A feasibility study asks whether and how a future study can be carried out. Its value comes from resolving uncertainties about recruitment, delivery, measurement, and study procedures. Treating it as a small effectiveness trial can leave those practical questions unanswered.
Begin with the decisions the next study depends on. Which uncertainty could prevent it from working, and what information would let you proceed, revise the design, or stop?
Identify the procedures you need to test. Depending on the project, these may include finding eligible participants, obtaining consent, delivering an intervention consistently, retaining participants, collecting outcomes, or linking records across sites.
A pilot study rehearses some or all of a future study on a smaller scale. Other feasibility work may focus on a specific uncertainty without rehearsing the entire design. Specify what your study will and will not test. If the future trial requires randomization, consider whether your feasibility questions require testing acceptance and implementation of that process.
For every measure, specify its numerator, denominator, observation window, and data source. “Recruitment was good” is not an analyzable outcome. Recruitment per active site-month, consent among eligible people approached, and the percentage of the recruitment target reached answer different questions.
Qualitative findings can explain why a procedure failed and whether a proposed change is credible. A favorable completion percentage alone does not reveal whether staff found the process sustainable.
Progression criteria connect feasibility evidence to a decision about the next study. Define them in advance, explain their basis, and identify who will review them. Traffic-light categories can distinguish proceeding, proceeding with changes, and not proceeding in the current form.
For a fictional example, a team might propose green for at least 85% follow-up completion, amber for 70% to below 85%, and red for below 70%, measured among enrolled participants due for follow-up. These numbers are invented, not recommended standards. The actual thresholds should reflect what the definitive study needs and what remedial action is possible.
Specify how uncertainty will inform the decision. With 17 completions among 20 participants, the point estimate is 85%, but it is imprecise. Decide whether categories concern a point estimate, an interval, or a formal decision rule, and evaluate the consequences of that choice. One participant can move a small study across a threshold.
Justify the sample by the precision or decision properties needed for the main feasibility outcomes. Include sites, staff, or other units when those are the units relevant to delivery. There is no single participant count that makes every feasibility study adequate.
Allow for denominators smaller than total enrollment, clustering, and the time required to observe outcomes. If a recruitment rate is central, document the exposure period. If your criterion concerns a proportion, explain the estimation method and expected uncertainty.
You may collect future trial outcomes to test procedures or inform planning. Report them with uncertainty and an explicit account of their purpose. A nonsignificant treatment comparison in a small feasibility study does not show that the intervention is ineffective. A significant one does not establish readiness for a definitive effectiveness claim.
A pilot treatment-effect estimate should not automatically become the effect size for the next trial. See how to choose a target effect for power analysis.
Report each criterion, its result, uncertainty, protocol deviations, and any proposed modification. Explain how conflicting findings were weighed. If the study reveals an important problem outside the original criteria, record it rather than ignoring it because it was not on the checklist.
Use the analysis-planning worksheet to record outcomes and intended decisions. Read when a pilot should progress to a full trial. Discuss a feasibility-study plan with DASS before recruitment begins.
CONSORT extension for randomized pilot and feasibility trials addresses reporting and progression criteria for that study type. Determining sample size for pilot trials discusses linking sample-size planning to feasibility objectives. Check your target journal’s current reporting requirements.
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