FDA Archives -

The Invisibility Machine of the Women’s Health Gap

A 300-year warning

The global timeline for gender equality is not merely stalling; it is a sobering indictment of our collective priorities as a society. Current estimates from the United Nations reveal a staggering distance to parity: at our current trajectory, it will take 300 years to end child marriage, 286 years to eliminate discriminatory laws and legal protection gaps, 140 years to achieve equal representation in workplace leadership, and 47 years to reach an equal footing in national parliaments.

These are not just social milestones; they are structural barriers that define the “Gender Health Gap.” This gap represents the inequitable, systematic differences in health outcomes between women and men — differences rooted in under-researched medical needs, chronic underfunding, and a “medical model” that has historically treated male biology as the universal baseline. To close this divide, we must recognize that health equity is a strategic imperative for global stability, health capital, and economic prosperity.

A ledger of health inequality: The data and the reasons behind the gender gap

Sex is a fundamental genetic modifier of biology, influencing everything from disease susceptibility to treatment response. Yet we remain trapped in a “health-survival paradox”: while women generally live longer than men, they endure higher burdens of morbidity and disability throughout their lives. Some examples are:

Diagnostic Delays: On average, women are diagnosed nearly four years later than men for the same diseases.
Misdiagnosis: Women are twice as likely to die following a heart attack than men, partly because they have a 50% higher chance of receiving an incorrect initial diagnosis.
AI Bias: Modern digital tools often entrench these disparities; AI-powered symptom checkers have been found to flag women experiencing heart attacks as needing psychological care rather than emergency medical intervention.
Invisible Conditions: Many women-specific conditions are severely underdiagnosed. For example, 8 in 10 women with menopause and 6 in 10 women with endometriosis remain undiagnosed. Adenomyosis affects up to 35% of women but is often invisible in medical records due to misdiagnosis as fibroids.

Some of the key reasons for the gender health gap are related to systematic underinvestment in research and innovation funding and the intersection of biology with social factors that historically displaced women’s equal position in society.

A primary driver of the health gap is the systemic neglect of female biology in scientific research:

Underfunding: Only 5% of global research and development funding is allocated to female-related research. Of this, a mere 1% goes toward women-specific conditions like menopause and fertility.
Clinical Trial Underrepresentation: The inclusion of women in clinical research only became a requirement in the 1990s. Today, women make up only about 41.2% of participants in key disease clinical trials. In cardiovascular drug trials, female participation averages only 34%, often failing to match the actual disease prevalence in the population.
Adverse Drug Reactions: Because many drugs are tested primarily on men, women have a 34% increased risk of severe adverse events. A notable example is the sleep aid Zolpidem, which stays in women’s systems longer than men’s; it took until 2013 for the FDA to require reduced dosing for women after decades of increased emergency room visits.

The gap is also influenced not only by the complex interplay of biological sex (genetics, hormones), but also by social gender (norms, roles) and societal roadblocks such as lack of female representation in leadership positions directly shaping inequalities in health policy development not only for women but for all marginalized communities.

Fact vs. fiction: Debunking women’s health misconceptions

Effective strategy requires dismantling the myths that have long perpetuated gender health inequality.

Women’s health is not synonymous with OB/GYN: Progress has been hindered by the misconception that women’s health is limited to reproductive and sexual needs. In reality, the gap spans every disease area, including neurology, immunology, and cardiovascular health, where women present with unique symptoms and risk profiles.
Longevity does not equal better health: The “morbidity burden” is a critical indicator of inequity. Women spend more years in poor health, facing higher disability-adjusted life year rates for musculoskeletal, neurological, and mental health disorders.
Inequality is not solely about race, but intersectionality is critical: While gender is a standalone driver of health outcomes, it does not exist in a vacuum. For example, Black and Native American women face the highest rates of pregnancy-related mortality, and Black women are three times more likely to die from heart failure than White women. These data points illustrate why an intersectional lens is non-negotiable for any health equity strategist.

Progress has remained largely stagnant over the last decade because women remain “invisible” in methodological and decision-making frameworks. The ICH Guidance on Technical Requirements for Pharmaceuticals for Human Use still refers to women as a “special subgroup” to be considered “when appropriate.” This classification is mathematically and medically absurd: women represent half of the global population. This invisibility fuels a self-perpetuating cycle of Data Poverty. The recent FDA guidance on addressing sex differences in clinical trials is, though, a positive step towards recognition of such impact in clinical development.

The roadblocks to reform health technologies and decision-making frameworks to address women health needs and considerations are not just scientific — they are structural. They include a lack of political will, the absence of gender indicators for evaluation, and a strong position of gender norms and laws that favor the lack of protection of women on health matters and beyond.

Conclusion

Health equity does not need to take 300 years — though some of those glacial aspects must be addressed for true success to be achieved.

Big data, digital technologies, and advanced analytics provide the means to overcome the challenges to achieving women’s health equity in the coming years. Gender health equity is not an act of morality — it is the foundation of a sustainable, healthy, and economically stable future for all.

FDA’s New Default: One Pivotal Trial for Drug Approval

A Paradigm Shift Sparking Optimism and Questions

In February 2026, the U.S. Food and Drug Administration (FDA) announced a landmark policy change that one adequate and well‑controlled pivotal trial, supplemented by confirmatory evidence, will now serve as the default basis for drug approval. This decisive shift — articulated by FDA Commissioner Marty Makary and CBER director Vinay Prasad in The New England Journal of Medicine — effectively ends a decades‑long “two‑trial dogma” and reframes the evidentiary foundation of U.S. drug regulation.

“Going forward, the FDA’s default position is that one adequate and well-controlled study, combined with confirmatory evidence, will serve as the basis of marketing authorization of novel products. The FDA will carefully examine all aspects of study design with particular focus on controls, end points, effect size, and statistical protocols.”1

It is important to remember that it has always been possible to obtain a marketing authorization on the basis of a single adequate and well-controlled study in combination with confirmative evidence, but typically this approach was mainly applied in breakthrough program designation, accelerated approval, and priority review pathways.

Why the FDA is moving away from two trials

Makary and Prasad argue that requiring two trials made sense when biology was poorly understood and therapeutics were often blunt instruments rather than targeted molecular tools. In today’s world, duplicative trials may be unnecessarily costly, slow, and redundant.

The original argument for two clinical trials is a statistical one: If a substance does not have any efficacy, then the chances of showing an effect in two studies are much lower than showing it in only one study. The article quantifies this chance as 0.06% instead of 2.5%, assuming that the test is performed at the typically applied one-sided 2.5% level (but that calculation assumes that the two studies are independent of each other, which is not necessarily the case).

