In continuation from our previous blog on what real-world data (RWD) and real-world evidence (RWE) are and where sources are from, in this post, we will be illustrating the value of RWD and RWE, as well as discussing examples of their application.
Why Use RWD and RWE
The gold-standard randomized clinical trial provides a highly narrow snapshot of the efficacy and value of a medicine or treatment. Consequently, RWE has long been used by regulatory agencies for post-marketing surveillance of how a therapeutic is working in the real world.
RWD and RWE are increasingly being used in many aspects of the drug development and post-marketing process. Some of the reasons for this growth include:
- The digital age has enabled the collection of vast amounts of health-related data.
- In parallel, the development of analytical capabilities (e.g. machine learning) is facilitating rapid and meaningful mining of data.
- Such data are driving improvements in the design and conduct of clinical trials to answer questions that were previously unaddressed, while also helping to shorten trial times.
- Beyond clinical trials, RWE is helping to evaluate the value of a medicine and support health care providers’ decisions.
As noted above, RWD and RWE have established roles in assessing the efficacy of a therapeutic in the real world, including understanding serious and non-serious Adverse Events. By combining different sources of health claims data, it is possible to analyze Adverse Events within the scope of drug intake and doctors’ visits.
As an application of this, the study’s authors developed a software tool (named JADE) to mine claims data from Austrian health insurance companies. The aim of JADE is to enable non-specialists to analyze such RWD using a simple tool and pre-defined stat methods to gain valuable information on how drug interactions are related to possible adverse drug events. Results can then be used during the planning of clinical trials and the formulation of hypotheses.
Regarding effectiveness, pragmatic randomized controlled trials, which mimic typical clinical practice, provide significant sources of RWD. Physicians and policy-makers use such data to evaluate the value (e.g. cost-benefit and pharmacoeconomics) of available medicines in real-world settings. However, there are challenges to the use of RWE in comparative effectiveness research and relative effectiveness assessment. Research projects are ongoing to develop best practices for RWE analyses, including the use of advanced analytics and artificial intelligence.
Indications Using RWD/RWE and Limitations
Cardiovascular disease, cancer, and diabetes are among the leading causes of death in industrialized nations. Drug development efforts are consequently heavily focused in these areas. RWD and RWE are therefore playing increasingly important roles in decision-making processes.
For example, a systematic review reported that between 2002 and 2017 there was an upward trend in the use of RWD from heart failure registries in OECD (Organisation for Economic Co-operation and Development) countries. Most of the studies were published between 2013 and 2016, and, over time, the studies showed increasingly diverse outcomes and advanced statistical methods to improve their validity and reliability.
To truly leverage the power of RWD, the future may lie in big data analytics. In a perspective article, the authors called for the development of a federated RWD infrastructure that will enable centrally conducted big data analyses to be carried out on decentralized data. Their experience in attempting to analyze data from a national cancer registry highlighted several data gaps. For example, many patients from the registry had censored survival time, making the study results highly uncertain, or lacked long-term follow-up information, such as tumor response. Consequently, appropriate RWD research is limited.
In a survey of current and planned uses of RWD and RWE by pharmaceutical, biotechnology, and contract research organizations, 60% of companies reported that the greatest barrier to overcome is the availability of RWD and RWE data. Other challenges included the lack of external stakeholder trust in RWE, and the cost and effort of acquiring and integrating diverse types of data (e.g. electronic clinical outcomes, patient-reported outcomes, and mobile health).
It is clear that the adoption and application of RWD and RWE are increasing in all areas of the drug development process. However, numerous challenges are associated with this development. Various guidelines are now available to harness the value of RWD and RWE (see Fig. 1). Meanwhile, to maximize the value of RWD and RWE in decision-making, let XClinical help you manage your data collection for clinical trials and beyond.
Image Credit: sdecoret / Shutterstock.com