A Comprehensive Guide to Applying Qualitative Methodology in Clinical Trials Data Anonymization

In today’s data-driven landscape, the demand for transparency and the exchange of clinical trial data has grown exponentially. While this shift opens doors to more robust research and collaboration, it also presents unique challenges in safeguarding the privacy and confidentiality of trial participants and commercially confidential data. Balancing the need to protect individual privacy while retaining the clinical value of shared data is critical. One approach to navigate this challenge is through data anonymization, specifically using the qualitative methodology.

In this in-depth guide, we will explore the nuances of qualitative anonymization in clinical trials, covering key principles, best practices, and critical considerations to help you apply it effectively. The goal is to help researchers strike the delicate balance between patient re-identification risks and retaining the utility of clinical trial data.

What is Qualitative Anonymization?

Before diving into the application of qualitative anonymization, it’s essential to understand what it entails. Unlike quantitative anonymization, which relies on measurable statistical analysis to ensure data anonymity and preservation of data utility, qualitative anonymization is based on a combination of a set of rules, judgment, expert knowledge, and a case-by-case review of sensitive information. This method introduces subjectivity, meaning researchers must apply a flexible and context-driven approach to protect participant data.

The goal of data anonymization is twofold:

  1. To minimize the risk of participant re-identification.
  2. To preserve the utility of the data for meaningful clinical insights.

The qualitative anonymization process involves defining rules for handling personally identifiable information (PII) and other sensitive data points within clinical trial documents. Given that no statistical models are used in the qualitative approach, the effectiveness largely depends on human expertise, manual review, and contextual understanding.

Key Considerations When Applying Qualitative Anonymization

A well-executed qualitative anonymization process begins with a firm understanding of several core considerations. These guiding principles ensure that data is anonymized appropriately while still retaining its clinical value. Below are the five key considerations to keep in mind:

1. Contextual Judgment

In qualitative anonymization, contextual judgment is critical. Unlike quantitative methods, which rely on automated algorithms or statistical models, qualitative anonymization involves subjectivity. This means researchers must make informed decisions on what data to anonymize, retain, or generalize based on the context of the trial.

Each clinical trial is unique. The identifiers in one study may not pose the same risks as in another. For example, a trial focused on a rare disease could make even minor personal details highly identifying, whereas the same information might pose less risk in a more common disease setting.

Researchers must ensure that the anonymization rules they apply are tailored to each trial, identifying sensitive data and making informed decisions about how to handle it. Contextual judgment helps protect participant privacy while retaining relevant data that contributes to the study’s overall integrity.

2. Manual Review

One of the hallmarks of qualitative anonymization is the reliance on manual review. While automated systems can help identify and classify personal data, the ultimate decision whether to redact or retain potentially sensitive information will always be a manual process.

Manual review is particularly important for high-focus sections of clinical trial documents, such as patient narratives, aggregate-level data, or personal contact information. These sections often contain intricate details that may inadvertently lead to re-identification if not properly anonymized. Conducting a detailed review ensures that identifiers are not overlooked and that any retained data is purposefully kept, rather than being missed.

3. Expert Knowledge Redaction

Subject matter experts (SMEs) play a crucial role in qualitative anonymization. These individuals must have a deep understanding of the clinical trial, the study design, and the data in question. Their knowledge allows them to make well-informed decisions about what data to redact, retain, or transform.

SMEs are responsible for ensuring that sensitive data points are handled correctly and that the anonymization process is both effective and compliant with regulatory guidelines. They also help identify high-priority areas that require special attention, such as adverse events or unique medical histories that might pose a higher re-identification risk.

4. Redaction vs. Transformation

A critical decision in the anonymization process is determining when to redact data and when to transform it. Redaction involves completely removing identifiable information, while transformation refers to replacing it with more generalized or abstract categories.

For example, instead of removing all geographical information, researchers might transform "United States" into the broader category of "North America." Similarly, for gender-specific trials, "Female" might be retained in the dataset for clarity.

These decisions are made based on trial-specific factors, such as whether the information has already been publicly disclosed (e.g., on ClinicalTrials.gov), if it is a single-race or single-gender study or how critical the data is for the study’s integrity. The choice between redaction and transformation has a significant impact on the balance between protecting participant privacy and preserving the utility of the data.

Further, the process of anonymizing the data is more complex than straight redaction. Purpose built software solutions may be needed to accomplish this, especially for large projects that may involve anonymization of hundreds and commonly thousands of pages of sensitive participant information.

5. Iteration and Validation

Given the subjectivity and human element involved in qualitative anonymization, it’s vital to approach the process iteratively. This means applying multiple rounds of review and validation to ensure that the anonymization rules are consistently applied and that no sensitive data has been overlooked.

Iteration allows researchers to revisit the rules they initially defined and adjust them based on findings from the manual review process. This ongoing validation ensures that anonymization is effective, while also ensuring consistency across different datasets and study documents.

Defining Anonymization Rules

Once the key considerations are understood, the next step is to define specific rules for anonymization. These rules are not static and may evolve as the trial progresses or as new data becomes available. Researchers often revisit and refine these rules periodically to ensure they remain relevant and effective.

Below is an example of how anonymization rules are applied to specific data categories:

Each research team or organization will need to decide what anonymization or redaction rules to apply.  

Anonymization of Adverse Events: A High-Priority Consideration

One of the most critical elements in qualitative anonymization is the disclosure and protection of adverse event data. Adverse event data is often prioritized by regulatory bodies, meaning that even in heavily redacted or suppressed datasets, adverse events should be disclosed wherever possible

Regulatory agencies emphasize the importance of adverse event retention because of its impact on understanding the safety profile of a drug. However, qualitative methodologies must strike a careful balance to avoid inadvertently exposing sensitive participant information.

There are two main strategies for dealing with adverse events:

  1. Selective Retention: Researchers can identify rare, sensitive and observable adverse events and review them within the context of the study. If these events are relevant to the drug’s safety profile or the trial indication, they may be retained. Otherwise, they may be anonymized or generalized to a higher-level group term.
  2. Complete Retention: In certain cases, all adverse events are retained, despite the potential risk of re-identification. This approach requires careful consideration, as retaining all adverse event data is likely to increase the risk of participant re-identification.

Contextual Review in Anonymization of Adverse Events

Contextual review is a key component of qualitative anonymization, particularly when it comes to assessing adverse events. The context in which a term appears can determine whether it is retained, generalized, or redacted.

For example, in a diabetes study, an adverse event like "amputation of the left foot" may be retained because it is relevant to the disease being studied. In contrast, in a non-psychiatric trial, a term like "schizophrenia" might be generalized to "psych disorder" if it is unrelated to the study drug or trial indication.

Contextual review allows researchers to make more informed decisions about how to handle specific data points, ensuring that the data remains useful without compromising participant privacy.

