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The complete guide to using real world data and evidence in clinical trials and research

Hannah Gaimster, PhD

Hannah Gaimster, PhD

May 2024

Author: Hannah Gaimster, PhD

Contributors: Amanda White 

 

 

 

In this article:

 

  1. Defining real world data (RWD) and real world evidence (RWE)
  2. Sources of RWD
  3. Key characteristics of RWD
  4. The benefits of using RWD
  5. How much is RWD being used for clinical research and trials currently?
  6. Challenges and considerations in using RWD in research
  7. Examples of use cases for RWD in clinical research
  8. What is the current state of guidance around using RWE in research, clinical trials and healthcare policy and practice?
  9. Emerging trends and future directions for RWD/E 
  10. Conclusions: from insights to impact 

 

 

Introduction

 

Real world data (RWD) and real world evidence (RWE) are emerging as transformative tools in clinical research and trials.

This complete guide describes the diverse applications of RWD and RWE across clinical research, as well as the benefits that incorporating RWD can bring. It explores some of the difficulties of using RWD in clinical trials and research, and suggests potential solutions to these challenges.

The guide also considers the current state of guidance surrounding use of RWD/E in clinical research and trials globally and concludes by speculating on the key emerging trends surrounding RWD/E use.

By understanding the sources, challenges, regulatory considerations, and best practices outlined in this guide, researchers and stakeholders can harness the full potential of RWD to advance evidence-based medicine and drive innovation in research and healthcare.


1. Defining real world data and real world evidence

 

Real world data (RWD) refers to data collected from various sources outside the controlled environment of clinical trials, reflecting the everyday clinical practice and patient experiences. 

Real world evidence (RWE) is generated from the analysis of RWD to provide insights into the effectiveness, safety, and utilization of healthcare interventions in real world settings. These are valuable data sources that can supplement traditional clinical trials including randomized controlled trials (RCTs), epidemiological studies, and lab-based experiments.

Below, Lifebit’s precision medicine lead, Dr Chiara Bacchelli, discusses the key roles of clinical trials in the development of novel drugs.

 

 

 

 


A crucial step in the drug development process, clinical trials assess the efficacy and safety of novel medications and medical treatments. These investigations usually involve the collection of data in small, controlled settings. 

These clinical research trials might not, however, fairly reflect the significant variations observed in bigger, more varied populations. This is where incorporating RWD into clinical research and trials can have a significant advantage.

 


2. Sources of real world data

 

RWD encompasses a wide array of information collected from diverse sources, so offers a comprehensive view of patient demographics, treatment patterns, and healthcare outcomes.

 

Real world data comes from a variety of sources including electronic health records, claims data and patient registries

 

 


Some common sources of RWD include:

 

  • Electronic Health Records (EHRs): EHRs contain detailed patient information, including medical history, diagnoses, medications, laboratory results, and treatment plans. EHRs provide a longitudinal view of patient care and enable retrospective analysis of real world treatment outcomes.

  • Claims Data: Claims data are generated from healthcare billing and reimbursement processes, capturing information on healthcare services provided to patients, including procedures, medications, and associated costs. Claims data offer insights into treatment patterns, healthcare utilization, and economic outcomes.

  • Patient Registries: Patient registries are databases that systematically collect and store data on patients with specific medical conditions, treatments, or demographics. Registries may be disease-specific or population-based, facilitating the study of rare diseases, treatment effectiveness, and long-term outcomes in real world settings.

  • Electronics and wearable devices: These devices collect RWD by seamlessly integrating into users' daily lives and capturing various aspects of their health and activity. These devices, ranging from fitness trackers to smartwatches, continuously monitor parameters such as heart rate, sleep patterns, exercise routines, and even environmental factors like temperature and humidity.

 

 

3. Key characteristics of real world data

 

Despite RWD coming from various sources, it often shares key characteristics. Some of these characteristics bring key advantages to using RWD in clinical research and trials, however some cause significant challenges in utilizing RWD.

