Trial landscape analysis before you design a protocol

Every design choice in a protocol has a precedent somewhere. Others have already tested related interventions in your indication, chosen endpoints, set eligibility criteria and tried to recruit in the countries you have in mind.

Looking at that body of work first answers practical questions early, while they are still cheap to change.

• Which primary endpoints have regulators and investigators already accepted in this indication?
• Which comparators are standard, and is placebo still acceptable?
• How strict are typical eligibility criteria, and how does that affect recruitment?
• How many participants do comparable trials enrol, and how long do they run?
• Where are competing trials recruiting the same patients right now?
• Which trials stopped early, and why?

A landscape also supports conversations outside the design team. Ethics committees, regulators, investigators and internal governance boards all ask how a new study relates to existing evidence, and a documented landscape answers that question in one place.

Collect the same fields for every relevant trial, so you can compare them in one table.

• Phase and study type, to separate exploratory work from confirmatory trials and observational studies.
• Population, including disease stage, line of therapy and key subgroups.
• Interventions and comparators, with doses and schedules where the record gives them.
• Primary and secondary endpoints, with the time frame of each.
• Eligibility criteria, especially the exclusions that shrink the eligible population.
• Enrolment, noting whether the figure is anticipated or actual.
• Duration, from start date to primary completion and study completion.
• Countries and number of sites.
• Overall status: not yet recruiting, recruiting, active, completed, terminated, suspended or withdrawn.
• Whether results were posted, and whether a publication exists.
• Sponsor and sponsor type.

Terminated and withdrawn trials deserve their own column. The reason for stopping, when the registry records it, is some of the most useful information in the landscape: slow accrual, a safety signal, a futility analysis or a business decision each carry a different lesson for your design.

1. Frame the question. Write the population, intervention, comparator, outcomes and study types you care about. The PICOS framework gives a simple structure.

2. Choose the sources. Search ClinicalTrials.gov, the EU registers and the WHO ICTRP for registered trials, and PubMed for published results. Registries show what was planned and what is running, publications show what was found.

3. Build the term lists. Collect synonyms for the condition and the intervention, including code names and drug classes, and keep the list with your notes.

4. Run broad searches first. Start with the condition and the intervention class, read the results, then narrow by phase, status and dates.

5. Deduplicate. Link records for the same trial across registries using secondary IDs, then titles, sponsors and dates.

6. Screen for relevance. Decide for each trial whether it belongs in the landscape, and note the reason when you exclude one.

7. Extract the fields. Fill the table with the fields above, opening the registry record for every trial you keep.

8. Read the stopped trials. For terminated, suspended and withdrawn studies, record the stated reason and any related publication.

9. Summarize patterns. Describe the usual endpoints, comparators, sizes, durations and countries, and the places where your planned design differs.

10. Record the date and the search. Landscapes change weekly, so note when and how the data was collected.

The table is only useful once it informs choices. A few questions help translate it.

Endpoints. If most confirmatory trials in the indication use the same primary endpoint and time frame, a different choice needs a clear justification. If endpoints vary widely, that is worth discussing with regulators early.

Comparators. Check what recent trials used as control and whether standard of care has shifted since older trials were designed.

Eligibility. Compare your inclusion and exclusion criteria with those of similar trials. Criteria much narrower than the norm can slow recruitment, and much broader criteria can dilute the effect you hope to show.

Size and duration. Comparable enrolment and duration figures give a realistic starting point for planning, alongside your own statistical calculation of the required number of participants.

Competition. Overlapping trials recruiting in the same countries and sites compete for the same patients, which matters for feasibility and site selection.

Failures. The reasons trials stopped can reveal recruitment barriers or safety questions you can address in advance.

• Searching one registry only, which misses trials registered elsewhere.
• Counting records instead of trials, so multinational studies are counted several times.
• Mixing anticipated and actual enrolment in one summary.
• Treating planned dates as actual dates.
• Ignoring publications, so completed trials look as if they never reported.
• Using exact condition terms only, so trials described with other words are missed.
• Skipping terminated and withdrawn trials because they seem irrelevant.
• Forgetting to date the analysis, so an outdated landscape is presented as current.

Each of these errors is easy to avoid once you know it, and each one changes the conclusions a design team draws from the table.

A landscape is usually reviewed by several people: the medical lead, the statistician, the clinical operations team and sometimes external advisors. Exports should keep the sources attached.

• A spreadsheet with one row per trial and the NCT number in its own column, for filtering and further analysis.
• A document with a narrative summary and a full reference list, for governance and regulatory discussions.
• Citations in RIS or BibTeX, so publications go straight into a reference manager.
• The search date and term lists, so the work can be updated rather than repeated.

Keep one master version of the table and update it as trials change status. A landscape built once and never refreshed quickly drifts away from reality, especially in active indications where new trials register every month.

When you share the landscape outside the core team, include a short methods note: sources searched, terms used, filters applied, the date of the search and how duplicates were handled. Reviewers trust a landscape they can reproduce.

ClinicalEvidence turns the first steps of this workflow into one question. You describe the indication and intervention in plain English, and the answer is written from registered trials and published studies, with an NCT number or PMID on every sentence.

• The clinical trial table lists phase, population, interventions, primary and secondary endpoints, results posted, sponsor, status, enrolment, dates and locations for every matching study, sortable and filterable.
• Filters cover condition, intervention, phase, status, study type, date range, sponsor type, country and results available.
• Compare interventions in clinical trials side by side, two to four at a time.
• Export clinical trial data as CSV, XLSX or DOCX with a reference list, or citations as RIS and BibTeX, depending on your plan.
• Save the question as an alert to hear about newly registered trials by email.

Feasibility teams can read more on our clinical feasibility page.