A Guide to Real-World Evidence in Clinical Trials

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.

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:

1. 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.
   
   

2. 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.
   
   
   
   

3. Enhancing Preclinical Studies

   In preclinical studies, RWE enhances the assessment of potential drugs by providing insights into their safety and efficacy based on historical data. Traditional preclinical studies often depend on animal models and in vitro experiments, which may not fully predict human responses. RWE supplements these studies with data from actual clinical experiences, helping to identify potential safety signals and efficacy trends early in the drug development process. For instance, historical data from EHRs and patient registries can highlight adverse effects observed in similar drug classes or reveal real-world usage patterns that inform dose optimisation. By integrating RWE into preclinical research, developers can better design their studies to address potential risks and improve the likelihood of successful outcomes in subsequent clinical trials.
   
   

4. 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. 
   
   

5. Efficient Study Designs 

   RWE has the ability to optimise clinical trials by analysing the RWD and identifying patient populations most likely to benefit from a new treatment which in turn enables more efficient study designs. RWE offers valuable insights that support the development of adaptive trial designs, allowing researchers to make modifications based on interim data. This is done by monitoring RWD and identifying potential issues early and modifying ongoing trials to investigate safety concerns. This flexibility not only streamlines the trial process but also enhances the relevance and applicability of the study outcomes. By leveraging RWE, researchers can identify patient populations that are most likely to benefit from the investigational treatment, thereby reducing recruitment time and costs. Furthermore, the use of RWE can minimise the resources RCTs, ultimately facilitating faster access to new therapies for patients. This efficiency in study design accelerates the drug development process and brings effective treatments to market more swiftly, benefiting both patients and the healthcare system as a whole.
   
   

6. Efficiencies by improving the speed of data analysis

   RWE enables rapid and easy access to data. This accessibility allows researchers to analyse and utilise up-to-date information, facilitating timely insights into treatment efficacy, safety, and patient outcomes. The speed and ease of data access in RWE enable more dynamic and responsive research processes, allowing for faster decision-making and the ability to address emerging health concerns promptly. This ultimately leads to more efficient and effective healthcare interventions, improving patient care and outcomes.
   
   

7. 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.
   
   

8. 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. Traditionally, regulatory approval has relied heavily on data from RCTs, which may not fully capture the complexities of real-world patient experiences. As mentioned, 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.
   
   

9. Health Economics

   RWE is key as it provides us with more accessible and affordable healthcare. Traditional clinical trials often focus on efficacy under ideal conditions, which may not fully translate to real-world effectiveness and value. 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. 
   
   

10. 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.

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:

1. 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.
   
   

2. 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 paramount, 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.
   
   

3. 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.
   
   

4. 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.
   
   

5. 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.

EMA's Approach to RWE

The EMA has also embraced the potential of RWE, particularly through its "Regulatory Science to 2025" strategy. This strategy 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.
• Leverage 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.

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.

The integration of EHRs into RWE studies enhances the accessibility and usability of health data. Researchers can use EHR data to track treatment outcomes, monitor disease progression, and identify trends in patient care. Moreover, EHRs facilitate the linkage of data across different healthcare settings, providing a more comprehensive view of patient health. This integration enables researchers to conduct longitudinal studies that capture the long-term effects of treatments and interventions, contributing to a deeper understanding of patient outcomes in real-world settings.

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.

Technology Advancements in RWE

Predictive Analytics and Personalised Medicine

By analysing diverse patient data, RWE facilitates the tailoring of treatments to individual patient needs, genetic profiles, and environmental factors. This precision approach enhances the efficacy of treatments while minimising adverse effects, significantly improving patient outcomes. Predictive analytics, powered by advanced machine learning algorithms, can identify patients at high risk for specific conditions or those likely to benefit from particular therapies. This data-driven approach supports the development of personalised treatment plans that align with each patient's unique health profile, leading to more effective and targeted interventions.