Best Practices for Successful Qualitative Anonymization

To ensure the success of a qualitative anonymization strategy, the following best practices should be followed:

  1. Frequent Iteration: Because qualitative anonymization is subjective, multiple rounds of review are essential. This allows researchers to revisit their rules and refine them as needed to ensure consistency and effectiveness.
  2. Expert Involvement: SMEs are crucial to the success of qualitative anonymization. Their knowledge of the trial and its data ensures that anonymization is applied correctly and in compliance with regulatory requirements.
  3. Balancing Redaction and Data Utility: Over-redaction can strip a dataset of its clinical value, while under-redaction can expose participants to re-identification risks. Researchers must carefully balance these competing priorities to ensure that the data remains both secure and useful.
  4. Regulatory Compliance: It’s critical to adhere to regulatory guidelines when applying qualitative anonymization. This includes understanding the requirements of agencies like Health Canada, the FDA, and the European Medicines Agency (EMA), all of which have specific standards for data anonymization.

Conclusion

Qualitative anonymization offers a flexible and adaptable approach to data protection in clinical trials. While it requires more manual effort and subjective judgment than quantitative methods, its flexibility allows researchers to tailor anonymization practices to the unique characteristics of each trial, should they choose to do so.

By following best practices—such as thorough manual reviews, leveraging subject matter expertise, and applying a context-specific approach—researchers can minimize the risk of participant re-identification. The iterative nature of qualitative anonymization ensures that any sensitive information is adequately protected while allowing for adjustments and improvements in the anonymization strategy over time. This is especially important in high-stakes areas like adverse event data, where careful balance is needed between maintaining data integrity and ensuring privacy. 

Additionally, a successful qualitative anonymization process must maintain compliance with global regulatory standards, such as those set by the FDA, EMA, or Health Canada. Regular audits, validations, and updates to anonymization protocols help ensure the data remains both compliant and usable for ongoing research efforts.

Qualitative anonymization can support compliance with data protection requirements, however, it often comes with a challenge: preserving data utility while remaining within acceptable risk thresholds. This balance has been known to lead to excessive redaction. Further, understanding the true risk of re-identification is difficult if not impossible as the resulting anonymized data is not statistically assessed. Further, the information loss or resulting data utility is not analyzed which makes the value of the resulting anonymized data unknowable. Quantitative anonymization, on the other hand, results in clear and measurable criteria for achieving a defined risk threshold while providing the highest possible level of data utility. This highlights the significant differences between the two methodologies. 

Ultimately, qualitative anonymization can empower researchers to share clinical trial data, contribute to the advancement of science and protect the privacy of participants. By applying thoughtful, context-driven anonymization techniques, clinical trial data can be disseminated more widely, fostering collaboration and driving innovation in medical research without compromising individual confidentiality.

 Before selecting an anonymization approach for your clinical data, we recommend understanding the similarities and differences between qualitative and quantitative methodologies such that you can make an informed choice.  Real Life Sciences provides comprehensive services and software solutions for both qualitative and quantitative anonymization. For inquiries or to discuss potential projects, please reach out to us at inquiry@rlsciences.com.

Navigating Adverse Event and Medical History Clinical Data Anonymization  

In the ever-evolving landscape of clinical trials, managing data responsibly while adhering to regulatory and company defined disclosure requirements is crucial. This post will explore a best-in-class   methodology to assess and anonymize  Adverse Event (AE) and Medical History (MH) data.  

Understanding Adverse Event Assessment

The Objective

At the heart of Adverse Event assessment is the goal to accurately evaluate and report AEs while minimizing the risk of patient re-identification. Achieving this requires a systematic process that incorporates both quantitative and qualitative methods.

A Hybrid Approach

At RLS, we employ a hybrid approach to navigate the complexities of AE assessment. This method integrates quantitative models—which provide metrics, automation, and efficiency—with qualitative insights that derive from domain knowledge and the specific context of the trial and the participant narrative itself. The quantitative component primarily focuses on risk-based anonymization assessments applicable to participant quasi-identifiers, which help us define equivalence classes for the data.

By evaluating similar trials a broader participant population may be applied resulting  in equivalence classes smaller than traditional classes. This allows us to confidently retain more data while protecting participant privacy. Other methods include setting a risk of re-identification threshold—say, 9% depending on how the data will be shared. Using assessment capabilities such as reference populations and risk of re-identification thresholds combined with a qualitative review of the participant narratives found in clinical documents, informed decisions regarding retaining the AE data can be concluded.

The Assessment Process

The RLS assessment process unfolds in three key steps:

  1. Automated AE Processing: The first step involves filtering AEs through the RLS internal Rare Sensitive and Observable database. This flags AEs that require further scrutiny, allowing us to focus on those that are truly rare and sensitive - which are most important to evaluate more closely.
  2. Quantitative Assessment: In the second step, we apply the quantitative assessment results to retain AEs occurring 11 or more times, or occurring in classes or groups suggested by the k-sample. The k-sample value is the result of the analysis of quasi-identifiers which creates smaller groupings that adhere to the threshold.  
  3. Contextual Review: For AEs that appear fewer than four times, we conduct a detailed contextual review. This step assesses each of the remaining AEs against the specific characteristics of the trial such as trial indication, safety drug profile or re-identifiability if it is observable, knowable or replicable, ensuring that we make informed decisions about anonymization or retention based on clinical relevance.

Through this systematic process, we are able to  maximize data utility while the necessary safeguards and risk of re-identification are fully understood before submitting the data  for regulatory and third party review.

Maximizing Data Utility

Balancing Re-Identification Risk and Usefulness of the Resulting Data

The overarching goal of our approach is to maximize the data utility of AEs.After all, AEs are a critical component for understanding the trial results and how it may impact secondary research efforts. Even if an event is rare or sensitive, it may still be crucial to the context of the study. For instance, if Schizophrenia appears among two participants as an AE in a trial focused on mental health, we prefer to retain this information due to its relevance, whereas other less pertinent AEs might be generalized to higher level terms. 

We often visualize the filtering process as a funnel. Starting with the broad pool of all AEs that have occurred in a trial, we progressively narrow down our focus through rigorous assessment through quantitative and qualitative means, ultimately retaining only those AEs that provide valuable insights without compromising patient privacy outside the desired re-identification risk threshold.

Examples of Contextual Review

To illustrate the significance of contextual review, consider three hypothetical AEs that occur less than four times within a study:

  1. Schizophrenia: If associated with a study on psychiatric disorders, this AE might be retained due to its relevance.
  2. Immunodeficiency Syndrome: If linked to one participant and viewed as potentially stigmatizing, it may be generalized to a higher level term to protect the individual’s identity.
  3. Burns: This could be transformed to a system organ class due to its sensitivity and potentially something that is permanently observable, therefore posing a higher risk of re-identification.