The more important argument is that modern drug development provides much more clarity on a precise mechanism of action, assessed by biomarkers as well as a variety of endpoints, thus supporting statistical with biologic inference.

They emphasize several points:

Modern science provides multiple layers of corroboration

Mechanistic data, class‑effect consistency, real‑world evidence, and surrogate endpoints can complement a single pivotal study.

Two trials don’t guarantee correctness

Even under the two‑trial regime, the FDA has approved drugs later found ineffective or unsafe — not because of too few trials, but because trial design quality matters more than quantity.

Lowering trial count may reduce costs and time

One pivotal trial can cost $30–150M and takes years to complete. Reducing this burden may spur innovation and could reduce price‑justification arguments tied to Research & Development investment.

Focus on trial design and analysis

The article clearly articulates the importance of various aspects of trial design to support the credibility of trial results, including the use of a contemporary control group, pre-specification of a hypothesis, choice of a primary endpoint, statistical power, randomization, and blinding. These are key statistical aspects documented in the ICH E9 guidance on Statistical Principles for Clinical Trials, and as such they have been underlying drug development for almost 30 years. What is new and encouraging is that the article specifically states that these can also be provided by a Bayesian framework, referencing the recently published draft FDA guidance on this topic, and described by Cytel’s Savina Jaeger..

Unclear implications for global drug development

For most companies, drug development is a global business, and as such it’s unclear whether this change in FDA policy will affect the expectations from regulatory authorities in other regions and countries. Will they follow suit or maintain their current expectations? Cytel’s Strategic Consulting group will be monitoring this closely as this will have a fundamental impact on designing trials for global approvals.

It is also uncommon for the FDA to announce a major change in policy through a publication, so we will also monitor FDA’s official channels for further announcements on this topic in the future.

Final takeaway: A defining regulatory moment

The FDA’s new one‑trial default represents a significant policy shift in U.S. drug regulation. It aligns with trends in precision medicine, leverages mechanistic and statistical advances, and may unlock faster access and lower development burdens. Yet it also raises profound questions about evidence standards, risk tolerance, and the balance between speed and certainty. Most importantly, though, it reinforces the importance of solid statistical principles underlying credible drug development, with a clear statement that both Bayesian and frequentist approaches can provide them.

FDA’s Bayesian Guidance: Strategic Considerations for Sponsors

The FDA’s January 2026 draft guidance, “Use of Bayesian Methodology in Clinical Trials of Drug and Biological Products,” clarifies how the Agency expects sponsors to justify Bayesian approaches, especially when an informative prior borrows external information to support primary inference. As a draft guidance, it is nonbinding and not for implementation.

This blog highlights strategic considerations that should inform development planning, protocol/SAP design, and FDA engagement.

Type I error control is not the only path

The guidance notes that calibrating Bayesian success criteria to a Type I error rate “may not be applicable or appropriate” when borrowing external information. In those settings, sponsors may instead define success using posterior probability criteria (e.g., Pr(d>a)>c) and, where appropriate, benefit-risk or decision-theoretic frameworks.

At the same time, the draft guidance also recognizes that Bayesian methods are often used within an overall frequentist framework (e.g., to facilitate complex adaptive designs), where Type I error calibration can remain appropriate. Regardless of the framework, success criteria should be pre-specified and justified.

Strategic implication:

When the FDA and sponsor agree that a design does not need to be calibrated to the Type I error rate (often discussed in pediatrics and rare diseases), the draft guidance describes alternative operating characteristics such as Bayesian power (probability of success averaged over a prior) and the probability of a correct decision (akin to positive predictive value). That flexibility increases the premium on a well-justified analysis prior, credible simulations, and early FDA alignment.

Prior specification is now a regulatory deliverable

The draft guidance recommends that sponsors pre-specify and justify the prior in the protocol, document external information sources (including exclusions), and quantify prior influence metrics. For informative priors, the FDA emphasizes a systematic, transparent review of the totality of relevant evidence — effectively bringing evidence-synthesis discipline into prior construction.

Key expectations:

Pre-defined source selection criteria before searching for external data
Patient-level data preferred over published summary statistics
Randomized controlled evidence is generally preferred over single-arm or observational sources
Documentation of sources considered and excluded, with rationale

Strategic implication:

Prior construction cannot be a post-hoc exercise. Build the evidence base for your prior prospectively — ideally while Phase 2 is ongoing — and then plan early for patient-level data access and any needed re-analyses to align primary estimand/estimators/strategies for handling intercurrent events. If patient-level data from prior studies are not accessible, negotiate data-sharing early or design natural history studies with Bayesian use in mind.

Dynamic discounting provides protection — with complexity

The draft guidance discusses both static and dynamic discounting approaches for borrowing external information. Dynamic approaches (e.g., commensurate/supervised power priors, mixture priors, Bayesian hierarchical models, elastic priors) can reduce borrowing when prior-data conflict emerges. These approaches can improve robustness but introduce additional parameters and assumptions that need justification. The FDA also notes the applicability of discounting methods is case-by-case and should be discussed with the Agency.

Strategic implication:

For rare diseases with uncertain external data relevance, dynamic discounting is often an important safeguard. For common diseases with robust and highly relevant prior data, simpler (static) discounting may suffice and can simplify the regulatory narrative. Either way, determine the discounting approach while still blinded to the results of the trials that will be borrowed — per the guidance’s explicit recommendation — and support the choice with simulations that span plausible degrees of prior data conflict.

Effective sample size is a central metric — Not Type I error inflation

The draft guidance recommends against using Type I error inflation to measure prior influence, calling it “philosophically inconsistent.” Instead, it highlights Effective Sample Size (ESS) and other metrics (e.g., the prior-only estimate) as more interpretable ways to quantify borrowing. The guidance also notes that multiple ESS calculation methods exist, and that ESS can exceed the source-study sample size when the variability in the target population variability is higher.

Strategic implication:

Quantify and present ESS across a plausible range of outcomes, including summary statistics such as maximum and mean values. For dynamic methods, show how ESS changes with different degrees of prior-data agreement. Be prepared to explain why ESS may differ from the original study’s nominal samples size and reassess influence after trial completion when dynamic priors are used.

Simulation standards are now explicit

The draft guidance recommends providing a comprehensive simulation report (including code, implementation details, and results) across pre-specified, plausible scenarios, including pessimistic assumptions about treatment effect. Simulations should address statistical parameters (e.g., variance, background rate, intercurrent events) as well as operational assumptions such as accrual rate. For MCMC-based analyses, computational settings (warmup/burn-in, iterations, chains, convergence diagnostics) and any other important algorithm-specific settings should be documented for reproducibility.

