Sponsor’s Response to DMC Recommendation: A Case Study from a Phase 2b/3 Trial

 The DMC or DSMB is now commonly used in the clinical trials, especially the late phase clinical trials. According to FDA guidance for industry "Use of Data Monitoring Committees in Clinical Trials", DMC Responsibilities include:

1.             1.    Monitoring of Trial Conduct

2.      Monitoring of Results of Interim Analysis of Trial Data

·         Safety – to determine if there is a credibly increased risk of a serious adverse outcome in subjects receiving the investigational product, indicating that enrollment should be stopped. To determine a safety risk, review of unblinded efficacy data should also be conducted by the DMC as they evaluate a benefit-risk assessment

·         Implementing a predefined adaptive feature

                                                              i.      Efficacy – to determine if there is statistically significant evidence of efficacy such that enrollment should be stopped

                                                            ii.      Futility – to determine if there is no longer a reasonable likelihood that the trial will reach a conclusion of effectiveness, so that enrollment should be stopped to protect subjects from further exposure to a potentially ineffective investigational product and to conserve resources

                                                          iii.      Other adaptations – a DMC or a separate adaptation committee should determine if a prespecified adaptive aspect of the trial design is to be implemented. This can include modifying the sample size, changing a randomization ratio, or restricting future enrollment to a prespecified subgroup (adaptive enrichment

3.    Consideration of External Data

4.    Recommendations and Documentation

When a DMC is established, a DMC charter will be established to describe DMC Obligations, Responsibilities, and Standard Operating Procedures. DMC charter may also specify if there is any stopping rules to implement, decision trees to be followed, and any adaptation rules to be implemented. 

DMC communicates with the sponsor through the DMC recommendations. The FDA guidance has the following about the DMC recommendations:

A fundamental responsibility of a DMC is to make recommendations to the sponsor concerning the continuation of the trial.  Most frequently, a DMC’s recommendation after an interim review is for the trial to continue as designed.  Other less frequent but possible recommendations, however, as discussed previously, include trial termination, trial continuation with major or minor modifications (such as implementation of prespecified adaptive elements), or temporary suspension of enrollment and/or trial intervention until an identified uncertainty is resolved. 

A DMC should express its recommendations clearly to the sponsor because a DMC’s actions potentially affect the safety of trial subjects.  Both a written recommendation and an oral communication, with opportunity for questions and discussion, can be valuable.  Recommendations for modifications are best accompanied by the minimum amount of data critical for the sponsor to make a reasonable decision about the recommendation, and the rationale for such recommendations should be as clear and precise as possible.  Sponsors may wish to develop internal procedures to limit the interim data released by a DMC after a recommendation and until a decision is made regarding acceptance or rejection of the recommendation in order to help maintain confidentiality of the interim results should the trial continue.  We recommend that a DMC document its recommendations and rationale in a manner that can be reviewed by the sponsor and then circulated, as appropriate, to IRBs, FDA, and/or other interested parties, when based on interim data.  Major trial changes—such as early trial termination, change in population or entry criteria, or change in trial endpoints—can have substantial impact on the validity of the trial and/or its ability to support the desired regulatory decision.  Sponsors should discuss with FDA any proposed protocol changes based on review of interim data that were not planned for, before implementation, and submit such changes to FDA in accordance with 21 CFR 312.30 and 812.35.  However, if the sponsor learns of information that presents an imminent safety hazard to trial participants, sponsors should implement the necessary changes as quickly as possible to ensure the safety and welfare of study subjects (see 21 CFR 312.30(b)(2)(ii) and 812.35(a)(2)). 

In most of situation, the study is as expected and it is easy for DMC to make a recommendation of no changes to the study. However, in complicated situation, the DMC needs to make tough decision and recommend the termination of the study. In a paper by Wittes et al "The Data Monitoring Committee: A Collective or a Collection?", the following suggestions of consensus operating were made:

In a typical DMC meeting, data emerge as expected. No worrisome safety concern arises; the efficacy data are not surprising; and the DMC deems that trial is progressing as planned with, perhaps, some lag in recruitment and a less than desirable rate of follow-up of participants and incomplete capture of important efficacy and safety data. These and other quality metrics affect decision-making and the DMC may discuss them with study leadership. When, however, evidence of an unexpected harm arises, or the study operations appear unacceptable, or efficacy appears much different from anticipated, the deliberations of the DMC may reveal initial, perhaps strong, differences of opinion. A requirement to vote may curtail discussion and may lead to the failure to produce a recommendation that all find acceptable. Instead, we agree with those who urge DMCs to operate by consensus . Operating by consensus means that a DMC can have an odd or even number of members. Prior to reaching consensus, the Chair may elicit the opinion of each member to gauge the general views of members of the DMC or even call an informal straw vote. Regardless of how the DMC reached consensus, all members should agree to the language summarizing its recommendations....