These examples emphasize that our recommended approach is not a one-size-fits-all solution; context plays a  significant role in finalizing the determination to retain, generalize or possibly redact.

Anonymization of Medical History Data

While managing AEs is critical, anonymizing Medical History data presents its own set of challenges. It’s essential to evaluate the information based on specific criteria to determine whether it should be retained, redacted, generalized, or suppressed.

Key Considerations

We identified four crucial considerations when anonymizing medical history data:

  1. Entities: Who does the Medical History reference pertain to? Is it participant-level, aggregate-level, or non-participant level (e.g., family history)? The answers to these questions will likely determine the approach taken to redact or retain per entity.
  2. Rule Level: What specific decisions and anonymization strategies apply? When should  the standard/global redaction rules vs project specific rules? There needs to be a clear understanding and alignment that project specific rules will take precedence over standard rules. For example, if Nephrolithiasis is a term generally protected but it is present in a Urinary Disorder narrative, hence it will be retained because it is relevant to the context of the patient narrative section. Therefore It’s critical to understand the hierarchy of said rules, especially in cases of conflicting information while considering the trial characteristics.
  3. Conditional Evaluations: Are there specific circumstances affecting how we treat certain Medical History terms? This ensures the approach is flexible and tailored to individual cases.
  4. Standards: Establishing a database of common terms helps us maintain consistency. This ensures that widely recognized terms that pose minimal risk of re-identification are retained.

Practical Applications

In practice, the approach to Medical History anonymization can vary significantly based on trial characteristics. For example:

Contextual Factors

The context of each Medical History term is critical in determining the most appropriate anonymization technique to apply. For example, if Diabetes is mentioned in a context that links it directly to an Adverse Event, it may be retained. However, if it appears without context, it might be redacted to protect against risk of re-identification.

Conclusion

In summary, navigating the complexities of anonymizing and disclosing Adverse Event and Medical History data requires a thoughtful and structured methodology. By applying a hybrid method that integrates quantitative and qualitative assessments, data utility can be  maximized while safeguarding patient privacy while adhering to a defined risk threshold. This careful balance is essential in the context of clinical trials where ethical,regulatory and company standards and policies must be met.

As we continue to refine our processes, we aim to contribute to a culture of transparency,  trust and participant privacy in clinical research, ensuring that vital data is preserved and utilized responsibly for the benefit of all stakeholders involved.

Clinical Data Anonymization: How to Approach Voluntary Data Sharing

In today's digital age, data is often hailed as the new currency. Individuals and organizations alike generate and collect vast amounts of data every day. With this influx of data comes the opportunity for valuable insights, innovations, and advancements. However, it also raises concerns about privacy, security, and ethical use.

One way to harness the power of data while respecting privacy is through voluntary data sharing. This practice involves individuals or organizations willingly sharing their data for secondary research, analysis, or other purposes. Voluntary data sharing can foster collaboration, drive innovation, and contribute to the greater good. But what if you're not familiar with anonymization techniques? How can you navigate this landscape responsibly? Let's explore some key steps:

Understand the Risks and Benefits

Before diving into voluntary data sharing, it's crucial to understand the risks and benefits involved. Sharing data without proper anonymization can compromise privacy and confidentiality. However, anonymized data can still carry risks, especially when combined with other datasets or advanced re-identification techniques. On the other hand, sharing data can also lead to valuable insights, improved services, and societal benefits. Assessing these trade-offs is essential for making informed decisions.

Educate Yourself

While you may not be familiar with detailed anonymization techniques, you can educate yourself on the key concepts. Learn about common methods and the differences between Qualitative Anonymization & Quantitative anonymization approaches. Understand the principles behind these techniques and their implications for data privacy. Real Life Sciences provides online resources, webinars, and materials to help you grasp the fundamentals of anonymization.

Below are four basic areas of data anonymization that are beneficial know and understand:

1. Removing Direct Identifiers

Removing direct identifiers involves the elimination of explicit personal information from datasets, such as names, addresses, social security numbers, and phone numbers. By stripping away these identifiers, the risk of re-identification is significantly reduced. However, it's essential to recognize that even seemingly innocuous pieces of information can potentially lead to re-identification when combined with other data sources. Therefore, careful consideration must be given to the context and sensitivity of the data being shared.

2. Pseudonymization

Pseudonymization is a technique that replaces identifiable information with artificial identifiers or pseudonyms. Unlike anonymization, which irreversibly removes identifying information, pseudonymization allows data to be linked back to individuals using additional information held separately (e.g., a key or lookup table). While pseudonymization offers a level of privacy protection, it's crucial to implement robust security measures to safeguard the link between pseudonyms and original identities.

3. Generalization

Generalization involves aggregating or summarizing data to a higher level of abstraction, thereby reducing the granularity of the information while preserving its utility for analysis. For example, instead of recording exact ages, data may be grouped into age brackets (e.g., 20-30, 30-40). While generalization helps protect individual privacy by minimizing the risk of re-identification, it can also introduce information loss and reduce the precision of analysis. Therefore, careful consideration must be given to the trade-off between privacy protection and data utility when applying generalization techniques.

4. Data Masking

Data Masking techniques involve altering or obscuring data values to prevent the identification of individuals while preserving statistical properties of the dataset. For example, data may be masked by replacing precise values with ranges or by adding random noise to numerical values. While these techniques help protect privacy, they can also introduce distortions that affect the accuracy and reliability of analysis. Therefore, it's essential to carefully assess the impact of masking on data quality and analytical outcomes.

Seek Expert Guidance

If you're uncertain about anonymization, always seek expert guidance. Reach out to professionals with expertise in data privacy, security, and ethics. They can provide valuable insights, recommend best practices, and help you navigate potential pitfalls. Collaborating with experts can enhance the effectiveness and reliability of your data sharing efforts.

Prioritize Transparency and Consent

Transparency and consent are fundamental principles of ethical data sharing. Clearly communicate the purpose of data sharing, how the data will be used, and any associated risks. Obtain explicit consent from individuals before sharing their data, ensuring they understand and agree to the terms. Transparency and consent build trust, promote accountability, and uphold individuals' rights over their data.

Consider Alternatives

If anonymization seems daunting or impractical, consider alternative approaches to data sharing. For example, you could aggregate data to remove personally identifiable information while still preserving valuable insights. Alternatively, you could establish data sharing agreements that restrict the use of sensitive information and prioritize privacy protection. Exploring different options allows you to find the approach that best balances utility and privacy. However, Data Anonymization is usually the most effective and reliable way to share data.

Embrace Continuous Learning

Data privacy and anonymization are complex and evolving fields. Embrace a mindset of continuous learning and improvement. Stay updated on the latest developments, techniques, and regulations related to data privacy. Engage with the broader transparency community through forums, conferences, and networking events. By staying informed and adaptable, you can effectively navigate the challenges of voluntary data sharing.

