Over the past decade, oncology has achieved a level of biological precision that would have been difficult to imagine even twenty years ago. Molecular stratification has redefined disease taxonomy, biomarker-driven therapies have reshaped treatment algorithms, and adaptive trial designs have accelerated development timelines. In many respects, oncology has become the most sophisticated field in modern medicine.
What has not evolved at the same pace is the representativeness of the patients enrolled in clinical trials.
Cancer remains, fundamentally, a disease of aging. By 2030, approximately 70% of all new cancer diagnoses in the United States are projected to occur in individuals aged 65 years and older. 1 And yet, the populations enrolled in oncology trials continue to skew younger, fitter, and less clinically complex than those encountered in routine practice. The result is a structural contradiction:the majority of patients are elderly, but the evidence base guiding their care is not built around them. 2
Besides being an inclusivity issue, this mismatch is also a limitation in how oncology generates knowledge.
The Quantitative Gap
The underrepresentation of older adults is well documented, but its most important feature is often overlooked: it becomes more pronounced with increasing age.
In a large analysis of 166 clinical trials involving 229,558 participants used to support regulatory approvals, adults aged 60–74 were often represented in proportions roughly aligned with disease prevalence. However, patients aged 75 years and older were consistently underrepresented, and in several indications, those aged 80 and above were nearly absent. 3
The implications become clearer when examined in disease-specific contexts.
In non-small cell lung cancer, one of the most extensively studied malignancies, patients aged ≥75 accounted for 42.8% of the disease population but only 7.3% of trial participants, while younger patients aged 40–64 were markedly overrepresented relative to their prevalence. 3
Participation data further reinforce the magnitude of the gap. Among older adults with hematologic malignancies, only 4.3% were enrolled in clinical trials within five years of diagnosis. 4
Representation vs. Reality: The “Fit Elderly” Problem
The issue is not only that older adults are underrepresented. It is that those who are included are often not representative of the broader elderly population.
Clinical trial participants labeled as “elderly” are frequently characterized by lower levels of frailty, fewer comorbidities, and better functional status than typical patients seen in routine practice, reflecting restrictive eligibility criteria, clinician selection, and logistical barriers to participation. 5
The consequence is a form of selection bias within age itself. A 70-year-old patient with preserved organ function and minimal comorbidity is fundamentally different from an 82-year-old with multimorbidity, reduced physiological reserve, and functional dependence, yet both are often grouped within the same analytical category, despite well-established heterogeneity in aging that is not captured by chronological age alone. 6, 7
This compression of heterogeneity has important implications. It creates the appearance of age inclusion while excluding the very features, frailty, complexity, and competing risks, that define real-world aging and influence treatment tolerance and outcomes 5, 8
In effect, trials do not study “older adults” as they exist clinically. They study a subset of older adults who most closely resemble younger populations, limiting the external validity of trial findings for routine oncology practice.
How Trial Design Produces Systematic Exclusion
The persistent underrepresentation of older adults is not the result of a single decision point. It emerges from the cumulative effect of how trials are designed, operationalized, and interpreted.
Eligibility criteria remain one of the most influential drivers. Older adults are more likely to present with multimorbidity, polypharmacy, renal impairment, and cardiovascular disease, all of which frequently conflict with protocol-defined thresholds. 9
Performance status requirements further narrow eligibility. Oncology trials frequently rely on strict performance status criteria, which can exclude patients with reduced functional reserve and limit the representativeness of enrolled populations. 10
Safety considerations add another layer. Aging is associated with changes in pharmacokinetics and pharmacodynamics, as well as increased variability in drug exposure and toxicity. While these factors warrant careful evaluation, contemporary guidance emphasizes that they should support inclusion with appropriate monitoring rather than broad exclusion. 5, 11
Operational barriers reinforce these patterns. Older adults are more likely to encounter challenges related to transportation, caregiver support, and the logistical demands of trial participation, factors that remain insufficiently addressed in trial design. 12
Finally, referral dynamics play a role. Older patients are less likely to be offered participation in clinical trials, reflecting persistent concerns regarding tolerability, competing risks, and perceived patient preferences, despite evidence that many are willing to enroll when given the opportunity.
Clinical Consequences: Uncertainty, Variability, and Undertreatment
The consequences of this underrepresentation are not theoretical. They shape clinical decision-making in direct and measurable ways.
When evidence is generated predominantly in younger, fitter populations, clinicians are required to extrapolate to older patients whose physiology, comorbidity burden, and functional reserve differ substantially. Age-related changes in drug metabolism, immune response, and organ function are well documented, and they influence both efficacy and toxicity. 1
In practice, this creates uncertainty at multiple levels.
Dosing becomes less predictable. Without robust age-specific data, clinicians often rely on empirical dose adjustments rather than evidence-based strategies. This introduces variability in treatment intensity and outcomes.