 

  • Heterogeneity: RWD sources can vary in terms of data format, structure, and quality, leading to heterogeneity in the data collected. This diversity poses significant challenges for data integration, standardization, and analysis.

  • Temporal Aspect: RWD captures data longitudinally over time, allowing for the examination of treatment patterns, disease progression, and long-term outcomes. The temporal aspect of RWD enables researchers to assess real-world effectiveness and safety beyond the confines of clinical trials which is a considerable advantage.

  • Real World Context: RWD reflects the complexities of routine clinical practice, including variations in patient populations, healthcare delivery systems, and provider practices. This real world context enhances the generalizability and external validity of research findings to diverse patient populations and healthcare settings. This also provides a key benefit to RWD use, compared to only using data collected in a clinical trial setting.

 

By leveraging these diverse sources of RWD and applying rigorous analytical methods, researchers can derive valuable insights to inform clinical decision-making, healthcare policy, and patient care practices.

 

4. The benefits of using real world data

 

Utilizing RWD and RWE in clinical research and trials has four main advantages. These include better post-market surveillance, enhanced generalizability, potentially expedited approval processes, and optimized cost effectiveness. When taken as a whole, these advantages can all work to improve patient outcomes.

 

Using RWD in clinical research and trials can bring many benefits to these processes

 

 

 

Increased diversity in RWD can help studies become more broadly applicable.

Researchers can learn more about how various patient subgroups react to therapies in real world contexts by utilizing RWD. This aids in our comprehension of the safety and efficacy of a medication for various populations. Since these populations are often excluded from conventional studies, RWD can help increase equity in access to research studies among minority populations, elderly adults, and people with numerous medical issues.


It is well known that the absence of diversity, equity, and inclusion in clinical research has a direct impact on patient care and outcomes. Exploring the existing access to trial participation in Europe and identifying limiting variables can be accomplished by robust RWD exploitation in the era of digital medicine. Europe and identifying limiting variables can be accomplished by robust RWD exploitation in the era of digital medicine.


Featured Resource: Catch up on our data diversity in genomics webinar..


The clip below shows highlights from experts in genomic data research and technology, including Victor Angel-Mosti, CEO and Founder of Omica.bio in Mexico, Prof Lygia da Veiga Pereira, gen-t Science in Brazil, Dr Matt Silver, Lead Genomic Data Scientist ‐ Diversity at Genomics England, and Dr Maria Dunford, CEO of Lifebit.


 

Quicker approval processes

Conventional clinical studies can take years to recruit people, carry out follow-ups, and analyze outcomes since they are frequently resource- and time-intensive. By utilizing already-existing RWD sources to find eligible patients, monitor results, and produce real-time evidence, RWD and RWE has the potential to speed up the clinical trial process. This time saving impact can help reduce the time and costs associated with bringing new therapies to market, benefiting patients and healthcare stakeholders alike.

Enhanced economy of cost

Using existing RWD speeds up the research process and may provide cost efficiency by reducing the time and resources needed for data collecting. Researchers can conduct cost-effective investigations without sacrificing the quality and rigor of their findings by making use of pre-existing, available data.

Improved post-purchase monitoring

In addition to helping with initial approvals, RWD can provide continuous longitudinal post-market surveillance to assess the long-term efficacy and safety of medical therapies. In order to guarantee that patients receive the most recent, evidence-based care possible, regulators, manufacturers, and prescribers can continuously analyse RWD in order to identify adverse events, evaluate the durability of treatments, and improve prescribing guidelines. It is clear there are multiple benefits that can be gained from incorporating RWD/E into clinical research and trials. It might be expected that a significant amount of trials and researchers leverage RWD to surface these many benefits. The next section explores the current use of RWD in clinical research.

 

 

5. How much is real world data being used for clinical research and trials currently?

 

The increasing prevalence of RWD in research is evident when considering the quantity of search results on PubMed for the term ‘real world data’- shown by the blue in the graph below. The number of results for this term has increased significantly in recent years, and this is a trend that shows no sign of slowing down.

