Real-world evidence (RWE) is derived from real-world data (RWD) sources, such as electronic health records (EHRs), claims data, data from product/disease registries, pharmacy data, social media, and pragmatic trials. RWE provides essential insights into the clinical experience, thereby complementing the information obtained from traditional randomized controlled trials (RCTs).(1)

RWE has been extensively used for post-marketing safety observations. Realizing the increasing importance and utility of RWE in the drug approval cycle, the USA passed the 21st Century Cures Act in 2016 which allowed pharma companies to utilize RWE to support drug approvals and update label claims. Subsequently, in 2018, the USFDA rolled out a draft guidance for submission of RWD for assessment of investigational new drugs (INDs), and new drug applications (NDAs).(2, 3) This recommendation represents a way to optimize the use of RWD and RWE and it will help standardize the applications with RWE for market approvals.(1)

Traditional RCTs are typically aimed at measuring the safety and efficacy of interventions, compared with standard treatment (or placebos), usually in double-blinded settings, and recruit a closely targeted population. This is done to minimize bias and confounding factors.(4) Traditional RCT findings are still the gold standard for regulatory approval of a medicine, for the expansion of a product label, and to support treatment guidelines.(4) However, these trials are expensive, and the patients enrolled in controlled settings often do not represent those in everyday clinical practice.(5) Thus, clinical research professionals are always looking at methods to optimize clinical trials.

With the increasing interest in RWD, a new method has emerged to incorporate RWE to optimize RCTs. For certain RCTs, previously collected RWD is used to prepare ‘synthetic’ control arms, which replace conventional ‘control’ group in RCTs. For instance, the USFDA approved Merck’s Bavencio (avelumab) in 2017 for metastatic Merkel cell carcinoma, which was based on a single-arm trial and a synthetic comparator arm that used historical control of matched patients. Roche expanded access to the treatment for non-small-cell lung cancer [Alecensa (alectinib)], making it available in 20 European countries, by using synthetic control data.(6)

The benefits of synthetic control arms include lower study costs, reduced delays and faster access to drugs. The use of RWE for creating synthetic control arms is still in its early phase. New technologies are being explored to assist in extracting relevant information, ensuring data quality, and ultimately for bringing out the full potential of this approach to the forefront, by never completely replacing RCTs, but by leveraging RWE. Specific care practices are encouraged to achieve advanced accuracy. Hence, all the stakeholders must work towards defining standards for ensuring quality.(6)

To facilitate integrating RWE with the RCTs, some stakeholders have laid down recommendations for preparing RWE suitable for regulatory decisions. For instance, the white paper launched in 2017 by Duke-Margolis Center for Health Policy, with support from the USFDA, talks about the regulatory use of RWE. This white paper was released after an open consultation with stakeholders, including academics, patients, and the industry.(7) Moreover, several areas have been identified for practical improvement of RWE that would fit regulatory decision-making. These include matching RWD sources with appropriate study designs and data collection, enhancing methods to address the research question, and transparent collaborations while sharing datasets. Professional societies, such as the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) and the International Society for Pharmacoepidemiology (ISPE) have also jointly published recommendations for good procedural practices for RWE aimed at building confidence about and expanding the current use of RWE in health care decision-making. (8, 9)

Study designs using RWD is a much-needed yet logical step in advancing the system of regulatory approvals. However, it will require collaborations among regulators, pharmaceutical companies, and RWE experts to optimize the scientific potential of RWD through innovative study designs which would generate solid and dependable RWE.(9)

