Real-world evidence (RWE) in clinical trials is changing drug development by using Real-World Data (RWD) from various sources to enable researchers to gather valuable insights into how drugs work in a wide range of populations. This move toward RWE not only deepens our understanding of treatment effects and patient outcomes, but also speeds up the drug development process, making it more efficient and centred around patients. In this article, we explore the role of RWE in clinical trials, discussing its benefits, challenges, and the regulatory environment that supports its use in healthcare decision-making. We also clarify how RWE relates to traditional clinical trial data, where each approach is most useful, and why they are typically most valuable when used together rather than treated as direct substitutes.
What is Real-Word Evidence (RWE)?
Real-World Evidence is the clinical evidence generated through the analysis of Real-World Data (RWD). Unlike traditional clinical trial data, which is collected under highly controlled conditions to ensure internal validity, RWE uses RWD to demonstrate the actual experiences of patients in everyday environments, capturing a broader range of patient behaviours, treatment responses, and health outcomes.
What is Real-Word Data (RWD)?
Real-World Data is patient health-related data collected outside of traditional clinical trials. RWD can come from a range of sources including;
- Electronic health records (EHRs), which provide comprehensive patient histories.
- Insurance claims data that reflect healthcare utilisation patterns.
- Patient registries that aggregate data on specific conditions.
- Digital health technologies such as:
- Wearable devices, that continuously monitor health metrics such as heart rate and physical activity.
- Mobile health apps, similar to wearable devices but could also require patients to input data manually.
- Electronic patient diaries, could take on the form of a mobile health app or other device but again require patient input when prompts.
The diversity in data sources allows for a broader understanding of patient health as RWD can capture the variability of real-life patient experiences, enabling the identification of trends and patterns that might not otherwise have been captured in a traditional clinical trial.
What Makes Real-World Data Fit for Purpose?
‘Fit for purpose’ means the data are suitable for the specific question being asked. A large dataset is not automatically useful if it does not capture the right population, exposure timing, outcomes, follow-up period, or key confounders. The relevant standard is therefore not just whether the data exist, but whether they are sufficiently complete, traceable, and analytically appropriate for the intended use.
This matters because the same source may be useful for one purpose and weak for another. Claims data may support utilisation analysis well, for example, but may be less suitable for clinical endpoints that depend on detailed disease severity or laboratory context. Assessing how suitable the data means checking whether the source, design, and analysis plan match the evidentiary question from the outset.
The Benefits of Real-World Evidence in Clinical Trials
There are several benefits to using RWE in clinical trials. Let’s look at these in more detail:
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Improved Drug Discoveries From Larger Sample Sizes
Randomised clinical trials (RCTs) generate a limited set of study analysis data. In contrast, RWE incorporates a vast array of RWD. This larger sample size allows researchers to detect patterns and correlations that might indicate new therapeutic opportunities. For example, analysing RWD can reveal unexpected benefits or less common effects of existing treatments in sub-populations that were not the focus of initial clinical trials. By providing a more comprehensive understanding of disease mechanisms and treatment effects, RWE can significantly shorten the time required to identify and validate new drug targets. -
Ability to Research More Diverse and High Risk Patient Groups
RWE enables research on more diverse and high-risk patient groups, which is often challenging RCTs. Through virtual trials, RWE facilitates easier patient enrolment, allowing for the inclusion of patients with rare diseases and those from various demographic backgrounds. This inclusivity extends to high-risk groups such as pregnant women and children, where conventional RCTs may have been impractical or ethically challenging. Therefore, RWE provides valuable insights and data that enhance our understanding of treatment efficacy and safety across a broader spectrum of patient populations.