This week, we saw an interesting example how the DMC recommendation was received and handled by the sponsor. Apparently, the sponsor did not trust the DMC's recommendation of stopping the study. The sponsor is now assembling an expert panel to review the unblinded data to determine how the DMC's recommendation is made and what are the rationales for DMC's recommendation.

Pliant Therapeutics announcedthat their phase 2b/3 study in IPF was suspended per DMC’s recommendation.  

          Pliant Brings in Outside Experts to Review IPF Study Pause 

          Pliant Therapeutics  has initiated assembly of outside panel of world-renowned experts to review 

          BEACON-IPF trial dataAnnounces Next Steps Following DSMB

SOUTH SAN FRANCISCO, Calif., Feb. 13, 2025 (GLOBE NEWSWIRE) -- Pliant Therapeutics, Inc. (Nasdaq: PLRX) today announced that, per the charter of the trial’s independent Data Safety Monitoring Board (DSMB), the Company has initiated the assembly of an outside expert panel to review unblinded data from the ongoing BEACON-IPF Phase 2b trial of bexotegrast in patients with idiopathic pulmonary fibrosis (IPF). The panel, consisting of world-renowned experts in pulmonary diseases and biostatistics, will provide an independent recommendation to Pliant regarding the BEACON-IPF trial. Subsequently, the panel will serve as part of an expanded DSMB with the goal to reach a consensus recommendation regarding BEACON-IPF. The decision to assemble the outside panel was taken as the Company has not been able, through review of blinded data, to determine the rationale for the DSMB’s recommendation to pause enrollment and dosing in the trial. The Company expects this process to conclude in two to four weeks.

Following the DSMB’s previously announced recommendation, Pliant voluntarily paused enrollment and dosing in the BEACON-IPF clinical trial. Pliant is committed to remaining blinded ensuring the data integrity of the BEACON-IPF 2b clinical trial with the goal of maintaining its potential to serve as a registrational trial.

It is very likely that the DMC focuses on the safety review and follows the FDA guidance which says:

Safety – to determine if there is a credibly increased risk of a serious adverse outcome in subjects receiving the investigational product, indicating that enrollment should be stopped. To determine a safety risk, review of unblinded efficacy data should also be conducted by the DMC as they evaluate a benefit-risk assessment

There may be an imbalance in the number of deaths or serious adverse events and there may no clear indication of efficacy. In such cases, the DMC's recommendation to stop the trial is based on a careful benefit-risk assessment. 

Understanding Mis-Stratification in Randomized Controlled Clinical Trials

Stratified randomization is a common practice in randomized, controlled clinical trials. It ensures that key characteristics are evenly distributed across treatment groups and the treatment assignments are balanced within each randomization stratum, enhancing the validity of study results. However, during the course of a trial, mis-stratification can occur—this happens when an incorrect stratification stratum is used during randomization. Let's explore what this means, why it happens, and how it impacts clinical trials.

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What is Mis-Stratification?

In clinical trials, stratification factors (e.g., age, disease severity, disease subgroup, or background medication use) are used to group participants before randomization. Stratified randomization is used to ensure that equal numbers of subjects with one or more characteristic(s) thought to affect the treatment outcome in efficacy measure will be allocated to each comparison group. Mis-stratification - a type of randomization errors, occurs when:

• An incorrect stratification factor is used for randomization, or
• A participant is placed in the wrong stratum due to an error.

Despite this, the treatment assignment and drug dispensation remain accurate, making it a minor deviation rather than a critical error. When mis-stratification occurs, the random code and the treatment assignment is pulled from an incorrect stratum. 

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Historical Approach: Intention-to-Treat Principle

Traditionally, clinical trials have adhered to the Intention-to-Treat (ITT) principle, where participants are analyzed according to the group they were originally randomized to, regardless of any errors. This approach maintains the integrity of the randomization process.

