Randomized controlled trials provide critically needed evidence of drugs’ safety and efficacy, yet their results are not always generalizable to diverse populations in less idealized settings. Thus, we need data beyond controlled trials. Further, there is growing inclination toward precision medicine for individualizing therapy to the patient, and there is a shift toward specialty medications with more narrow indications and smaller patient populations, creating a need for creative and nimble means for data collection and analysis. These emerging trends suggest that the drug development landscape is inherently evolving to a place where use of real-world data will not only grow but will become pivotal as long as some inherent challenges can be satisfactorily addressed.
Novel therapies bring hope to a number of patients around the globe whose clinical needs are unmet by existing therapies. The drug development process, however, can be long, expensive, and sometimes unsuccessful, with the cost of bringing a new drug to market often exceeding $2.5 billion and approximately 90% of drugs failing during the clinical trial process.1 The prominence of real-world data (RWD) generated from real-world health care practice settings outside of traditional clinical trials is growing throughout drug development and commercialization, with the potential to provide real value in time savings, efficiency, and costs.
Randomized controlled trials (RCTs) provide critically needed evidence on efficacy and safety in well-controlled environments among specific patient populations, yet their results are not always generalizable to more diverse populations and to those in less idealized settings. As a result, it is not possible to know everything we need to know about how well a drug works using only controlled trials. The pharmaceutical industry is under enormous pressure to continue to innovate without increasing drug prices. Further, there is growing inclination toward precision medicine for individualizing therapy to the patient, and there is a shift toward specialty medications with more narrow indications and smaller patient populations, creating a need for creative and nimble means for data collection and analysis. These emerging trends suggest that the drug development landscape is inherently evolving to a place where use of RWD will not only grow but will become pivotal as long as some inherent challenges can be satisfactorily addressed.
RWD and its Evolution
Any patient data generated outside of the traditional clinical trial setting can be regarded as RWD. This information has long been available in patient encounters in health care providers’ offices, from hospitals through patient charts and medical and pharmacy billing claims, and more recently its availability has skyrocketed through the widespread adoption of electronic health records, establishment of product- and disease-specific registries, collection of environmental, consumer and social media data, and patient-experience data extracted through wearable devices (Figure 1).
The increased availability of RWD has been fueled by the growing digitalization in the health care industry over the last two decades, coupled with increased computational capacity and the refinement of advanced analytic methods. These trends have unleashed new possibilities for RWD, which have the potential to lead to transformation in decision-making across the health care industry, encompassing regulatory and health technology assessment decisions, development of clinical guidelines by physicians, formulary decisions by pharmacy managers and others, and coverage decisions by payers.
Though RWD have long been leveraged in health insurer coverage and reimbursement decisions in the United States, the Food and Drug Administration (FDA) has historically used such data almost exclusively for postmarketing safety surveillance endeavors. More recently, the FDA has signaled an increased role for RWD in the drug development process through the 21st Century Cures Act, and the “Framework for FDA’s Real-World Evidence Program.”2 In May 2019, the FDA issued draft guidance delineating its existing thinking on RWD in regulatory submissions, suggesting openness toward use of RWD in support of effectiveness or safety for new product approvals, label changes for approved drugs, and postmarketing requirements.3 This cautious nod from the FDA elevates the potential for use of RWD in drug development.
Challenges With Use of RWD
Widespread adoption and expanded use of RWD in health care depends on overcoming some key challenges and limitations associated with its use. Historically (though this may be changing), RWD have not been collected for research purposes, making them infinitely messier to work with than data from controlled studies and thus prone to some well-known issues, such as missing and incomplete information, incorrect coding, and censoring when a patient moves or leaves a health care practice. Furthermore, the collection of data across health care provider practices and in health care systems is not standardized, yet these disparate RWD sources are often combined to garner insights, which can be clouded by the lack of standardized collection. The process of randomization in RCTs distributes both known and unknown confounding factors equally between exposure and intervention groups, thus reducing bias. In contrast, statistical techniques used in observational research can only control for known factors, leaving room for bias. Finally, in addition to suffering from messy, incomplete, and noisy information, RWD sources, such as patient charts or records from health care encounters or patient-reported experiences, can unwittingly contain patient-identifying information.
As a result of these challenges, before widespread acceptance and use, it is critically important that RWD undergo proper extraction and curation to ensure that they maintain privacy and confidentiality, and that they are accurate, consistent, and fit for use. Massive storage and computing power coupled with advances in algorithmic processing and artificial intelligence techniques like machine learning are enabling data scientists to both harness the power of RWD and address the key challenges to widespread use. These advances are fostering novel ways to clean and codify data as well as explore and integrate unstructured and structured data, paving the way for expanded acceptance and use.