 

 

RWD is currently being underused in the clinical trials sector

 

However, the purple line shows the number of search results on PubMed for the term ‘real world data clinical trial’- terms which are increasing in amount, but much more slowly than the search hits for just RWD. 

This seems consistent with literature reviews on the subject which have shown that RWD is currently being underused in the clinical trials sector.

One recent study assessed the application of RWE for FDA-approved new medications and biologics linked to neurology. Three applications (10%) out of these approvals were confirmed to contain RWE, with one of those applications using RWE as the principal evidence of effectiveness.

Another review assessed 89 papers clinical trial approvals and produced findings that were comparable. Less than 10% of trials employed RWD, despite the fact that a wide range of disorders were investigated. The articles examined for this investigation also discussed a variety of data-related difficulties, including problems with missing values in the data and restricted data availability and accessibility. 

 

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This indicates that finding high-quality, fully interoperable, and standardized data for research and clinical trials may be difficult for researchers, and that a current barrier to integrating RWD into research, development, and clinical trials may be a lack of secure access to data catalogs containing global health data.

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RWD has great potential to speed up the approval of clinical trials; nevertheless, in order to facilitate access to high-quality, worldwide datasets for research and clinical trials, it is imperative to offer researchers access to global data networks and secure platforms for data linkage and analysis.

 

6. Challenges and considerations in using real world data in research

 

 

Issues regarding use of RWD for clinical research and trials include limited data quality, interoperability, security and access considerations

 

There remain several significant obstacles that surround the application of RWD in clinical trials and research. RWD is typically larger and less structured than data collected from RCTs. As a result, it can frequently be challenging to safely collect, store, access, and analyze such enormous volumes of data. Challenges can include quality of the data, a wide range of data sources, privacy and ethical issues, and legislative uncertainties over the use of RWD. These  issues are explored in more detail in the drop downs below.

 

 

Poor quality of data

Complex analysis platforms are needed for RWD analysis in order to reduce confounding variables and guarantee causal inference. One of the primary challenges in utilizing RWD is ensuring data quality. RWD often comes from disparate sources, leading to inconsistencies, missing values, and inaccuracies. Addressing these issues is crucial to maintain the integrity and reliability of the data used in clinical trials.


For instance, a recent analysis that examined the application of RWD in clinical trial approvals detailed a number of difficulties relating to data, such as problems with data quality including missing numbers. This is sometimes called "garbage in, garbage out" since poor quality data can lead to conclusions and findings that are not reliable.

Incompatible data

One of the biggest obstacles to using RWD for clinical research is ensuring that it is compatible with data gathered from many sources. Data from EHRs, claims data, patient registries, and wearable devices may not always seamlessly integrate, making it challenging to derive meaningful insights.

Enhanced economy of cost

Using existing RWD speeds up the research process and may provide cost efficiency by reducing the time and resources needed for data collecting. Researchers can conduct cost-effective investigations without sacrificing the quality and rigor of their findings by making use of pre-existing, available data.

Privacy and security concerns

Protecting patient privacy and ensuring data security are paramount in utilizing RWD for clinical research. Safeguarding patient privacy and data integrity is essential to avoiding misuse, breaches, and unwanted access to private medical records like RWD. To protect patient privacy and preserve public confidence in RWE generation, RWD should be managed by a safe data solution that incorporates the best available security features.


Featured Resource: Read our white paper to learn about Lifebit's robust data security strategy.

Inconsistent RWD regulatory guidance

As RWE becomes more valued and recognized in regulatory decision-making, there is still a need to provide precise criteria and recommendations for integrating RWD into clinical trial approvals. To fully utilize RWE in healthcare decision-making, a global regulatory framework must be established. This requires cooperation from all relevant parties, including legislators, regulatory bodies, researchers, and healthcare providers.