In practice, this means using the original stratification data—even if incorrect—in the statistical analysis. A typical Statistical Analysis Plan (SAP) might state:

“All original stratification information used in the randomization procedure will be used for analyses, regardless of whether it was later found to be incorrect. All efficacy analyses will be performed primarily on the ITT Population.”

This approach minimizes bias and reflects the 'real-world' impact of treatment. However, in the mis-stratification situation, using the incorrect stratum information in analyses may be too harsh and too strict unnecessarily.

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Why Does Mis-Stratification Occur?

Mis-stratification can result from several factors, including:

• Too Many Stratification Factors: More factors increase the complexity and likelihood of error.
• Local vs. Central Lab Results: Differences between local and central lab measurements can lead to misclassification.
• Timing of Measurement: Stratification factors measured at different times (baseline vs. screening) may not align.
• Medication Use: Stratifying by prior or concomitant medication use can be complicated by variations in patient reporting or prescription practices.

These issues highlight potential flaws in protocol design and study quality, emphasizing the need for clear definitions and consistent procedures.

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Regulatory Perspective: FDA Guidance

The FDA's guidance document, “Adjusting for Covariates in Randomized Clinical Trials for Drugs and Biological Products,” provides clarity on handling mis-stratification:

“Randomization is often stratified by baseline covariates. A covariate adjustment model should generally include strata variables and can also include covariates not used for stratifying randomization. In some cases, incorrect stratification may occur and result in actual and as-randomized baseline strata variables. A covariate adjustment model can use either strata variable definition as long as this is prespecified.  “

This guidance supports the use of either the originally assigned stratification or the actual baseline data in the analysis, provided it is specified before data unblinding. This flexibility helps maintain the study's validity while addressing stratification errors transparently.

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Impact on Study Results

Mis-stratification is generally considered a minor deviation because its impact on efficacy and safety analyses is minimal. It does not affect treatment assignment or drug dispensation but only the stratum from which the assignment was drawn.

When incorrect stratification occurs, the actual stratification information is collected in the Electronic Data Capture (EDC) system and can be used in sensitivity analyses to evaluate the robustness of the study results.

There is an article "Handling misclassified stratification variables in the analysis of randomised trials with continuous outcomes" where the authors did the simulation study to investigate the impact of the mis-stratification on the statistical analyses. 

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Minimizing Mis-Stratification in Randomization

Too many mis-stratification errors indicate the poor quality of the clinical trial. To reduce the risk of mis-stratification, consider the following best practices:

• Limit Stratification Factors: Use the minimum necessary factors to reduce complexity.
• Consistent Measurement Timing: Align the timing of stratification factor measurements (e.g., always at baseline).
• Clear Definitions: Ensure stratification criteria are clearly defined, identified or measured, and uniformly applied.
• Training and Quality Checks: Provide thorough training for study personnel and implement rigorous quality checks.

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Conclusion

While mis-stratification is not ideal, its impact on clinical trial results is usually minimal. By adhering to the Intention-to-Treat principle and following regulatory guidance, researchers can maintain the integrity of their analyses. As clinical trial designs become more complex, understanding and managing mis-stratification will continue to be crucial for maintaining study quality and reliability.

Prognostic enrichment versus predictive enrichment

Prognostic enrichment and predictive enrichment are both strategies used in clinical trials to select patients for inclusion. Both strategies aim to improve trial efficiency but address different aspects of clinical trial design.

Prognostic enrichment
Selects patients who are more likely to experience a disease-related event or condition. This strategy can help reduce the sample size required for event-driven trials.

Predictive enrichment
Selects patients who are more likely to benefit from a treatment or intervention based on a physiological or biological mechanism.

From FDA's guidance "Enrichment Strategies for Clinical Trials to Support Determination of Effectiveness of Human Drugs and Biological Products":

FDA guidance includes some detail examples of using prognostic enrichment strategies or predictive enrichment strategies. 

