Key Points
- The terms real-world data (RWD) and real-world evidence (RWE) are often used inconsistently or interchangeably, including in submissions to the US Food and Drug Administration (FDA) involving RWE to evaluate the effectiveness of drugs and biologic products.
- Although a misconception sometimes exists that only non-interventional studies utilize RWD to generate RWE, the spectrum of study designs involves various combinations of data sources and design architectures which determine whether RWE is generated or not.
- Attempts by various stakeholders to identify the role of RWE in regulatory decision-making often do not focus on the contribution of RWD in specifically evaluating drug-outcome associations.
- Prior examples of FDA approvals demonstrate how RWD can be utilized to generate RWE as part of a marketing application for regulatory decision-making.
In accordance with the 21st Century Cures Act of 2016 (Cures Act),1 the Food and Drug Administration (FDA or the Agency) launched a program for evaluating the use of real-world evidence (RWE) to support regulatory decision-making. The Agency’s 2018 Framework for FDA’s Real-World Evidence Program defines real-world data (RWD) as “data relating to patient health status and/or the delivery of health care routinely collected from a variety of sources” and RWE as “clinical evidence about the usage and potential benefits or risks of a medical product derived from analysis of RWD.”2 Since passage of the Cures Act, the Center for Drug Evaluation and Research (CDER), in cooperation with the Center for Biologics Evaluation and Research (CBER) and the Oncology Center of Excellence, published a series of guidance documents related to RWD and RWE.3 (Although beyond the scope of this commentary, FDA’s Center for Devices and Radiological Health (CDRH) published a guidance4 describing expectations for the use of RWD and RWE for regulatory decision-making for medical devices and also published examples5 of “RWE-based” CDRH approvals.)
Despite the focus of the Cures Act on promoting research using RWD, types of data and study designs have not fundamentally changed since passage of the Cures Act. For example, the randomized controlled trial (RCT) is the archetype of study designs for assessing the safety and efficacy of a medical product. Other study design types can be used, however, including when randomization has feasibility challenges or ethical concerns. Although methodological challenges exist with nonrandomized research, such studies can contribute to drug development and support regulatory decision-making regarding effectiveness when adequately meeting evidentiary standards.6, 7 For example, data sources with well-characterized covariates and clinical endpoints are now more available for exploration using existing study design approaches and statistical methods in lieu of randomization. Nonrandomized studies also offer opportunities to study diverse patient populations and better understand long-term outcomes among patients receiving medical products.
Although FDA has been using what is now called RWE for years to assess the safety of medical products, since passage of the Cures Act, we have observed increased submissions involving RWE to evaluate effectiveness of drugs and biological products that analyze data collected during routine clinical care. At the same time, and given varying operational definitions of RWD and RWE in the stakeholder community, these terms have often been used inconsistently and sometimes interchangeably. As a result, confusion can arise when similar data sources and study designs are characterized differently in different settings.6
This commentary addresses RWD/RWE terminology for drugs and biological products, specifically related to studies of effectiveness submitted to FDA and whether they are classified as RWE by the agency. We have encountered a misconception8 that only non-interventional (observational) research7 utilizes RWD to generate RWE—in other words, a dichotomy of randomized controlled trials versus real-world evidence is said to exist.6 In reality, the spectrum of study design involves various combinations of data sources and design architectures; see Table 1. For example, externally controlled trials that utilize RWD in the comparator arm generate RWE, despite the treatment arm generating data according to a study protocol in a clinical trial environment. As another example, a randomized trial generates RWE if the primary outcome is based on an assessment of RWD (often referred to as a point-of-care trial).TABLE 1. Perspective on when studies generate real-world evidence for studies of effectiveness and safety.
| Is RWE generated? | ||
|---|---|---|
| Study design | No | Yes |
| Interventional studies | ||
| Randomized, controlled trials | ||
| Real-world data (RWD) used to develop a study (e.g., to identify potential participants, select trial sites) | ✓ | |
| RWD used to assess impact of various enrollment criteria | ✓ | |
| Data from trial-provided digital health technology | ✓ | |
| Open-label extension studies not using RWD | ✓ | |
| RWD used for trial endpoint | ✓ | |
| Data from digital health technology used in non-research settings | ✓ | |
| Open-label extension studies including RWD | ✓ | |
| Externally controlled trials | ||
| Single-arm trial with summary level estimate as comparator | ✓ | |
| External control arm data* from a clinical trial | ✓ | |
| External control arm data* from a RWD source | ✓ | |
| Non-interventional studies | ||
| Observational cohort study | ✓ | |
| Case–control study | ✓ | |
| Case-crossover study | ✓ | |
| Self-controlled case series | ✓ | |
- * Data on a specific group of patients as a comparator (e.g., not including publications with summary-level data).
Conversely, although RWD can be utilized to identify potential participants or trial sites in a traditional RCT (along with the ability to promote diversity of study populations), such data are not generating RWE to evaluate the drug-outcome association. In another scenario, a non-interventional study can use RWD to generate RWE even if a prespecified protocol exists to collect additional data, as in a patient registry, as long as the treatment is administered as part of routine clinical care (i.e., per a clinician’s judgment).7
Additional considerations arise when characterizing data from various RWD sources. A specific consideration is how to characterize data generated from digital health technologies (DHTs), such as software applications and sensors. If a DHT is used in a clinical trial according to protocol-driven procedures, the data are not considered RWD. In contrast, if data are obtained from personal use of DHTs outside of research setting, the data are considered RWD. When such data are determined to be reliable and relevant, they can be used to generate RWE for regulatory purposes.