Strategic implication:

Treat simulations and computational reproducibility as submission-grade deliverables, not just internal design exploration. Establish reproducible computational workflows from the start. Pre-specify scenarios and decision rules, and define contingency procedures for implementation issues (e.g., MCMC non-convergence) before the first interim look and before the final analysis.

Early FDA engagement is essential

The draft guidance states that “the time needed for FDA and the sponsor to align on an appropriate prior should be considered in the development of the intended trial” and recommends submitting information “as early as possible to ensure sufficient time for FDA feedback prior to initiation.” The draft guidance also states that sponsors should have early discussions with the Agency about the planned estimands, estimators, and approaches for handling missing data in the analyses of external data that will be borrowed, and any differences relative to the approaches planned for the prospective trial data.

Strategic implication:

Use early interactions (e.g., Pre-IND or End-of-Phase 2 meetings and, where applicable, the Complex Innovative Trial Design (CID) program) to align on prior specification, success criteria, operating characteristics, and simulation strategy before protocol finalization. Include detailed design comparisons in meeting packages — the draft guidance explicitly recommends comparing proposed Bayesian designs against an alternative, including simpler alternatives.

Interim analyses: Design the decision points upfront

The guidance emphasizes that in trials with interim decision-making (e.g., group sequential designs), success criteria should be specified for each decision point. When Bayesian success criteria are calibrated to Type I error rate, interim criteria can be constructed to preserve overall control of the family-wise error rate across looks.

For designs not calibrated to Type I error rate, operating characteristics are calculated relative to the prior and can be especially sensitive when the sample size is small — or when an early interim look makes the effective sample size small. The guidance also notes that skeptical (or enthusiastic) priors can be used in adaptive settings to temper early stopping behavior for efficacy (or futility), but the resulting decision framework should be demonstrated via simulation.

Key interim analysis considerations:

Pre-specify what decisions can be made at each look (e.g., stop for efficacy, stop for futility, adapt) and the exact posterior or predictive criteria that trigger each action.
Simulate interim timing under realistic accrual, endpoint maturation, and missing data patterns — not just idealized information fractions.
Plan prior sensitivity and robustness checks targeted at early looks, where prior influence is greatest (e.g., alternative priors and alternative borrowing strengths).
Operationalize Bayesian computation for interim timelines: reproducible pipelines, diagnostic thresholds, locked code/versioning, and contingency plans for non-convergence.
Protect safety and benefit-risk interpretability: consider minimum exposure or follow-up requirements even if an early efficacy threshold is met.

Strategic implication:

Treat interim analyses as part of the regulatory-facing Bayesian package, with pre-specified decision rules, simulations that stress-test early looks, and an execution plan that can be reproduced under tight timelines.

Rare vs. Common Disease Considerations

Consideration	Rare Diseases	Common Diseases
Justification for borrowing	Often straightforward: document infeasibility of a conventionally powered randomized trial (small populations and/or ethics) and explain how borrowing supports interpretable benefit-risk.	Higher burden: efficiency gains alone may not suffice; clearly demonstrate relevance, address potential bias, and explain why non-borrowing alternatives are not adequate.
Prior data availability	Often limited; may rely on natural history studies/registries, small prior trials, and/or structured expert elicitation.	Typically richer: Phase II/earlier indications, external trials, and real-world data may be available, but heterogeneity and relevance must be managed.
Recommended approach	Dynamic discounting and robust priors; success criteria not calibrated to Type I error may be appropriate when FDA and sponsor agree; plan extensive sensitivity analyses.	Bayesian methods embedded in a Type I calibrated frameworks when appropriate; borrowing (if used) is typically limited and carefully justified; pediatric extrapolation handled via separate extrapolation plan.
Key success factor	Prospective natural history characterization and early alignment on estimand definition and strategies to make external data relevant.	Early data-sharing to enable patient-level review, alignment on estimand definition and strategies, and covariance adjustment, plus a clear relevance narrative and drift/bias mitigation plan.

The bottom line

This draft guidance provides a clearer regulatory pathway for Bayesian methods, but that pathway requires substantial upfront investment in prior construction, estimand definition, and strategies for handling intercurrent events, simulations, and documentation at submission quality. The strategic question is not whether Bayesian methods are acceptable in principle — it is whether the efficiency gains justify the additional complexity and review burden for your specific program.

For rare diseases, the answer is often yes. Bayesian borrowing may be the only viable path to interpretable and approvable evidence. For common diseases, the calculus is more nuanced; borrowing typically needs a stronger relevance argument and may be most defensible when embedded in a Type I calibrated framework. Either way, the strategic decisions about prior specification, discounting method, and operating characteristics should be made early, documented thoroughly, and aligned with FDA before the pivotal trial initiation.

What’s clear is that biostatisticians must now be prepared to operate in both paradigms:

To calibrate Bayesian designs to Type I error when appropriate, and
To construct and defend fully Bayesian alternatives (including borrowing) when circumstances warrant.

The January 2026 draft guidance does not eliminate the traditional framework; it expands the toolkit. Using that expanded toolkit effectively will require new skills, new conversations, and new ways of thinking about evidence.

The statistical methodology exists. FDA expectations are clearer. The challenge is execution.

Interested in learning more?

Cytel invites you to an interactive Office Hours session with Melissa Spann and Savina Jaeger on Wednesday, March 4 at 9 am ET, where you will have the opportunity to ask questions about the FDA’s Draft Guidance for Industry: Use of Bayesian Methodology in Clinical Trials of Drugs and Biological Products:

FDA Guidance on Assessing Overall Survival in Oncology Trials: A DMC Perspective

Overall survival (OS) is the ultimate endpoint — it is easily measured and of the utmost clinical relevance. However, it also takes the longest time to develop and may not be as sensitive to treatment effect as other endpoints. In many oncology studies, for example, alternative endpoints are used, such as progression-free survival (PFS). The hope is that these alternative endpoints are indeed clinically relevant and allow for quicker results with fewer numbers of events and/or subjects needed than a study powered to detect a difference in OS. However, there has been growing concern over the use of endpoints such as PFS.

As a result, the FDA has pondered guidance on how to prioritize endpoints: whether OS or an alternative endpoint should be primary, or both should be co-primary. The FDA has also pondered whether accelerated approval might be permitted using a quicker-developing alternative while waiting for the OS data to mature and (hopefully) substantiate the efficacious results from the co-primary endpoint.

To address this, the FDA has produced the draft guidance “Approaches to Assessment of Overall Survival in Oncology Clinical Trials.”

Here, we will review this document as it pertains to Data Monitoring Committees (DMCs), and provide our own thoughts based on our 30 years of work and thousands of studies.