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Streamlining Clinical Trials from Improved Patient Recruitment and Retention
Traditional RCTs often require extensive recruitment efforts and prolonged follow-up periods. RWE, however, can help identify patient populations that are most likely to benefit from the investigational treatment, based on real-world usage patterns and outcomes. This targeted approach to recruitment not only reduces recruitment time but also enhances the relevance and applicability of trial results. In addition, because of the virtual trial setting, it is easier to recruit patients regardless of their location as they do not have to visit study sites. -
Improving Trial Efficiency, Study Design, and Data Analysis
RWE can help optimise clinical trials by analysing RWD to identify patient populations most likely to benefit from a new treatment, which in turn can support more efficient study designs. It can also provide insights that inform adaptive trial approaches, allowing researchers to respond to emerging issues and investigate safety concerns earlier during study conduct. In addition, the rapid availability of real-world data can support faster analysis and more timely insights into treatment efficacy, safety, and patient outcomes. Together, these advantages can make research processes more responsive, reduce the resources needed for some development questions compared with running new traditional studies in every case, and help bring effective therapies to patients more efficiently. -
Improving Patient Outcomes
The integration of RWE into healthcare practices leads to improved patient outcomes by enabling more personalised and effective treatment strategies. Traditional clinical trials often have strict inclusion and exclusion criteria, which may not represent the broader patient population seen in everyday clinical practice. RWE provides a more accurate reflection of how treatments perform in diverse, real-world settings, capturing a wide range of patient demographics, co-morbidities, and adherence behaviours. This comprehensive data allows healthcare providers to tailor treatments to individual patient needs, enhancing therapeutic effectiveness and reducing adverse effects. For example, RWE can identify sub-groups of patients who respond particularly well to a specific therapy or those at higher risk for certain side effects, enabling more precise and personalised care. -
Supporting Regulatory Decisions
Regulatory bodies are increasingly incorporating RWE into their decision-making processes to ensure that new therapies meet high standards of safety and efficacy, because RWE provides additional evidence that complements RCT data, offering a broader perspective on how treatments perform in everyday clinical practice. This comprehensive view helps regulatory agencies make more informed decisions about drug approvals, labelling changes, and post-marketing surveillance. For instance, RWE can support the approval of new indications for existing drugs by demonstrating effectiveness in real-world settings. The incorporation of RWE into regulatory frameworks enhances the robustness and relevance of safety and efficacy assessments, ultimately benefiting patients . -
Health Economics
RWE provides evidence of how treatments perform in routine clinical practice, including their impact on healthcare utilisation, costs, and patient outcomes. This information is essential for payers when evaluating the cost-effectiveness and overall value of new therapies. By demonstrating real-world benefits, such as reduced hospitalisations or improved quality of life, RWE supports negotiations for reimbursement and formulary inclusion, facilitating broader patient access to innovative treatments. -
Supporting Pharmacovigilance
Using RWE to monitor the safety of medicines in the post-marketing phase brings efficiencies to pharmacovigilance. This includes identifying adverse events, understanding long-term safety profiles, and detecting rare side effects that may not be apparent in pre-approval clinical trials.
How Real-World Evidence Differs From Traditional Clinical Trial Data
In practice, traditional clinical trial data and real-world evidence often answer different but related questions. Randomised controlled trials are typically designed to test efficacy under defined conditions and support stronger causal inference, while RWE is more often used to understand how treatments perform across broader populations, routine care settings, and longer follow-up periods.
One reason these evidence types can lead to different findings is bias. In observational RWD studies, treatment allocation is not random, so the patients receiving one treatment may differ systematically from those receiving another before the treatment effect itself is considered. Confounding, missing variables, differences in follow-up, coding limitations, treatment switching, and inconsistent outcome capture can all influence the results. This does not make RWE unhelpful, but it does mean findings should be interpreted in light of the design and data source rather than treated as automatically equivalent to randomised evidence. Common sources of bias in RWE studies include selection bias, confounding, missing data, inconsistent outcome capture, and differences in follow-up between patient groups. These limitations do not make RWE unusable, but they do mean findings need to be interpreted in light of the data source, study design, and analytical approach.
Pragmatic trials sit somewhere between highly controlled explanatory trials and fully observational real-world evidence. They are designed to test how an intervention performs in conditions closer to usual clinical practice, often using broader eligibility criteria, routine care delivery, and outcomes that are more directly relevant to everyday settings. For teams working with RWE, pragmatic trials can therefore act as a bridge between tightly controlled randomised evidence and real-world data, improving the practical relevance of trial findings without abandoning prospective trial methodology.
Can Real-World Data Replace Randomised Clinical Trials?
Not usually as a direct one-for-one replacement. Randomised clinical trials remain important for reducing selection bias and supporting causal interpretation, while real-world data are more often used to complement trial evidence or support questions that are difficult to answer through a traditional trial alone. In practice, whether RWD can stand in for a traditional trial depends on the question being asked, the quality and completeness of the data, and how much uncertainty decision-makers can accept.