The differences between prognostic enrichment and predictive enrichment can be summarized as following:

	Prognostic Enrichment	Predictive Enrichment
Definition	Selecting patients based on their likelihood of experiencing a specific outcome (e.g., disease progression or event) regardless of treatment.	Selecting patients based on their likelihood of responding to a specific treatment due to a biomarker or characteristic.
Goal	To increase the event rate or outcome frequency in the trial population, improving statistical power.	To identify patients who are more likely to benefit from the investigational treatment.
Focus	Focuses on the natural history of the disease or risk of an outcome.	Focuses on the interaction between the treatment and a specific patient characteristic (e.g., biomarker).
Patient Selection	Patients are selected based on prognostic factors (e.g., disease severity, biomarkers, or risk scores).	Patients are selected based on predictive factors (e.g., presence of a biomarker or genetic mutation).
Outcome	Increases the proportion of patients who experience the outcome of interest.	Increases the likelihood of observing a treatment effect in the selected population.
Example	Enrolling patients with advanced-stage cancer to ensure a higher rate of disease progression. Enrolling severe patients who may be more likely to develop clinical worsening events	Enrolling only patients with a specific genetic mutation (genetic biomarker) that is targeted by the therapy. Enrolling only patients in a specific etiology sub-group who are expected to be more responsive to the investigational treatment
Impact on Trial	Reduces sample size and trial duration by enriching for patients with a higher event rate.	Improves treatment effect size by focusing on patients who are more likely to respond.
Statistical Benefit	Increases statistical power by reducing variability in the control group.	Increases effect size by reducing heterogeneity in treatment response.
Risk	May exclude patients who could still benefit from the treatment.	May limit generalizability of trial results to a broader population.
Common Use Cases	Trials where the primary endpoint is time-to-event (e.g., survival, disease progression).	Trials where the treatment mechanism is targeted (e.g., precision medicine, biomarker-driven therapies).

Further Reading: 

• FDA's guidance for industry (2019) "Enrichment Strategies for Clinical Trials to Support Determination of Effectiveness of Human Drugs and Biological Products":
• Bob Temple (2013) Enrichment Strategies for Clinical Trials
• Stanski and Hong (2020) Prognostic and predictive enrichment in sepsis. Nature Reviews Nephrology
• Moscicki and Tandon (2017) Drug-Development Challenges for Small Biopharmaceutical Companies. NEJM

Survivorship Bias and its Occurrences in Clinical Trials

Survivorship bias or survival bias is the logical error of concentrating on entities that passed a selection process while overlooking those that did not. This can lead to incorrect conclusions because of incomplete data. Survivorship bias is a selection bias that occurs when an individual only considers the surviving observation without considering those data points that didn’t “survive” in the event. It can lead to incorrect or misleading conclusions.

 
Most famous example of survivorship bias came from the World War II time. During World War II, the high rate of planes being shot down posed a significant challenge. To address this, a team was tasked with improving the protection and armor of the aircraft to reduce their vulnerability. The team analyzed the planes that returned from missions, documenting the locations of damage and creating a damage map. They observed heavy damage on the wings and tail sections, leading them to reinforce these areas. However, despite these modifications, the rate of plane losses did not significantly decrease.

The breakthrough came when the team realized a critical oversight: they were only examining the planes that had successfully returned, meaning the damage they observed was survivable and not the kind that would cause a plane to be lost. This insight shifted their perspective, highlighting that the most vulnerable areas were likely those that hadn’t been hit on the returning planes—areas where damage would be fatal. By focusing on reinforcing these previously overlooked sections, the team was able to better protect the aircraft and pilots, ultimately saving lives. This story underscores the importance of considering not only the visible data but also the hidden or missing information, as what you don’t know can be just as critical as what you do know.

Survivorship Bias in Clinical Trials. 

Survivorship bias occurs when we focus only on the "survivors", "completers", "responders", or successful outcomes in a dataset while ignoring those that didn’t make it. This can lead to overly optimistic or inaccurate conclusions because the full picture isn’t being considered. In the context of clinical trials, survivorship bias can distort our understanding of a treatment’s effectiveness or safety by excluding data from participants who dropped out, didn’t respond to the treatment, or experienced adverse events.

Survivorship Bias are very common in clinical trials even though the term "survivorship bias" may not be explicitly used and may be described under "selection bias".

How Does Survivorship Bias Manifest in Clinical Trials?