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References

1. Naidoo, P, et al. Real-world evidence and product development: Opportunities, challenges and risk mitigation. Wien Klin Wochenschr 2011; 133:840-846.
2. US Food and Drug Administration. Framework for FDA’s real world evidence program. 2018. Available at: https://www.fda.gov/media/120060
3. S. Department of Health and Human Services, Food and Drug Administration. Submitting Documents Using Real-World Data and Real-World Evidence to FDA for Drugs and Biologics Guidance for Industry. 2019. Available at: https://www.fda.gov/regulatory-information/search-fdaguidance-documents/submitting-documents-using-real-world-data-and-real-world-evidence-fda-drugs-and-biologics-guidance.
4. Katkade VB, et al. Real world data: an opportunity to supplement existing evidence for the use of long-established medicines in health care decision making. J Multidiscip Healthc 2018; 11:295-304.
5. Xia AD, et al. RWE Framework: An Interactive Visual Tool to Support a Real-World Evidence Study Design. Drugs – real world outcomes 2019; 6(4):193-203.
6. CHR Sparks. Synthetic control arms – use of RWE in clinical trials. Available at: https://www.camhcr.com/blog/synthetic-control-arms-use-of-rwe-in-clinical-trials
7. Duke-Margolis Center for Health Policy. A framework for regulatory use of real-world evidence. 2017. Available at: https://healthpolicy.duke.edu/sites/default/files/atoms/files/rwe_white_paper_2017.09.06.pdf.
8. Berger ML, et al. Good practices for real-world data studies of treatment and/or comparative effectiveness: recommendations from the joint ISPOR-ISPE Special Task Force on Real-World Evidence in Health Care Decision Making. Value in Health 2017; 20(8):1003-1008.
9. Andre EB, et al. Trial designs using real-world data: The changing landscape of the regulatory approval process. Pharmacoepidemiol Drug Saf 2020; 29:1201-1212.

In a data-driven world, where everything can be digitalized, drug regulators and sponsors are increasingly looking beyond the confines of clinical trials. The use of real-world data (RWD) is becoming common in the clinical development process of a drug. RWD enhances the drug approval process, helps sponsors understand how a particular intervention really performs, and assists in clinical trial planning.

RWD is the data that is generated around patients’ healthcare status or through delivery of healthcare to the patients, in a real-life setting. (1) Real world evidence (RWE) is the evidence that is generated by the analysis of RWD.

Since RWD dramatically reduces the drug approval time, regulatory agencies have emphasized its incorporation into Randomized Controlled Trials (RCTs). This advancement was made possible in the USA by the USFDA’s 21^st Century Cures Act, (2) which incorporates the perspectives of patients in the development of a drug, biologics or a device. This act enhances the ability to modernize the clinical trial design and assessing the clinical outcomes in the real world, accelerating the overall drug development process

Though the data from RCTs are considered as the gold standard for drug approval process, drug agencies view RWD as a new avenue to inform the regulators of drug development and expedite the approval process. RWD provides insights into the clinical outcomes of diverse groups of patients in response to various treatments and interventions in real world. This is in stark contrast to the highly regulated or manicured atmosphere in which clinical trials are conducted – where a new intervention is tested on homogeneous group of patients. Since the clinical trial environment cannot be replicated in a routine care setting, the treatment outcomes may vary from what was observed during a clinical trial and how the intervention performs in real world scenario.

With the advent of technology and digitalization in the past decade, it has become easier to collect and store the data into a digital database. The increasing use of smartphones, computers, biosensors and wearable devices into RCTs provides real-time, rigorous and robust data. RWD can be collected prospectively as well as retrospectively from pragmatic trials or from observational studies. Additional sources of gathering real-world data are electronic health records (EHRs), insurance billing and claims, patient registries, patient reported outcomes (PROs), and biometric monitoring devices.

FDA’s Historical Uses of RWE

Using RWD and RWE in regulatory decision-making for drugs and devices has been on the agenda of regulatory agencies for a few years now. There has been a surge in approvals of more drugs with the help of RWE by the USFDA. From 1995 to 1997, FDA’s approvals based on RWE were 19.4 percent, which jumped to 47.2 percent between 2015 and 2017. (3)

A few notable examples where the USFDA has used RWE for regulatory purposes are as follows:

