Inclusion and exclusion (I/E) criteria sit at the foundation of every clinical trial protocol. They determine who can participate, who cannot, and whether a study can realistically recruit patients in a safe, timely way.
When designed thoughtfully, I/E criteria help define a study population that aligns with the research question — accounting for factors like age, disease stage, treatment history, or geography — while also protecting patients who may be at higher risk or more likely to confound results.
But while these criteria are essential, they’re also a common source of downstream effects. That’s especially true when their real-world impact isn’t examined closely enough, or early enough.
I/E criteria are often finalized late in protocol development and based more on precedent or assumption than on evidence from real patient populations. As a result, study populations may not fully reflect how the disease shows up in the real world.
The Hidden Cost of Overly Restrictive I/E Criteria
There’s no shortage of discussion around the importance of diversity and representativeness in clinical trials. Yet in practice, many studies still fall short of recruiting populations that mirror real-world patients.
Poor representation drives up costs, slows drug development timelines, and can widen existing health disparities for already vulnerable populations.
Overly restrictive eligibility criteria are a major contributor. Many protocols continue to exclude patients based on criteria that may no longer be clinically necessary, or that disproportionately affect certain groups. Common examples include:
- Age limits that exclude older adults, even when they make up a large share of real-world patients
- Exclusions for common comorbidities that are more prevalent in certain racial or ethnic populations
- Requirements for frequent in-person visits that create geographic or socioeconomic barriers
- Language or technology requirements that limit participation without clear scientific justification
A large external validity analysis of nearly 44,000 randomized controlled trials found that more than 40% of adults over 70 (and over 90% of patients with multimorbidity) were excluded from trials, despite routinely receiving the same therapies in real-world practice. When patients most likely to use a treatment are systematically left out of studies, gaps in safety and effectiveness data are almost inevitable.
Restrictive criteria also create operational challenges. Smaller eligible populations mean longer recruitment timelines and higher screen failure rates.
Scenario Modeling: Testing Protocol Decisions Before You Commit
Traditional feasibility assessments often rely on historical enrollment metrics or limited site surveys. While useful, these approaches frequently miss how individual eligibility decisions compound to narrow the eligible population far more than expected.
That’s why eligibility “pre-work” is critical. Before a protocol is finalized, sponsors need a clearer view of how each criterion reflects the size, demographic, and geographic distribution of real-world patients.
With access to comprehensive, and most importantly, representative real-world data, teams can model eligibility scenarios early and pressure-test protocol assumptions by asking questions like:
- What happens to the eligible population if patients with condition X are excluded?
- How does tightening or loosening a lab threshold change enrollment across regions or age groups?
- Where are eligible patients actually receiving care — and do planned sites align with those care settings?
A scenario/sensitivity analysis allows teams to identify risks before they become problems. Instead of discovering enrollment challenges months into a study, criteria can be refined earlier to strike a better balance between scientific rigor, feasibility, and representation.
Designing Trials That Better Reflect Real-World Patients
Designing trials that reflect real patients doesn’t mean compromising on safety or data quality. It means grounding decisions in evidence rather than assumptions.
Real-world data can help support:
- Broader, more inclusive eligibility criteria where clinically appropriate
- Site selection based on where eligible patients actually receive care
- Pragmatic or hybrid trial designs that reduce burden on participants
- A clearer understanding of how therapies perform in patients with comorbidities
- Informing the study’s clinical endpoints
Trials designed with real-world context are more likely to generate insights that translate into everyday clinical practice — not just idealized study settings.
How PurpleLab® Can Help
PurpleLab’s Patient and Market Navigator, powered by billions of medical and pharmacy claims enriched with specialty datasets, including demographic and mortality data, is designed to help clinical teams turn these insights into action. Teams can explore eligibility scenarios, understand patient distribution, and evaluate protocol decisions before they’re locked in.
The earlier the real-world impact of I/E criteria is understood, the fewer surprises emerge later — and the more likely a trial is to enroll efficiently and deliver results that matter.