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Challenges in Implementing Real-World Evidence
As much as there are benefits to RWE, there are also challenges and it is not always straight forward for drug developers to incorporate RWE into their study analysis. Here are some key challenges to RWE:
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Data Quality and Standardisation
One of the significant challenges in implementing RWE is ensuring the quality and standardisation of RWD as it comes from a variety of different sources. Whereas with data from clinical trials, where there are CDASH standards in place for data collection, there is currently no equivalent standards to adhere by whilst collecting and storing RWD. Each source can have different formats, structures, and levels of detail. This variability can lead to inconsistencies and potential biases in the data, complicating its analysis and interpretation. To address this challenge, robust data governance frameworks and standardisation protocols must be established. Initiatives such as the adoption of Health Level Seven International (HL7) and Fast Healthcare Interoperability Resources (FHIR) standards facilitate the harmonisation of health data across different systems. Ensuring data quality also involves rigorous validation processes to identify and correct errors, missing values, and inconsistencies, thereby enhancing the reliability and comparability of RWD. -
Privacy and Security
The use of RWD raises significant privacy and security concerns due to the sensitive nature of health information. Protecting patient confidentiality is of utmost importance, requiring strict adherence to data protection regulations such as the General Data Protection Regulation (GDPR) in Europe and the Health Insurance Portability and Accountability Act (HIPAA) in the United States. These regulations mandate robust safeguards to prevent unauthorised access, disclosure, and misuse of patient data. Ensuring compliance involves implementing advanced encryption methods, secure data storage solutions, and stringent access controls. Additionally, data anonymisation and de-identification techniques are essential to minimise privacy risks while allowing meaningful analysis. Ethical considerations also play a crucial role, necessitating transparent communication with patients about how their data will be used and obtaining informed consent where applicable. -
Regulatory Acceptance
Despite individual regulatory bodies accepting and encouraging the use of RWD as mentioned, achieving regulatory acceptance of RWE poses a challenge due to varying standards and requirements across different regions and the fact there are multiple regulatory bodies. While agencies like the FDA and EMA have begun to incorporate RWE into their frameworks, harmonising these standards globally remains complex. Each regulatory body may have different expectations regarding data quality, methodology of using RWD, and evidentiary thresholds.
Strict data anonymisation regulations in the European Union make it challenging for researchers to access and use patient-level data efficiently. These regional differences underscores the necessity for a global agreement on RWD in relation to data privacy. Similar data restrictions are emerging in the United States, where data privacy laws vary significantly between states.
To navigate these challenges, ongoing collaboration between pharmaceutical companies, regulators, and other stakeholders is essential. Developing clear guidelines and best practices for the generation and use of RWE can help align expectations and facilitate broader acceptance. Continuous dialogue and engagement with regulatory agencies are crucial to ensure that RWE studies meet the necessary standards and contribute effectively to regulatory decision-making. -
Analytical Complexity
The heterogeneous and unstructured nature of RWD presents significant analytical challenges. Unlike traditional clinical trial data, RWD often lacks the controlled conditions and standardised formats that facilitate straightforward analysis. Advanced statistical methods are often required to manage and interpret this complex data. Techniques such as propensity score matching, machine learning algorithms, and natural language processing are increasingly employed to address these challenges. Ensuring the validity and reliability of RWE analyses involves careful study design, appropriate handling of confounders, and sensitivity analyses to assess the robustness of findings. Developing and validating these advanced analytical techniques is critical for extracting meaningful insights from RWD. -
Ethical Considerations
Maintaining ethical standards in the collection and use of RWE is essential to uphold the trust and integrity of the research process. Ethical considerations include ensuring patient consent, maintaining transparency in data usage, and addressing potential biases in the data. Obtaining broad consent for future research use of health data can help address concerns about autonomy and privacy. Transparency involves clear communication with patients and the public about how their data will be used, the benefits and risks of RWE research, and the measures taken to protect their privacy. Additionally, addressing potential biases in RWD, such as disparities in data availability across different populations, is crucial to ensure that RWE research does not exacerbate health inequities. Ethical frameworks and oversight mechanisms must be in place to guide the responsible and equitable use of RWE.
Regulatory Perspectives on Real-World Evidence
As mentioned, regulatory agencies are open to the use of RWE to help drug developers demonstrate a therapy’s safety and efficacy. In this section we will review the regulatory stance on RWE in more detail.