Integration of New Technologies

Technological advancements are set to further revolutionise the collection and analysis of real-world data (RWD). The integration of artificial intelligence (AI), machine learning, and big data analytics is transforming how RWD is processed and interpreted. AI algorithms can analyse vast amounts of data to identify patterns, predict outcomes, and generate actionable insights. For example, AI can predict disease progression, treatment responses, and potential adverse events based on historical data. Additionally, wearable devices and mobile health apps continue to generate a wealth of RWD, providing continuous real-time data streams that enrich RWE studies. These technologies enable more precise and comprehensive analyses, supporting more informed decision-making in drug development and patient care.

Evolving Regulatory Framework

As RWE gains prominence, regulatory bodies such as the FDA and EMA are evolving their frameworks to accommodate and encourage its use in drug approvals and monitoring. These evolving frameworks emphasise the importance of data quality, methodological rigor, and transparency in RWE studies. Collaborative initiatives between regulatory agencies, pharmaceutical companies, and technology providers are crucial for establishing standards and guidelines that ensure the reliability and ethical use of RWE. These initiatives aim to facilitate the integration of RWE into regulatory decision-making processes, enhancing the relevance and applicability of regulatory assessments to real-world clinical practice.

Collaborative Initiatives

Collaborative initiatives are essential for the successful integration of RWE into healthcare and pharmaceutical research. Partnerships between pharmaceutical companies, regulatory agencies, healthcare providers, and technology firms foster innovation and facilitate the development of comprehensive frameworks for RWE utilisation. These collaborations enable the sharing of expertise, resources, and data, supporting the generation of high-quality, reliable RWE. By working together, stakeholders can address common challenges, such as data standardisation, privacy concerns, and ethical considerations, and develop best practices that promote the responsible and effective use of RWE. Collaborative efforts also support the creation of robust infrastructure and data governance frameworks, ensuring that RWE can be seamlessly integrated into healthcare decision-making process.

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. 

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FAQs for Real-World Evidence in Clinical Drug Development

What are the potential biases in Real-World Evidence (RWE) studies, and how can they be mitigated?

Potential biases in Real-World Evidence (RWE) studies include selection bias, information bias, and confounding. Selection bias occurs when the study population is not representative of the broader patient population. Information bias arises from inaccuracies in data collection, while confounding occurs when extraneous variables affect the observed relationship between the treatment and outcome. These biases can be mitigated through robust study designs, such as propensity score matching, rigorous data validation, and advanced statistical methods to control for confounders.

How does Real-World Evidence (RWE) facilitate comparative effectiveness research?

Real-World Evidence (RWE) facilitates comparative effectiveness research by providing data on how different treatments perform in real-world settings. By analysing data from electronic health records, patient registries, and other sources, researchers can compare the effectiveness, safety, and costs of multiple treatment options across diverse patient populations. This comparative data helps healthcare providers and policymakers make evidence-based decisions about which treatments offer the best outcomes for specific patient groups.

What role does patient-reported outcomes (PROs) play in Real-World Evidence (RWE) studies?

Patient-reported outcomes (PROs) play a crucial role in Real-World Evidence (RWE) studies by capturing patients' perspectives on their health status, treatment experiences, and quality of life. PROs provide valuable insights into the real-world impact of treatments from the patient's viewpoint, complementing clinical and administrative data. Incorporating PROs into RWE studies enhances the understanding of treatment benefits and risks, helping to inform patient-centered care and improve healthcare decision-making.

What are the challenges of using RWE for rare disease research?

Using Real-World Evidence (RWE) for rare disease research presents challenges such as limited data availability, small patient populations, and variability in disease presentation. Rare diseases often lack extensive real-world data due to their low prevalence, making it difficult to generate robust evidence. To address these challenges, researchers can leverage global patient registries, collaborate across multiple institutions, and employ advanced statistical methods designed for small sample sizes. These approaches can enhance the quality and reliability of RWE in rare disease research.