FDA draft guidance: “Approaches to Assessment of Overall Survival in Oncology Clinical Trials”

A key section of the FDA guidance related to DMC activity is the following:

This text reinforces what Cytel has long advocated — more interim analyses, both for futility (and possibly harm) and benefit — and reinforces the independence of the DMC and the group directly facilitating the DMC’s work. Approaches to constructing and implementing interim analyses for OS can be flexible — for example, assessing futility at 50% information fraction of OS, and then assessing both futility and benefit at 75% information fraction.

In addition to the section above, there are other references within the draft guidance for how a DMC or other parties would use and interpret OS data. Some particular references are below:

Using and interpreting overall survival data

It is important the DMC understands the operating characteristics of these interim analyses. The analyses might be at a high level in the DMC charter, or more specifically described in the protocol, or study SAP or interim analysis plan (IAP). Translating statistical methodology informally into actual counts is helpful to ensure audiences, including the DMC, understand what the “tipping points” might be. For example, a stopping rule for futility at 50 deaths (with the stopping rule perhaps defined by O’Brien-Fleming or by Bayesian analysis) would helpfully be presented to the DMC that translates to clearly understandable events as a reality check — perhaps the study design and stopping rules might lead to the conclusion that 28 deaths on active vs. 22 deaths on placebo (using reasonable assumptions for timing of events and other aspects of the data) would be the tipping point to trigger futility. This could be informally presented to the DMC at their organizational meeting to ensure the DMC understands the high-level approach and how that might play out in real numbers.

It is also important for the group creating the analyses to communicate to the DMC the uncertainty of early results so that the DMC does not overinterpret. This is especially true in the (theoretic) presence of non-proportional hazards.

Overall survival data: A key safety parameter

OS is a critical variable to help the DMC assess the study even outside of formal interim analyses — both as the DMC interprets safety (more deaths on the active arm) and efficacy (fewer deaths on the active arm). Together, this provides a more complete way to assess risk-benefit in the context of other safety concerns. While some sponsors prefer a “safety only” DMC and are hesitant to provide endpoint data, it is typically well understood that, as a key safety parameter, OS data is

Therefore, we typically present a death table to the DMC, and likely two: one summary of deaths for the safety population and one for the randomized population. This is particularly relevant for open-label studies where there might be an imbalance in subjects not treated and therefore not included in the safety population. Note that, traditionally, not all deaths are included in the adverse event (AE) dataset. In many studies, AEs occurring after treatment has finished and/or due to underlying disease might not be captured in the AE dataset.

Cytel also may tabulate investigator-assessed cause of death. (We do note to DMCs the hazards of interpreting investigator-reported causality though. That is part of the reason OS is appealing, as it is immune to the potential biases of ascertaining relationship.) Two treatment arms might have the same number of deaths, but the interpretation of the DMC could be different if there is an excess of deaths due to disease progression on one arm, relative to an excess of deaths due to serious adverse events (SAEs)/toxicity on another arm. It is traditional to also show timing in a table (perhaps deaths <30 days after last dose vs. deaths ≥ 30 days after last dose). The DMC may drill down into the fatal SAEs/toxicity to look for patterns and perhaps make recommendations to mitigate that risk. We have seen examples of excess fatal interstitial lung disease (ILD), fatal COVID-19, and fatal infections on active arms compared to control arms. DMCs in these situations have thought hard about mitigation strategies, as they would for any concerning imbalance in a major safety domain.

A Kaplan-Meier figure of time to death (possibly without any inferential statistics) is also typically presented to the DMC — typically based on the randomized population. It can be important for the DMC to understand the timing of the deaths to help answer questions such as whether most deaths occur early in the study or if there is a differential pattern over time (i.e., crossing curves implying non-proportional hazards) between the treatment arms. The definitions used for programming the Kaplan-Meier figure might not match the exact definitions at end of study if used in a non-inferential way — the censoring rules might be flexible based on the data available (e.g., the choice of whether to censor subjects still alive and in study follow-up at their last known contact, or whether to censor at the data cut-off date). In some situations, especially where OS is the primary endpoint, the sponsor might be hesitant to even show a non-inferential Kaplan-Meier figure of time to death to the DMC outside of formal analyses (if those exist). Nonetheless, the DMC could argue these are needed and, at minimum, are available upon request by the group facilitating the DMC’s discussion.

For formal interim analyses of OS that show inferential statistics (e.g., p-values, hazard ratio, confidence intervals), there would be appropriate effort in advance to ensure that the precise censoring algorithm and inferential statistics specified in in the study SAP are put in place for the DMC review.

Oversight on study integrity and interpretability

The DMC has oversight on study integrity and interpretability in conjunction with the sponsor, but it is important to have the independent thoughts of the DMC. In this domain, the DMC may well voice concern if an excessive rate of subjects is being lost to long-term survival surveillance. That would be particularly important if an imbalance in arms is developing for subjects lost to follow-up for long-term survival (as could happen in open-label studies). The DMC might not explicitly state that observation externally, but an imbalanced rate of lost to follow-up would be particularly concerning to the interpretation of OS at the end of the study.

And the DMC may voice concern if OS is the primary endpoint or it is critical to have a certain number of deaths for secondary analysis, but the overall rate of deaths is appreciably lower than expected, which seems likely to extend the duration of the study by years – bringing into question whether the study will be relevant and funded for the additional years needed. The DMC may request current projections (with computed confidence interval) for when in calendar time the expected number of deaths will occur. This can be complicated if the minimum number of deaths is required in a subgroup, and particularly so if the minimum numbers of deaths is in a blinded subgroup (a blinded biomarker population, or in a subgroup of treatments for studies with more than two treatment groups).

The DMC should understand that OS data early in a study is statistically unreliable. And there might even be some expectation for excess deaths early on (if late benefit is expected, in the presence of early toxicity). There might even be the expectation of no OS benefit — there might still be overall value to the new treatment even if there is no difference in OS — if the treatment is less toxic or more easily administered or cheaper.