FDA Framework and Guidelines
The FDA has been proactive in integrating RWE into its regulatory processes, recognising its potential to enhance drug development and regulatory decision-making. The FDA's framework for evaluating RWE encourages the use of RWE to support regulatory submissions, including new drug approvals and post-approval studies. The framework emphasises several key principles:
Purpose and Scope
The FDA's RWE framework is designed to evaluate real-world evidence to support new drug indications, fulfil post-approval study requirements, and make other regulatory decisions, in line with the 21st Century Cures Act.
Data Quality and Study Design
It emphasises the need for high-quality, reliable real-world data and robust, transparent study designs. The framework encourages diverse data sources and methodologies, requiring pre-specified protocols to ensure credibility and reproducibility.
Public Engagement and Transparency
The FDA aims to share insights from RWE studies through public workshops and case studies to promote understanding and acceptance of RWE in regulatory contexts.
Submission and Review Process
A structured process for submitting RWE proposals is outlined, where the FDA collaborates with sponsors to ensure studies meet regulatory standards.
In practice, the most important point is less about any single framework document and more about the direction of travel: regulators increasingly expect RWE to be credible, question-led, and methodologically transparent.
EMA's Approach to RWE
The EMA has also embraced the potential of RWE, particularly through its wider regulatory science and evidence-generation work, including ongoing efforts to improve access to and use of real-world data in medicines regulation. That work underscores the importance of leveraging RWE for a deeper understanding of diseases, treatment pathways, and the real-life use of medicines. The EMA focuses on several key areas
Innovate Clinical Trials
The EMA aims to leverage RWD to include diverse populations and endpoints in clinical trials. They are focused on modernising oversight for decentralised trials and developing robust digital endpoints.
Enhance Benefit-Risk Assessment
The EMA emphasises the inclusion of patient preferences in benefit-risk assessments and is developing guidance for incorporating patient-reported outcomes to ensure patient-centric evaluations.
Support Special Populations
Using RWD, the EMA seeks to accelerate access to treatments for special populations, addressing unmet medical needs more effectively.
Advance Modelling and Simulation
The EMA is advancing the use of RWD to improve predictive tools and decision-making through enhanced modelling and simulation techniques.
Utilise Digital Technology and AI
To analyse large datasets, the EMA is leveraging digital tools and artificial intelligence. They are establishing a digital innovation lab and developing comprehensive guidelines for AI use in regulatory processes.
Improve Patient Access
The EMA integrates Health Technology Assessment (HTA) and payer evidence early in drug development to improve patient access. They also utilise RWD for post-licensing evidence generation and the detection of drug safety issues.
Strengthen Network Competence
The EMA is building a sustainable platform for accessing and analysing healthcare data across the EU, ensuring data quality and strengthening the regulatory network's capability to handle big data submissions.
Across both FDA and EMA contexts, the practical takeaway is similar. RWE can support regulatory and post-approval decision-making, but it is most persuasive when the research question, data provenance, study design, and analytical limitations are clearly defined.
Technology Advancements in RWE
Capturing and analysing RWD would not be possible if it was not for the advancements in digital technologies. Below we review these technologies in more detail and how they have made RWE possible.
Wearable Devices and Mobile Health Apps
Wearable devices and mobile health apps have revolutionised the collection of patient health data, offering opportunities for real-time monitoring and continuous data collection. These technologies include fitness trackers, smart watches, and biosensors that can track a wide range of physiological parameters such as heart rate, activity levels, sleep patterns, and even glucose levels. The constant stream of data generated by these devices provides a detailed picture of a patient's health and lifestyle, offering valuable insights for RWE studies.
Mobile health apps complement wearable devices by providing platforms for patients to manage their health conditions, track medication adherence, and communicate with healthcare providers. These apps can collect a wide array of data, from self-reported symptoms and medication intake to data collected via integrated smartphone sensors. The combination of wearable devices and mobile health apps enables the collection of comprehensive and continuous health data, which can be used to enhance the precision and relevance of RWE studies.
Big Data Analytics and AI
The integration of big data analytics and artificial intelligence (AI) is transforming the collection and analysis of real-world data (RWD). Big data analytics involves processing vast amounts of data rapidly, enabling researchers to uncover patterns and correlations that inform healthcare decision-making. Advanced analytics techniques, such as machine learning and natural language processing, can handle the complex and unstructured nature of RWD. This is particularly useful when extracting meaningful insights that may not be apparent through traditional analysis methods.