Future of RWD
The future of RWD is bright, as openness to use and sophisticated technologies continue to evolve in ways that will enable us to address inherent limitations and challenges. In addition to the historical tried-and-true uses in demonstrating disease burden, cost of illness, treatment journey, and comparative effectiveness, RWD are now routinely leveraged to optimize the RCT process through protocol feasibility exercises and efficient site identification and patient recruitment. These data highlight the implications of various inclusion/exclusion criteria and identify sites in locations where pockets of eligible patients live. Going a step further, RWD from electronic health records and other patient-reported sources will also be useful for adaptive design trials in which prospectively planned modifications to one or more design aspects (eg, dropping or adding dose, increasing trial size or duration, enhancing the study population) are implemented based on accumulating data from participants in the trial.4 RWD are beginning to inform the modeling and simulation work needed to design, plan, and implement these trials, enabling investigators to understand factors such as patient recruitment rates, time to effect, and effect size.5
There is soaring industry attention on rare disease and orphan drugs with worldwide sales expected to grow at a cumulative annual growth rate of 12.3% from 2019 to 2024—about double the expected rate for the nonorphan drug market.6 These drugs are expected to capture one-fifth of worldwide prescription sales to reach $242 billion by 2024.6 RCTs are extremely challenging, if not impossible, to conduct in rare disease as well as oncology given the difficulty of finding and enrolling patients. In situations such as these, where a control arm may not be feasible or ethical, RWD from historical standard-of-care cohorts can be used for comparison in certain studies.
Related to this, the growing trend toward individualization of care requires a deep understanding of specific patients and their experience to evaluate treatment and effects in an individual patient. Biotech companies are increasingly using RWD to evaluate specific biological profiles for these purposes. Furthermore, leveraging the range of increasingly available molecular and clinical data, prediction modeling using powerful machine learning techniques is beginning to be used to identify molecules that have a higher likelihood of being developed into drugs that may be more likely to succeed.7
A report released by three former FDA commissioners in August 2019 proposes several ways to expand the use of RWD and real-world evidence, noting, “As medicine becomes more personalized and drugs become targeted for smaller populations, traditional, large-scale, RCTs will become increasingly less feasible. Additional approaches will be needed to assure safety and efficacy and protect the public’s health.”8 This is bearing out, as at least a dozen product approvals to date have used RWD on historical controls in demonstrating efficacy.9
Use of RWD has evolved to serve as an invaluable companion to RCTs as well as an independent pillar in drug development and product value demonstration. The continued emphasis that regulators and policymakers place on cost containment and individualized medicine, along with the cautious optimism shown by the FDA, suggest that the value of RWD will continue to grow, especially as we are able to maintain patient confidentiality and improve data quality and analysis.
1. DiMasi JA, Grabowski HG, Hansen RW. Innovation in the pharmaceutical industry: new estimates of R&D costs. J Health Econ. 2016;47:20-33. doi:10.1016/j.jhealeco.2016.01.012
2. Food and Drug Administration. Framework for FDA’s Real-World Evidence Program. December 2018. Accessed April 27, 2020. https://www.fda.gov/media/120060/download
3. Food and Drug Administration. Submitting documents using real-world data and real-world evidence to FDA for drugs and biologics: guidance for industry. May 2019. Accessed April 27, 2020. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/submitting-documents-using-real-world-data-and-real-world-evidence-fda-drugs-and-biologics-guidance
4. Food and Drug Administration. Adaptive design clinical trials for drugs and biologics: guidance for industry. November 2019. Accessed April 27, 2020. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/adaptive-design-clinical-trials-drugs-and-biologics-guidance-industry
5. AMGEN® Science. A strategy for making clinical trials more successful. Accessed April 27, 2020. https://www.amgenscience.com/features/a-strategy-for-making-clinical-trials-more-successful/
6. EvaluatePharma®. Orphan Drug Report 2019, 6th ed. April 2019. Accessed April 27, 2020. https://bit.ly/2xUM8SV
7. McKinsey & Company. How big data can revolutionize pharmaceutical R&D. April 2013. Accessed April 27, 2020. https://www.mckinsey.com/industries/pharmaceuticals-and-medical-products/our-insights/how-big-data-can-revolutionize-pharmaceutical-r-and-d
8. Bipartisan Policy Center. Expanding the use of real-world evidence in regulatory and value-based payment decision-making for drugs and biologics. August 2019. Accessed April 27, 2020. https://www.fdanews.com/ext/resources/files/2019/08-22-19-RealWorldEvidenceReport.pdf?1566498099
9. Silverman B. A baker’s dozen of US FDA efficacy approvals using real world evidence. August 7, 2018. Accessed April 27, 2020. https://pink.pharmaintelligence.informa.com/PS123648/A-Bakers-DozenOf-US-FDA-Efficacy-Approvals-Using-Real-World-Evidence