWE becomes plays an increasingly important role in regulatory decision making, the data collection process should be considered to ensure a high level of data quality and the process should be implemented in high quality and regulatory grade research studies.

All research protocols need to be well-designed and be incorporated with a comprehensive assessment of good research principles. Both prospective and retrospective designs should consider the following elements or provide justification why it may not be applicable:

  • Research question: clearly defined and well-developed
  • Milestones: timelines for key milestones in the data collection process
  • Research Design: clearly articulated, including the type of data collected and specifications of measures of occurrence, association and reporting of adverse events
  • Source and Study Populations: well-defined and should provide an appropriate level of information
  • Exposure Definition and Measurement: The exposure of the subject in the study to the therapy of interest (i.e. dose, dosage form and dosage regimen) should be well-defined and measured, and the validity of any measures should be addressed
  • Outcome Definition and Measurement: All primary and secondary outcomes, including dose delivery and intensity, should be clearly defined and measured, and the protocol should address the validity of any measures
  • Bias: describe potential sources of confounding, or other sources of bias (e.g. selection bias, information bias)
  • Effect Measure Modification: address the collection of items that could modify the effect and how they were included in the analysis
  • Data Source and Collection: describe the data sources utilized and the appropriateness of these data to capture all relevant exposures, outcomes and covariates of interest
  • Statistical Analysis Plan: well-described description and justification for the chosen approach for statistical analyses
  • Data Management and Quality Control: describe data storage, management and statistical software
  • Feasibility and Limitations: discuss limitations on the ability to draw conclusions from the data
  • Ethical and Data Protection Issues: Efforts to protect study participants should be included
  • Amendments and Deviations: amendments or deviations to the protocol should be dated, well-described, and justified
  • Plan for Communication of Study Results: The plans for both communicating study results and disseminating results externally should be discussed

If you have questions about the new regulations and how to become compliant, contact us today.