Data accessibility and availability

Despite the abundance of data generated daily, accessing high-quality, standardized RWD remains a challenge. Researchers often face hurdles in obtaining secure access to comprehensive datasets necessary for conducting robust clinical trials. This is likely a key reason why RWD is currently being underused in clinical trials.

Featured Resource: Lifebit's federated technology provides secure access to a deep, diverse data catalog from over 100 million patients . Researchers worldwide can securely connect and analyze valuable real world, clinical and genomic data in a compliant manner.

 

 

 

7. Examples of use cases for real world data in clinical research

 

Whilst the use of RWD in clinical trials may be in its infancy and not without considerable challenges- the studies that have been successful in using it have been truly groundbreaking.

 

Real world data (RWD) and real world evidence (RWE) are emerging as transformative tools in clinical research and trials

 

The use of RWE in monitoring the efficacy and safety of COVID-19 vaccinations during the post-authorization phase serves as an illustration of its ability to help to quickly, reactively and transparently inform decision-making.

An important example from the UK is the COALESCE (Capacity and capability Of UK-wide Analysts to LEverage health data at Scale using COVID-19 as an Exemplar) study.  The main objective of the COALESCE project is to give the UK researchers and clinicians the data they need to take effective action to increase the adoption and coverage of the COVID-19 vaccination.

The study provides a comprehensive picture of the under-vaccination rate against COVID-19 and the related hazards across the UK. This was the first epidemiological study that uses EHR data at the individual level for the whole population (approx 67 million people) of the United Kingdom.  

Another example from the EU was a population based study using the French National Health Data System linked to the national COVID-19 vaccination database. This work showed that there was no significant increase for any cardiovascular events following vaccination.

In the US, the National COVID Cohort Collaborative (N3C) was set up to facilitate the analysis of RWD on COVID-19 and potential therapies by researchers and clinicians. Within the N3C Data Enclave, data from over 60 healthcare facilities nationwide was standardized into a common format. 

 

Featured Resource: Catch up on our recent webinar, where Dr Melissa Haendel, PhD, Chief Research Informatics Officer and Marsico Chair of Data Science at University of Colorado gave insightful explanations of how the N3C is set up to guarantee agile, at-scale data harmonization with ongoing quality control and monitoring.

 

The earliest and most representative data to forecast long-term COVID risk were made available by NC3, which was also able to assist in informing health policy through the harmonization and combination of RWD.

 

 

8. What is the current state of guidance around using real world evidence in research, clinical trials and healthcare policy and practice?

 

Realizing the importance of RWE in health research, different regulatory bodies have begun to develop guidelines for using RWD in generating RWE to be used in clinical research and trials.

 

As the use of RWD and RWE becomes more prevalent in clinical research and trials, there is a growing need for guidance and policies to ensure the quality and reliability of the data being used.


It is clear that policies and guidelines are becoming increasingly necessary to guarantee the quality and dependability of the data being used. This is particularly crucial when utilizing RWE to support regulatory approvals, for example approving novel drugs.

In the absence of explicit policies and procedures, using biased or inaccurate data entails a risk that could negatively impact patient health. Additionally, as regulatory bodies develop guidelines to ensure safe access to and use of RWE, this could help to boost public trust in the use of RWE.

 



Current Guidance from the US


The U.S. Food and Drug Administration (FDA), for instance, is becoming more receptive to taking RWD/E into account when making regulatory decisions, such as approving clinical trials and supporting its evaluation of medical products. The FDA is the federal agency tasked with safeguarding public health in the USA by regulating and overseeing the safety and effectiveness of food, drugs, vaccines, and medical devices.

This is a part of the FDA's evaluation of how RWD and RWE can be used as the foundation for regulatory approvals in submissions, as mandated by the Prescription Drug User Fee Act and the 21st Century Cures Act.

The FDA published a framework in December 2018 that described how it will assess the possibility of using RWD/E to assist in regulatory decision-making. 


