Real-world data (RWD) are data relating to patient health status and/or the delivery of health care collected from a variety of sources, while Real-world evidence (RWE) is the evidence produced from the analysis of this RWD. RWD typically consists of routinely collected data such as electronic medical records, medical claims data and disease and patient registries but can also include data such as patient reported outcomes (PROs) and data collected from wearable devices.
RWD is regularly analysed in post-marketing and safety surveillance studies to offer valuable insights into how treatments perform and are used in routine clinical practice, outside the confines of a randomised clinical trial. It is also increasingly being used to support the evaluation of treatments and devices as part of the approval process. The FDA, EMA and MHRA have all published guidance on the use of RWD and RWE in supporting regulatory decisions, though their definitions do differ slightly, the basic premise is the same, they are open to the use of RWD to support submissions with the caveat that the data used is relevant and reliable.
The use of RWD offers an opportunity to explore a variety of elements such as treatment adherence and use of concomitant medications in practice, to populations and sub-groups in which the efficacy is better or worse, to occurrence of rare and long-term outcomes and the identification of groups for which there are safety concerns.
While there are advantages of using real-world data to support drug development there are also many challenges that need to be considered both in the design and analysis of RWE studies.
Data collected in real-world settings is rarely perfect, with significantly higher levels of missing data and often quality issues that cannot be rectified in the same way as may be possible within a clinical trial setting. Therefore care needs to be taken to ensure that all potential data issues are accounted for in a way that demonstrates that the data used are reliable. In retrospective studies this will mainly be focused on implementing rules to ensure the data used for any analysis is correct and consistent. It can also be of interest to include sensitivity analyses to examine the outcome under different sets of assumptions.
In prospective studies it is important to design the study and data collection tools so as to reduce the potential for issues with data quality and completeness. Consideration needs to be given to the key information that needs to be collected in the study to evaluate the outcome, how this will be collected and recorded and what can be done to reduce the possibility of errors in data collection.
Studies that aim to use an external control arm derived from RWD sources needs to carefully consider how they will select their controls to minimise any potential bias and how they will ensure that the information that will be collected in the treatment arm of the clinical trial is available for the control arm also.
The use of RWD has the potential to reduce costs and time associated with drug development and can help to answer questions that are difficult to examine within a clinical trial setting but the value of their use will depend heavily on how they are used. If you’d like advice on how to design and analyse your real-world data studies get in touch with our team today.