Artificial intelligence, particularly machine learning algorithms, plays a crucial role in analysing RWD by identifying trends, predicting outcomes, and generating actionable insights. For example, AI can be used to predict disease progression, treatment responses, and potential adverse events based on historical data. The combination of big data analytics and AI enhances the precision and scalability of RWE studies, allowing for the analysis of larger and more diverse patient populations.
Electronic Health Records (EHRs)
These are digital versions of patients' paper charts, offering comprehensive records of medical history, diagnoses, treatment plans, immunisation dates, and test results. The digitisation of patient records has been pivotal in streamlining the collection of RWD, making it more accessible for research purposes. EHRs provide a wealth of data that can be used to generate RWE, including information on patient demographics, comorbidities, treatment outcomes, and healthcare utilisation patterns.
Case Studies of RWE in Action
Pfizer’s IBRANCE®
Pfizer's use of Real-World Evidence (RWE) to expand the label of IBRANCE® (palbociclib) is a notable example of how RWE can influence regulatory decisions and broaden treatment options. Initially approved in 2015 for treating HR+/HER2- advanced or metastatic breast cancer in postmenopausal women, Pfizer sought to expand its use to include male patients, a population not included in the initial clinical trials. To achieve this, Pfizer conducted an observational study using real-world data from electronic health records (EHRs) to demonstrate the effectiveness of IBRANCE® in men with breast cancer. Based on the real-world evidence presented, the FDA expanded the label of IBRANCE® in 2019 to include male patients, marking a significant instance where RWE directly influenced drug labelling and expanded treatment options for a broader patient population.
Novartis’ Entresto®
Novartis leveraged RWE to support the real-world effectiveness and safety of Entresto® (sacubitril/valsartan), a treatment for heart failure. Clinical trials had already demonstrated its benefits over enalapril in reducing the risks of death and hospitalisation due to heart failure. After approval, Novartis continued to gather real-world data from patients using Entresto® through registries and observational studies. This data provided ongoing evidence of the drug's effectiveness and safety in a broader, more diverse patient population outside the controlled environment of clinical trials. The real-world data supported the findings from the clinical trials and helped further establish the real-world effectiveness and safety profile of Entresto®, reinforcing its position in treatment guidelines and clinical practice for heart failure.
AstraZeneca’s TAGRISSO®
AstraZeneca's TAGRISSO® (osimertinib) is a medication used in treating non-small cell lung cancer (NSCLC) with specific mutations. Its approval and subsequent label expansions have been supported by a combination of clinical trial data and RWE. Real-world evidence has been particularly useful in demonstrating the drug's effectiveness in real-world settings, including its use in populations and scenarios not fully covered in clinical trials. This includes data on long-term survival rates, quality of life, and effectiveness in treating central nervous system (CNS) metastases. The integration of RWE has helped provide comprehensive evidence of TAGRISSO®'s benefits, supporting its widespread adoption in clinical practice for the treatment of NSCLC with EGFR mutations.
COVID-19 Vaccine Approvals
The unprecedented global effort to develop, approve, and monitor COVID-19 vaccines has relied heavily on RWE. This includes post-authorisation safety and effectiveness studies conducted in real-world populations. Governments and pharmaceutical companies have utilised health records, vaccine registries, and other sources of health data to monitor the safety and effectiveness of COVID-19 vaccines in the general population. This ongoing collection of RWE is crucial for identifying rare side effects, understanding long-term immunity, and making informed decisions about booster doses. The real-world evidence gathered has played a key role in ensuring public confidence in the vaccines, guiding public health policies, and adapting vaccination strategies based on emerging data about vaccine performance against variants of the virus.
Conclusion
In summary, RWE provides a comprehensive picture of how treatments perform across diverse patient populations and healthcare settings. This makes RWE a critical tool for understanding the effectiveness, safety, and value of medical interventions in real-world practice.
The insights gained from RWE can inform clinical guidelines, support regulatory decisions, and enhance patient care by aligning treatment approaches with real-world patient needs and experiences. When used carefully, RWE can extend, contextualise, and strengthen the evidence base around clinical development rather than trying to stand apart from it.
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