Conclusion

Voluntary data sharing offers tremendous potential for driving innovation and societal progress. However, it also poses challenges, particularly for those unfamiliar with anonymization techniques. By understanding the risks and benefits, educating yourself, seeking expert guidance, prioritizing transparency and consent, considering alternatives, and embracing continuous learning, you can navigate voluntary data sharing responsibly and ethically. Together, we can harness the power of data while respecting privacy and empowering individuals.

Clinical Data Sharing: Why It's Important To Share Clinical Results

Clinical data sharing plays a crucial role in advancing scientific knowledge and improving patient care. 

  1. Transparency and Scientific Advancement: Data sharing allows researchers to access clinical trial data, fostering transparency and enabling scientific advancements. Pharmaceutical companies have recognized this importance and committed to sharing participant-level data, study-level data, and protocols1.
  2. Reducing Research Waste: By sharing data, we can avoid duplicative efforts and reduce research waste. Access to existing clinical trial data helps researchers build upon previous work, leading to more efficient resource utilization.
  3. Patient-Centered Care: Data sharing contributes to better decision-making for patients and healthcare professionals. When data is accessible, clinicians can make informed choices based on evidence from a broader pool of studies.
  4. Challenges and Commitment: Implementing good data sharing principles requires resources, time, and commitment. Despite challenges, enhancing data sharing remains essential for scientific collaboration and patient well-being.

This particular Blog will focus on advancing research - how the sharing of participant level data with qualified researchers can accelerate novel research and inform the design of future clinical trials, some examples of successful sharing initiatives and an outline of common challenges associated with data sharing.

Transparency and Scientific Advancement

  1. Transparency and Open Science:
    • Clinical trial data, including participant-level data, study protocols, and results, are valuable resources. When researchers share this information openly, it promotes transparency.
    • Transparency is essential for scientific advancement. By allowing others to scrutinize and build upon existing research, we accelerate progress.
    • Open science practices, such as pre-registering studies and sharing raw data, enhance credibility and reproducibility.
  2. Collaboration and Replication:
    • Data sharing encourages collaboration among researchers, institutions, and disciplines. It fosters a sense of community and collective effort.
    • Replication studies become feasible when data is accessible. Replicating findings ensures robustness and confirms or challenges initial results.
  3. Pooling Data for Insights:
    • Combining data from multiple studies (meta-analysis) provides statistical power. Researchers can draw more accurate conclusions about treatment efficacy, safety, and adverse events.
    • For rare diseases or conditions, pooling data across trials is especially valuable.
  4. Challenges and Solutions:
    • Challenges include privacy concerns, data governance, and intellectual property. Striking a balance between openness and protecting sensitive information is crucial.
    • Initiatives like the FAIR principles (Findable, Accessible, Interoperable, Reusable) guide responsible data sharing.
    • The Clinical Research Data Sharing Alliance (CRDSA) is a multi-stakeholder consortium that services the clinical data sharing ecosystem with a shared goal of accelerating the discovery and delivery of life-saving and life-changing therapies to patients by expanding the research value of the data collected through the clinical development process

Some examples of successful clinical data sharing initiatives include:

  1. PhRMA/EFPIA Commitment (2013):
    • In 2013, the Pharmaceutical Research and Manufacturers of America (PhRMA) and the European Federation of Pharmaceutical Industries and Associations (EFPIA) endorsed a commitment to share participant-level data, study-level data, and protocols from clinical trials of US and EU registered medicines with qualified researchers. They also aimed to provide public access to clinical study reports (CSRs) or at least synopses from trials submitted to the FDA, EMA, and EU Member States2.
  2. Advancements in Drug Development:
    • Successful data sharing has led to tools that optimize drug development. These initiatives help bring new therapies to patients in need by accelerating the research process and improving decision-making3.
  3. Clinical Research Data Sharing Alliance (CRDSA):
    • The CRDSA focuses on sharing patient data generated from clinical trials. By doing so, they transform the trial process itself, improve the patient experience, and deliver life-saving and life-changing therapies faster and at a lower cost to society4.


Data sharing comes with certain challenges that are often faced by researchers.  

  1. Cultural Shift
    • Challenge: Establishing a culture where data sharing is the norm requires overcoming traditional practices and concerns.
    • Recommendation: Stakeholders should foster an environment where data sharing is expected and commit to responsible strategies5.
  2. Timelines and Compliance:
    • Challenge: Meeting deadlines for sharing various types of clinical trial data (e.g., full analyzable datasets, metadata, and analytic datasets) can be demanding.
    • Recommendation: Sponsors and investigators should adhere to specified timelines for data sharing6.
  3. Sensitive Data Risks:
    • Challenge: Sharing sensitive clinical trial data while protecting privacy and managing risks.
    • Recommendation: Implement operational strategies like data use agreements, independent review panels, and transparency7.
  4. Organizing and Presenting Data:
    • Challenge: Researchers struggle with organizing data in a useful and presentable manner.
    • Recommendation: Providing clarity on copyright, licensing, repository options, and metadata standards can help8.

Remember, addressing these challenges is essential for advancing scientific knowledge and benefiting patients.

Real Life Sciences specializes in data anonymization for regulatory compliance and voluntary sharing.

Risk-Based Clinical Data Anonymization, EMA Policy 0070, and Health Canada’s PRCI: A Comprehensive Overview

Introduction

In the era of digital health, the importance of clinical data cannot be overstated. However, the need for data privacy and protection is equally paramount. This has led to the development of risk-based clinical data anonymization strategies, regulatory policies like the European Medicines Agency’s (EMA) Clinical Data Publication (CDP) Policy 0070, and initiatives like Health Canada’s Public Release of Clinical Information (PRCI).

Risk-Based Clinical Data Anonymization

Risk-based clinical data anonymization is a strategy that measures the probability of re-identifying individuals (in this case, subjects that have participated in a clinical trial) through indirectly-identifying pieces of information. This probability is then reduced through various data transformations, such as offsetting dates, generalizing disease classifications or demographic values, or removing outlier values. The goal is to balance the need for data utility and the requirement for privacy.

Why EMA and Health Canada Prefer Risk-Based Anonymization

Balance: Risk-based anonymization allows for a more nuanced approach that balances privacy protection with the need for transparency and research. Traditional methods often remove too much data, hindering its usefulness for research purposes.

Minimizing Data Loss: By assessing the risk of re-identification for each data attribute, risk-based approaches can retain more valuable information while still protecting privacy. This allows for more comprehensive analysis and better insights.

Adaptability: The risk of re-identification can vary depending on the context and available information. Risk-based methods can adapt to these changing factors, ensuring appropriate protection in different scenarios.

Compared to other techniques, risk-based anonymization provides a more sophisticated and balanced approach to protecting privacy while enabling valuable research and data sharing in the life sciences industry. This aligns with the goals of regulators like EMA and Health Canada to promote public health and transparency while upholding individual privacy rights.

