Patient Recruitment · 2026

Clinical Trial Recruitment Benchmarks by Study Type

“Is our enrolment rate good?” is one of the most commonly asked questions in recruitment operations, and it’s almost impossible to answer with a single number — because the right benchmark depends on what kind of study is asking.

Clinical trial recruitment benchmarks by study type exist because a recruitment pace that’s a strong result for a rare-disease study would represent serious underperformance for a common-condition trial — comparing both against the same number tells you nothing useful about either.

What are clinical trial recruitment benchmarks by study type, and why does one number not work?

A benchmark is only useful if it’s read against the right frame of reference. Applying a generic industry average to every study treats a rare-disease trial screening for a small, dispersed population the same as a common-condition trial with a broad eligible pool — and that comparison sets one team up to look like they’re failing and the other to look like they’re coasting, regardless of how well either is actually being run.

The most useful benchmark for any given study isn’t an external industry number at all — it’s the study’s own stated expectation, set against its own reported position. The size of the gap between expectation and reality is what actually tells you something, because it controls for the study’s own difficulty level by design.

Why doesn’t a single industry benchmark work across studies?

Recruitment difficulty is driven overwhelmingly by how many eligible patients exist and how hard they are to find and reach — not by anything about the study team’s competence. A study screening for a highly specific, rare genetic condition is working against a fundamentally smaller and more dispersed population than a study recruiting for a common chronic condition with a wide diagnostic base, a distinction covered from the channel-strategy side in patient source mix for rare disease vs common conditions.

What actually drives recruitment difficulty from one study to the next?

A handful of factors consistently separate easier recruitment contexts from harder ones, and they compound rather than act independently.

What does the evidence say about how differently rare and common studies actually perform?

A 2018 register-based study in PLOS ONE compared trial characteristics between rare and non-rare disease studies directly. Rare-disease trials enrolled far fewer participants at every level: a mean of 18.3 versus 40.2 subjects at the national level, 181.0 versus 626.9 across the EEA, and 335.8 versus 1,406.3 across a whole multinational trial. Notably, overall trial duration showed no significant difference (38.3 vs 36.4 months) — the gap is about how many patients exist, not about studies running slower.

Diagnostic pathway complexity compounds this further. The EURORDIS Rare Barometer survey of over 10,000 patients across 42 countries and 1,675 distinct rare diseases found an average of 4.7 years from symptom onset to confirmed diagnosis, with a quarter of patients needing eight or more clinician consultations and 60% experiencing an initial misdiagnosis. And protocol burden adds a third dimension: as procedures and eligibility criteria per protocol grew, one industry analysis found trial cycle time increased 69.6% while enrolment rates fell from 75% to 59% over the same period.

What does this mean for source strategy and channel mix?

Population difficulty doesn’t just change what counts as an acceptable pace — it changes which recruitment channels are actually appropriate. A rare-disease study is often better served by fewer, more targeted channels: clinical referral networks, patient advocacy organisations, and specialist investigator relationships, where relevance matters more than volume. A common-condition study can typically draw on a wider mix, including broader digital and search channels, because the pool of eligible patients is large enough that reach becomes a useful lever — the full comparison is covered in patient source mix for rare disease vs common conditions.

This is also where a limited source mix means something different depending on context. Running only two or three channels is a minor observation for an easy-to-reach population with many viable channels available. The same limited mix is a material finding for a difficult population, where narrowing the channel set further shrinks an already small pool of reachable patients.

What does this mean for a study that hasn’t opened yet?

Study status matters as much as study type. A trial that hasn’t yet opened for recruitment shouldn’t be benchmarked against enrolment pace at all — it should be assessed on readiness: is the source strategy appropriate for the population, is the referral pathway built and tested, is the team positioned to respond with the kind of first-contact speed that matters once recruitment opens. Applying an enrolment-pace benchmark to a study that hasn’t started recruiting produces a meaningless comparison in either direction.

How do you build the right benchmark for your own study?

• What expectation was set at the outset, and was it realistic given the population and protocol as understood at that time?
• How does current position compare to that expectation, and how significant is the gap given how much of the recruitment window remains?
• Which parts of the study are actually affected by that gap — timeline, budget, statistical power, site burden — rather than treating the shortfall as a single undifferentiated problem?
• Has the study's population difficulty — prevalence, diagnostic complexity, protocol burden, community structure, standard-of-care alternatives — actually been accounted for before comparing to any benchmark at all?

A shortfall that’s small relative to a difficult population and early in the recruitment window looks very different from the same percentage shortfall in a study recruiting a common condition well into its enrolment period. The number alone can’t tell you which situation you’re in — the context around it can, which is exactly what a proper under-enrolment diagnosis is built to surface.

Frequently asked questions