Important takeaways from the FDA framework are as follows:

  • Fit-for-Purpose Approach: Depending on the particular environment and the regulatory topic being addressed, the kind and quantity of RWE required to support a given regulatory decision should vary. The FDA will take into account a range of RWD sources, such as data gathered from mobile health technology, claims data, patient registries, and electronic health records.

  • Validity and Data Quality: The FDA expects that the RWD utilized to create RWE will be valid, pertinent to the research question, and obtained in a way that complies with all applicable regulations and standards.

  • Submissions and Communications: Early in the process, sponsors that wish to use RWE to assist a regulatory submission should communicate with the FDA to explain their ideas and get input. 




More recently, in August 2023, the FDA released another report, entitled Considerations for the Use of Real-World Data and Real-World Evidence To Support Regulatory Decision-Making for Drug and Biological Products. 

This document gives a framework for the FDA's approach to utilizing RWE in regulatory decision-making, once again emphasizing the importance of data quality, validity, and engagement between stakeholders and regulatory agencies. 

It is likely that additional rules and regulations to guarantee the accuracy and dependability of RWD being utilized are going to be implemented as RWD/E usage grows. The FDA is becoming more willing to employ RWE, but it is crucial to remember that this is still a developing process.

In general, the FDA anticipates that RWE/RWD will be applied with scientific rigor, openness, and regulatory restrictions taken into account. More guidelines appear likely to be forthcoming in 2024, according to a recent FDA blog post about "Realizing the Promise of Real-World Evidence."

 

 

Current guidance from the UK


In terms of guidance for UK regulators, the Medicines & Healthcare products Regulatory Agency have provided some guidance, which has very similar themes to that provided by the FDA.


The guidelines emphasize understanding the data's accuracy, reliability, and validity, along with ensuring privacy and security measures. Clinical trials sponsors are encouraged to validate data sources and describe data handling methods in study protocols. The MHRA also conducts inspections to ensure compliance with data quality and regulatory standards.

For additional advice beyond the guidelines, sponsors can request scientific advice meetings with MHRA experts to discuss specific plans related to licensing, clinical trials, medical devices, and data management.

It is clear that as use of RWD begins to increase, regulators globally will have to refine their guidance to ensure proper use of RWD/E in clinical trials and research. 

Watch the clip below from our recent webinar, where Dr Sandeep Pawar, PhD, Head of Ecosystem Partnerships, Verana Health discusses the key aspects of RWD that global regulators are considering. 





 

9. Emerging trends and future directions for real world data and evidence

 

Emerging trends in RWD suggest a shift towards more robust data collection methodologies, including wearables and mobile apps. These larger and varied additional data sources demand advanced analytics like machine learning and AI to derive meaningful insights efficiently.

 

As RWD/E continues to evolve, its impact on personalized medicine, healthcare policy, and patient outcomes is expected to grow significantly.

 

Future directions for RWD/E involve enhancing data quality, ensuring patient privacy, and fostering data sharing collaborations among stakeholders. Key challenges include data standardization, regulatory compliance, and ethical considerations. As RWD/E continues to evolve, its impact on personalized medicine, healthcare policy, and patient outcomes is expected to grow significantly.

 

 

10. Conclusions: From insights to impact

 

The integration of RWD into clinical research and trials represents a paradigm shift in evidence generation and healthcare delivery. RWD offers a wealth of opportunities for enhancing clinical research, improving patient outcomes, and informing healthcare decision-making. 

However, key challenges remain in obtaining secure access to vital RWD for research. Overcoming these obstacles requires collaboration among stakeholders, including researchers, healthcare providers, policymakers, and technology innovators.

 

 

About Lifebit

 

Lifebit's federated technology provides secure access to deep, diverse data access from over 100 million patients. Researchers worldwide can securely connect and analyze valuable real world, clinical and genomic data in a compliant manner. 

 

 

 

Discover our precision medicine data catalog and book a data consultation with one of our experts now.

 

 

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