Increasing the confidence of subgroup analysis results

The DMC may rightfully be concerned upon seeing an excess of deaths on the active arm. There are options for the DMC, as for any potential safety concern. One of the first steps traditionally undertaken is to investigate which subgroup of patients is most at risk for the excess death — but with the full understanding of the hazards of subgroup analysis. It is easy for an unwary reviewer to overinterpret the results of subgroup analyses. The DMC should consider factors that increase the confidence of subgroup results when looking for consistency of signal or trying to identify a subgroup that could specifically have some risk mitigation plan implemented:

Biologic rationale
Larger sample size
Consistent findings in other trials
Included as a stratification factor

Ad hoc interim analyses for OS

If OS is the primary endpoint, and no statistical futility in place but the DMC sees worrisome or neutral trends in OS, the DMC might consider ad hoc analysis of OS for futility. This is usually in the context of excess Grade 3+ AEs or SAEs as well as this OS result. The DMC might see if a lower limit of a confidence interval of hazard ratio (suitably adjusted for the interim nature by using information fraction and perhaps O’Brien-Fleming boundary) excludes 0.9 or lower. Or perhaps the DMC will request the supporting CRO to compute conditional power. We have seen DMC recommend termination or major change based on the totality of the data — largely influenced by neutral or negative OS results. Obviously, it is preferred to simply have pre-planned looks for OS futility.

The DMC might also see amazing benefit of OS, but without any formal interim analysis planned to assess benefit. The DMC could decide that the scientific question has been answered and that it is unethical to continue and therefore alert the sponsor. We have seen this occur and after intense focused discussion between the DMC and sponsor and regulatory agencies the decision was made to move forward towards regulatory approval, which accelerated the regulatory process by years. Clearly, it is preferred to simply have pre-planned looks for OS benefit.

A recent situation involved a relatively high toxicity observed, but the intervention was expected to provide a long-term OS benefit. The DMC decided to recommend halting enrollment in the population with the lowest baseline disease severity. These subjects were expected to have the same rate of toxicity as all groups, but with minimal expected absolute OS benefit.

Difficult recommendations

DMCs must make difficult recommendations as PFS and OS data emerge, if PFS is a primary or co-primary endpoint. The DMC should ask for and be provided with (perhaps non-inferential) summaries of both PFS and OS. DMC recommendations are challenging if results are discrepant. Most common would be an interim analysis of PFS for benefit that crosses a boundary for benefit, but OS results are extremely immature or neutral or perhaps even in the negative direction. The DMC hopefully has the flexibility to explain the context of the situation to a senior liaison at the sponsor for discussion if the study should continue and obtain additional valuable OS data to help answer questions about OS more precisely. This is particularly problematic if there is a discrepancy between investigator-provided PFS and blinded independent review committee (BICR) PFS, or a delay or unavailability of BICR data to the DMC.

Controversies exist when the co-primary endpoint achieves full statistical information before OS does and deciding what the DMC’s role is at the point, and after that point in time. In some situations, the DMC’s obligations for oversight of the study conclude once PFS has completed. The sponsor (perhaps the full study team, or a subset of the study team, or a separate team) becomes unblinded to final PFS (and likely interim OS), and oversight for safety of ongoing patients during continued OS surveillance is undertaken by the sponsor. The assumption is that unblinding and knowledge of PFS results will not bias the ongoing OS collection. However, if results of PFS are impressive and that knowledge becomes public, that might impact behavior that affects future collection of OS — especially in an open-label study.

Therefore, many DMCs have argued that the DMC should still be involved in oversight of OS even after final analysis of PFS — perhaps in conjunction with making sure that the final PFS results are handled very securely and led by DMC and not sponsor personnel. The value of this approach is that the continued OS is not impacted by patients, sites, or general sponsor team knowledge of the PFS results or interim OS results. The DMC can take the lead on communicating final PFS results and perhaps interim OS results to a small group within the sponsor or perhaps (with agreement from all parties) directly to regulatory agencies.

Final takeaways

We believe, and the FDA agrees, that DMC access to overall survival results can be critical to the DMC’s remit. Hopefully, DMC members will insist on receiving the outputs that meet their needs, which could include either non-inferential or inferential analyses of overall survival; CROs will be able to create the outputs needed in an accurate and timely way; and sponsors will trust DMCs to act responsibly with the outputs provided to them.

Interested in learning more?

Download our white paper, “DMCs for Oncology Studies”:

The FDA’s Roadmap to Reducing Animal Testing in Preclinical Safety Studies

For a number of years, the FDA and other regulatory agencies have been concerned about the number of animals used in drug research, particularly with regard to toxicology studies. This concern is not only based on animal welfare considerations, it is also based on the increasing realization that animal toxicology data does not always predict human toxicity.

Here, we discuss these challenges and the FDA’s new roadmap for reducing animal testing in preclinical safety studies.

Animal toxicity often does not predict human toxicity

There have been a number of drugs that appeared safe in animals while under development but were later shown to be toxic in humans.

Some examples include:

Fialuridine: No significant toxicity was observed in mice, rats, or dogs, but in a Phase II clinical trial, several cases of fatal hepatic failure occurred. The reason for this was later shown to be species-specific differences in how nucleoside analogs affect mitochondrial function.1
Troglitazone: No significant toxicity recorded in rats, mice, or dogs, but several reports of liver failure were reported post-approval, which ultimately led to the withdrawal of troglitazone from the U.S. market in 2000. It was later determined that toxic reactive metabolites were formed in humans, but not in animals.2
Rofecoxib: In non-clinical species, there were no safety signals observed. Post-approval, an increased risk of myocardial infarction and stroke were observed during long-term use. The reason for this difference in response is thought to be that rofecoxib may increase the susceptibility of human low-density lipoprotein and cellular membrane lipids to oxidative damage, which then may lead to plaque instability and thrombus formation in humans.3

According to the FDA, over 90% of drugs that appear safe and effective in animals do not ultimately receive FDA approval in humans largely due to safety and/or efficacy concerns.4 Conversely, some medications that are generally considered safe in humans may never have passed animal testing. Such physiological differences underscore why animals may not always provide adequate models of human health and disease.5

A new roadmap: Reducing animal testing in preclinical studies

In April 2025, the FDA published its Roadmap to Reducing Animal Testing in Preclinical Safety Studies.6 The roadmap outlines a long-term plan to reduce or possibly eliminate animal toxicology testing, starting with monoclonal antibodies, by using what is termed the “New Approach Methodologies (NAMs),” which include the use of human tissue-based systems, such as organs-on-chips, in silico modeling, and other innovative approaches.

Organs-on-chips (OoC): Systems that contain engineered or natural miniature tissues grown inside microfluidic chips.7
In silico modeling: Using computational modeling to leverage existing data to predict safety, immunogenicity, and pharmacokinetics, reducing the need for new animal testing. Key tools include PBPK modeling, AI/ML, and so on.8

Other approaches mentioned include ex vivo human tissues, high-throughput cell-based screening, microdosing and imaging in human volunteers, and refined in vivo methods. The Roadmap highlights that these methods all address one or more aspects of animal testing, and thus it will be essential to use an integrative strategy.