Both EMA and Health Canada have specific guidelines and regulations outlining their expectations for risk-based anonymization. This ensures consistency and accountability.

Key Considerations of Risk Based Anonymization

Risk Threshold

The risk threshold in the context of clinical data anonymization is defined as the minimum amount of de-identification that must be applied to a dataset for it to be considered de-identified.

In more practical terms, it refers to the probability of correctly assigning an identity to a participant (or clinical trial subject) described in the clinical reports. This is also referred to as the probability of re-identification.

For instance, both the European Medicines Agency (EMA) and Health Canada have set an acceptable probability threshold at 0.09. This means that the likelihood of re-identifying an individual from the anonymized data should be less than 9 in 100 for the data to be considered sufficiently anonymized.

The number of data attributes in the dataset requiring anonymization depends on the dataset’s risk score. Higher risk scores mean more fields must be anonymized. The goal is to ensure that the probability of re-identification is very small, thereby protecting the privacy of individuals while still allowing the data to be useful for research purposes.

The risk threshold in clinical data anonymization is determined based on several factors:

  1. Data Disclosure Precedents and Industry Benchmarks: The risk threshold is often set based on historical data disclosure precedents and industry benchmarks.
  2. Regulatory Guidance: Regulatory authorities such as the European Medicines Agency (EMA) and Health Canada provide guidance on acceptable risk thresholds.
  3. Risk Assessment: Anonymization requires a risk assessment to a predetermined threshold (often 0.09) to determine the probability of re-identification of a clinical trial subject.
  4. Dataset Characteristics: The number of data attributes in the dataset requiring anonymization depends on the dataset’s risk score. Higher risk scores mean more attributes must be anonymized.
  5. Sources of Re-identification Risk: Factors such as the number of participants, whether the trial is in a rare disease, subjective assessment of potential socioeconomic harm to patients if there is re-identification, and the perceived re-identification risk of certain pieces of information (whether they would be knowable by potential adversaries) are considered.

Determining the risk threshold is a complex process that involves considering various factors, including industry standards, regulatory guidance, and the specific characteristics and risks associated with the dataset.

Clinical Data Utility

Data utility in the context of clinical data anonymization refers to the usefulness of the data after it has been anonymized. The goal of risk-based anonymization is to protect the privacy of individuals in a quantifiable manner, but it’s equally important to ensure that the anonymized data remains useful for research purposes.

Preserving data utility during the anonymization process involves quantitative measurements at the document/data level and a well-defined and precise implementation of the selected rules to prevent over-redaction or over-anonymization.

For instance, pseudonymization, which replaces identifiers with a pseudonym, retains more data utility than anonymization, which may involve redacting or masking identifiers. This is because pseudonymization allows for meaningful secondary analyses and follow-on research while maintaining patient confidentiality.

In summary, data utility is a critical aspect of data anonymization. It ensures that the anonymized data can still provide valuable insights and contribute to scientific research, public health, and other secondary purposes.

Methods of Risk Based Clinical Data Anonymization

Clinical data anonymization involves various techniques to ensure the privacy of individuals while maintaining the utility of the data for research purposes. Here are some commonly used methods:

  1. Generalization: Specific values are categorized into groups or ranges. For example, exact ages might be replaced with age groups, and countries might be grouped into continents.
  2. Suppression or Redaction: This involves removing or redacting sensitive attributes entirely.
  3. Masking: Parts of the data are replaced with symbols like (*, $, #).
  4. Date Offsetting: This involves altering an identifiable date related to an individual and applying an alternative or random date throughout the data or document(s). To maintain usefulness of the data, offset dates maintain the same duration between events as compared to the original dates.
  5. Recoding: Categories of a variable are recoded into broader categories.
  6. Local Suppression: Specific values of a variable are suppressed.

These techniques can be used individually or in combination, depending on the specific requirements of the data set and the level of anonymization required.

Evaluating and Selecting a Specialized Risk Based Anonymization Partner

Choosing the right company for risk-based anonymization of clinical data is crucial, as it requires balancing utility with robust privacy protection. These are the principles Real Life Sciences is built upon. Here are some key considerations:

Expertise and experience:

Technology and infrastructure:

Company reputation and ethics:

Conclusion

Risk-based clinical data anonymization, EMA Policy 0070, and Health Canada’s PRCI are all significant strides towards a future where clinical data is both accessible and secure. These initiatives not only foster transparency and trust but also pave the way for innovation and advancement in clinical research.

While these initiatives are a step in the right direction, it is crucial to continue refining these strategies to ensure the balance between data accessibility and privacy is maintained. As we move forward, the focus should be on developing robust, scalable, and efficient methods for data anonymization and public release, keeping in mind the ever-evolving landscape of digital health and data privacy regulations.

When implementing a risk based anonymization approach, engage with experts, like Real Life Sciences for assistance. This will accelerate your project and increase your probability of a high quality and on time project.

Unlocking the Secrets: Navigating Company Confidential Information (CCI) in Clinical Trial Transparency

In the realm of clinical trials, transparency is not just a buzzword but a fundamental principle guiding the advancement of medical science. However, amidst the push for transparency across regulators such as Health Canada, the FDA and EMA, one significant challenge looms large: handling company confidential information (known also as confidential business information (CBI) when referring to Health Canada). 

What exactly constitutes company confidential information in the context of clinical trials, why is it so challenging to manage, and what potential solutions exist to strike a balance between transparency and protecting sensitive data?

Defining Company Confidential Information

Before delving into the complexities, it's crucial to establish what falls under the umbrella of company confidential information in clinical trials. Simply put, it encompasses proprietary data that clinical trial sponsors consider integral to maintaining a competitive edge. This could include:

For further clarification, EMA defines CCI as:

“...any information contained in the clinical trial information submitted to the CTIS which is not in the public domain, or publicly available, and where disclosure may undermine the legitimate economic interest or competitive position of the owner of the information.

Examples of What is NOT Considered Confidential Information: 

The Tightrope Walk: Why It's Challenging

Balancing the imperative of transparency with the need to protect company confidential information is akin to walking a tightrope. Several factors contribute to the complexity of this task:

Charting a Course: Potential Solutions

While navigating the labyrinth of confidentiality in clinical trials may seem daunting, several strategies can help reconcile competing interests:

Practical CCI Strategies & Considerations

Things to Know

  1. Before you redact, Sponsors should be aware of the information already available in the public domain for their product’s development
  2. The extent of the redaction should be limited only to the word(s), figure(s) and pieces of text that can be considered CCI
  3. For CTR projects, EMA has indicated you may indicate CCI in non-public version documents for purposes of Member State awareness (e.g.; highlight or apply a red box around the perimeter of CCI text) 
  4. For EMA Policy 0070 projects, preparing a robust justification for your CCI redactions is critical to avoid unnecessary rejections. While justifications are not required for CTR public version documents, we recommend you create an internal justification log and store it in case it is needed in response to an Request for Information (RFI).

