EMA and FDA define Real-World Evidence (RWE) as the information derived from the analysis of Real-World Data (RWD) about a medicinal product. RWD includes the data relating to a patient’s health status or the delivery of health care collected from various sources. These sources exclude traditional clinical trials, although FDA considers that hybrid or pragmatic trial designs can generate data for RWE.
The controlled environment of Randomized Controlled Trials (RCTs) and the efficacy and safety conclusions they derive are usually completed by those known through a drug profile built under the variable conditions of worldwide patients’ treatment. Compared to RWE studies it is relevant to highlight that the population included on RCTs might not be representative of clinical practice, and adherence or drug exposure might be inferior to the real world (there is a considerable effort to maintain compliance with treatments elevated in RCTs), while outcomes are measured much more accurately on RCTs and it is also impossible to analyze placebo arms in RWE, reinforcing RCTs indispensability.
However, through data collected outside of the gold standard RCTs, RWE allows us to obtain complementary information, such as treatment patterns, disease burden, patient behaviors, and product performance in settings and populations representative of everyday clinical practice. RWD can be obtained from claims, medical records, disease registries, but might also be generated through post-marketing observational trials or case series data. By using this kind of data to identify stakeholders’ unmet needs, companies can describe their products’ real effectiveness and thereby increase their value proposition.
A RWE complementary role could be divided into clinical (e.g. real-life remission rates, or treatment adhesion), economical (e.g. cost associated with treatments), and humanistic (e.g. health-related quality of life). The RWE everyday clinical practice groundwork branches from its ability to show how physicians prescribe medicines and how patients use them, encompasses a look at diverse populations, including patients typically excluded from RCTs, and helps gather data not usually collected during RCTs, such as the financial costs of disease burden. Despite these advantages, the less expensive and fast-paced RWE studies also allow biopharma to make comparisons that might not have been made on the RCTs developed (e.g. between competitor products in different efficacy or safety endpoints) and help us assess potential efficacy/effectiveness gaps or support the scientific findings of RCT’s.
Evidence-based clinical research can be additionally sustained by RWE, beyond its complementary potential. Regulators have reviewed the applicability of RWD as primary data in clinical research for regulatory decision making and it has been acceptable in single-arm interventional trials where a parallel control arm was considered not feasible or unethical (e.g. oncology and rare disease trials). RWD has also been used by FDA and EMA to support the approval of label extensions in several medical areas, potentially accelerating approval time. Literature revisions describe how different proportions of the various sources of RWD have been used by EMA and FDA to support primary approvals (e.g. medical records, registries, historic pooled data) versus label extensions (e.g. post-marketing experience). Furthermore, the different recognition of RWD between these regions, the different goals, and RWD quality requirements generate distinct pharmaceutical and regulatory environments on RWE.
Regulatory authorities have created guidance frameworks for the pharmaceutical industry to outline how evidence collected from RWE is evaluated, allowing for the development of best practices, defining endpoints with greater clarity and analytic methods to be used for generating reproducible data. The evolving diversity of source data (SD), barriers in data privacy, and the variable possibilities to review SD pose challenges that are surmountable to regulators, who face difficulties when defining how successful a RWE study was regarding confounding adjustment. One consistent requirement for RWE is the fit-for-purpose data to ensure the methodology validation. Among others, the data needs to be fully traceable and data transformations registered from SD to final reports. Furthermore, the RWE study analysis plan should be framed with causality establishment methods that transpire confidence in face of the confounding factors, the selection of the proper hypotheses, meaningful patient populations, measurement of exposure status and specificity and sensitivity of endpoints design, all of which account for the fitness for the purpose of the data.
Looking at traditional strategies within pharmaceutical companies, RCTs lead to approvals, and subsequent new trials or sub-studies are initiated to explore special disease or efficacy predictors, label extension, and post-marketing long-term safety data (e.g. major adverse cardiac events). RWE gives companies the opportunity to think about those differences, creating space for the rise of new pathways to address these same hypotheses earlier on clinical drug development (e.g. when a pivotal study for another disease already took place). This means early use of RWE might support label extension and be a second robust option for addressing effectiveness after consolidated evidence from RCTs.
RWE might create opportunities through expansion of some of the hypotheses generated after pivotal trials, enabling the creation of external control arms (in rare diseases or oncology), the augmentation of existing clinical trials by looking at additional sub-groups that sometimes are too small in an RCT population setting, generate additional endpoints and even demonstrate the efficacy and safety of a placebo-controlled trial against a comparator group, all the while generating scientific knowledge and contributing to patients’ well-being.
Both RCTs and RWE studies have limitations in the design, interpretation, and extrapolation of their results. Regardless of the potential for bias and data quality issues, the inclusion of RWE is expected to continue expanding with the development of new analytical systems and the upcoming harmonization of guidelines for RWD quality and RWE generation. Integration of RWE in drug development will expectedly continue to be further established among pharmaceutical companies’ clinical operations strategies, better targeting their goals, and supplying regulatory authorities with higher degrees of evidence for the use of medicines.
Ultimately, RWE is expected to provide a better understanding of both the treatment and the disease, generate new hypotheses, drive strategic decision making, and enhance HCPs trust beyond RCTs information and physicians’ experience.
José Sendim do Nascimento e Novais Miranda, PharmD
José finished his PharmD from the Faculty of Pharmacy of the University of Porto, Portugal, which allowed him to additionally gather experience within academic research in biochemistry, immunology, and pharmaceutical technology. While completing a postgraduate degree in Clinical Trials Monitoring and Medical Affairs in Barcelona, he joined AbbVie’s clinical site management team in Portugal as an intern, joining the team as a Clinical Research Associate in 2021. José is enthusiastic about cross-functional collaboration, healthcare providers’ engagement, and being at peace with the leading-edge scientific news.
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