Key questions for success

Key questions that need to be answered in order for these approaches to be successful include:

How predictive are NAMs with regard to determining drug safety?
How are NAMs best utilized during the early stages of development, including how studies are to be designed?
How consistent are the results across various manufacturers of NAMs?

Unlike animals, new approach methodologies (such as organs-on-chips) may differ significantly in terms of cell types, genetics, and composition of the overall structure.

Final takeaways

The FDA has laid out an ambitious long-term strategy for reducing or even eliminating animal testing initially for monoclonal antibodies, with the potential to extend this to small molecules and therapeutic proteins. The success of this strategy will depend in part on close cooperation between industry stakeholders and the FDA, as well as other regulatory bodies in the ICH.

Interested in learning more?

Join Cytel’s Michael Fossler, Nelia Padilla, and Mammoth Preclinical’s Edwin Garner for their upcoming webinar, “FDA’s Roadmap to Reducing Animal Testing in Monoclonal Antibody Development” on December 9 at 9 am ET:

FDA OCE Project Frontrunner: Accelerating First-Line Oncology Drug Development

The U.S. Food and Drug Administration’s Oncology Center of Excellence (OCE) launched Project Frontrunner to shift the paradigm in oncology drug development. Traditionally, novel oncology drugs gain approval for use in patients with later-stage disease and who have exhausted other treatment options. Project Frontrunner challenges this model by encouraging sponsors to pursue initial drug approvals in the earliest feasible disease setting, particularly first-line or treatment-naïve populations.

The conventional late-line strategy for oncology drug development offers fewer regulatory hurdles and facilitates faster enrollment. However, it delays access to potentially life-extending or curative therapies for patients with early-stage disease. Moreover, the biology of tumors in heavily pretreated patients often differs significantly from earlier stages, limiting generalizability. Project Frontrunner seeks to reverse this trend, thereby aligning trial design with patient-centric outcomes.

Here, I discuss the key elements of Project Frontrunner, the statistical complexities of first-line trial design, and the potential impact on sponsors.

Key elements of Project Frontrunner

First-line indication targeting: Encourages drug developers to pursue marketing applications based on trials in treatment-naïve populations, not just refractory or relapsed disease settings.
Regulatory support and early engagement: The FDA offers early scientific engagement with sponsors through Type B and Type C meetings, helping optimize development plans for first-line indications.
Use of randomized controlled trials (RCTs): Promotes the use of RCTs in early-stage disease rather than single-arm studies in late-stage patients, aiming for more robust and generalizable evidence.
Expedited programs compatibility: Supports use of breakthrough therapy designation, priority review, and accelerated approval, even when targeting earlier lines of therapy.

Practical implications for trialists

Trial design complexity: Sponsors must design larger, more rigorous trials, often needing comparator arms, which may increase cost and duration but improve scientific robustness.
Patient recruitment considerations: Recruiting treatment-naïve patients can be more competitive and ethically challenging, requiring careful protocol development and site coordination.
Strategic endpoint selection: Trialists must select endpoints that reflect long-term clinical benefit (e.g., progression-free survival, overall survival), rather than short-term surrogate markers typically used in late-line settings.

Statistical complexities in first-line trial design

Designing oncology trials for first-line indications — as encouraged by Project Frontrunner — brings increased statistical and methodological complexity compared to traditional late-line trials. The rigor demanded by earlier-stage settings requires careful planning to ensure validity, power, and regulatory acceptability.

Randomized comparators and control integrity

Trials typically require active control arms rather than historical controls. Selecting an appropriate standard-of-care comparator and maintaining blinding (where feasible) becomes essential to minimize bias and strengthen inference.

Longer time horizons for endpoints

In first-line disease, progression-free survival (PFS) and overall survival (OS) require longer follow-up, increasing risk of loss to follow-up and requiring more robust methods for censoring and handling missing data.

Multiplicity adjustments and hierarchical testing

With multiple endpoints — such as PFS, OS, objective response rate (ORR), and quality of life — multiplicity becomes a critical issue. Sponsors may need hierarchical testing strategies or gatekeeping procedures to control Type I error.

Interim analysis and adaptive design considerations

Sponsors may wish to incorporate group-sequential designs or adaptive features (e.g., sample size re-estimation), but these add statistical complexity and must be pre-specified with strong rationale to be acceptable to regulators.

Subgroup analyses and biomarker stratification

Treatment-naïve populations may be heterogeneous. Stratification by biomarkers or disease subtype may be necessary, but raises statistical power concerns and increases the risk of false discovery if not pre-specified and adjusted.

Likely impact on sponsors

Project Frontrunner presents both opportunities and challenges for drug developers aiming to target earlier lines of oncology treatment. Below are key advantages and disadvantages for sponsors engaging with this program:

Advantages

Market leadership and differentiation: Gaining approval for a first-line indication can position a therapy as the standard of care, offering strategic advantage over drugs only approved for late-line use.
Extended commercial exclusivity: Earlier approval typically translates into longer duration of market exclusivity, enhancing revenue potential before generics or biosimilars enter the market.
Clinical value and branding: Drugs proven effective in first-line settings may be perceived as more effective and versatile, strengthening the sponsor’s brand and clinical reputation across stakeholders, including physicians and payers.

Disadvantages

Higher development costs and risk: Trials in earlier-stage patients typically require larger sample sizes, randomized designs, and longer follow-up, increasing overall trial costs and investment risk.
Increased regulatory scrutiny: Early-line trials are subject to higher evidentiary standards, with greater emphasis on demonstrating long-term clinical benefit (e.g., overall survival), making approval more difficult.
Competitive recruitment environment: Enrolling treatment-naïve patients is often slower and more competitive, as these patients may have multiple treatment options and may be hesitant to join experimental arms.

Final thoughts

Project Frontrunner represents a bold step by the FDA to reshape oncology drug development. While it demands more rigorous trial designs and greater investment from sponsors, it aligns closely with patient-centric goals: bringing promising therapies to those who need them most, earlier in their disease journey. For sponsors willing to embrace these challenges, the program offers a chance to lead in an increasingly competitive oncology landscape.

James Matcham, VP Strategic Consulting, and Pranav Yajnik, Senior Consultant, will be hosting a Cytel webinar on August 20, 2025, where they will provide an overview of Project Frontrunner and its implications for oncology drug development. They will also explore, using a case study, how innovative trial design strategies can lead to faster, more robust pathways to market for oncology therapies.

FDA Guidance on Integrating RCTs into Clinical Practice and the Growing Potential of RWE

Patient recruitment remains one of the most challenging and costly parts of clinical trials. One approach to tackle this has been partnering with healthcare providers to capture data gathered during routine clinical practice for use in clinical trials.

