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In today’s value-driven healthcare system, real-world studies are increasingly becoming mandated by regulators as a condition of approval for new medicines.
Why taking a right-sized approach to real-world studies will help keep sites and patients around for the duration.
Years ago, long-term real-world evidence collection was viewed as an academic exercise to see how a product was performing in the real patient population, perhaps used for publication purposes. But in the current value-driven healthcare system, real-world studies are increasingly becoming mandated by regulators as condition of approval for new medicines. In May 2016, then-FDA Commissioner Robert Califf told participants at the Food and Drug Law Institute's annual conference that leveraging real-world evidence to inform FDA decision-making is the “top programmatic priority” for his tenure.1
The European Medicines Agency (EMA) has made similar statements. Real-world evidence collection is already in routine use in the European Union, and the EMA recently indicated that it is interested in “increasing the use of real-world evidence to support lifecycle product development and monitoring, and to improve decision-making for regulation and HTA (health technology assessment).”2
A real-world study involves research using data collected in standard-of-care settings. A real-world study can be observational, where all treatment decisions are made by the physician according to their normal practice, or it can be interventional, where an initial treatment decision is dictated by the study protocol. Generally, all follow-up care and diagnostic testing after the initial treatment is consistent with standard of care, meaning no mandated visits, tests or interventions other than those indicated by the physician’s usual practice. Real-world studies that collect data prospectively might be carried out in any care setting; however, these sites would likely differ from those that would carry out a traditional early phase clinical trial, as the goal is to capture information in a naturalistic fashion-a task best done at sites where typical patients are treated. A real-world study can also use secondary data, which is data collected for other purposes that can be re-used for research with appropriate privacy protection consistent with all applicable regulations.
The growing interest from regulatory authorities underscores the value that observational studies bring to our understanding of how a drug is used post-marketing, allowing biopharma companies to evaluate clinical results in a real-world setting and validate the safety and efficacy of their products. It can also help uncover prescription trends, identify safety signals in vulnerable patients and determine whether prescribing physicians and patients are adhering to expected criteria. It can allow the product to be used for off-label indications as well. All of this data is of keen interest to regulators, and can help biopharma companies identify new opportunities for an approved product and related research.
However, the increasing interest in real-world data is putting pressure on drug manufacturers to integrate post-marketing studies into the product development lifecycle, and that can be a challenge. Many of these companies lack the experience to successfully deliver real-world studies, and mistakenly assume they can use the same technology, design process, and data management practices from their clinical research environment for real-world studies. This mistake can lead to high rates of attrition among sites and patients, and result in incomplete or unusable data from their efforts.
Keep it simple
It can be tempting for biopharma companies to want to use these studies to collect lots of data related to many different variables, and to establish high expectations for data capture and cleaning. However, over-burdening sites and patients will quickly drive them away; real-world studies require a much lighter touch to be successful.
Unlike rigorously controlled clinical research trials, much of the data collected in real-world studies comes from loosely scheduled doctor’s visits and/or a review of patients’ health records-and the studies can last for many years. The physicians and staff involved in these studies generally do not have the time or training, nor are they being offered the financial incentive to gather and clean additional data to meet heavy protocol requirements. As real-world studies, this data collection is being performed by doctors whose main priority is patient care, not research, and they don’t have the time to learn how to operate complex electronic data capture (EDC) platforms or complete electronic case report forms (eCRF) that demand precise responses to many queries. Because patient visits are infrequent and not mandated, the site staff may batch-enter data after multiple patients are enrolled in the study or complete physician visits at their sites. In addition, most data is being collected only if it is a part of routine practice; so not all desired data may be available for all, or most, patients.
Biopharma companies need to acknowledge these differences in the design and implementation of their real-world studies. This begins with setting realistic and relevant data-collection goals, and choosing EDC and eCRF design that are fit for the purpose. An ideal EDC platform will be user-friendly with easy-to-understand fields, and simple edit check tools that can be learned by staff without extensive training and support. The platform should also offer real-time feedback features so queries are presented at the time of data entry, as the site may not return to the EDC very often.
Once a platform is chosen, biopharma companies should define appropriate criteria and validation rules that take into account the minimal time and expertise users will have to meet study objectives, and the long-term nature of the studies. Establishing a simple and succinct protocol and platform is the best way to prevent attrition of sites and patients, and to ensure that meaningful results can be confidently extrapolated from the data.
To get the most value from these studies, Biopharma organizations should start planning for them early in the drug development lifecycle, so they can think about what data they will want to collect, and how these studies will be supported by the data gathered during clinical research and through already existing databases. By starting early, and working with real-world study design experts, biopharma companies can be confident that they are choosing the right technology, design and study protocols to capture the information that regulators will expect to see.
In an era where such studies are becoming a requirement of bringing new drugs to market, it is increasingly important that biopharma organizations make the best choices for these studies from the outset of their research, and work with partners who can help them make critical decisions about their technology, protocols, and data management strategies so that they can confidently validate the safety and efficacy of their products in real-world settings.
Zia Haque is Senior Director, Data Management, Real-World & Late Phase Research, QuintilesIMS; Charles-Louis Wang is Manager, Clinical Data Management, QuintilesIMS