• The first instance of USFDA using RWE for regulatory approval happened in June 2017, when the USFDA approved a new indication for a medical device. The device, which is called a ‘Transcatheter Aortic Valve Replacement (TAVR)’ device, was first approved in 2011 on the basis of RCTs. Subsequent to its approval, the manufacturer established a product registry, which had around 1,00,000 TAVR records. Among these, there were around 600 instances of off-label use of TAVR for a ‘valve-in-valve’ procedure; based on this registry data, the USFDA approved TAVR for the new procedure, without requiring any RCTs, saving both time and money. (4)
• On May 24, 2019 the USFDA approved Zolgensma (Onasemnogene abeparvovec) for a specific type of Spinal Muscular Atrophy (SMA) in children less than two years of age. (3)
• On August 14, 2019, the USFDA approved pretomanid tablets as part of a combination regimen with bedaquiline and linezolid for treating a specific type of highly treatment-resistant pulmonary tuberculosis. (3)

Incorporating RWD into Clinical Trials: Smarter Clinical Trials

RWD and clinical trials is not an “either-or” situation. In fact, researchers can combine RWD and RCTs while investigating a medicinal product. RWD can support many activities during the clinical trial phases of drug development. For example, RWD can be used during the initial study design and planning phases to validate the study protocol feasibility. The use of RWD speeds up patients’ recruitment and numbers, compresses startup time lines and reduces the cost of evidence generation.

Researchers have developed “mosaic methodologies” to blend the components of traditional RCTs into newly found RWE. These methodologies are as follows: (5)

• Extension: This approach begins with an RCT and patients consent to link their data from sources like EHRs. Thus, initially an RCT is conducted and follow-up data is linked later.
• Augmentation: Here the RWE is used as a control data for a single arm study.
• Enrichment: It combines primary data from patients and physicians with secondary data from EHRs and other sources. e.g., registries
• Pragmatic randomization: In this approach patients are randomized first, initial data are collected and, follow-up is conducted using RWD. This is the best approach as it uses randomization and provides RWD for generalizability.

Nonetheless, there are certain barriers to integrate RCTs with RWE. The major barrier is the ‘traditional mindset’ that the results from RCTs are the best possible evidence. These are evidence hierarchies that put RCTs at the top and RWE at a lower level. In order to make change, these hierarchies need to be revisited. There is a lot of work needed on the technical as well as methodological sides to convince the decision makers that the RWD is addressing an important clinical question in a robust and valid manner.

Thus, the smart integration of RWD has the potential to improve the quality, impact, validity, generalizability, and trustworthiness of RCTs. Probably in near future, using RWD in clinical trials will be second nature, but we – as an industry – must work incessantly to get to that point.

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References 

1. Real-World Evidence. Available from: https://www.fda.gov/science-research/science-and-research-special-topics/real-world-evidence. Accessed 18 July 2020
2. 21^st Century Cures Act. Available from: https://www.fda.gov/regulatory-information/selected-amendments-fdc-act/21st-century-cures-act. Accessed 18 July 2020
3. Christina Purpura. The surge in FDA approvals supported by RWE: A look at the three recent FDA developments. Available from: https://www.aetion.com/post/the-surge-in-fda-approvals-supported-by-rwe-a-look-at-three-recent-fda-decisions. Accessed 29 July 2020
4. Neil A. Belson. FDA’s Historical use of Real-World Evidence. Available from: https://www.fdli.org/2018/08/update-fdas-historical-use-of-real-world-evidence. Accessed 4 August 2020
5. Barriers and Disincentives to the Use of Real-World Evidence and Real-World Data. Available from: https://www.ncbi.nlm.nih.gov/books/NBK540112/. Accessed 13 Aug 2020

Real-world evidence (RWE) research is gaining significant importance in biopharmaceutical product development as well as its commercialization. The increasing need of the industry to seek broader information about the safety and effectiveness in the real-world setting, which typically impacts the ensuing reimbursement and utilization of new products, is determined by regulators, public and private payers, and prescribers – in order to understand the impact of a new product in a such a setting. As a result, RWE is now included earlier in the research and development phase. (1)

Real-world evidence or real world data (RWD) is nothing but the information collected under normal day-to-day circumstances that is found outside of a randomized clinical trial. This data is considered to be RWE when it is looked at from and analysed within the context of what is being measured. Ultimately, RWE can be used to evaluate treatment effectiveness in daily settings to guide clinical decision-making and answer scientific questions. (2)