As part of the U.S. FDA’s Real-World Evidence (RWE) Program, the agency has issued a new draft guidance on the integration of randomized control trials (RCTs) into routine clinical practice.1 The draft is open to public comment until December 17, 2024.

Here we discuss what you need to know about the new draft guidance and the implications for the future of clinical trials.

Bringing together clinical research and clinical practice

Traditional randomized controlled trials gather a large amount of patient information, some of which is also collected during routine clinical care. Considering this overlap, data for a clinical trial could potentially also be gathered from patients in other clinical settings with health care providers.

Such integrated RCTs, often referred to as point-of-care or large simple trials, are designed to be more convenient and accessible for participants since they can reduce the need for trial sites, and thus can ultimately lead to more representative and generalizable results.

Additionally, there has been increasing interest in incorporating real-world data (RWD) similarly collected during routine clinical care into clinical studies.

While integrating clinical trials into routine clinical practice is not new — indeed efforts to do so have been going on for decades — recent tools, such as the use of electronic health records, have made such trials and the use of RWD far more feasible.

Integrating RCTs into routine clinical practice: What to know

The new draft guidance, “Integrating Randomized Controlled Trials for Drug and Biological Products into Routine Clinical Practice: Guidance for Industry,” aims to “support the conduct of randomized controlled drug trials (RCTs) with streamlined protocols and procedures that focus on essential data collection, allowing integration of research into routine clinical practice.”

The guidance emphasizes a few key points:

The role of established healthcare institutions and existing clinical expertise

The guidance highlights the importance of leveraging established healthcare institutions and existing clinical expertise, discussing the roles of sponsors, clinical investigators, and healthcare providers. This can reduce start-up times and speed up enrollment, making the trial process more efficient.

Streamlining RCTs to align with clinical practice

Here the guidance emphasizes that trials will be most successfully integrated with clinical practice when the data needed is collected routinely and does not require additional procedures or visits. Where this is not possible, a hybrid approach should be considered.

A quality by design approach

Successful integration will rely on designing trials that follow a set of principles that involves considering such aspects as eligibility criteria, choosing suitable investigational drugs, study endpoints, and so on.

Implications for the future of clinical trials

Enhanced accessibility and efficiency

By integrating RCTs into routine clinical practice, this guidance aims to make participation easier for patients, which could lead to higher enrollment rates and more diverse participant pools. This will be especially important for rare diseases, as the overall pool of patients is smaller, and trials compete for enrollment.

Furthermore, streamlined protocols and procedures are expected to reduce administrative burdens and costs, making trials more efficient and potentially accelerating the development of new therapies.

Improved generalizability of results

The use of RWD and the integration of trials into everyday clinical settings can produce findings that are more applicable to real-world patient care. This can enhance the external validity of trial results and improve their utility in clinical decision-making.

Faster innovation cycles

The ability to conduct trials more quickly and efficiently can shorten the time from discovery to market for new treatments. This can foster a more dynamic and responsive healthcare innovation ecosystem.

Integrating clinical trials with clinical practice: Challenges and perspectives

Although the emphasis has been put on the integration of clinical trials with clinical practice, such integration may face challenges in the short term. Over time, infrastructure and scientific advances could help us overcome them.

Quality, integrity, and accuracy of trial data

The guidance emphasizes that sponsors must ensure the quality, integrity, and accuracy of trial data. However, sponsors may encounter inconsistencies with how data is collected by healthcare providers and find that some study procedures cannot be performed within routine clinical practice without causing significant disruption.

Additionally, the lack of standardized formats and terminologies across different data sources can also make it difficult to integrate and analyze data uniformly. Although standards and common data models (CDM) are converging towards more standardization, the progress has been very slow.

Obtaining informed consent

When conducting a traditional clinical trial, sponsors must obtain consent from trial participants, but doing so within routine clinical practice may present additional hurdles. To overcome this when integrating an RCT into clinical practice, the guidance suggests that one solution can be to embed informed consent documents into EHRs.

However, when using RWD retrospectively, obtaining appropriate consent remains a significant challenge, and may need future changes in jurisdictions waiving such consent.

Controlling for bias

When incorporating a clinical trial into clinical practice, blinding may be difficult to ensure. According to the guidance, blinding may add complexity to trial implementation, require greater resources, increase costs, and require longer timelines. And when not possible to include blinding, identifying potential sources of bias and including measures to address them in the trial design will add additional challenges.

Data privacy and security

Ensuring the privacy and security of patient data is crucial. The use of RWD must comply with stringent data protection regulations, which can complicate data sharing and integration.

Facilitating secure and compliant data sharing between institutions and across borders also remains a significant hurdle.

Methodological challenges

Developing robust methodologies to analyze RWE and integrate it with traditional clinical trial data is essential to ensure the reliability and validity of the results.

RWD may also be subject to biases that can affect the validity and generalizability of findings. Ensuring that the data accurately represents the broader patient population is crucial.

Addressing these challenges requires collaboration among stakeholders, including researchers, healthcare providers, regulatory bodies, and technology developers. By overcoming these hurdles, the integration of RWD into clinical trials can enhance the relevance and efficiency of clinical research, ultimately leading to better patient outcomes.

Final takeaways

The FDA’s new draft guidance represents a significant step toward modernizing clinical trial methodologies, making them more patient-centric and reflective of real-world conditions. This evolution is poised to enhance the relevance, efficiency, and impact of clinical research in the coming years.

This approach is also consistent with the growing trend of considering the “entirety of evidence.” The conventional hierarchy of evidence, which opposes clinical trials with real-world evidence study designs, may also need to be revisited for a more complex and holistic consideration of evidence that includes a variety of needs on the one hand and a continuum of study designs and data sources on the other.

Notes

1 U.S. FDA. (September 2024). Integrating Randomized Controlled Trials for Drug and Biological Products into Routine Clinical Practice: Draft Guidance for Industry.

Maximizing the Potential of Real-World Data with Bayesian Borrowing

In response to concerns about data quality in real-world evidence (RWE) generation, including issues such as bias and small sample sizes, resulting in low precision estimates with questionable accuracy and thus interpretability challenges, regulatory submissions have increasingly incorporated advanced methodologies to enhance the robustness of RWE.