Limiting Company Confidential Information

  1. Limit unnecessary sharing of confidential information during the authoring process; often referred to as “minimization”
  2. Involve Real Life Sciences who retains relevant and practical experience with scientific and technical skills in the CCI identification process
  3. Follow a consistent decision-making process and tag references to CCI during authoring 

A Suggested Three Step Process for Handling CCI

  1. Rule out information in the public domain
  2. Confirm the information is innovative and could undermine the economic interest of the business
  3. As an exception, determine if the information is not deemed to be innovative but could still undermine the economic interest or competitive position of the business

Use Technology to Streamline your CCI Identification and Redaction Process

A purpose built enterprise-ready system, such as RLS Protect, is designed to support your clinical document redaction process from end-to-end. Example capabilities include, but are not limited to

Efficiency and Quality

Collaboration & Visibility 

Security

Conclusion

In the quest for transparency in clinical trials, navigating the labyrinth of company confidential information is a formidable challenge. Yet, by adopting a balanced approach that safeguards sensitive data while promoting openness, stakeholders can forge a path toward greater transparency without compromising innovation or commercial interests. As the landscape evolves, maintaining a nuanced understanding of confidentiality will be paramount in unlocking the secrets to advancing medical science for the benefit of all.

The EMA Policy 0070 and Health Canada Public Release of Clinical Information (PRCI) Anonymization Report

How the New Anonymization Report Template Impacts Study Sponsors

Collaboration between Health Canada & EMA

While Health Canada PRCI and EMA Policy 0070 are distinct policies, Health Canada and EMA collaborate on harmonizing approaches to clinical trial transparency. In most circumstances, both recommend a 9% re-identification risk threshold for disclosure submissions and provide a unified Anonymization Report Template. Further, both share a similar scope for submission with focus on ICH CTD/eCTD M2.5, M2.7 and M5.3.

What is the Anonymization Report Template?

The Anonymization Report Template is a crucial document used in these disclosure submissions. . The report captures the methods you applied to anonymize the clinical data and assessment of risk of re-identification applicable for your submission.

Thoroughly completing and thoughtfully justifying your anonymization methods in the report is crucial for a smooth and successful EMA Policy 0070 submission. If you have any specific questions about the template or the anonymization process, consider contacting Real Life Sciences for more information.

Here's a breakdown of the key aspects of the anonymization template (key terms defined below):

Structure and Content:

Importance and Benefits:

Key terms

Risk of Reidentification

In the context of clinical trial transparency, the risk of re-identification refers to the probability of an adversary successfully identifying  a specific individual who participated in  the trial, even when their data has been anonymized or de-identified. This re-identification could potentially happen through a combination of factors, such as:

Risk Threshold

Risk Threshold is the maximum acceptable probability of an individual being correctly re-identified from anonymized clinical trial data. It's a crucial measure for balancing data privacy and transparency.

Residual Risk 

Residual risk is the remaining risk that individuals could be re-identified from a dataset, even after anonymization techniques have been applied. It's the risk that lingers even after safeguards are in place.

Data Utility 

Data utility is the usefulness and accuracy of the anonymized data for its intended purpose. It essentially describes how well the anonymized dataset retains its value for analysis and insights, while still protecting the privacy of individuals.

Why a New Template?

In designing the new Anonymization Report Template (launched in 2023), EMA and Health Canada aimed to achieve the following benefits:

Contact Real Life Sciences:

If you have questions or would like some consultation prior to starting your EMA Policy 0070 or Health Canada PRCI project, please contact us directly at inquiry@rlsciences.com.

What To Know Before Embarking On Your EMA Policy 0070 Submission Project

Making the Most of the European Medicines Agency (EMA) Policy 0070 Pre-Submission Meeting

Policy 0070 Background

EMA Policy 0070  (otherwise known as Clinical Data Publication, or CDP), is a policy implemented by the European Medicines Agency (EMA) that governs the publication of clinical data for medicinal products for human use. The policy aims to increase transparency and public access to clinical data while protecting patient confidentiality and commercially sensitive information. 

History of CDP/Policy 0070

Key Policy 0070 Terms

Committee for Medicinal Products for Human Use (CHMP): The scientific committee of the EMA that provides opinions on marketing authorization applications.

Marketing authorization application (MAA): The formal application submitted by a pharmaceutical company to obtain marketing authorization for a medicinal product.

Marketing authorization holder (MAH): The individual or company responsible for the marketing of a medicinal product.

Pre-submission Meeting (PSM): The meeting held between Sponsor and EMA to review the scope, strategy and timescales for the Policy 0070 submission.

See our Policy 0070 FAQ page for more background info and key terms.

What is the EMA Policy 0070 Pre-Submission meeting?

The EMA Policy 0070 pre-submission meeting is a critical  opportunity for sponsors to discuss their plans for complying with the policy with the European Medicines Agency (EMA) before formally submitting their Proposal Package (PP). 

The pre-submission meeting will confirm in-scope and out-of-scope content, anonymization strategy, use of vendor, CCI (if applicable) and submission schedule. This meeting can help to avoid delays and ensure a smooth submission process. It is especially helpful and valuable for sponsors submitting a Policy 0070 package for the first time.

Where in the MAA process does the Pre-Submission meeting occur?

The pre-submission meeting occurs prior to submitting the Proposal Package (PP). The value of the meeting comes from aligning early with the Health Authority on the personal data anonymization strategy and approach to protection of confidential information.

Ideally, your pre-submission meeting should occur no later than when you receive the invitation letter from EMA. You can (and should) begin your Policy 0070 project work prior to the invitation letter.  Please refer to the timeline illustration below for more details.

Reviewing and confirming your anonymization strategy with EMA early in the process allows you to incorporate their feedback into your Proposal Package, thereby streamlining the validation process and potentially preventing future delays or rework.

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Figure 1F

The Pre-Submission Meeting

 Here's what you need to know:

Purpose of the Pre-Submission meeting

Who should attend?

What to prepare

Key Topics for Discussion

Benefits of a Pre-Submission Meeting

Additional Preparation Items Before Embarking on Your Policy 0070 Project

  1. Assemble your team including Clinical Transparency, BioStats, Regulatory
  2. Engage with Real Life Sciences for expert advice, preparation for and facilitation of the pre-submission Meeting and delivery of the complete proposal and final packages
  3. Gather the in-scope Policy 0070 CTD document set - specifically ICH CTD/eCTD M2.5, M2.7 and M5
  4. Gather the applicable trial datasets which can be used in support of an empirical analysis of subject data and later applied to the in-scope documents by RLS
  5. Review the EMA guidance for Policy 0070 
  6. Plan an internal kickoff for your project with help from RLS 

Policy 0070 Moving Forward

During the Policy 0070 relaunch in 2023 EMA and Health Canada demonstrated increased partnership which is expected to continue moving forward. This is evidenced, for example, through the jointly revised Anonymization Report Templated to be used by Sponsors when submitting their Policy 0070 (and Health Canada PRCI) Proposal Packages. This collaboration between the two Health Authorities benefits Sponsors given the similarities in the two policies. 

