The evaluation of new treatment effectiveness and patient safety has traditionally been dominated by clinical trials. These trials, particularly randomized controlled trials (RCTs), are conducted in highly controlled environments to ensure the reliability and validity of the results. However, the controlled nature of these trials can sometimes be limiting when it comes to their use in real-world settings, where patient populations, healthcare and drug availability, as well as many other conditions, can vary greatly. This is where real-world evidence (RWE) comes into play, offering a broader perspective on how treatments perform outside the confines of clinical trials.
While trial data is collected under very controlled circumstances, by trained professionals and often on-site, RWE is derived from the analysis of data collected from real-world settings and includes electronic health records (EHRs), insurance claims, patient registries and various other data sources that reflect the actual experiences of patients. This type of evidence provides insight into the effectiveness and safety of treatments in diverse patient populations under varying conditions, which is often not possible to fully capture in clinical trial settings.
Here, we discuss the limitations inherent in clinical trials and highlight what RCTs can sometimes overlook. We will also discuss how RWE serves as a valuable complement to RCTs, enhancing our understanding of treatments in actual healthcare practices, and underscoring the importance of integrating RWE into decision-making processes, ultimately leading to more informed and effective patient care strategies.
The gap between clinical trial outcomes and real-world results
Clinical trials, particularly RCTs, often have strict inclusion and exclusion criteria to ensure the study population is as homogeneous as possible. While this helps in isolating the effects of the treatment being studied, it can lead to a patient population that does not fully represent the diversity found in real-world settings, where patients may have a variety of comorbidities, be on multiple medications, and have different demographic characteristics, not always represented in clinical trials.
RCTs also often exclude certain patient groups, such as those with multiple comorbidities, pregnant women, or the elderly. This exclusion can lead to a lack of data on how these groups respond to treatments, which is a significant limitation from a research perspective. It can result in gaps in understanding the full safety and efficacy profile of a treatment across all potential patient populations. For example, a 2023 study found that over a period of 13 years, patients treated in real-world settings for multiple myeloma had death rates 75% higher than those in clinical trials[i]. Findings showed limitations in using clinical trial data in isolation when predicting outcomes for patient groups typically excluded from trials due to their health status.
In areas such as rare diseases and oncology, the small number of patients often makes it difficult to recruit enough participants to achieve statistically significant results. The heterogeneity of cancer types and stages can complicate the design and interpretation of RCTs, and the rapid evolution of treatment standards can make it difficult to maintain a consistent control group[ii]. This strict criteria for selection can also limit the applicability of results to the broader patient population and may even withhold potentially beneficial treatments.
Implications for research
From a research perspective, the exclusion of diverse patient groups in RCTs can have significant implications. It can result in limited external validity, meaning that the findings of the trial may not be applicable to the broader patient population who will use the treatment in real-world settings, undermining the generalizability of the trial results.[iii] This exclusion of certain groups can lead to incomplete safety and efficacy data, and the gap in data may leave unknown risks or benefits unaddressed in the populations that were not included in the trials.
For example, a patient with atrial fibrillation (AF) may wish to know how anticoagulant use will affect their liver cirrhosis, but because most patients with hepatic conditions are under-represented or excluded in cardiovascular RCTs, this information is scarce. Using this example, researchers could not find any results for patients with both AF and cirrhosis, as patients with hepatic conditions are under-represented or excluded in cardiovascular RCTs.[iv] These exclusions can go on to pose challenges in regulatory approval processes with regulatory bodies requiring additional studies to fill data gaps, delaying the availability of new treatments.
How does RWE complement research from RCTs?
RWE has the potential to complement traditional clinical trial findings by adding perspective to the results, empowering physicians to make more targeted and appropriate care plans for patients. For instance, clinical notes can reveal social determinants of health, such as socioeconomic status, lifestyle choices and even environmental exposures, that may influence disease progression and symptom development. This aspect is particularly crucial in understanding how various factors interplay in the manifestation of diseases, which can lead to more tailored and effective treatment strategies.
RWE also plays a vital role in fulfilling post-market regulatory requirements. Regulatory bodies like the U.S. Food and Drug Administration (FDA) and European Medicines Agency (EMA) are increasingly relying on RWE to assess the long-term safety and effectiveness of approved therapies. After a new medication is approved, RWE can be used to track patient outcomes and adverse events within the general population, offering a broader perspective on a drug’s safety profile.
However, RWE is only as good as the real-world data used to generate it, physicians and other healthcare providers play a critical part in gathering this data. For example, clinicians enter structured clinical data such as diagnoses, lab tests and results, and prescribed medications as well as unstructured, clinical notes into electronic health record (EHR) systems. Providers will also submit claims to payers. All this data can be de-identified and used to generate real-world evidence.
One primary concern sponsors may have with the quality of RWE, stems from how it is collected and logged, which can impact its interpretability. However, advancements in technology are helping to address these challenges, and according to analysts at GlobalData, the number of initiated studies using RWE elements in 2024 was 16%, compared with 13% for 2023[v].
Additionally, regulatory authorities such as the FDA and EMA are actively promoting the use of RWE trials, with the FDA releasing a framework for the RWE program in 2018. The proactive stance of regulatory authorities such as the FDA towards RWE trials signifies a transformative shift in the pharmaceutical industry.
Veradigm’s comprehensive RWE solutions
Generating meaningful insights from various, disparate sources of real-world data can be challenging. Today’s healthcare teams can’t afford to waste time and resources generating insights from fragmented sources of data, but Veradigm can support these teams with real-world evidence research.
As a leading provider of healthcare technology, Veradigm’s RWE solutions empower life sciences organizations to transform fragmented datasets and disparate data sources into clear, actionable insights that drive better clinical, regulatory, and commercial outcomes. Veradigm RWE experts bring deep expertise in generating insights from industry-leading databases, including Veradigm’s Real World Data assets. Veradigm’s comprehensive suite of data solutions and RWE services ensures that Veradigm remains at the forefront of real-world evidence generation, providing valuable insights and data-driven solutions.
For more on how Veradigm can help with your real-world evidence needs, download the free paper below.
[i] https://www.prnewswire.com/news-releases/studies-uncover-drivers-of-health-disparities-and-opportunities-to-enhance-equity-302010728.html
[ii] K. Verkerk, E.E. Voest,
Generating and using real-world data: A worthwhile uphill battle, Cell, Volume 187, Issue 7, 2024, Pages 1636-1650, ISSN 0092-8674, https://doi.org/10.1016/j.cell.2024.02.012. (https://www.sciencedirect.com/science/article/pii/S0092867424001788)
[iii] Comparing clinical trial population representativeness to real-world populations: an external validity analysis encompassing 43 895 trials and 5 685 738 individuals across 989 unique drugs and 286 conditions in England Tan, Yen Yi et al. The Lancet Healthy Longevity, Volume 3, Issue 10, e674 – e689
http://thelancet.com/action/showCitFormats?doi=10.1016%2FS2666-7568%2822%2900186-6&pii=S2666-7568%2822%2900186-6
[iv] Lai, A.G., Chang, W.H., Parisinos, C.A. et al. An informatics consult approach for generating clinical evidence for treatment decisions. BMC Med Inform Decis Mak 21, 281 (2021). https://doi.org/10.1186/s12911-021-01638-z
[v] https://www.clinicaltrialsarena.com/features/accurate-data-interpretation-key-expanding-rwe-trials/