Another consideration is how to characterize summary-level aggregated data from the medical literature. Although stakeholders sometimes classify non-patient-level data as RWD generating RWE, such information from the literature is not central to the goal of using RWE for regulatory approvals. For example, literature citations that report on disease prevalence or drug utilization are included in most regulatory submissions and may not be viewed as RWE for regulatory purposes.
Two FDA approvals help illustrate how RWD can be utilized to generate RWE as part of a marketing application for regulatory decision making. In 2021, the FDA approved Prograf® (tacrolimus) in combination with other immunosuppressant drugs to prevent organ rejection in adult and pediatric patients receiving lung transplantation.9 The evidence in support of approval of this new indication included a non-interventional study using RWD from a US-based registry, compared to historical controls. The FDA considered the RWD fit-for-use and the non-interventional study using these data to be the adequate and well-controlled clinical investigation necessary for establishing substantial evidence of effectiveness for approval. RCTs of Prograf® in other (liver, kidney, and heart) transplant settings provided confirmatory evidence.
Another scenario, wherein the RWE played a lesser role, was the approval in 2019 of Ibrance® (palbociclib) for male patients with metastatic breast cancer (MBC) in combination with letrozole, an aromatase inhibitor. The sponsor submitted a supplemental new drug application including data from prior RCTs (PALOMA-1, PALOMA-2, and PALOMA-3) including only women, along with descriptive analyses of electronic health record and medical claims data for men with MBC.10, 11 Substantial evidence of effectiveness relied on the previous RCT data in women, based on the knowledge that the natural history of the disease, response to therapy, and safety would be expected to be similar in men and women. The data from electronic health records on men provided information on safety, indicating the safety profile for the use of palbociclib in combination with hormonal therapies in men was consistent with the known adverse event profile.
More generally, external attempts to identify the role of RWD and RWE in regulatory decision-making can lead to different characterizations than those made by the FDA.12–16 For example, an article14 examined the role of RWE in FDA-approved new drug and biologics license applications from 2019 to 2021 and identified studies as RWE when “used to support the application’s therapeutic context (e.g., prevalence and incidence of a disease).” Another article15 tracking submissions to the European Medicines Agency from 2018 to 2019 considered the use of RWD “to assess the representativeness of the control arm [of a traditional randomized trial]” as constituting RWE for label expansion and marketing authorization. By not focusing directly on the evaluation of drug-outcome associations, these examples indicate how different applications of real-world terminology can create inconsistency when tracking approvals based on RWE across regulatory agencies.
As part of the FDA RWE Program, published guidance17 includes recommendations for sponsors to accurately describe data sources and design attributes in their submission cover letter to the Agency. In that guidance,17 FDA recommends providing specific information describing the regulatory purpose, types of study design, and RWD source; see Table 2. More recently, FDA announced an Advancing RWE Program18 to fulfill a commitment under the Prescription Drug User Fee Act (PDUFA) VII for fiscal years 2023 through 2027. The program includes a new mechanism for identifying approaches to generate RWE that meet regulatory requirements in support of labeling for effectiveness; it also includes a commitment to publicly report on RWE submissions to CDER and CBER starting in 2024. Use of consistent terminology can help FDA classify and quantify RWE and promote better understanding of reports by external entities regarding the use of RWE for regulatory purposes.TABLE 2. Example of how sponsors can describe data and design attributes in cover letters of submissions to FDA containing RWD/RWE (based on Reference 17).
| • Generic/proprietary name of product |
| • Disease/medical condition |
| • Purposes of Using RWD/RWE as Part of the Submission (select all that apply) |
| ° To support safety and/or effectiveness for a product not previously approved by FDA |
| ° To support labeling changes for an approved product, including |
| – Add or modify an indication |
| – Change dose, dose regimen, or route of administration |
| – Expand the labeled indication of the product to a new population |
| – Add comparative effectiveness information |
| – Add or modify safety information |
| – Other labeling change—specify |
| ° To support or satisfy a PMR/PMC |
| • Study Designs Using RWD to Generate RWE (select all that apply) |
| ° Randomized controlled trial with pragmatic elements involving RWD or those using RWD to supplement a control arm |
| ° Single-arm trial that uses RWD as an external control arm |
| ° Non-interventional (observational) study |
| ° Other study design—specify |
| • RWD Sources Used to Generate RWE (select all that apply) |
| ° Electronic health records data |
| ° Medical claims data |
| ° Product, disease, or other registry data |
| ° Data from digital health technologies in non-research settings |
| ° Other data sources (e.g., questionnaires) that can inform on health status—specify |
- Abbreviations: RWD, real-world data; RWE, real-world evidence.
The FDA Real-World Evidence Program seeks to address current challenges in using RWE for regulatory decision-making, along a spectrum from supportive to pivotal contributions. Inconsistent use of the terms RWD and RWE complicates efforts among regulators to track such data and evidence, causing potential confusion during communications among regulatory agencies, sponsors, and other stakeholders. Although the distinction between studies that generate RWE and those that do not may at times seem confusing to some in the stakeholder community, careful consideration of data and design elements can help sponsors and regulators better describe and characterize RWE.
Source: https://onlinelibrary.wiley.com/doi/10.1002/pds.5715