The revised Anonymization Report Template requires trial sponsors to indicate which type of anonymization methodology they have adopted for the submission, among other important points pertinent to the project. If trial sponsors do not indicate a quantitative  risk-based methodology they must include justification as to why. A quantitative anonymization methodology, otherwise referred to as “risk-based anonymization” applies empirical measures to assess the risk of reidentification of the resulting anonymized data - based on analysis of the subject data itself. 

In contrast, a qualitative or “rules based anonymization” approach applies a static set of anonymization rules to the data, however, the results are not quantifiable and therefore the risk of r identification is not measured. As the Anonymization Report Template has evolved, including this most recent set of revisions as published Fall 2023, it is becoming increasingly clear the Health Authorities have a strong preference to a Quantitative risk-based anonymization methodology. While RLS supports both quantitative and qualitative approaches, our experience suggests most Sponsors prefer quantitative methods because it is measurable (the risk of reidentification is known) and it supports maximizing the clinical utility of the resulting data.  

Given EMA and Health Canada appear to prefer a risk-based anonymization method when evaluating Policy 0070 submissions, and because of the inherent benefits of a risk-based anonymization approach, RLS recommends sponsors adopt this methodology. That said, there may be certain specific situations where a rules based or qualitative methodology is best.  Most certainly this underscores the importance of the pre-submission meeting. As mentioned above, the trial sponsor should discuss with EMA the preferred approach  during the pre-submission meeting.  Having the methodology agreed with EMA no later than the pre-submission will help the trial sponsor avoid delays when delivering the Proposal and Final packages to EMA.

Contact Real Life Sciences

If you have questions or would like some consultation prior to starting your EMA Policy 0070, CTR or Health Canada PRCI project, please contact us directly at inquiry@rlsciences.com.  RLS has completed dozens of CDP/Policy 0070, Health Canada PRCI and other clinical transparency projects with pharmaceutical manufacturers of all types and sizes. 

For more information on Policy 0070 please visit our Policy 0070 overview page.

European Medicines Agency's (EMA's) Clinical Trial Regulation (CTR) & Clinical Trial Information System (CTIS): Trial Sponsor and CRO Frequently Asked Questions & Regulatory Announcements

What may be considered CCI when redacting CTA documents for submission to CTIS and how should I go about redacting this information? 

Some examples of confidential information may be names of manufacturers, details of the manufacturing process, future development plans, innovative analytics methods and equipment used at the clinical sites.

Once you confirm the information that is confidential with your IP Legal, Regulatory, Clinical and non-Clinical teams, the redactions in the CTA document should be limited only to the specific information that is deemed confidential. This means that full paragraphs and pages will not be redacted while specific content or syntax within a sentence, table or figure will be redacted.

Prior to finalizing your CCI decisions, remember to double check applicable public sources such as global trial registries, publications and abstracts to confirm the information indeed remains confidential and has not already been made public.
Link to EMA Guidance document on how to approach the protection of personal data and commercially confidential information in documents uploaded and published in the Clinical Trial Information System (CTIS).

How can I streamline my internal review and approval process of redacted CTIS CTA documents?

To optimize and streamline your internal approvals, consider using a log to capture each proposed redaction and the associated justification. This information will assist reviewers and approvers and help mitigate potential questions and delays. Further, by building a list, you can repurpose the information for other documents and studies, as applicable.

An example log may include a column for each of the following:

  1. Document type e.g.; Investigator Brochure
  2. Document filename e.g.; IB_9301_redacted_draft
  3. Document version: v2.1
  4. Page reference e.g.; page 7
  5. Non-redacted syntax e.g.; 3 mg
  6. Proposed redacted syntax e.g.; 3
  7. Justification category: e.g.; proprietary manufacturing technique or method
  8. Justification: e.g.; text justification of why this information is proposed to be redacted
  9. Transparency Redactor: e.g; Jane Smith
  10. Redaction date: 15 Oct 2023

Which studies are required to transition to the CTR and what is the deadline to complete this?

Since the launch of the CTR in 2022, the timescales and expectations for transition studies from EudraCT have been socialized by EMA. The authorization of transition studies in CTIS requires advanced planning to prepare and submit the necessary documents prior to the deadline of 30 January 2025.

The EMA is estimating up to 6,000 studies under the Clinical Trials Directive (CTD) will require transition and approval in CTIS prior to the deadline of 30 January 2025. To date, approximately 320 studies have completed the transition.

Trials authorized under the CTD with at least one active site in the EU must be transitioned by 30 January 2025. Trials that have ended locally in all EU member states but have active sites in other regions of the world do not need to transition. Key to achieving the deadline of 30 January 2025 is not just submitting the necessary information and documents into CTIS but receiving the authorization itself.

There are specific considerations and deliverables for each transition study as outlined in the following documents that are made available by EMA.

Guidance for the transition of clinical trials

CTCG’s best practice guide and cover letter template

New EMA announcement 06 Oct 2023 explains planned changes to Transparency Rules for 2024. Revisions to EMA’s CTIS Transparency rules will impact all trial sponsors

Following a public consultation period in May and June 2023, on 06 October 2023, the EMA announced upcoming changes to CTIS Transparency Rules that will become effective in 2024.

To be implemented in 2024, changes include removal of the deferral mechanism for all trials, an abbreviated list of ‘for publication’ documents and simplification of required data fields in CTIS.

With these changes, EMA aims to simplify and streamline the transparency of information for patients and healthcare professionals while balancing protection of commercial confidential information and personal data for trial sponsors.

Trial sponsors will need to continue to manage their CTIS projects under the current rules while anticipating the changes for mid-year 2024.

EMA Announcement

Revised CTIS Transparency Rules

What happens if I receive an RFI regarding redacted version ‘for publication’ documents submitted to CTIS and how can I avoid this?

Avoiding an RFI is critical to your study timelines. RFI’s require a short turnaround time. Awareness of the CTR and its guidance are critical to mitigating the risk of an RFI. Read on to learn more about avoiding RFIs due to CCI redactions and examples of acceptable CCI.

RFI’s related to ‘for publication’ documents have recently been noted bay several study sponsors. In these cases, the Member State has noted dense redactions in these documents and the RFI has indicated the sponsor needs to resubmit a revised version of the document that adheres to Section 1.3 Legal Framework in EMA’s CCI Guidance document (including references to Article 81.4 of the CTR).

Avoiding an RFI is critical to your study timelines. RFI’s require a short turnaround time and therefore the operational considerations of turning a revised document in an expedited fashion are often challenging. 

