Design Your Study for Successful Enrollment
If your clinical trial feels more like a challenge—silence from sites, disappearing participants, and ineffective procedures—you’re not alone. Many enrollment problems begin long before the first patient arrives. The usual culprits? Overly optimistic assumptions, vague eligibility criteria, impractical procedures, poorly defined control groups, and endpoints that are difficult to measure when needed most.
Fortunately, with a well-crafted study design, you can address these issues before they disrupt your trial.
1. Overly Optimistic Assumptions: The Invisible Patient Problem
There are two key aspects of successful enrollment: identifying the right patients and finding them.
What goes wrong: The “ideal” patient in your concept deck does not equal the available patient at your sites. Restrictive inclusion/exclusion (I/E) criteria, an over‑reliance on a single specialty, or a site network that doesn’t intersect with the true care pathway leads to empty screening logs.
How to prevent it: Defining your ideal study participants is a process. A carefully defined target population that aligns with your eventual target market is a first step. Aligning the target market with the intended use population ensures your study will have relevance in the marketplace and will get you regulatory approval to support that. Finally, understanding the practical realities of each specific I/E criteria is vital. Certain criteria are necessary to ensure patient safety, but other criteria intended to reduce variability can greatly reduce the available patient pool. It does not matter if your study is statistically well-powered if it can’t be enrolled.
Making sure you understand how study entry criteria will affect the patient pool requires you to understand where patients are coming from. Again, the process is stepwise. First, understand where patients present along the care pathway. Finding sites on this pathway will not only be a practical source of participants for your study but will help you gain visibility to the sites and physicians who will use your device in the marketplace. All of this will inform you about the pool of sites you’ll consider. From there, you can validate real site-level counts using the specific inclusion/exclusion criteria. For a large study with a long term recruitment, look for data over the last 12 months (if possible) to avoid being misled by seasonal or short-term trends. For a smaller study that should recruit quickly, understand how many patients at each site are “ready to go”.
Practical steps:
- Validate with real counts—before the first participant is enrolled.
- Require each candidate site to provide 12 months of counts for your top three inclusions and top three exclusions, plus referral sources and competing trials. Numbers beat anecdotes.
- Map the patient journey.
- Identify where patients enter the system (emergency departments, imaging, specialty clinics, or primary care) and which stakeholders influence treatment selection. If your trial recruits in one setting while patients are diagnosed elsewhere, build referral bridges or revise the site mix.
- Use lead indicators, not lagging disappointment.
- Track metrics weekly: referrals per active site, pre‑screen eligible rate, consent‑to‑enroll conversion, and time from consent to index procedure. These metrics spotlight feasibility issues before you lose months.
2. Vague or Impractical Eligibility Criteria: The Challenge of Perfection
Having the right inclusion and exclusion criteria requires striking the right balance.
What goes wrong: Ambiguous or subjective eligibility criteria and long, multi-step screening visits depress consent-to-enroll conversion.
Even with adequate prevalence, procedural friction quietly erodes conversion: long screening visits, duplicative imaging, pre-authorization delays, or multi-operator steps that require more hands than a site can spare creates a difficult path to enrollment.
Each added assessment increases visit duration, costs, and the chance of no-shows. Trying to define the perfect criteria leads to chasing perfection.
How to prevent it: Write criteria that are necessary and testable, the same way at every site (what test, who reads it, within what window). If a criterion excludes many otherwise indicated patients without improving safety or endpoint integrity, reconsider it.
Practical steps:
- Run a “day-in-the-life” simulation at 3–5 sites.
- Walk (and time) the entire screening visit and index procedure: check-in → eligibility confirmation → consent → assessments → scheduling. Anything that routinely pushes screening beyond 90–120 minutes without clear necessity will increase no-shows.
- Specify who does what, when, and how.
- For each criterion and assessment, define the responsible role, test/modality, collection window, required documentation, and decision rule for borderline cases. Remove or postpone steps that don’t protect safety or endpoint integrity.
- Equip coordinators to succeed.
- Provide pocket I/E cards, eligibility algorithms, patient education sheets, and call scripts for referrals. Pair this with 10–20-minute micro-training modules that reinforce tricky steps (e.g., functional assessments).
- Plan for the device learning curve.
- Early cases often take longer and yield more variability. Neutralize this with on-call proctors, checklists, and brief post-case debriefs to capture lessons while they’re fresh.
- Keep safety and adjudication clean from day one.
- Safety case report forms should capture onset, severity, relatedness, action taken, device status, and outcome—using definitions that fit your device and indication. If you use adjudication (e.g., clinical events committee), define packet contents, blinding rules, and tie-breaker processes upfront so data doesn’t pile up.
3. Control Group Selection: Addressing Concerns of Randomization
Are the options you’re presenting for clinicians and patients acceptable?
What goes wrong: A scientifically optimal control group may not be practically feasible.
How to prevent it: If it’s possible to design a single arm trial (with comparisons to a literature-based performance goal, or with a patient as their own control) you can reduce the fear factor that accompanies the randomization process.