1. Dropout Rates and Missing Data
   Clinical trials often experience participant dropouts due to side effects, lack of efficacy, or personal reasons. If researchers only analyze data from participants who completed the trial, they may overestimate the treatment’s effectiveness or underestimate its risks. For example, a drug might appear highly effective because only the patients who benefited from it stayed in the trial, while those who didn’t respond or experienced severe side effects left.
2. Selective Reporting
   Researchers or sponsors may inadvertently (or intentionally) focus on positive outcomes while downplaying or omitting negative results. This can create a skewed perception of a treatment’s success. For instance, if a trial reports only the patients who improved and ignores those who didn’t, the treatment may seem more promising than it actually is.
3. Long-Term Follow-Up Gaps
   Many clinical trials focus on short-term outcomes, which can miss long-term effects. Patients who experience adverse events or relapse after the trial ends may not be included in the final analysis, leading to an incomplete understanding of the treatment’s safety and efficacy.
4. Population Selection
   Clinical trials often exclude certain populations, such as older adults, pregnant women, or individuals with comorbidities. While this is sometimes necessary for safety or feasibility, it can create a biased sample that doesn’t reflect the real-world population. The "survivors" in this case are the participants who met the strict inclusion criteria, potentially limiting the generalizability of the results.

Real-World Consequences of Survivorship Bias

Survivorship bias in clinical trials can have serious implications for patients, healthcare providers, and policymakers. For example:

• Overestimation of Treatment Efficacy: If a drug appears more effective than it truly is, patients may be prescribed a treatment that doesn’t work for them, wasting time and resources.
• Underestimation of Risks: Ignoring data from participants who dropped out due to side effects can lead to an incomplete understanding of a treatment’s safety profile.
• Misguided Policy Decisions: Policymakers relying on biased trial results may approve treatments that aren’t as effective or safe as they seem, potentially putting public health at risk.
  

How Can We Address Survivorship Bias?

1. Intent-to-Treat Analysis or Treatment Policy Strategy in Estimand Framework
   One of the most effective ways to mitigate survivorship bias is to use an intent-to-treat (ITT) analysis or treatment policy strategy in handling the intercurrent event, which includes all participants who were randomized in the trial, regardless of whether they completed it. This approach provides a more realistic picture of the treatment’s effectiveness in real-world conditions.
2. Avoid performing the analyses only on completers
3. Encourage the patients with intercurrent events to remain in the study to minimize the dropouts
4. Transparency in Reporting
   Researchers should report all outcomes, including dropouts, adverse events, and negative results. Journals and regulatory agencies can encourage this by requiring comprehensive data disclosure.
5. Long-Term Follow-Up
   Extending the follow-up period can help capture long-term outcomes and provide a more complete understanding of a treatment’s benefits and risks.
6. Diverse Participant Populations
   Including a broader range of participants, such as older adults and individuals with comorbidities, can improve the generalizability of trial results and reduce bias.
7. Independent Oversight
   Independent review boards and data monitoring committees can help ensure that trials are conducted and analyzed objectively, minimizing the risk of bias.

Conclusion

Survivorship bias is a subtle but significant issue in clinical trials that can distort our understanding of medical treatments. By focusing only on the "survivors", "completers", "responders", or successful outcomes, we risk overlooking critical data that could impact patient care and public health. Addressing this bias requires a commitment to transparency, rigorous analysis, and inclusive research practices. As we continue to advance medical science, it’s essential to remember that what we see isn’t always the full picture—and that the missing pieces may hold the key to better, safer, and more effective treatments.

By being aware of survivorship bias and taking steps to mitigate it, we can ensure that clinical trials provide the most accurate and reliable evidence possible, ultimately improving outcomes for patients everywhere.

Further Reading:

• Elston (2021) Survivorship bias. Journal of the American Academy of Dermatology
• Francesco Pasqualetti (2023) The impact of survivorship bias in glioblastoma research. Critical Reviews in Oncology/Hematology
• Mark É. Czeisler (2021) Uncovering survivorship bias in longitudinal mental health surveys during the COVID-19 pandemic. Epidemiology and Psychiatric Sciences
• Conner (2019) Survivorship Bias in Analyses of Immune Checkpoint Inhibitor Trials. JAMA oncology
• Selection Bias: Selection Bias: The Art of Choosing the Right Data
• WHO (2019) WHO consolidated guidelines on drug-resistant tuberculosis treatment

Bioethics with Biotechnology, Bioengineering, and Genetic Engineering

Biotechnology and bioengineering are advancing at an unprecedented pace. Through genetic engineering, possibilities once considered unthinkable are now within reach. While it is widely accepted—both morally and ethically—to use genetic engineering for treating diseases and addressing organ transplant shortages, its application for human enhancement remains highly controversial.