Today, healthcare industry is rapidly expanding with varied data sources, such as electronic health records (EHRs), insurance claims data, patient registries, surveys, medical devices, imaging, genomics, etc. that capture enormous amounts of patient health and medical information. These data reflect patient’s routine valuable health information in the context of real clinical practice. This evidence from real-world setting can be used to study different aspects, such as epidemiology and burden of a disease, co-morbidities, treatment patterns, adherence, and outcomes of different treatments. Therefore, RWE can be used to design clinical studies, inform hypotheses, thereby improving the probability of approval and successful treatment take-off. These applications not only facilitate compressed clinical trial timelines and consequent cost-savings, but they can also serve as a powerful complement to evidence gathered from randomized control trials (RCTs), which is a gold standard for assessing biopharmaceutical drug safety and efficacy among researchers. (3)

Real-world evidence can significantly impact clinical study design. Here’s how: An effective trial design normally begins with creating a hypothesis and defining the patient cohort. This process requires the most extensive research and analysis, which is why it is a lengthy and iterative one, requiring many sequences of refinement. Real-world evidence can be optimized to test the hypotheses across diverse datasets quickly. With RWD, which consists of multiple and expansive datasets, specific insights to indication and severity of interest (e.g. rheumatoid arthritis, stage 4 chronic kidney disease, lymphoma, etc.) can be achieved in order to recognize clinical phenotypes, outcomes, unmet needs, and much more. Real-world evidence can provide answers pertaining to clinical gaps and the consequent findings can then be used to strengthen a hypothesis and further to design and tune the RCTs to all possible unmet needs. (3)

This is because, conventional primary tools used to generate a hypothesis are limited to 1) existing published literature from previous studies conducted in the patient population of interest or 2) expensive and lengthy primary chart studies. These methods fail to provide extensive information, particularly regarding rare diseases and less common disease subsets. Advances in RWE technologies, therefore, in these instances, can provide researchers the ability to quickly test hypotheses to assess clinical relevance along with care and treatment pathways of the desired cohort. (3)

In case of regulatory applications of RWE, various large data initiatives are developing standardized and consistent concepts across multiple data types and sources, in addition to bringing forward the importance of RWE in addressing concerns, such as medical product safety, patient-centred outcomes (PROs), and the value of new technologies and care delivery programs. There are many ways in which RWE can be harnessed to improve regulatory decision-making, such as to support the change of label change for new dosing administration or facilitate post-marketing safety surveillance. Furthermore, some applications can also apply RWE in historical controls or the progress and regulatory appraisal of products intended to treat rare disease populations. These examples have been fairly well characterised and followed by US FDA and industry stakeholders in recent years. (4)

In our view, RWE studies are useful complements to RCTs, since they reproduce the routine utility of drugs, devices and other products, providing a more wide-ranging view of patient response to medications, improved assessment of disease patterns, additional information on safety as well as economic analyses. Moreover, since RWE provides the actual care that patients receive in clinics, which is not limited by a strict inclusion and exclusion criteria; it generates long term efficacy and safety data as well as economic assessment – all in a real-world setting. Additionally, it allows for a comparison between multiple interventions. (5) It can also facilitate well-informed healthcare and policy decisions. Healthcare industry, therefore, needs to keep up with new developments in RWE, data sources, analytical techniques, and study methodologies to ensure competitiveness as well as maximum ROI on new products. (2)

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References

1. Cziraky M, Pollock M. Real world evidence studies. October, 2015. 
2. Jadhav S. Using real world data to enhance clinical trials. January, 2017.
3. Ahmed R, Rusli E. Using Real-World Evidence to Optimize Clinical Trials- Improving Trial Design, Patient Recruitment, and Data Analysis. Shyft Analytics. 
4. Incorporating Real-World Evidence in Regulatory Decision-Making: A Pragmatic Approach to Randomization in the Clinical Setting. Margolis Center for Health Policy- Duke University. 
5. Mahajan R. Real world data: Additional source for making clinical decisions. International Journal of Applied and Basic Medical Research 2015; 5(2):82.