Among these methods, Bayesian borrowing stands out as an approach that can significantly increase the scientific potential of real-world data. By leveraging data from multiple sources that may all have different weaknesses, Bayesian borrowing can combine these and enhance the power of comparisons with trial data for comparisons beyond those from a randomized control trial. Bayesian borrowing can also be used to create hybrid control arms, enabling a smaller control cohort to address ethical concerns and patient availability issues.1

The Bayesian borrowing concept

Bayesian borrowing methods make use of external data, potentially from multiple sources, by using a prior distribution that adjusts for the possibility that this external data may come from a different population. While using external or historical data can enhance the precision and accuracy of parameter estimates in a study, directly simple pooling of this data could lead to bias if the external population differs from the current one.2,3,4 To address this, priors such as a power prior is used to adjust the influence of the external data, which is more diffuse than complete pooling of current study dataset and the external dataset, reducing the possible bias but also the eventual precision of the parameter estimate.

In drug development, Bayesian borrowing is primarily applied in situations involving rare diseases, pediatric trials, or when there are no existing approved treatments for the same conditions.5

Figure 1. Bayesian borrowing

Quantitative bias analysis (QBA) plays a crucial role in supporting studies that employ Bayesian borrowing by assessing the impact that the weaknesses in the data being integrated has on study results. When leveraging external or historical data through Bayesian methods, such as Bayesian borrowing, there is always a risk that the borrowed data may introduce bias due to elements that cannot be addressed directly in analysis specifications, such as missing or unmeasured data, or other quality issues. QBA helps to quantify the extent of these biases and provides a structured approach to adjust for them, thereby enhancing the interpretation possibilities of the results, ultimately supporting study validity and scientific integrity.

By applying QBA alongside Bayesian borrowing, researchers can transparently account for uncertainties in the borrowed data and ensure that the final estimates are more robust, credible, and defensible in both regulatory and clinical decision-making contexts.

Figure 2. Example of QBA for Bayesian borrowing

FDA and HTA submissions incorporated with Bayesian borrowing methods

In recent years, the acceptance of Bayesian borrowing approaches has been evolving from both regulatory and Health Technology Assessment (HTA) perspectives.

The FDA has highlighted this shift through initiatives like a podcast discussing the use of Bayesian statistics, including a case where Bayesian methods were used to borrow data from an adult trial to assess an asthma product’s treatment effects in pediatric patients.6 Additionally, the FDA recommended that GSK apply Bayesian dynamic borrowing to integrate adult trial data for a pediatric study for post-marketing activities, and these results were subsequently accepted.7

HTA bodies are also considering Bayesian methods; for example, NICE recommended using Bayesian hierarchical models, which are closely related to Bayesian borrowing, in the technical appraisal of larotrectinib for NTRK-fusion positive solid tumors in 2020.8

Furthermore, the FDA plans to release draft guidance on the use of Bayesian methods in clinical trials for drugs and biologics by the end of 2025.

The future of Bayesian borrowing

Although Bayesian methods have garnered increasing attention from regulatory and HTA bodies, their practical implementation has been somewhat limited. Challenges such as organizational resistance to novel approaches, resource constraints, and difficulties in applying these advanced methods effectively can hinder their adoption in regulatory and HTA submissions. However, as awareness grows and best practices are established, these barriers are likely to diminish, paving the way for more widespread use of Bayesian methods.

Notes

1 Dron, L., Golchi, S., Hsu, G., & Thorlund, K. (2019). Minimizing Control Group Allocation in Randomized Trials Using Dynamic Borrowing of External Control Data – An Application to Second Line Therapy for Non-Small Cell Lung Cancer. Contemporary Clinical Trials Communications, 16(1).

2 Viele, K., Berry, S., Neuenschwander, B., Amzal, B., Chen, F., Enas, N., Hobbs, B., Ibrahim, J. G., Kinnersley, N., Lindborg, S., Micallef, S., Roychoudhury, S., & Thompson, L. (2013). Use of Historical Control Data for Assessing Treatment Effects in Clinical Trials. Pharmaceutical Statistics, 13(1).

3 Struebing, A., McKibbon, C., Ruan, H., Mackay, E., Dennis, N., Velummailum, R., He, P., Tanaka, Y., Xiong, Y., Springford, A., & Rosenlund, M. (2024). Augmenting External Control Arms Using Bayesian Borrowing: A Case Study in First-Line Non-Small Cell Lung Cancer. Journal of Comparative Effectiveness Research, 13(5).

4 Mackay, E. K. & Springford, A. (2023). Evaluating Treatments in Rare Indications Warrants a Bayesian Approach. Frontiers in Pharmacology, 14(1).

5 Muehlemann, N., Zhou, T., Mukherjee, R., Hossain, M. I., Roychoudhury, S., & Russek‑Cohen, E. (2023). A Tutorial on Modern Bayesian Methods in Clinical Trials. Therapeutic Innovation & Regulatory Science, 57(1).

6 Clark, J. (2023). Using Bayesian Statistical Approaches to Advance our Ability to Evaluate Drug Products. CDER Small Business and Industry Assistance Chronicles, U.S. FDA.

7 Best, N., Price, R. G., Pouliquen, I. J., & Keene, O. N. (2021). Assessing Efficacy in Important Subgroups in Confirmatory Trials: An Example Using Bayesian Dynamic Borrowing. Pharmaceutical Statistics, 20(1).

8 NICE. (2020). Appraisal Consultation Document: Larotrectinib for Treating NTRK Fusion-Positive Solid Tumours.

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A 300-year warning

A ledger of health inequality: The data and the reasons behind the gender gap

Fact vs. fiction: Debunking women’s health misconceptions

Conclusion

A Paradigm Shift Sparking Optimism and Questions

Why the FDA is moving away from two trials

Focus on trial design and analysis

Unclear implications for global drug development

Final takeaway: A defining regulatory moment

Type I error control is not the only path

Strategic implication:

Prior specification is now a regulatory deliverable

Key expectations:

Strategic implication:

Dynamic discounting provides protection — with complexity

Strategic implication:

Effective sample size is a central metric — Not Type I error inflation

Strategic implication:

Simulation standards are now explicit

Strategic implication:

Early FDA engagement is essential

Strategic implication:

Interim analyses: Design the decision points upfront

Key interim analysis considerations:

Strategic implication:

Rare vs. Common Disease Considerations

The bottom line

Interested in learning more?

FDA draft guidance: “Approaches to Assessment of Overall Survival in Oncology Clinical Trials”

Using and interpreting overall survival data

Overall survival data: A key safety parameter

Oversight on study integrity and interpretability

Increasing the confidence of subgroup analysis results

Ad hoc interim analyses for OS

Difficult recommendations

Final takeaways

Interested in learning more?

Animal toxicity often does not predict human toxicity

A new roadmap: Reducing animal testing in preclinical studies

Key questions for success

Final takeaways

Interested in learning more?

Key elements of Project Frontrunner

Practical implications for trialists

Statistical complexities in first-line trial design