Recently, I watched a YouTube video titled "Rewriting Genomes to Eradicate Disease and Aging" featuring Dr. George Church. In the discussion, Dr. Church covered topics like synthetic genomics, germline editing, and more. When speaking about xenotransplantation, he stated:

“…to have something that’s enhanced is immunologically superior—that is less rejected, resistant to pathogens, resistant to cancerous senescence, and capable of cryopreservation. All of these things have been demonstrated in animals, and now we want to either get them into humans via cell or organ transplants.”

This statement highlights the transformative potential of genetic engineering in medicine, particularly in creating organs that are less prone to rejection and more resilient to diseases. However, it also raises important ethical questions about how far these advancements should be pursued.

I am a fan of the Harvard philosopher Michael Sandel. I thoroughly enjoyed watching his online course, "Justice: What's the Right Thing to Do?" and reading his book, "What Money Can't Buy: The Moral Limits of Markets." Sandel has also weighed in on the ethical issues surrounding genetic engineering.

In a lecture held in the Netherlands, Sandel engaged with the audience to explore these ethical dilemmas in depth. I have included the video below for those interested.

The Perfect Human Being

In the video, Michael Sandel posed several thought-provoking questions to the audience:

• Should biotechnology aim to create the "perfect" human being? And perhaps an even harder question—what would "perfection" mean?
• Should parents be able to choose the sex of their child? This is already possible through embryo screening and other methods. Imagining yourself as a parent, would you find it morally permissible or objectionable to select whether to have a boy or a girl?
• What about selecting a child’s sexual orientation? If technology existed to predetermine whether a child would be straight or gay, should parents have the freedom to make that choice?
• What about enhancing traits like intelligence, appearance, or talents? Suppose it became possible (and safe) to select for a smarter, more attractive, athletically gifted, or musically talented child. Would parents have a responsibility to use these technologies to give their children the best possible advantages?
• Should genetic engineering be used for self-enhancement? For instance, could interventions—genetic, pharmacological, or surgical—be morally justified if they were used to make oneself smarter, improve memory, or enhance cognition? Should individuals have the freedom to do whatever they want with their own brains?
• If biotechnology enabled us to live far longer—perhaps even forever—would that be desirable? How many people would want to live to 1,000 years old, for example?
• Should humanity evolve without limits? What would be the implications of such limitless evolution? Could it lead to a genetic arms race?

Sandel ended by encouraging the audience to reflect on these questions: What would the world look like if everyone could use biotechnology to become the smartest or if people no longer died?

Sandel wrote a book "The Case against Perfection: Ethics in the Age of Genetic Engineering" to explore the ethical implications of genetic engineering and other forms of human enhancement. Sandel argues that the pursuit of perfection through biotechnology undermines core human values and raises profound moral concerns.

• Ethical Limits of Enhancement

Sandel critiques the drive to enhance human traits—such as intelligence, physical abilities, or appearance—through genetic engineering. He contends that this pursuit reflects a problematic desire for mastery over life, rather than an acceptance of human imperfections.

• The Giftedness of Life

A central argument is the importance of appreciating the "giftedness" of human life. Sandel suggests that genetic enhancement erodes this appreciation, replacing humility with hubris and diminishing our capacity to accept the unbidden aspects of existence.

• Moral and Social Implications

Genetic engineering risks exacerbating social inequalities by creating a divide between the "enhanced" and the "unenhanced." Sandel also highlights how it could lead to a commodification of human traits, treating them as products to be optimized.

• Parenthood and the Drive for Perfection

The book explores how the desire for "designer babies" transforms the relationship between parents and children, shifting the focus from unconditional acceptance to a mindset of control and customization.

• The Ethical Boundary

While Sandel acknowledges the benefits of genetic engineering for therapeutic purposes (e.g., curing diseases), he argues that enhancement for non-medical reasons crosses a crucial ethical boundary.

Sandel urges society to resist the temptation to pursue perfection through biotechnology and instead embrace the inherent imperfections that define humanity. He advocates for humility and a respect for the natural limits of human life, warning that unchecked enhancement technologies could compromise the moral fabric of society.