Awareness of the CTR and its guidance are critical to mitigating the risk of an RFI. Per the CTR guidance, when deferring publication of documents, redactions should be minimal. Further, sponsors should be redacting ‘for publication’ documents based on what is anticipated to be confidential at the time of publication (not at the time of upload to CTIS).

Examples of information that may be deemed acceptable to redact as CCI are listed below (note: this is not an exhaustive list). It is recommended you check the guidance to confirm your suggested CCI data for your study meet the necessary criteria.

Link to more information: EMA CCI CTIS Guidance

Are Member States reviewing public version documents in CTIS and could my ‘for publication’ document redactions result in an RFI?

The guidance from EMA has been consistent: if you are deferring, ‘for publication’ version documents should contain minimal if any redactions upon upload to CTIS. Further, the guidance states that Sponsors should not defer and redact.

At the start of the CTIS launch in 2022 it became clear that Member States were not reviewing ‘for publication’ documents to ensure conformance to this expectation - although it was expected that study sponsors adhere to the guidance at all times. However, more recently, multiple study sponsors have begun to receive RFIs as a result of heavy redaction in ‘for publication’ documents. It is important for all study sponsors to remember that when deferring, only commercially sensitive information at the time of publication (following the deferral period, if applicable) should be redacted. EMA is expecting all ‘for publication’ documents to be meaningful for the public.

Link to Sponsor Handbook with guidance regarding deferral and ‘for publication’ document redaction.
Link to EMA
CTR Questions & Answers Version 6.5, see Question 6.5, paragraphs 259-261.

My organization considers the dosing information for my CTIS project as Confidential (CCI). However, CTIS requires metadata fields be populated with dosing information. How can a Sponsor approach the protection of this information?

Depending on the phase of the trial, Sponsors can protect this information from becoming public despite certain CTIS fields requiring an input. Below is EMA’s guidance from section 3.3 “Q&A on the protection of Commercially Confidential Information and Personal Data while using CTIS”:

How can dose details be protected from disclosure from CTIS for certain trials category falling within category 2?
The main characteristics of medicinal products used in clinical trials falling under category 2 are subject to publication rules after a decision on the clinical trial application is issued by the first Member State concerned.

These main characteristics also include structured data fields on daily dose allowed and maximum dose allowed for the medicinal product under investigation. In some instances, depending on the trial development phase, dose details may be considered to be CCI. In such instances, sponsors can include ‘dummy data’ (e.g. 00 digits) in the related structured data field(s) of CTIS.

The full information on the posology should, however, be provided to the Member States for assessment in the document version ‘not for publication’ and can be redacted in the corresponding documents to be published.

This approach would be acceptable only on justified grounds, i.e. when the sponsor proves that the specific information on the posology is not in the public domain and constitutes patentable matter, the disclosure of which before a patent application is filed (typically, after the completion of the trial and during the trial readout) would jeopardize its protection.

This might be applicable for example to integrated phase I/phase II trials that are to be marked in CTIS as category 2 trials. The grounds for considering dose details as CCI should be clearly documented in the cover letter of the application. Link to additional Guidance

Transparency: An integral part of the clinical trial process.

With the primary aim of fostering research innovation and improving transparency of the clinical trial process in Europe, the Clinical Trials Regulation (CTR) was implemented in January of 2022. To facilitate the implementation and management of the CTR, the EMA simultaneously launched a digital portal known as Clinical Trials Information System (CTIS). Beginning January 31, 2023, all new clinical trial applications must be submitted via the CTIS.

These changes are leading a cultural shift in clinical trial transparency  [See: A Cultural Shift Is Happening] and the operational impact of this change needs to be discussed in further detail.        

Why adopt transparency as a critical component in the clinical trial lifecycle?   

The EMA has elevated transparency and disclosure of in-process and approved trials to another level. With the CTR, transparency has become an integral part of the clinical trial process in Europe. Using the CTIS, a publicly accessible database, anyone interested in the clinical trials conducted in Europe can search to obtain useful information. Interested parties may be current patients being treated for a disease or chronic illness, participants of a clinical trial seeking detailed information regarding his or her trial, researchers who are seeking details about past and in-process trials and their results, and everyday citizens seeking knowledge of a disease or treatment.

Increased transparency will help to mitigate redundancy or duplication of clinical trial initiatives. If a clinical trial had negative or inconclusive results, the CTIS will make  this trial information available to researchers and individuals. For pharmaceutical companies, this visibility will help in avoiding the repetition of unsuccessful study designs. Increased transparency also helps in improving the quality of data. In other words, the structure and workflow that the CTIS requires is providing clarity and consistency in what stakeholders can expect when querying clinical trials in Europe. 

In a nutshell, although these increased transparency requirements brought about by the CTR will trigger changes to pharmaceutical company’s current workflow, embracing transparency as a high value component in clinical trials will help to expedite research in a given therapy area while building trust and confidence with the public who seek to learn and understand about the trial itself.

Operationally, based on the information that is required in CTIS about a particular clinical trial,  study sponsors need to assume all trial documents and information will become public, with few exceptions. All functional teams must understand that disclosure is now an integral part of the clinical process and prepare submission documents accordingly.

How can trial sponsors manage this change?

Over the next several years, as the industry embraces this change, the resulting impact to clinical teams and risks brought about  with the disclosure of clinical documents  and Commercially Confidential Information (CCI) must be managed carefully. Study sponsors may need to conduct a review of existing processes such as medical writing practices and awareness training regarding transparency of confidential information. A gap analysis of roles, responsibilities, tasks, internal policies and procedures may be required. Study sponsors may choose to start with foundational aspects such as how end-to-end CTR information flow will be managed in addition to the preparation of public version documents. Further, a risk-based approach to anonymizing personal data will be a necessary step in the process before sharing trial results and patient narratives.

As of January 31, 2023, the use of CTIS for new trial applications is mandatory. Study sponsors need to assess their internal capabilities, map their updated business processes, prioritize action items and seek support from vendors who specialize in identification of personal data and can support the redaction and anonymization of this information in a timely manner.

Is there any support available to navigate these changes?

Transparency requirements have indeed disrupted the historical workflows previously implemented by study sponsors and CROs. Study sponsors are looking for more guidance during these times of change. The EMA has provided guidance documentation for study sponsors which is a good place to start. Additionally, to cope with the increased workload related to standardized document templates and document redactions, some companies are choosing to collaborate with external software and service providers such as Real Life Sciences (RLS).

Redactions can be less stressful purpose built digital tools are used to complete the document redaction and anonymization tasks. At RLS,  solutions such as RLS Protect can help to automate the identification of personal data and confidential information and prepare public version documents. However, minimizing the need for redactions is one strategy that study sponsors can adopt. In the next blog of this series, let’s delve deeper into “lean” authoring...