Clinical Trial Feasibility Assessment: A Practical Guide

Clinical trial feasibility is often reduced to a question sent to prospective sites: how many participants could you recruit? That question matters, but it comes too late and asks too little. A useful feasibility assessment starts at the study level. It asks whether the protocol, as designed, is realistic for the people who would participate and the organisations expected to deliver it.

For startup and university life science teams, that distinction is important. A clinical program may be carrying an ambitious evidence goal, a developing product, limited time, and a fundraising milestone that depends on credible progress. The right response is not to make the plan conservative by default. It is to test the assumptions early enough that the team can choose a better path while meaningful choices are still open.

Start with the study decision

Begin with the decision the study needs to support. Is the team trying to establish clinical performance, generate initial safety evidence, test usability in a defined setting, or decide whether a larger program is justified? The answer shapes the population, endpoint, visit schedule, follow-up, product handling, and evidence standard. A feasibility exercise that begins only with site counts can miss the more consequential question: whether the design can generate a trustworthy answer.

This connects directly to a clinical development plan. That plan explains why a study belongs in the evidence sequence. Feasibility tests whether the proposed study is workable in the real world. Keep the two documents connected. If feasibility exposes an unworkable criterion or an overly demanding schedule, the development plan may need to change, not merely the operational forecast.

The ICH E8(R1) guideline on clinical studies emphasises clear objectives and quality designed around factors that matter to reliable results. In practical terms, define the question first, then test the people, procedures, data flow, and operating conditions needed to answer it.

Separate study feasibility from site selection

Study-level feasibility asks whether the proposed protocol is executable across the intended setting. It looks at the realistically eligible population, treatment context, competing studies, the demands placed on participants and sites, country considerations, and the estimated footprint needed to recruit on time. Site selection then asks which particular investigators and sites have the capacity, experience, access, and systems to deliver that study.

Keeping these decisions separate avoids a familiar trap. A site may have a strong reputation and still be unable to enrol the narrow population described by a protocol. Equally, a protocol may be workable overall but unsuitable for a site without a specialist procedure, a required laboratory relationship, or staff capacity. The study-level feasibility distinction is useful because it keeps a questionnaire from becoming a substitute for a decision about the design itself.

Six questions a feasibility assessment should answer

A polished spreadsheet is not the goal. The assessment should make its assumptions visible and turn them into decisions. The questions below provide a practical structure for an early team.

1. Who is realistically eligible?

Start with the population described by the actual inclusion and exclusion criteria, not the broad prevalence of a condition. A disease may be common while the eligible group is small once prior treatment, biomarker, disease stage, comorbidities, geography, or timing requirements are applied. Use the strongest available source for the question, which may include published epidemiology, registries, claims data, electronic health record queries, or carefully qualified historical screening data.

Objective data can change a design decision. In a published NIH StrokeNet case study, testing proposed criteria against a population-based resource led the team to revise four key exclusions. The authors estimated that the share of acute stroke patients eligible for the study rose from 2.4% to 4%, and that 57% of enrolled participants qualified only through the modified criteria. The lesson is not that criteria should always be broadened. It is that a team should understand the recruiting consequence of each important criterion before locking it.

2. What does participation ask of people and sites?

Read the participant journey from first contact through follow-up. Count visits, travel, procedures, assessments, diary or device tasks, washout periods, and the time between each step. Then read the site journey: staff roles, training, investigational product handling, specialised equipment, sample processing, data entry, safety reporting, and coordination with vendors. A study can be scientifically well conceived and operationally fragile when its demands do not fit everyday care or a site’s practical capacity.

Ask where the burden sits. A complex procedure may be acceptable when it is central to the evidence question and available at selected sites. It becomes a feasibility problem when it is required repeatedly, difficult to schedule, poorly aligned with ordinary care, or dependent on a handoff the team cannot reliably control. Those findings should feed into clinical trial risk management, where the team assigns ownership and controls for the conditions that could affect participants or result reliability.

3. Does the protocol fit the care setting?

Compare the proposed population, comparator, background therapy, visit schedule, and procedures with the way care is delivered in the intended setting. A requirement that departs sharply from usual care may make recruitment harder for a sound clinical reason: clinicians may be reluctant to disrupt treatment, and patients may reasonably decline an unfamiliar or burdensome pathway. Investigator conversations can test these assumptions, but they should be structured around the protocol rather than a general request for enthusiasm.