Known knowns and Unknown Unknowns

On several occasions during scientific presentations, I have come across citations of Donald Rumsfeld's statement. Donald Rumsfeld was one of the most famous US Secretary of Defense.

"There are known knowns; there are things we know that we know. 

There are known unknowns; that is to say, there are things we now know we don't know.

But there are also unknown unknowns - there are things we do not know we don't know."

Wikipedia includes an entry on the phrase 'there are unknown unknowns,' a term popularized by Donald Rumsfeld. He famously used it in response to a question about the absence of evidence linking the Iraqi government to the supply of weapons of mass destruction to terrorist groups.

With respect to awareness and understanding, unknown unknowns can be compared to other types of problems in the following matrix:

In clinical trials, comparing an experimental therapy to a control group is often complicated by confounding factors—both known and unknown. Randomization is a key method for addressing these challenges, as it helps balance these factors across treatment groups. By randomly assigning participants to different groups, randomization ensures that potential confounders are evenly distributed, enabling a more accurate comparison of treatment effects.

For known confounding factors, stratified randomization can be employed. This approach involves dividing participants into strata based on specific factors and then randomizing them within each stratum, ensuring an equal probability of assignment to either treatment group within each category. For unknown known or unknown unknown confounding factors, the only way to minimize the impact is to utilize the randomization. 

Randomization is regarded as the cornerstone of causal inference in randomized controlled trials (RCTs). It enables researchers to attribute differences in outcomes between groups to the treatment under investigation, rather than to pre-existing differences among participants, thereby strengthening the validity of the findings.

The awareness-understanding matrix, which includes concepts like 'known unknowns' and 'unknown unknowns,' can be applied to scenarios such as xenotransplantation—for instance, the transplantation of porcine organs into humans. In the context of xenotransplantation, there is always a potential risk of zoonotic infections, where pathogens may be transmitted from animals to humans. There are known pathogens (viruses) and there are unknown pathogens. As Dr Jay Fisherman discussed the issue in his paper "Xenotransplantation-associated infectious risk: a WHO consultation":

"In xenotransplantation, there is the unique potential risk for the transmission of both known and unknown zoonotic infectious agents of animal origin into human recipients and into the wider human population."

"Xenotransplantation will necessitate the development of surveillance programs to detect known infectious agents as well as previously unknown or unexpected pathogens in the absence of recognizable clinical syndromes. This may include assays for known infectious agents, probes for classes of infectious agents (e.g., common genes or antigens of herpesviruses), and assays for unknown pathogens in a variety of tissues."

 "Unknown pathogens: Organisms not known to be human pathogens, not known to be present in the source animals, or for which clinical syndromes and microbiologic assays are poorly described or unknown"

The awareness-understanding matrix is dynamic. With advancements in science, today's unknown unknowns may eventually evolve into known unknowns or even known knowns.

Real world data (RWD) and Real world evidence (RWE) in Drug Development

The 21st Century Cures Act (Cures Act), signed into law on December 13, 2016, is designed to accelerate medical product development and bring new innovations and advances faster and more efficiently to the patients who need them. Following the passing of the Cures Act, the Food and Drug Administration (FDA) has created a framework for evaluating the potential use of real-world evidence (RWE) to help support the approval of a new indication for a drug already approved or to help support or satisfy drug postapproval study requirements.

In December, 2018, FDA issued "Framework for FDA’s Real-World Evidence Program" and FDA's CDER and CBER divisions (now also including the oncology center of excellence) created the RWE program. A series of guidance documents were released. 

DEFINITION of RWD and RWE:

FDA GUIDANCE DOCUMENTS on RWD and RWE (as of November 2024):

Topic	Title	Category	Current Status
EHRs and claims data	Real-World Data: Assessing Electronic Health Records and Medical Claims Data to Support Regulatory Decision-Making for Drug and Biological Products	Data considerations	Final, July 2024
Registry data	Real-World Data Assessing Registries to Support Regulatory Decision-Making for Drug and Biological Products	Data considerations	Final, December 2023
Data standards	Data Standards for Drug and Biological Product Submissions Containing Real-World Data	Data submission	Final, December 2023
Regulatory considerations	Considerations for the Use of Real-World Data and Real-World Evidence to Support Regulatory Decision-Making for Drug and Biological Products	Applicability of regulations	Final , August  2023
Submitting RWE	Submitting Documents Using Real-World Data and Real-World Evidence to FDA for Drug and Biological Products	Procedural	Final, September 2022
Externally controlled trials	Considerations for Design and Conduct of Externally Controlled Trials for Drug and Biological Products	Design considerations	Draft, February 2023
Non-interventional studies	Considerations Regarding Non-Interventional Studies for Drug and Biological Products	Design considerations	Draft, March 2024
RCTs in clinical practice settings	Integrating Randomized Controlled Trials for Drug and Biological Products Into Routine Clinical Practice	Design considerations	Draft, September 2024