Treatment selection is similarly affected. In situations where benefit-risk profiles are unclear, clinicians may be less likely to recommend newer or more aggressive therapies for older patients, particularly in the absence of clear trial data supporting their use in this population.
Toxicity management becomes more complex. Older patients are at higher risk of adverse events, yet without detailed age-stratified data, it is difficult to anticipate and mitigate these risks effectively.
Over time, these uncertainties converge into a recognizable pattern: clinical conservatism.
Older adults are more likely to receive reduced doses, simplified regimens, or supportive care alone—not necessarily because this reflects optimal treatment, but because the evidence base does not provide sufficient confidence to support more intensive approaches.
Research examining disparities in oncology care has shown that older adults are less likely to receive standard-of-care therapies, even after adjusting for comorbidity and performance status. 1
This is how underrepresentation becomes undertreatment.
It is not driven by neglect, but by uncertainty. And because older adults constitute the majority of cancer patients, this uncertainty affects the core of oncology practice.
The Reporting Gap
Even when older adults are enrolled in trials, they are often not adequately represented in the data that inform clinical decisions.
In an analysis of 286 randomized oncology trials, only 26.2% reported the proportion of older adults enrolled, and just 15% reported outcomes stratified by age. 13
This creates a second-order problem. Patients may be included in trials, but the absence of age-specific reporting limits the clinical utility of the data.
Without stratified outcomes, clinicians cannot determine how efficacy, toxicity, and tolerability vary across age groups. As a result, decision-making remains uncertain, even when trial participation has occurred.
Early Development Decisions Shape the Entire Evidence Base H2
The age gap is not confined to late-phase trials. It is embedded early in development.
Across oncology programs, trial participants have been shown to be on average 6–7 years younger than the corresponding disease population. 1
This has important implications. Early-phase trials shape dosing strategies, safety signals, and development trajectories. If these studies are conducted in younger populations, the assumptions they generate may not fully apply to older patients.
Notably, younger enrollment does not consistently correlate with reduced rates of serious adverse events. This challenges the assumption that excluding older patients necessarily improves safety or data clarity.
Instead, it suggests that the trade-off between control and representativeness may not be as favorable as traditionally assumed.
Emerging Solutions
There are clear signs that the field is beginning to evolve.
Structured geriatric assessment is increasingly recognized as a valuable tool for evaluating functional status, cognition, comorbidity, and vulnerability in older patients. Incorporating these assessments into clinical care and trial design can improve risk stratification and support more individualized treatment decisions. 1
At the same time, trials specifically designed for older populations are beginning to emerge. These studies demonstrate that prospective research in elderly cohorts is both feasible and informative, particularly when endpoints and dosing strategies are tailored to the realities of aging (source).
However, these approaches remain inconsistently applied. Many trials continue to rely on traditional eligibility frameworks and do not fully integrate the complexity of aging populations.
The result is a fragmented landscape in which solutions exist, but are not yet embedded at scale.
A Strategic Blind Spot in Clinical Development
For sponsors, the underrepresentation of older adults is often framed as an ethical or regulatory issue. Increasingly, it should be understood as a strategic one.
A therapy developed in a non-representative population may achieve regulatory approval, but it enters clinical practice with limitations in external validity. Physicians must interpret trial data in the context of patients who differ from those studied, leading to variability in adoption and use.
This can translate into reduced clinician confidence, slower uptake, and increased reliance on post-marketing evidence generation.
Conversely, programs that generate robust data in older populations are better positioned to demonstrate real-world applicability and clinical value. Evidence that reflects the actual treatment population supports more confident decision-making and strengthens the overall value proposition of the therapy. 3
In an increasingly competitive oncology landscape, representativeness becomes a differentiator.
The Majority That Remains Underrepresented
Older adults are not a niche population in oncology. They are the majority.
They account for most diagnoses, most treatment decisions, and most outcomes in cancer care. And yet, they remain systematically underrepresented in the trials that define modern oncology.
This is no longer a gap that can be attributed to historical precedent or methodological convenience. It is a structural limitation in how evidence is generated.
For clinical development leaders, the implication is clear.
The question is no longer whether elderly participation should be improved.
It is whether oncology trials can continue to produce credible, actionable, and clinically meaningful evidence while underrepresenting the very population they are designed to serve.
About Arc Nouvel
Arc Nouvel supports biopharma sponsors navigating the growing complexity of modern oncology development. We partner with clients across all stages of clinical trials and drug development, from early strategy and protocol design through later-phase execution and evidence generation.
Our work includes helping sponsors address critical challenges such as patient recruitment, trial representativeness, and the inclusion of underrepresented populations, including older adults, ensuring that clinical evidence aligns with real-world oncology practice.