For a randomized study, while a sham or placebo treatment may offer the most rigorous scientific comparison, patients may not be willing to consent to the chance of not receiving treatment. In some cases, this can be mitigated by allowing subjects to eventually crossover to treatment. In other cases, judicious active control may be possible but may frighten off physicians who have a preferred available treatment and who are unwilling to leave therapeutic choices to chance. Vet your control group choice through meaningful discussions with representative clinicians, by finding a relevant successful precedent from other trials, or through pilot testing (i.e., a randomized feasibility study).
- Figure out your options.
- More than one type of study design may support regulatory approval. While it’s important to understand precedents and expectations for a therapeutic area, multiple options may be available. If you’re not aggressive in what you consider and what you ask for, someone else may be, and will beat you to market.
- Choose a control that clinicians respect and sites can operationalize.
- Active, placebo/sham, or external/historical comparisons all have trade-offs. The right choice depends on ethics, standard of care, feasibility of randomization/blinding, as well as business goals. Document why your selection best answers the decision you need to make.
- Test what you can.
- If it’s possible to run a randomized feasibility, you can gain insights into your initial choice. Other times, conversations with practicing clinicians who will participate in your trial may be able to provide solid recommendations. Consider informal surveys of patient groups to gain further insights.
- Know when to say when.
- In certain cases, your original plan may have to change. Maybe the treatment landscape has changed, and there is no equipoise for randomization; a single arm study may be possible. Other times, a competitive study may emerge that raises the bar, causing you to reconsider your design. Talking to regulatory authorities early and providing a clear rationale and justification for design changes may help you salvage a previously unworkable design.
4. Endpoints: Ensuring Meaningful Outcomes
Endpoints need to be aligned with study goals and sufficiently practical.
What goes wrong. A study can be impeccably executed but effectively worthless if endpoints are only loosely connected to your intended claim, payer coverage argument, or clinical adoption goal. If the endpoint makes sense scientifically but is operationally brittle—rare, late, or measured in ways that vary by operator, you may also end up with no useful results.
How to fix it. The choice of endpoint can be driven by considerations of statistical power, clinical interest, or precedent. However the choice is made, it’s important to ensure your primary endpoint is both clinically meaningful and operationally feasible.
Clinical relevance can ensure investigator engagement which pays off in rapid enrollment. Operational feasibility is also important, but in a different regard. Enthusiasm and rapid enrollment do not solve issues of missing data that emerge when an endpoint is not operationally feasible.
Assess whether or not endpoint data collection fits into standard clinical practice, how much site training and support are needed to facilitate collection, and what mitigations can be considered if issues do emerge. Again, understanding precedent can help, as can initial assessments of practicality in pilot testing. Also provide design mitigations up front to minimize missing data; wider visit windows, alternative modalities as back-up, etc.
- Draft the estimand first
- Write one paragraph that states: Population (who), the primary endpoint variable(s) (what), intercurrent events and how you’ll handle them (e.g., treatment-policy, hypothetical, composite, while-on-treatment approaches), and a summary measure. Share it with clinical, regulatory, and biostatistics leaders. If they can’t repeat it back, it isn’t clear.
- Tie each endpoint to one business‑critical decision.
- For the primary and each key secondary endpoint, name the decision it supports (label language, clinical guideline anchor, coverage criterion). If you can’t, you’ve likely chosen an endpoint that will create noise without supporting your goals.
- Design for reliable capture.
- Specify assessment windows wide enough for real clinic workflows but narrow enough to preserve interpretability. Standardize measurement (scripts, calibration procedures, core labs/central reads) where appropriate.
Frequently Asked Questions (FAQs)
Can we amend our endpoint without restarting the trial?
Perhaps—if the change aligns with your scientific and regulatory goals and is clearly justified. Common amendments include refining eligibility criteria or clarifying endpoint definitions. Just be sure to update your analysis plan and retrain sites. Also notify the FDA for regulated trials.
Yes, especially when early feasibility reveals misalignment. It’s not ideal, but it’s better than letting the trial become a ghost story.
Will adding more sites fix enrollment issues?
Not if the design is the bottleneck. More sites just spread the issue. If missing data is the real issue, you’ll need to address bias and friction—not just expand your sites.
If the actual problem is missing data, patients or sites may be trying to solve the wrong problem. If there is bias due to missing data, adding more sites may just mean we have more precise estimates, but have done nothing to address the underlying potential bias. Careful planning of statistical analyses to handle missing data, including sensitivity analysis, is necessary.
We suspect our primary endpoint is too hard to capture. Can we amend without undermining credibility or restarting the trial?
Potentially, yes, if your change preserves (or corrects) the alignment of the scientific intent with the regulatory/reimbursement end goal, and you explain the rationale clearly. Update your analysis plan and retrain sites so execution matches the new text. More drastic changes may be possible but will require stronger justification. A drastic change without a clear rationale may raise credibility or integrity concerns.