WEBINARS for RWD/RWE:

FDA officials have given various webinars to explain these RWD/RWE guidance documents and encourage the sponsors to apply the RWE to the drug approval process. A non-profit organization, the Reagan-Udall Foundation for the FDA, in collaboration with the Food and Drug Administration (FDA), hosted a series of free, public webinars to discuss FDA-issued guidance in the RWD/RWE. 

Title	Webinar Series	Date
Real-World Data: Assessing Electronic Health Records and Medical Claims Data to Support Regulatory Decision-Making for Drug and Biological Products	https://reaganudall.org/news-and-events/events/public-webinar-series-fda-issued-guidance-real-world-evidence	November 4, 2021
Data Standards for Drug and Biological Product Submissions Containing Real-World Data	https://reaganudall.org/news-and-events/events/real-world-data-webinar-series-data-standards	December 3, 2021
Real-World Data Assessing Registries to Support Regulatory Decision-Making for Drug and Biological Products	https://reaganudall.org/news-and-events/events/real-world-data-webinar-series-registries	January 28, 2022
Considerations for the Use of Real-World Data and Real-World Evidence to Support Regulatory Decision-Making for Drug and Biological Products	https://reaganudall.org/news-and-events/events/real-world-data-webinar-series-considerations-use-rwd-and-rwe	February 11, 2022
Submitting Documents Using Real-World Data and Real-World Evidence to FDA for Drug and Biological Products	No webinar was conducted
Considerations for Design and Conduct of Externally Controlled Trials for Drug and Biological Products	https://www.youtube.com/watch?v=5rfInDy7osw&t=1s	April 13, 2023
Considerations Regarding Non-Interventional Studies for Drug and Biological Products	https://reaganudall.org/news-and-events/events/real-world-evidence-webinar-series-considerations-regarding-non	May 30, 2024
Integrating Randomized Controlled Trials for Drug and Biological Products Into Routine Clinical Practice	https://reaganudall.org/news-and-events/events/real-world-evidence-webinar-series-integrating-randomized-controlled-trials https://youtu.be/VRaQyOvn3AM?si=YrM9pY6JhL3LBr_o	November 22, 2024

Duke Margolis Center for Health Policy, in collaboration with the FDA, also conducted a series of free, public webinars to discuss the application of RWD/RWE: 

Webinar Title/Link	Date
Optimizing the Use of Real-World Evidence in Regulatory Decision-Making for Drugs and Biological Products – Looking Forward	December 12, 2024
2024 State of Real-World Evidence Policy	July 25, 2024
The State of Real-World Evidence Policy 2023	September 28, 2023
Understanding the Use of Negative Controls to Assess the Validity of Non-Interventional Studies of Treatment Using Real-World Evidence	March 8, 2023
Workshop on Draft Guidance on Real-World Data: Electronic Health Records/Medical Claims Data and Data Standards	February 27, 2023
The State of Real-World Evidence Policy	May 12, 2022
An Introduction to Real-World Data & Real-World Evidence: A Virtual Training Series for the Patient Community	March 12, 2021

SUMMARY:

RWD / RWE play an increasingly vital role in drug development by complementing traditional clinical trial data. Derived from sources such as electronic health records, insurance claims, registries, and patient-reported outcomes, RWD provides insights into how drugs perform in diverse, routine care settings. RWE, generated by analyzing RWD, helps assess the safety, efficacy, and value of treatments in real-world populations, addressing gaps that controlled clinical trials may leave. These insights are particularly valuable in identifying long-term outcomes, supporting regulatory decisions, designing pragmatic trials and comparative effectiveness researches, and informing post-market safety surveillance. Regulatory agencies like the FDA and EMA are encouraging the integration of RWE to enhance decision-making, optimize study designs, and support label expansions or accelerated approvals.