4. What other studies are recruiting the same people?

Review the active study landscape before calculating recruitment capacity. ClinicalTrials.gov makes it possible to check recruiting studies by condition, intervention, location, and other relevant terms. The point is not to create a perfect prediction of every competing referral. It is to identify where overlapping eligibility, the same specialist sites, or the same geography may compress an already limited pool.

This is also a useful moment to look for a better study position. A change in country mix, site type, enrolment window, or protocol sequence can sometimes improve feasibility without changing the evidence question. When that cannot be done, the honest outcome may be a different timeline, larger footprint, or a narrower near-term claim. A credible choice made now is more valuable than an optimistic forecast that causes the program to stall later.

5. What is the credible country and site footprint?

Turn the eligibility and burden analysis into a transparent forecast. State the recruitment target, the recruitment period, the expected enrolment rate per active site, the screen-failure assumption, expected activation time, and the contingency for underperforming sites. Then show how those assumptions lead to the number of countries and sites proposed. The aim is not artificial precision. It is to expose the assumptions leadership needs to challenge.

Country choices deserve the same discipline. Consider expected regulatory and ethics timelines, product import or sample-export needs, language and translation requirements, care patterns, site infrastructure, and access to the relevant patient group. The regulatory affairs work should be in the room early, because a country that looks attractive for recruitment may introduce a timing or evidence issue the clinical operations view alone cannot see.

6. What would make the plan change?

Every forecast should name the assumptions that would trigger a review. Examples include a new competing trial, lower than expected screening yield, a delayed product release, a protocol amendment, changed standard of care, a new safety signal, or a site capability gap. Give each trigger an owner, a review point, and a decision path. This keeps feasibility from becoming a static startup document.

Run the assessment as a focused decision session

An early team does not need a large feasibility department to do useful work. Start with a short protocol synopsis and assemble the people who understand the clinical question, product, regulatory pathway, quality requirements, operations, data, and business milestone. Ask each person to distinguish facts from assumptions. Then work through the six questions above and document where the answer is uncertain.

Use a simple evidence table with five fields: the assumption, the evidence supporting it, the confidence in that evidence, the consequence if it is wrong, and the next action. This format helps a team avoid two unhelpful extremes: treating every assumption as equally risky, or debating a concern without deciding how to resolve it. It also makes the work easier to revisit when the protocol changes.

Close the session with a clear outcome. The protocol may proceed as drafted, proceed with operational safeguards, return for targeted design changes, or require a wider program decision. None of these outcomes is a failure. The failure is discovering the mismatch after contracts, training, and expectations have already been set.

Common mistakes to avoid

Using broad prevalence as the recruitment forecast

Prevalence is a starting point, not an enrolment plan. Apply the real eligibility criteria, care pathway, referral pattern, geography, and competing studies before relying on a population estimate.

Asking sites for a number without showing the protocol burden

A site estimate is more useful when people can see the procedures, schedule, staffing, and product requirements behind it. Otherwise the number reflects an incomplete picture.

Keeping regulatory and quality work separate until later

Clinical feasibility can change when a regulatory consideration alters the study sequence, product controls, country choices, or evidence required. Bringing quality assurance and regulatory affairs into the review early keeps the plan joined up.

Confusing a feasibility assessment with a feasibility study

A feasibility assessment is a planning activity that tests whether a proposed protocol can be run. A feasibility study, sometimes called a pilot study, is a study in its own right that may test recruitment, procedures, or acceptability. The terms overlap in ordinary conversation, but they lead to different decisions and should not be treated as interchangeable.

How Nexus Life Sciences can help

Nexus Life Sciences helps startup and university teams connect clinical programs, regulatory affairs, and quality assurance with the product, business plan, team, and fundraising decisions surrounding a study. For feasibility work, that means looking beyond a site count to the evidence question, patient pathway, product reality, regulatory context, and practical operating conditions that need to fit together.

A useful feasibility assessment gives a team a more credible next decision, whether that is refining a protocol, changing a study sequence, preparing a site strategy, or explaining a clinical plan to investors and collaborators. If you are preparing a study or testing its evidence path, book a consultation to discuss the work ahead.