FDA’s July 2026 final guidance on cancer-trial performance status changes feasibility work before the first site is selected. Broader eligibility can improve representativeness and access, but it also changes assumptions about addressable populations, retention, safety monitoring, site support and enrollment forecasts. Those assumptions now need to be tested—not inherited.
Why does this guidance change feasibility now?
The FDA’s final performance-status eligibility guidance recommends including adults with ECOG performance status 2, or Karnofsky scores of 60–70, unless established safety considerations provide a scientific or clinical rationale for exclusion. It also says later-phase trials should generally reflect the population expected to use the treatment, while exclusion rationales should be explicit in the protocol.
This is guidance, not a one-size-fits-all mandate. Trial objectives, mechanism of action, accumulated safety evidence and the intended population still matter. Yet the operational implication is clear: teams should stop copying narrow performance-status criteria from earlier protocols without testing whether those criteria remain justified for the current study.
The FDA also recommends collecting baseline performance-status data, considering stratification where the range is broad, and complementing clinician ratings with patient-reported or other functional assessments. For feasibility teams, that means a protocol change may alter more than the eligible headcount; it may change assessment burden, visit design, retention support and the data required from participating sites.
What should feasibility teams recalculate?
1. The truly eligible population
A wider eligibility statement does not automatically produce a proportional increase in recruitable participants. Historical enrollment data were generated under historical protocols. If earlier studies routinely excluded people with lower performance status, those records may understate both potential access and the operational demands of enrolling a broader population.
The ASCO–Friends joint research statement evidence argues for eligibility criteria that protect participants while avoiding restrictions unsupported by the objectives and available safety evidence. More recent performance-status enrollment research findings reinforce that lower-functioning adults remain frequently excluded from cancer trials. Feasibility should therefore model the proposed criteria directly instead of treating an old recruitment rate as a universal constant.
2. Where the population can realistically participate
Country prevalence, site experience and investigator history are useful starting points, but they are not the same as reachable participants. Teams should layer the revised eligibility rules onto geography, competing trials, referral pathways, travel burden and the availability of caregiver or decentralized support. A site near a large patient population may still be a poor fit if its practical access pathway is weak.
3. Retention, monitoring and sample-size assumptions
The FDA notes that enrolling people with lower performance status may affect adverse-event patterns, treatment completion, visit attendance and retention. It also discusses prespecified exploratory cohorts and early stopping rules when safety concerns remain. Feasibility models should therefore include scenarios—not one point estimate—for screening, randomization, discontinuation, visit completion and cohort behavior.
Published analysis of oncology eligibility practices and a recent KRAS G12C eligibility analysis show why protocol criteria deserve study-specific scrutiny. The useful question is not simply how many records match. It is how assumptions change when each criterion is broadened, retained or removed, and whether the resulting population still supports the scientific and safety objectives.
4. What each site must support
Broader inclusion can change site workload. Feasibility questionnaires should test current staffing, functional assessments, safety-escalation capacity, accessible visit pathways, caregiver accommodation and any decentralized elements required by the protocol. Historical site performance should be treated as evidence, then confirmed against the proposed study and present-day operating conditions.
How do teams build a living feasibility model?
A defensible model separates source data from assumptions. It records which eligibility criteria were applied, the dates and geographies covered, how competing trials were defined, and which values came from internal experience rather than public evidence. The ClinicalTrials.gov protocol data element definitions help standardize interpretation of registry fields, but registry records still require quality checks and operational validation.
Teams should then run alternative scenarios: conventional performance-status limits, the proposed broader criteria, slower retention, staggered cohort opening, or different country and site mixes. Each scenario should expose the assumptions driving projected enrollment. As safety data, site responses and the competitive landscape evolve, the model should be refreshed rather than archived after the initial go/no-go decision.
Where can connected intelligence help?
Teams can explore Anju’s Data Science suite for clinical and commercial intelligence across trial design, diversity strategy, feasibility and site identification. They can also review TA Scan feasibility capabilities for identifying experienced investigators and sites, reviewing recruitment capacity, visualizing diversity data, understanding competing trials and generating enrollment simulations.
Teams can also review Anju’s trial feasibility guidance, which explains how external intelligence and sponsor-specific inputs can be combined in scenario planning. No analytics platform removes the need for clinical judgment or direct site confirmation. Its value is in making assumptions visible, comparable and easier to update as the protocol and evidence mature.
Questions to ask before the next decision
- Which performance-status exclusions are supported by current study-specific safety evidence?
- How does broader eligibility change the addressable population in each target geography?
- Which sites can support the assessments, retention needs and escalation pathways required?
- What happens to enrollment and sample-size assumptions under conservative scenarios?
- Who owns refreshes when safety data, site capacity or competing trials change?
Frequently asked questions
What did the FDA finalize in July 2026?
The FDA finalized guidance on performance-status eligibility in adult oncology trials. It recommends broader inclusion when scientifically and clinically appropriate, with explicit justification when established safety considerations support exclusion.
Does broader eligibility guarantee faster enrollment?
No. It may enlarge the potentially eligible population, but recruitment still depends on geography, referral pathways, competing studies, site capacity, participant burden and retention support.
Should historical recruitment rates still be used?
Yes, but as inputs rather than fixed answers. Historical rates should be segmented by relevant protocol criteria and tested against the proposed population, current competition and present site conditions.
What makes a feasibility model reviewable?
A reviewable model preserves data sources, definitions, assumptions, scenario changes and decision ownership. Another reviewer should be able to understand why the forecast changed and which evidence drove the change.
Why feasibility teams use TA Scan
Anju develops purpose-built software and data solutions for clinical research, Medical Affairs and life-sciences intelligence. TA Scan connects public clinical, publication, presentation and site data so teams can examine feasibility assumptions in context while keeping sponsor expertise, protocol knowledge and study-specific judgment central to every decision.
Teams can explore TA Scan feasibility intelligence to see how connected evidence can strengthen population, geography and site-capacity planning.